diff --git a/.env.example b/.env.example index 78a3b72c07..97d4077fc3 100644 --- a/.env.example +++ b/.env.example @@ -1,16 +1,37 @@ -# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API） -# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/ -# 注意消耗较大，可先进行小于40轮的模拟尝试 -LLM_API_KEY=your_api_key_here +# ===== MiroFish Agent Engine 默认配置 ===== +MIROFISH_MODE=agent +MIROFISH_LLM_PROVIDER=agent_queue +MIROFISH_GRAPH_PROVIDER=graphiti +MIROFISH_RUNS_DIR=./runs + +# ===== Graphiti / Neo4j 图谱配置 ===== +NEO4J_URI=bolt://localhost:7687 +NEO4J_USER=neo4j +NEO4J_PASSWORD=password +NEO4J_DATABASE=neo4j + +# ===== Embedding 配置（可选）===== +# GraphitiCompatibilityStore 的 no-LLM 主路径不需要 embedding。 +# fulltext 不需要 Ollama；仅 semantic/hybrid + ollama 时 doctor 会强制检查 Ollama。 +MIROFISH_GRAPH_SEARCH_MODE=fulltext +MIROFISH_EMBEDDING_PROVIDER=none +OLLAMA_BASE_URL=http://localhost:11434 +OLLAMA_EMBEDDING_MODEL=nomic-embed-text + +# ===== Legacy OpenAI-compatible LLM provider（可选）===== +# 仅当 MIROFISH_LLM_PROVIDER=openai_compatible 时需要。 +LLM_API_KEY= LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 LLM_MODEL_NAME=qwen-plus -# ===== ZEP记忆图谱配置 ===== -# 每月免费额度即可支撑简单使用：https://app.getzep.com/ -ZEP_API_KEY=your_zep_api_key_here +# ===== Legacy Zep Cloud graph provider（可选）===== +# 仅当 MIROFISH_GRAPH_PROVIDER=zep 时需要。 +ZEP_API_KEY= + +# ===== 加速 LLM 配置（legacy 可选）===== +LLM_BOOST_API_KEY= +LLM_BOOST_BASE_URL= +LLM_BOOST_MODEL_NAME= -# ===== 加速 LLM 配置（可选）===== -# 注意如果不使用加速配置，env文件中就不要出现下面的配置项 -LLM_BOOST_API_KEY=your_api_key_here -LLM_BOOST_BASE_URL=your_base_url_here -LLM_BOOST_MODEL_NAME=your_model_name_here \ No newline at end of file +# ===== Legacy UI / simulation behavior ===== +MIROFISH_PRESERVE_SIMULATIONS_ON_RELOAD=true diff --git a/AGENT_KIT.md b/AGENT_KIT.md new file mode 100644 index 0000000000..30a498201e --- /dev/null +++ b/AGENT_KIT.md @@ -0,0 +1,107 @@ +# MiroFish Agent Kit + +MiroFish now includes a full agent engine for desktop tools such as Codex, Claude Code, Cursor, and opencode. + +Default agent mode: + +```bash +export MIROFISH_MODE=agent +export MIROFISH_LLM_PROVIDER=agent_queue +export MIROFISH_GRAPH_PROVIDER=graphiti +export MIROFISH_RUNS_DIR=./runs +``` + +Agent mode does not require `LLM_API_KEY`, `OPENAI_API_KEY`, or `ZEP_API_KEY`. Model work is written to `runs//requests/*.json`; a desktop agent writes matching response JSON into `runs//responses/*.json`; MiroFish validates the response and resumes the run. + +Local graph service setup: + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/setup_agent_deps.sh --neo4j desktop +``` + +Agent dependencies are installed through the backend `agent` optional extra. Legacy OpenAI-compatible and Zep Cloud SDKs are not part of the default agent path; install them only when explicitly using `MIROFISH_LLM_PROVIDER=openai_compatible` or `MIROFISH_GRAPH_PROVIDER=zep`: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv sync --extra legacy +``` + +Neo4j Desktop, Homebrew/native Neo4j, and existing Neo4j instances are supported. Docker Compose is optional only. `mirofish-agent doctor --json` does not fail because Docker is missing; it fails when required Graphiti/Neo4j dependencies, Neo4j connectivity, or Neo4j `5.26+` are unavailable. Ollama checks are required only when `MIROFISH_GRAPH_SEARCH_MODE=semantic` or `hybrid` and `MIROFISH_EMBEDDING_PROVIDER=ollama`; `fulltext` search with `MIROFISH_EMBEDDING_PROVIDER=none` does not require Ollama. + +Minimal demo: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 +uv run mirofish-agent run --run ../runs/chip-2036 --json +uv run mirofish-agent requests show --run ../runs/chip-2036 --request-id req_000001 --json +uv run mirofish-agent responses validate --run ../runs/chip-2036 --response ../runs/chip-2036/responses/req_000001.json --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +Follow-up questions also use the same provider boundary and queue: + +```bash +uv run mirofish-agent followup ask --run ../runs/chip-2036 --question "先进AI芯片出口限制有什么影响?" --json +uv run mirofish-agent followup show --run ../runs/chip-2036 --request-id req_000007 --json +``` + +Supported agent queue task types: + +```text +extract_triples +generate_ontology +generate_oasis_profiles +generate_simulation_config +simulate_agent_action +summarize_round +update_memory +generate_report +answer_followup_question +answer_agent_question +answer_agent_questionnaire +summarize_questionnaire +ask_report_question +validate_json_output +repair_invalid_json +``` + +Web Console and interaction commands: + +```bash +# Generate the interactive Web Console +uv run mirofish-agent web generate --run ../runs/chip-2036 --json + +# List agents and ask questions +uv run mirofish-agent agents list --run ../runs/chip-2036 --json +uv run mirofish-agent agents ask --run ../runs/chip-2036 --agent-id agent_1 --question "What is the biggest risk?" --json +uv run mirofish-agent agents answer --run ../runs/chip-2036 --request-id req_XXXX --json + +# Send questionnaires +uv run mirofish-agent questionnaire send --run ../runs/chip-2036 --questions questions.json --json +uv run mirofish-agent questionnaire show --run ../runs/chip-2036 --questionnaire-id q_XXXX --json + +# Ask questions about the report +uv run mirofish-agent report-question ask --run ../runs/chip-2036 --question "Summarize key risks" --json +uv run mirofish-agent report-question answer --run ../runs/chip-2036 --request-id req_XXXX --json +``` + +Full smoke: + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/smoke_agent_queue_full.sh +bash scripts/smoke_mcp_full.sh +python scripts/check_provider_boundaries.py +``` + +Tool-specific guides: + +- [Codex](./docs/agent-usage/codex.md) +- [Claude Code](./docs/agent-usage/claude-code.md) +- [Cursor](./docs/agent-usage/cursor.md) +- [opencode](./docs/agent-usage/opencode.md) +- [MCP](./docs/agent-usage/mcp.md) + +The Flask/Vue UI is preserved as the legacy interactive interface. Legacy API calls that hit `agent_queue` return a structured HTTP 202 `need_agent_response` payload instead of a 500, but full checkpointed agent-mode orchestration remains CLI/MCP-first. diff --git a/IMPLEMENTATION_SUMMARY.md b/IMPLEMENTATION_SUMMARY.md new file mode 100644 index 0000000000..2bcf16c310 --- /dev/null +++ b/IMPLEMENTATION_SUMMARY.md @@ -0,0 +1,212 @@ +# Implementation Summary + +## Changed Areas + +- Added `backend/app/agent_engine/` for strict request/response schemas, filesystem queue, persistent run state, output contracts, shared runner, and CLI. +- Added `backend/app/adapters/llm/` with `AgentRuntime`, `agent_queue`, `mock`, legacy `openai_compatible`, and `AgentModelBackendAdapter`. +- Added `backend/app/adapters/graph/` with `GraphProvider`, `GraphitiGraphProvider`, `GraphitiCompatibilityStore`, and legacy `ZepGraphProvider`. +- Added `backend/app/mcp_server/server.py` with FastMCP lifecycle tools. +- Refactored direct model/Zep call sites in LLM client, graph builder, Zep entity/tools/memory facades, OASIS profile/config generators, and simulation scripts. +- Tightened agent response validation so `skipped` outputs still match `expected_schema`, `req_*.json` filenames must match response `request_id`, and invalid stage responses create `repair_invalid_json` requests when retry policy allows. +- Added stable output schemas for every declared agent task type: `extract_triples`, `generate_ontology`, `generate_oasis_profiles`, `generate_simulation_config`, `simulate_agent_action`, `summarize_round`, `update_memory`, `generate_report`, `answer_followup_question`, `validate_json_output`, and `repair_invalid_json`. +- Persisted graph provider overrides in run metadata so `graph build --provider graphiti` survives the `waiting_agent` pause/resume boundary. +- Made explicit stage entrypoints idempotent while waiting for agent responses: repeated `graph build`, `simulate start`, and `report generate` calls return the existing request instead of creating duplicates. +- Made `responses submit` attach generated `repair_invalid_json` requests to the current waiting stage, so later `resume` reuses the repair request instead of creating duplicates. +- Extended `GraphitiCompatibilityStore` Neo4j mode so episodes, agent memory, snapshot export/import, and timeline data stay in the Neo4j-backed compatibility layer instead of falling back to file storage. +- Aligned `GraphitiCompatibilityStore` default behavior with production `doctor`: `MIROFISH_GRAPHITI_STORE=auto` uses Neo4j, and offline file storage requires explicit `MIROFISH_GRAPHITI_STORE=file`. +- Added CLI and MCP follow-up Q&A through `answer_followup_question`, with GraphProvider retrieval context and queue-validated responses. +- Added Web Console generation (`generate_web_console`) producing an interactive HTML page at `runs//artifacts/web/index.html` with embedded artifact data, agent Q&A forms, questionnaire builder, report question input, and API-driven polling. The console gracefully degrades to static data when the Flask backend is offline. +- Added `backend/app/api/interaction.py` — Flask Blueprint providing REST endpoints for the Web Console: agent listing, agent Q&A, questionnaire submission, report questions, request polling, response submission, and artifact retrieval. All LLM work routes through `AgentRuntime / agent_queue`. +- Added interaction task types: `answer_agent_question`, `answer_agent_questionnaire`, `summarize_questionnaire`, `ask_report_question` with matching output schemas and mock provider coverage. +- Added CLI commands: `agents list/show/ask/answer`, `questionnaire send/show`, `report-question ask/answer`, `web generate`. +- Added MCP tools: `mirofish_generate_web_console`, `mirofish_list_agents`, `mirofish_get_agent`, `mirofish_ask_agent`, `mirofish_get_agent_answer`, `mirofish_send_questionnaire`, `mirofish_get_questionnaire_result`, `mirofish_ask_report_question`, `mirofish_get_report_question_answer`. +- Interaction artifacts are persisted to `runs//artifacts/interactions/agent_questions/`, `interactions/questionnaires/`, and `interactions/report_questions/`. +- Added staged workflow support alongside the existing auto workflow. Staged runs pause after `seed_input`, `prediction_requirement`, `simulation_settings`, `graph_build`, `profile_and_config`, and `simulation_run` until the user approves, rejects, updates settings, or reruns a stage. +- Added hard simulation settings to CLI/MCP run creation: `rounds`, `round_unit`, `minutes_per_round`, `pause_each_round`, `agent_count`, and `simulation_name`. `rounds` is persisted in `state.json` and no longer depends on natural-language requirement parsing. +- Added staged CLI commands under `mirofish-agent stage ...` and matching MCP tools for current-stage inspection, settings updates, stage approval/rejection, and reruns. +- Exposed `AgentModelBackendAdapter.last_need_agent_response` so OASIS/CAMEL runtime calls that hit `agent_queue` can be detected by orchestration code even when CAMEL expects a `ChatCompletion` object. +- Added a Flask `NeedAgentResponse` handler so legacy API calls return HTTP 202 structured `need_agent_response` payloads instead of generic 500s. +- Removed API-key default reads from legacy business facades; provider keys are now validated only inside legacy providers or provider-aware Flask guards. +- Moved OpenAI-compatible and Zep Cloud SDK packages out of default backend dependencies and into the optional `legacy` extra, so default agent installs do not require legacy SDKs. +- Strengthened `scripts/check_provider_boundaries.py` to reject direct legacy provider imports and Graphiti/Neo4j schema assumptions outside `GraphitiCompatibilityStore`. +- Changed ontology Python code generation to use provider-neutral Pydantic base classes instead of emitting graph SDK import strings. +- Added tests under `backend/tests/` and smoke/static scripts under `scripts/`. +- Added `docker-compose.agent.yml` and `scripts/setup_agent_deps.sh` for local agent dependencies and services. +- Updated `.env.example`, `AGENT_KIT.md`, README, and `docs/agent-usage/*`, including opencode usage. + +## Providers + +- `MIROFISH_LLM_PROVIDER=agent_queue`: writes strict request JSON and returns `need_agent_response`. +- `MIROFISH_LLM_PROVIDER=mock`: deterministic offline provider for tests. +- `MIROFISH_LLM_PROVIDER=openai_compatible`: legacy provider; the OpenAI SDK import is isolated to `backend/app/adapters/llm/openai_compatible.py`. +- `MIROFISH_GRAPH_PROVIDER=graphiti`: default agent graph provider. +- `MIROFISH_GRAPH_PROVIDER=zep`: legacy provider; Zep SDK imports are isolated to `backend/app/adapters/graph/zep.py`. + +## Dependencies And Local Services + +- Graphiti source code is not copied, cloned, or vendored into this repository. +- Graphiti is installed through backend dependency management with the optional `agent` extra: `uv sync --extra agent --group dev`. +- The `agent` extra includes `graphiti-core`, `neo4j`, and `mcp`. +- Legacy OpenAI-compatible and Zep Cloud SDKs are isolated in the optional `legacy` extra: `uv sync --extra legacy`. +- Neo4j is an external local graph database service. Supported non-Docker setup paths are Neo4j Desktop, Homebrew/native install, or an existing Neo4j 5.26+ instance. +- Docker Compose remains available through `docker-compose.agent.yml`, but Docker and Docker Compose are optional and are not required by `doctor`. +- Ollama is conditional. `doctor` only hard-fails Ollama checks when `MIROFISH_GRAPH_SEARCH_MODE=semantic` or `hybrid` and `MIROFISH_EMBEDDING_PROVIDER=ollama`. +- MiroFish only maintains `GraphitiGraphProvider` and `GraphitiCompatibilityStore`; all Graphiti/Neo4j schema assumptions are isolated there. + +## Minimal Demo + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/smoke_agent_queue_full.sh +``` + +Manual CLI: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 --json +uv run mirofish-agent run --run ../runs/chip-2036 --json +``` + +Staged CLI: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-agent create-run \ + --seed /path/to/seed.md \ + --requirement "预测未来10年全球芯片能力格局变化" \ + --output ../runs/chip-2036 \ + --mode staged \ + --rounds 10 \ + --round-unit year \ + --json +uv run mirofish-agent stage status --run ../runs/chip-2036 --json +uv run mirofish-agent stage approve --run ../runs/chip-2036 --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +When `need_agent_response` is returned, write the response file and run: + +```bash +uv run mirofish-agent responses validate --run ../runs/chip-2036 --response ../runs/chip-2036/responses/req_000001.json --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +## Codex Triple Extraction + +Use this prompt when processing an `extract_triples` request: + +```text +读取 request_file，严格按照 expected_schema 生成 response_file。只输出 JSON，不要添加解释。每个 triple 必须包含 subject、predicate、object、fact、evidence、confidence。不要编造现实种子中没有的事实。无法确认的关系不要写入，或将 confidence 降低。 +``` + +## Agent Engine Coverage + +Implemented full CLI/MCP lifecycle for: + +- ontology request +- triple extraction request +- profile request +- simulation config request +- batched simulation action request +- report request +- follow-up Q&A request +- agent question request +- agent questionnaire request +- questionnaire summary request +- report question request +- `report.md`, `verdict.json`, `timeline.json`, `graph_snapshot.json` +- interactive Web Console at `artifacts/web/index.html` + +Staged workflow maps to the original UI-style process: + +- `seed_input`: saves and summarizes `seed.md`. +- `prediction_requirement`: records the prediction target. +- `simulation_settings`: records hard settings such as `rounds=10` and `round_unit=year`. +- `graph_build`: runs ontology and triple extraction requests, then writes Graphiti/Neo4j. +- `profile_and_config`: generates profiles and simulation config; config rounds are forcibly taken from `simulation_settings`. +- `simulation_run`: runs the configured number of rounds and updates simulation progress. +- `report_generation`: writes report artifacts and records actual rounds in `verdict.json` and `timeline.json`. +- `followup_question`: uses AgentRuntime plus GraphProvider retrieval. + +Auto mode remains available for scripts and smoke tests. Staged mode is intended for QoderWork, Codex, Claude Code, Cursor, and other desktop agents that need user confirmation between phases. + +Legacy Flask/Vue UI is preserved but not fully upgraded to drive every checkpointed `agent_queue` stage. Legacy API calls return structured HTTP 202 `need_agent_response` payloads when model work needs an external agent. Full agent-mode orchestration is CLI/MCP-first. + +## Graphiti Limits + +`GraphitiCompatibilityStore` is a version-sensitive compatibility layer. It provides a no-LLM triplet write path and hides all Neo4j/Cypher/schema assumptions from business code. When Graphiti’s public fact triple API is available and stable in the installed version, this layer can be adapted internally without changing `GraphProvider`. + +Offline tests use explicit `MIROFISH_GRAPHITI_STORE=file`. Production Graphiti/Neo4j mode uses `MIROFISH_GRAPHITI_STORE=auto` or `neo4j` with `NEO4J_URI`, `NEO4J_USER`, `NEO4J_PASSWORD`, and `NEO4J_DATABASE`. + +## Verification Results + +Latest local verification: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend && uv run pytest -q +# 105 passed, 710 warnings + +cd /Users/leaf/Documents/future/MiroFish && bash scripts/smoke_agent_queue_full.sh +# CLI full agent_queue smoke passed, including follow-up Q&A + +cd /Users/leaf/Documents/future/MiroFish && bash scripts/smoke_agent_queue_staged.sh +# CLI staged agent_queue smoke passed, including stage approvals and rounds=10 artifacts + +cd /Users/leaf/Documents/future/MiroFish && bash scripts/smoke_mcp_full.sh +# MCP lifecycle smoke passed, including staged tool/schema checks, request validation, graph search/export, doctor, artifacts, and follow-up Q&A + +cd /Users/leaf/Documents/future/MiroFish && python scripts/check_provider_boundaries.py +# Provider boundary check passed + +cd /Users/leaf/Documents/future/MiroFish && bash -n scripts/setup_agent_deps.sh +# script syntax check passed + +cd /Users/leaf/Documents/future/MiroFish/backend && uv run mirofish-agent doctor --json +# status: ok; Neo4j 2025.04.0 connectable; hard_failures: [] + +# Live Graphiti/Neo4j provider smoke: +# add_triples/search/export_snapshot/clear_run_graph passed with MIROFISH_GRAPHITI_STORE=auto. + +# Live CLI Graphiti/Neo4j smoke: +# CLI Neo4j agent_queue smoke passed with graph search and final artifacts. +``` + +Frontend build must be run from the detected frontend package directory: + +```bash +cd /Users/leaf/Documents/future/MiroFish/frontend && npm run build +# built successfully; Vite reported only chunk-size/dynamic-import warnings +``` + +Doctor result in the current shell: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend && uv run mirofish-agent doctor --json +# status: ok +# graphiti_package: ok +# mcp_package: ok +# neo4j_package: ok +# neo4j_connectable: ok, bolt://localhost:7687 +# neo4j_version_supported: ok, 2025.04.0 +# docker: optional warning only when Docker is not installed +# docker_compose: optional warning only when Docker Compose is not installed +# graph_search_mode: fulltext +# embedding_provider: none +# ollama_connectable: optional; fulltext search does not require Ollama +# ollama_embedding_model: optional +# hard_failures: [] +``` + +Setup helper result in the current shell: + +```bash +cd /Users/leaf/Documents/future/MiroFish && bash scripts/setup_agent_deps.sh --neo4j desktop --skip-services +# Python agent dependencies installed/audited +# warning: Docker optional, skipped +# ok: Neo4j version 2025.04.0 +# warning: Ollama optional, skipped because MIROFISH_GRAPH_SEARCH_MODE=fulltext uses no semantic embedding +# ok: agent dependency and required service checks completed +``` + +This means the offline no-LLM compatibility path and the production Graphiti/Neo4j path are both verified. Docker is not required. Ollama is only required for semantic/hybrid graph search when `MIROFISH_EMBEDDING_PROVIDER=ollama`. diff --git a/README.md b/README.md index de082935a7..6486b5a1b3 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,26 @@ Click the image to watch MiroFish's deep prediction of the lost ending based on ## 🚀 Quick Start +### Agent Engine Mode + +MiroFish now supports CLI/MCP-driven agent mode. In this mode desktop agents process model tasks through request/response files, so `LLM_API_KEY`, `OPENAI_API_KEY`, and `ZEP_API_KEY` are not required. + +See [AGENT_KIT.md](./AGENT_KIT.md), [docs/agent-usage/codex.md](./docs/agent-usage/codex.md), and [docs/agent-usage/mcp.md](./docs/agent-usage/mcp.md). + +Install agent engine dependencies from the backend when using CLI/MCP mode: + +```bash +cd backend +uv sync --extra agent --group dev +``` + +Legacy OpenAI-compatible and Zep Cloud SDKs are optional: + +```bash +cd backend +uv sync --extra legacy +``` + ### Option 1: Source Code Deployment (Recommended) #### Prerequisites @@ -109,10 +129,30 @@ Click the image to watch MiroFish's deep prediction of the lost ending based on # Copy the example configuration file cp .env.example .env -# Edit the .env file and fill in the required API keys +# Edit the .env file. Agent mode does not require model or Zep API keys. +``` + +**Default Agent Environment Variables:** + +```env +MIROFISH_MODE=agent +MIROFISH_LLM_PROVIDER=agent_queue +MIROFISH_GRAPH_PROVIDER=graphiti +MIROFISH_RUNS_DIR=./runs + +NEO4J_URI=bolt://localhost:7687 +NEO4J_USER=neo4j +NEO4J_PASSWORD=password +NEO4J_DATABASE=neo4j + +MIROFISH_GRAPH_SEARCH_MODE=fulltext +MIROFISH_EMBEDDING_PROVIDER=none ``` -**Required Environment Variables:** +**Legacy Compatibility Variables:** + +These are required only when explicitly using `MIROFISH_LLM_PROVIDER=openai_compatible` or `MIROFISH_GRAPH_PROVIDER=zep`. +Install the optional legacy SDKs with `uv sync --extra legacy`. ```env # LLM API Configuration (supports any LLM API with OpenAI SDK format) @@ -200,4 +240,4 @@ MiroFish's simulation engine is powered by **[OASIS (Open Agent Social Interacti Star History Chart - \ No newline at end of file + diff --git a/backend/app/__init__.py b/backend/app/__init__.py index aba624bba9..9908c50e01 100644 --- a/backend/app/__init__.py +++ b/backend/app/__init__.py @@ -9,10 +9,11 @@ # 需要在所有其他导入之前设置 warnings.filterwarnings("ignore", message=".*resource_tracker.*") -from flask import Flask, request +from flask import Flask, jsonify, request from flask_cors import CORS from .config import Config +from .adapters.llm.agent_runtime import NeedAgentResponse from .utils.logger import setup_logger, get_logger @@ -61,12 +62,18 @@ def log_response(response): logger = get_logger('mirofish.request') logger.debug(f"响应: {response.status_code}") return response + + @app.errorhandler(NeedAgentResponse) + def handle_need_agent_response(error): + """Expose agent_queue waits as structured API responses instead of 500s.""" + return jsonify(error.result.to_dict()), 202 # 注册蓝图 - from .api import graph_bp, simulation_bp, report_bp + from .api import graph_bp, simulation_bp, report_bp, interaction_bp app.register_blueprint(graph_bp, url_prefix='/api/graph') app.register_blueprint(simulation_bp, url_prefix='/api/simulation') app.register_blueprint(report_bp, url_prefix='/api/report') + app.register_blueprint(interaction_bp, url_prefix='/api/interaction') # 健康检查 @app.route('/health') @@ -77,4 +84,3 @@ def health(): logger.info("MiroFish Backend 启动完成") return app - diff --git a/backend/app/adapters/__init__.py b/backend/app/adapters/__init__.py new file mode 100644 index 0000000000..6914e7c032 --- /dev/null +++ b/backend/app/adapters/__init__.py @@ -0,0 +1 @@ +"""Provider adapters for model and graph backends.""" diff --git a/backend/app/adapters/graph/__init__.py b/backend/app/adapters/graph/__init__.py new file mode 100644 index 0000000000..63f3fbc4e3 --- /dev/null +++ b/backend/app/adapters/graph/__init__.py @@ -0,0 +1,6 @@ +"""Graph provider adapters.""" + +from .base import GraphProvider, GraphTriple +from .factory import create_graph_provider + +__all__ = ["GraphProvider", "GraphTriple", "create_graph_provider"] diff --git a/backend/app/adapters/graph/base.py b/backend/app/adapters/graph/base.py new file mode 100644 index 0000000000..7ce1569acb --- /dev/null +++ b/backend/app/adapters/graph/base.py @@ -0,0 +1,76 @@ +"""Unified graph provider contract.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, ConfigDict, Field + + +class GraphTriple(BaseModel): + model_config = ConfigDict(extra="forbid") + + subject: str + predicate: str + object: str + fact: str + valid_at: Optional[str] = None + invalid_at: Optional[str] = None + source: Optional[str] = None + source_file: Optional[str] = None + evidence: str + confidence: float = Field(ge=0.0, le=1.0) + metadata: Dict[str, Any] = Field(default_factory=dict) + + +class GraphProvider(ABC): + name: str + + @abstractmethod + def add_episode(self, run_id: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def add_triples(self, run_id: str, triples: List[Dict[str, Any] | GraphTriple]) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def search(self, run_id: str, query: str, limit: int = 20) -> List[Dict[str, Any]]: + raise NotImplementedError + + @abstractmethod + def neighbors(self, run_id: str, entity: str, depth: int = 2) -> List[Dict[str, Any]]: + raise NotImplementedError + + @abstractmethod + def list_entities(self, run_id: str) -> List[Dict[str, Any]]: + raise NotImplementedError + + @abstractmethod + def get_entity(self, run_id: str, entity: str) -> Optional[Dict[str, Any]]: + raise NotImplementedError + + @abstractmethod + def update_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def get_agent_memory(self, run_id: str, agent_id: str) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def write_agent_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def export_snapshot(self, run_id: str, output_path: str) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def import_snapshot(self, run_id: str, input_path: str) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def clear_run_graph(self, run_id: str) -> Dict[str, Any]: + raise NotImplementedError diff --git a/backend/app/adapters/graph/factory.py b/backend/app/adapters/graph/factory.py new file mode 100644 index 0000000000..85b11934fe --- /dev/null +++ b/backend/app/adapters/graph/factory.py @@ -0,0 +1,24 @@ +"""Graph provider factory.""" + +from __future__ import annotations + +import os +from typing import Optional + +from .base import GraphProvider +from .graphiti import GraphitiGraphProvider +from .zep import ZepGraphProvider + + +def create_graph_provider(provider: Optional[str] = None) -> GraphProvider: + provider_name = provider or os.environ.get("MIROFISH_GRAPH_PROVIDER") + if not provider_name: + mode = os.environ.get("MIROFISH_MODE", "agent") + provider_name = "graphiti" if mode == "agent" else "zep" + + provider_name = provider_name.lower() + if provider_name == "graphiti": + return GraphitiGraphProvider() + if provider_name == "zep": + return ZepGraphProvider() + raise ValueError(f"Unsupported MIROFISH_GRAPH_PROVIDER: {provider_name}") diff --git a/backend/app/adapters/graph/graphiti.py b/backend/app/adapters/graph/graphiti.py new file mode 100644 index 0000000000..54b7d62a20 --- /dev/null +++ b/backend/app/adapters/graph/graphiti.py @@ -0,0 +1,642 @@ +"""Graphiti provider with no-LLM triplet write compatibility. + +The compatibility store intentionally hides all Neo4j/Cypher and Graphiti +schema assumptions from business code. +""" + +from __future__ import annotations + +import hashlib +import importlib.util +import json +import os +import re +from pathlib import Path +from typing import Any, Dict, List, Optional + +from .base import GraphProvider, GraphTriple + + +class GraphitiDependencyError(RuntimeError): + pass + + +class GraphitiCompatibilityStore: + """Version-sensitive Graphiti/Neo4j no-LLM triplet store.""" + + def __init__(self, *, store_path: str | None = None, require_neo4j: bool = False): + self.store_mode = os.environ.get("MIROFISH_GRAPHITI_STORE", "auto").lower() + if self.store_mode not in {"auto", "neo4j", "file"}: + raise GraphitiDependencyError("MIROFISH_GRAPHITI_STORE must be auto, neo4j, or file") + self.neo4j_uri = os.environ.get("NEO4J_URI") + if self.store_mode in {"auto", "neo4j"} and not self.neo4j_uri: + self.neo4j_uri = "bolt://localhost:7687" + self.neo4j_user = os.environ.get("NEO4J_USER", "neo4j") + self.neo4j_password = os.environ.get("NEO4J_PASSWORD", "password") + self.neo4j_database = os.environ.get("NEO4J_DATABASE", "neo4j") + self.store_path = Path(store_path or os.environ.get("MIROFISH_GRAPHITI_COMPAT_PATH", "./runs/.graphiti_compat_store.json")) + self.driver = None + + should_use_neo4j = self.store_mode in {"auto", "neo4j"} + if should_use_neo4j: + spec = importlib.util.find_spec("neo4j") + if not spec: + raise GraphitiDependencyError( + "neo4j Python package is required for GraphitiCompatibilityStore neo4j/auto mode; " + "set MIROFISH_GRAPHITI_STORE=file for offline compatibility tests" + ) + else: + from neo4j import GraphDatabase + + self.driver = GraphDatabase.driver( + self.neo4j_uri, + auth=(self.neo4j_user, self.neo4j_password), + ) + self._ensure_constraints() + + if not self.driver: + self.store_path.parent.mkdir(parents=True, exist_ok=True) + if not self.store_path.exists(): + self._write_file_store({"runs": {}}) + + def close(self) -> None: + if self.driver: + self.driver.close() + + def normalize_entity(self, value: str) -> str: + return re.sub(r"\s+", " ", value.strip()).casefold() + + def add_triplet(self, run_id: str, triple: GraphTriple) -> Dict[str, Any]: + if self.driver: + return self._add_triplet_neo4j(run_id, triple) + return self._add_triplet_file(run_id, triple) + + def add_triples(self, run_id: str, triples: List[GraphTriple]) -> Dict[str, Any]: + for triple in triples: + self.add_triplet(run_id, triple) + return {"provider": "graphiti", "store": "neo4j" if self.driver else "file", "triples_added": len(triples)} + + def get_or_create_entity_node(self, run_id: str, name: str, labels: Optional[List[str]] = None) -> Dict[str, Any]: + normalized = self.normalize_entity(name) + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + record = session.run( + """ + MERGE (e:MiroFishEntity {group_id: $run_id, normalized_name: $normalized}) + ON CREATE SET e.uuid = $uuid, e.name = $name, e.labels = $labels, e.created_at = datetime() + ON MATCH SET e.name = coalesce(e.name, $name) + RETURN e + """, + run_id=run_id, + normalized=normalized, + uuid=self._entity_uuid(run_id, normalized), + name=name, + labels=labels or ["Entity"], + ).single() + node = record["e"] + return self._to_jsonable(dict(node.items())) + + data = self._read_file_store() + run = self._file_run(data, run_id) + entities = run["entities"] + if normalized not in entities: + entities[normalized] = { + "uuid": self._entity_uuid(run_id, normalized), + "name": name, + "normalized_name": normalized, + "labels": labels or ["Entity"], + "summary": "", + "attributes": {}, + } + self._write_file_store(data) + return entities[normalized] + + def search_facts(self, run_id: str, query: str, limit: int = 20) -> List[Dict[str, Any]]: + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + """ + MATCH (s:MiroFishEntity {group_id: $run_id})-[r:MIROFISH_FACT]->(o:MiroFishEntity {group_id: $run_id}) + WHERE toLower(r.fact) CONTAINS toLower($search_query) + OR toLower(s.name) CONTAINS toLower($search_query) + OR toLower(o.name) CONTAINS toLower($search_query) + RETURN s, r, o + LIMIT $limit + """, + run_id=run_id, + search_query=query, + limit=limit, + ) + return [self._record_to_fact(row) for row in result] + + data = self._read_file_store() + run = self._file_run(data, run_id) + query_lower = query.casefold() + terms = self._query_terms(query_lower) + matches = [] + for triple in run["triples"].values(): + haystack = " ".join( + [ + triple.get("subject", ""), + triple.get("predicate", ""), + triple.get("object", ""), + triple.get("fact", ""), + triple.get("evidence", ""), + ] + ).casefold() + compact_haystack = re.sub(r"\W+", "", haystack) + if query_lower in haystack or any(term in haystack or term in compact_haystack for term in terms): + matches.append(triple) + return matches[:limit] + + def search_nodes(self, run_id: str, query: str, limit: int = 20) -> List[Dict[str, Any]]: + nodes = self.list_entities(run_id) + query_lower = query.casefold() + return [node for node in nodes if query_lower in node.get("name", "").casefold()][:limit] + + def neighbors(self, run_id: str, entity: str, depth: int = 2) -> List[Dict[str, Any]]: + normalized = self.normalize_entity(entity) + max_depth = max(1, min(int(depth), 10)) + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + f""" + MATCH path=(start:MiroFishEntity {{group_id: $run_id, normalized_name: $normalized}})-[*1..{max_depth}]-(n:MiroFishEntity {{group_id: $run_id}}) + RETURN nodes(path) AS nodes, relationships(path) AS relationships + LIMIT 100 + """, + run_id=run_id, + normalized=normalized, + ) + return [ + { + "nodes": [self._to_jsonable(dict(n.items())) for n in row["nodes"]], + "relationships": [self._to_jsonable(dict(r.items())) for r in row["relationships"]], + } + for row in result + ] + + data = self._read_file_store() + run = self._file_run(data, run_id) + frontier = {normalized} + seen = {normalized} + facts = [] + for _ in range(max(depth, 1)): + next_frontier = set() + for triple in run["triples"].values(): + subject = self.normalize_entity(triple["subject"]) + obj = self.normalize_entity(triple["object"]) + if subject in frontier or obj in frontier: + facts.append(triple) + if subject not in seen: + next_frontier.add(subject) + if obj not in seen: + next_frontier.add(obj) + seen.update(next_frontier) + frontier = next_frontier + if not frontier: + break + return facts + + def list_entities(self, run_id: str) -> List[Dict[str, Any]]: + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + "MATCH (e:MiroFishEntity {group_id: $run_id}) RETURN e ORDER BY e.name", + run_id=run_id, + ) + return [self._to_jsonable(dict(row["e"].items())) for row in result] + + data = self._read_file_store() + return list(self._file_run(data, run_id)["entities"].values()) + + def get_entity(self, run_id: str, entity: str) -> Optional[Dict[str, Any]]: + normalized = self.normalize_entity(entity) + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + row = session.run( + "MATCH (e:MiroFishEntity {group_id: $run_id, normalized_name: $normalized}) RETURN e", + run_id=run_id, + normalized=normalized, + ).single() + return self._to_jsonable(dict(row["e"].items())) if row else None + + data = self._read_file_store() + return self._file_run(data, run_id)["entities"].get(normalized) + + def update_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + current = self.get_agent_memory(run_id, agent_id) + current.update(memory) + return self.write_agent_memory(run_id, agent_id, current) + + def get_agent_memory(self, run_id: str, agent_id: str) -> Dict[str, Any]: + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + row = session.run( + """ + MATCH (m:MiroFishAgentMemory {group_id: $run_id, agent_id: $agent_id}) + RETURN m.memory_json AS memory_json + """, + run_id=run_id, + agent_id=agent_id, + ).single() + if not row: + return {} + return json.loads(row["memory_json"] or "{}") + + data = self._read_file_store() + return self._file_run(data, run_id)["memory"].get(agent_id, {}) + + def write_agent_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + session.run( + """ + MERGE (m:MiroFishAgentMemory {group_id: $run_id, agent_id: $agent_id}) + ON CREATE SET m.uuid = $uuid, m.created_at = datetime() + SET m.memory_json = $memory_json, m.updated_at = datetime() + """, + run_id=run_id, + agent_id=agent_id, + uuid=self._memory_uuid(run_id, agent_id), + memory_json=json.dumps(memory, ensure_ascii=False), + ) + return {"run_id": run_id, "agent_id": agent_id, "memory": memory} + + data = self._read_file_store() + run = self._file_run(data, run_id) + run["memory"][agent_id] = memory + self._write_file_store(data) + return {"run_id": run_id, "agent_id": agent_id, "memory": memory} + + def export_snapshot(self, run_id: str, output_path: str) -> Dict[str, Any]: + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + snapshot = self.snapshot(run_id) + path.write_text(json.dumps(snapshot, ensure_ascii=False, indent=2), encoding="utf-8") + return {"output_path": str(path), "nodes": len(snapshot["entities"]), "triples": len(snapshot["triples"])} + + def import_snapshot(self, run_id: str, input_path: str) -> Dict[str, Any]: + snapshot = json.loads(Path(input_path).read_text(encoding="utf-8")) + if self.driver: + self.clear_run_graph(run_id) + for entity in snapshot.get("entities", []): + name = entity.get("name") + if name: + self.get_or_create_entity_node(run_id, name, entity.get("labels")) + triples = [GraphTriple.model_validate(self._clean_triple_payload(triple)) for triple in snapshot.get("triples", [])] + self.add_triples(run_id, triples) + for episode in snapshot.get("episodes", []): + self.add_episode(run_id, episode.get("content", ""), episode.get("metadata", {})) + for agent_id, memory in snapshot.get("memory", {}).items(): + self.write_agent_memory(run_id, agent_id, memory) + return { + "run_id": run_id, + "imported": True, + "entities": len(snapshot.get("entities", [])), + "triples": len(triples), + "episodes": len(snapshot.get("episodes", [])), + } + + data = self._read_file_store() + data["runs"][run_id] = { + "entities": {self.normalize_entity(e["name"]): e for e in snapshot.get("entities", [])}, + "triples": {self._triple_uuid(run_id, t): t for t in snapshot.get("triples", [])}, + "episodes": snapshot.get("episodes", []), + "memory": snapshot.get("memory", {}), + } + self._write_file_store(data) + return {"run_id": run_id, "imported": True} + + def clear_run_graph(self, run_id: str) -> Dict[str, Any]: + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + session.run( + """ + MATCH (n {group_id: $run_id}) + DETACH DELETE n + """, + run_id=run_id, + ) + return {"run_id": run_id, "cleared": True} + + data = self._read_file_store() + data["runs"].pop(run_id, None) + self._write_file_store(data) + return {"run_id": run_id, "cleared": True} + + def add_episode(self, run_id: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + metadata_json = json.dumps(metadata or {}, ensure_ascii=False, sort_keys=True) + episode_uuid = self._episode_uuid(run_id, content, metadata_json) + if self.driver: + with self.driver.session(database=self.neo4j_database) as session: + session.run( + """ + MERGE (ep:MiroFishEpisode {group_id: $run_id, uuid: $uuid}) + ON CREATE SET ep.created_at = datetime() + SET ep.content = $content, + ep.metadata_json = $metadata_json, + ep.updated_at = datetime() + """, + run_id=run_id, + uuid=episode_uuid, + content=content, + metadata_json=metadata_json, + ) + return {"run_id": run_id, "uuid": episode_uuid} + + data = self._read_file_store() + run = self._file_run(data, run_id) + episode = {"content": content, "metadata": metadata or {}} + run["episodes"].append(episode) + self._write_file_store(data) + return {"run_id": run_id, "episode_index": len(run["episodes"]) - 1} + + def snapshot(self, run_id: str) -> Dict[str, Any]: + if self.driver: + return { + "run_id": run_id, + "provider": "graphiti", + "store": "neo4j", + "entities": self.list_entities(run_id), + "triples": self._list_facts_neo4j(run_id), + "episodes": self._list_episodes_neo4j(run_id), + "memory": self._list_memory_neo4j(run_id), + } + + data = self._read_file_store() + run = self._file_run(data, run_id) + return { + "run_id": run_id, + "provider": "graphiti", + "store": "neo4j" if self.driver else "file", + "entities": list(run["entities"].values()), + "triples": list(run["triples"].values()), + "episodes": run["episodes"], + "memory": run["memory"], + } + + def timeline(self, run_id: str) -> List[Dict[str, Any]]: + triples = self.snapshot(run_id)["triples"] + return sorted( + [ + { + "valid_at": triple.get("valid_at"), + "invalid_at": triple.get("invalid_at"), + "fact": triple.get("fact"), + "source": triple.get("source"), + } + for triple in triples + ], + key=lambda item: item.get("valid_at") or "", + ) + + def _ensure_constraints(self) -> None: + with self.driver.session(database=self.neo4j_database) as session: + session.run( + "CREATE CONSTRAINT mirofish_entity IF NOT EXISTS FOR (e:MiroFishEntity) REQUIRE (e.group_id, e.normalized_name) IS UNIQUE" + ) + session.run( + "CREATE CONSTRAINT mirofish_episode IF NOT EXISTS FOR (ep:MiroFishEpisode) REQUIRE (ep.group_id, ep.uuid) IS UNIQUE" + ) + session.run( + "CREATE CONSTRAINT mirofish_agent_memory IF NOT EXISTS FOR (m:MiroFishAgentMemory) REQUIRE (m.group_id, m.agent_id) IS UNIQUE" + ) + + def _list_facts_neo4j(self, run_id: str, limit: int = 100_000) -> List[Dict[str, Any]]: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + """ + MATCH (s:MiroFishEntity {group_id: $run_id})-[r:MIROFISH_FACT]->(o:MiroFishEntity {group_id: $run_id}) + RETURN s, r, o + LIMIT $limit + """, + run_id=run_id, + limit=limit, + ) + return [self._record_to_fact(row) for row in result] + + def _list_episodes_neo4j(self, run_id: str) -> List[Dict[str, Any]]: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + """ + MATCH (ep:MiroFishEpisode {group_id: $run_id}) + RETURN ep + ORDER BY ep.created_at + """, + run_id=run_id, + ) + episodes = [] + for row in result: + episode = dict(row["ep"].items()) + metadata = json.loads(episode.pop("metadata_json", "{}") or "{}") + episodes.append({"content": episode.get("content", ""), "metadata": metadata, "uuid": episode.get("uuid")}) + return episodes + + def _list_memory_neo4j(self, run_id: str) -> Dict[str, Any]: + with self.driver.session(database=self.neo4j_database) as session: + result = session.run( + """ + MATCH (m:MiroFishAgentMemory {group_id: $run_id}) + RETURN m.agent_id AS agent_id, properties(m) AS props + """, + run_id=run_id, + ) + return {row["agent_id"]: json.loads((row["props"] or {}).get("memory_json") or "{}") for row in result} + + def _add_triplet_neo4j(self, run_id: str, triple: GraphTriple) -> Dict[str, Any]: + subject_norm = self.normalize_entity(triple.subject) + object_norm = self.normalize_entity(triple.object) + triple_id = self._triple_uuid(run_id, triple.model_dump()) + with self.driver.session(database=self.neo4j_database) as session: + session.run( + """ + MERGE (s:MiroFishEntity {group_id: $run_id, normalized_name: $subject_norm}) + ON CREATE SET s.uuid = $subject_uuid, s.name = $subject, s.labels = ['Entity'], s.created_at = datetime() + MERGE (o:MiroFishEntity {group_id: $run_id, normalized_name: $object_norm}) + ON CREATE SET o.uuid = $object_uuid, o.name = $object, o.labels = ['Entity'], o.created_at = datetime() + MERGE (s)-[r:MIROFISH_FACT {uuid: $triple_id, group_id: $run_id}]->(o) + SET r.predicate = $predicate, + r.fact = $fact, + r.valid_at = $valid_at, + r.invalid_at = $invalid_at, + r.source = $source, + r.source_file = $source_file, + r.evidence = $evidence, + r.confidence = $confidence, + r.metadata_json = $metadata_json + """, + run_id=run_id, + subject_norm=subject_norm, + object_norm=object_norm, + subject_uuid=self._entity_uuid(run_id, subject_norm), + object_uuid=self._entity_uuid(run_id, object_norm), + subject=triple.subject, + object=triple.object, + triple_id=triple_id, + predicate=triple.predicate, + fact=triple.fact, + valid_at=triple.valid_at, + invalid_at=triple.invalid_at, + source=triple.source, + source_file=triple.source_file, + evidence=triple.evidence, + confidence=triple.confidence, + metadata_json=json.dumps(triple.metadata, ensure_ascii=False), + ) + return {"uuid": triple_id} + + def _add_triplet_file(self, run_id: str, triple: GraphTriple) -> Dict[str, Any]: + data = self._read_file_store() + run = self._file_run(data, run_id) + self.get_or_create_entity_node(run_id, triple.subject) + self.get_or_create_entity_node(run_id, triple.object) + data = self._read_file_store() + run = self._file_run(data, run_id) + triple_data = triple.model_dump() + triple_data["uuid"] = self._triple_uuid(run_id, triple_data) + run["triples"][triple_data["uuid"]] = triple_data + self._write_file_store(data) + return {"uuid": triple_data["uuid"]} + + def _record_to_fact(self, row: Any) -> Dict[str, Any]: + rel = dict(row["r"].items()) + return { + "subject": row["s"].get("name"), + "predicate": rel.get("predicate"), + "object": row["o"].get("name"), + "fact": rel.get("fact"), + "valid_at": rel.get("valid_at"), + "invalid_at": rel.get("invalid_at"), + "source": rel.get("source"), + "source_file": rel.get("source_file"), + "evidence": rel.get("evidence"), + "confidence": rel.get("confidence"), + "metadata": json.loads(rel.get("metadata_json") or "{}"), + "uuid": rel.get("uuid"), + } + + def _to_jsonable(self, value: Any) -> Any: + if isinstance(value, dict): + return {key: self._to_jsonable(item) for key, item in value.items()} + if isinstance(value, list): + return [self._to_jsonable(item) for item in value] + if hasattr(value, "iso_format"): + return value.iso_format() + if hasattr(value, "isoformat"): + return value.isoformat() + return value + + def _query_terms(self, query: str) -> List[str]: + terms = {query} + terms.update(token for token in re.split(r"\s+", query) if len(token) >= 2) + for chunk in re.findall(r"[\w\u4e00-\u9fff]+", query): + if len(chunk) >= 2: + terms.add(chunk) + if len(chunk) >= 5: + max_size = min(12, len(chunk)) + for size in range(max_size, 3, -1): + for start in range(0, len(chunk) - size + 1): + terms.add(chunk[start : start + size]) + return sorted(terms, key=len, reverse=True) + + def _entity_uuid(self, run_id: str, normalized: str) -> str: + return hashlib.sha256(f"{run_id}:entity:{normalized}".encode("utf-8")).hexdigest() + + def _episode_uuid(self, run_id: str, content: str, metadata_json: str) -> str: + return hashlib.sha256(f"{run_id}:episode:{content}:{metadata_json}".encode("utf-8")).hexdigest() + + def _memory_uuid(self, run_id: str, agent_id: str) -> str: + return hashlib.sha256(f"{run_id}:memory:{agent_id}".encode("utf-8")).hexdigest() + + def _clean_triple_payload(self, triple: Dict[str, Any]) -> Dict[str, Any]: + return {key: triple.get(key) for key in GraphTriple.model_fields} + + def _triple_uuid(self, run_id: str, triple: Dict[str, Any] | GraphTriple) -> str: + payload = triple.model_dump() if isinstance(triple, GraphTriple) else triple + stable = json.dumps( + { + "subject": self.normalize_entity(payload["subject"]), + "predicate": payload["predicate"], + "object": self.normalize_entity(payload["object"]), + "fact": payload["fact"], + "valid_at": payload.get("valid_at"), + "invalid_at": payload.get("invalid_at"), + }, + ensure_ascii=False, + sort_keys=True, + ) + return hashlib.sha256(f"{run_id}:triple:{stable}".encode("utf-8")).hexdigest() + + def _file_run(self, data: Dict[str, Any], run_id: str) -> Dict[str, Any]: + return data.setdefault("runs", {}).setdefault( + run_id, + {"entities": {}, "triples": {}, "episodes": [], "memory": {}}, + ) + + def _read_file_store(self) -> Dict[str, Any]: + if not self.store_path.exists(): + return {"runs": {}} + return json.loads(self.store_path.read_text(encoding="utf-8")) + + def _write_file_store(self, data: Dict[str, Any]) -> None: + self.store_path.parent.mkdir(parents=True, exist_ok=True) + self.store_path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") + + +class GraphitiGraphProvider(GraphProvider): + name = "graphiti" + + def __init__(self, store: Optional[GraphitiCompatibilityStore] = None, *, require_graphiti_package: bool = False): + if require_graphiti_package and importlib.util.find_spec("graphiti_core") is None: + raise GraphitiDependencyError( + "graphiti_core is not installed. Install Graphiti or use the no-LLM compatibility store." + ) + self.store = store or GraphitiCompatibilityStore() + + def add_episode(self, run_id: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + return self.store.add_episode(run_id, content, metadata) + + def add_triples(self, run_id: str, triples: List[Dict[str, Any] | GraphTriple]) -> Dict[str, Any]: + parsed = [triple if isinstance(triple, GraphTriple) else GraphTriple.model_validate(triple) for triple in triples] + return self.store.add_triples(run_id, parsed) + + def search(self, run_id: str, query: str, limit: int = 20) -> List[Dict[str, Any]]: + facts = self.store.search_facts(run_id, query, limit) + if len(facts) < limit: + facts.extend({"node": node} for node in self.store.search_nodes(run_id, query, limit - len(facts))) + return facts[:limit] + + def neighbors(self, run_id: str, entity: str, depth: int = 2) -> List[Dict[str, Any]]: + return self.store.neighbors(run_id, entity, depth) + + def list_entities(self, run_id: str) -> List[Dict[str, Any]]: + return self.store.list_entities(run_id) + + def get_entity(self, run_id: str, entity: str) -> Optional[Dict[str, Any]]: + return self.store.get_entity(run_id, entity) + + def update_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + return self.store.update_memory(run_id, agent_id, memory) + + def get_agent_memory(self, run_id: str, agent_id: str) -> Dict[str, Any]: + return self.store.get_agent_memory(run_id, agent_id) + + def write_agent_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + return self.store.write_agent_memory(run_id, agent_id, memory) + + def export_snapshot(self, run_id: str, output_path: str) -> Dict[str, Any]: + return self.store.export_snapshot(run_id, output_path) + + def import_snapshot(self, run_id: str, input_path: str) -> Dict[str, Any]: + return self.store.import_snapshot(run_id, input_path) + + def clear_run_graph(self, run_id: str) -> Dict[str, Any]: + return self.store.clear_run_graph(run_id) + + def export_timeline(self, run_id: str, output_path: str) -> Dict[str, Any]: + timeline = self.store.timeline(run_id) + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(timeline, ensure_ascii=False, indent=2), encoding="utf-8") + return {"output_path": str(path), "events": len(timeline)} diff --git a/backend/app/adapters/graph/zep.py b/backend/app/adapters/graph/zep.py new file mode 100644 index 0000000000..f06980c67b --- /dev/null +++ b/backend/app/adapters/graph/zep.py @@ -0,0 +1,117 @@ +"""Legacy Zep Cloud graph provider. + +This is the only backend/app path allowed to import the Zep SDK. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Dict, List, Optional + +from .base import GraphProvider, GraphTriple +from ...config import Config + + +class ZepGraphProvider(GraphProvider): + name = "zep" + + def __init__(self, api_key: Optional[str] = None): + self.api_key = api_key or Config.ZEP_API_KEY + if not self.api_key: + raise RuntimeError("ZEP_API_KEY is required for legacy zep graph provider") + try: + from zep_cloud.client import Zep + except ImportError as exc: + raise RuntimeError( + "zep-cloud package is required for legacy zep graph provider; " + "install with `uv sync --extra legacy`" + ) from exc + + self.client = Zep(api_key=self.api_key) + + def add_episode(self, run_id: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + try: + from zep_cloud import EpisodeData + except ImportError as exc: + raise RuntimeError( + "zep-cloud package is required for legacy zep graph provider; " + "install with `uv sync --extra legacy`" + ) from exc + + result = self.client.graph.add(graph_id=run_id, data=EpisodeData(data=content, type="text", metadata=metadata or {})) + return {"run_id": run_id, "result": str(result)} + + def add_triples(self, run_id: str, triples: List[Dict[str, Any] | GraphTriple]) -> Dict[str, Any]: + parsed = [triple if isinstance(triple, GraphTriple) else GraphTriple.model_validate(triple) for triple in triples] + content = "\n".join(triple.fact for triple in parsed) + self.add_episode(run_id, content, {"source": "agent_triples"}) + return {"run_id": run_id, "triples_added": len(parsed), "mode": "zep_episode_compat"} + + def search(self, run_id: str, query: str, limit: int = 20) -> List[Dict[str, Any]]: + result = self.client.graph.search(graph_id=run_id, query=query, limit=limit, scope="edges", reranker="rrf") + rows = [] + for edge in getattr(result, "edges", []) or []: + rows.append( + { + "uuid": getattr(edge, "uuid_", None) or getattr(edge, "uuid", ""), + "predicate": getattr(edge, "name", ""), + "fact": getattr(edge, "fact", ""), + "source_node_uuid": getattr(edge, "source_node_uuid", ""), + "target_node_uuid": getattr(edge, "target_node_uuid", ""), + } + ) + return rows + + def neighbors(self, run_id: str, entity: str, depth: int = 2) -> List[Dict[str, Any]]: + matches = self.search(run_id, entity, limit=50) + return matches[: max(depth, 1) * 20] + + def list_entities(self, run_id: str) -> List[Dict[str, Any]]: + nodes = self.client.graph.node.get_by_graph_id(graph_id=run_id) + return [self._node_to_dict(node) for node in nodes] + + def get_entity(self, run_id: str, entity: str) -> Optional[Dict[str, Any]]: + entity_lower = entity.casefold() + for node in self.list_entities(run_id): + if node.get("name", "").casefold() == entity_lower: + return node + return None + + def update_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + return self.write_agent_memory(run_id, agent_id, memory) + + def get_agent_memory(self, run_id: str, agent_id: str) -> Dict[str, Any]: + result = self.search(run_id, f"agent memory {agent_id}", limit=10) + return {"agent_id": agent_id, "facts": result} + + def write_agent_memory(self, run_id: str, agent_id: str, memory: Dict[str, Any]) -> Dict[str, Any]: + self.add_episode(run_id, json.dumps({"agent_id": agent_id, "memory": memory}, ensure_ascii=False), {"type": "agent_memory"}) + return {"run_id": run_id, "agent_id": agent_id, "memory": memory} + + def export_snapshot(self, run_id: str, output_path: str) -> Dict[str, Any]: + nodes = self.list_entities(run_id) + edges = self.search(run_id, "", limit=1000) + snapshot = {"run_id": run_id, "provider": "zep", "entities": nodes, "triples": edges} + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(snapshot, ensure_ascii=False, indent=2), encoding="utf-8") + return {"output_path": str(path), "nodes": len(nodes), "triples": len(edges)} + + def import_snapshot(self, run_id: str, input_path: str) -> Dict[str, Any]: + data = json.loads(Path(input_path).read_text(encoding="utf-8")) + self.add_episode(run_id, json.dumps(data, ensure_ascii=False), {"type": "snapshot_import"}) + return {"run_id": run_id, "imported": True, "mode": "zep_episode_compat"} + + def clear_run_graph(self, run_id: str) -> Dict[str, Any]: + self.client.graph.delete(graph_id=run_id) + return {"run_id": run_id, "cleared": True} + + def _node_to_dict(self, node: Any) -> Dict[str, Any]: + return { + "uuid": getattr(node, "uuid_", None) or getattr(node, "uuid", ""), + "name": getattr(node, "name", ""), + "labels": getattr(node, "labels", []) or [], + "summary": getattr(node, "summary", "") or "", + "attributes": getattr(node, "attributes", {}) or {}, + } diff --git a/backend/app/adapters/llm/__init__.py b/backend/app/adapters/llm/__init__.py new file mode 100644 index 0000000000..c28240a5f7 --- /dev/null +++ b/backend/app/adapters/llm/__init__.py @@ -0,0 +1,6 @@ +"""LLM provider adapters.""" + +from .agent_runtime import AgentRuntime, NeedAgentResponse +from .factory import create_llm_provider + +__all__ = ["AgentRuntime", "NeedAgentResponse", "create_llm_provider"] diff --git a/backend/app/adapters/llm/agent_queue.py b/backend/app/adapters/llm/agent_queue.py new file mode 100644 index 0000000000..3dc4077073 --- /dev/null +++ b/backend/app/adapters/llm/agent_queue.py @@ -0,0 +1,42 @@ +"""LLM provider that delegates model work to external desktop agents.""" + +from __future__ import annotations + +from pathlib import Path + +from .base import LLMProvider, LLMProviderResult, LLMTask +from ...agent_engine.queue import AgentQueue + + +class AgentQueueLLMProvider(LLMProvider): + name = "agent_queue" + + def __init__(self, run_dir: str | Path | None = None): + self.run_dir = Path(run_dir) if run_dir else None + + def run_task(self, task: LLMTask) -> LLMProviderResult: + if not self.run_dir: + raise RuntimeError("agent_queue provider requires run_dir") + + queue = AgentQueue(self.run_dir) + need = queue.create_request( + run_id=task.run_id, + task_type=task.task_type, + stage=task.stage, + expected_schema=task.expected_schema, + input_text=task.input_text, + input_files=task.input_files, + structured_input=task.structured_input, + system_prompt=task.system_prompt, + user_prompt=task.user_prompt, + validation_rules=task.validation_rules, + retry_policy=task.retry_policy, + context_refs=task.context_refs, + output_contract=task.output_contract, + ) + return LLMProviderResult( + status="need_agent_response", + request_id=need.request_id, + request_file=need.request_file, + expected_response_file=need.expected_response_file, + ) diff --git a/backend/app/adapters/llm/agent_runtime.py b/backend/app/adapters/llm/agent_runtime.py new file mode 100644 index 0000000000..538ee385f1 --- /dev/null +++ b/backend/app/adapters/llm/agent_runtime.py @@ -0,0 +1,62 @@ +"""Business-facing model runtime facade.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from .base import LLMProvider, LLMProviderResult, LLMTask +from .factory import create_llm_provider + + +class NeedAgentResponse(RuntimeError): + def __init__(self, result: LLMProviderResult): + self.result = result + super().__init__(f"Agent response required: {result.request_file}") + + +class AgentRuntime: + def __init__(self, provider: Optional[LLMProvider] = None, *, run_dir: str | None = None): + self.provider = provider or create_llm_provider(run_dir=run_dir) + self.run_dir = run_dir + + def run_task( + self, + *, + run_id: str, + task_type: str, + stage: str, + expected_schema: Dict[str, Any], + input_text: Optional[str] = None, + input_files: Optional[List[str]] = None, + structured_input: Optional[Dict[str, Any]] = None, + system_prompt: str = "", + user_prompt: str = "", + validation_rules: Optional[Dict[str, Any]] = None, + retry_policy: Optional[Dict[str, Any]] = None, + context_refs: Optional[List[str]] = None, + output_contract: Optional[Dict[str, Any]] = None, + ) -> LLMProviderResult: + task = LLMTask( + run_id=run_id, + task_type=task_type, + stage=stage, + expected_schema=expected_schema, + input_text=input_text, + input_files=input_files, + structured_input=structured_input, + system_prompt=system_prompt, + user_prompt=user_prompt, + validation_rules=validation_rules, + retry_policy=retry_policy, + context_refs=context_refs, + output_contract=output_contract, + ) + return self.provider.run_task(task) + + def require_output(self, **kwargs: Any) -> Dict[str, Any]: + result = self.run_task(**kwargs) + if result.status == "need_agent_response": + raise NeedAgentResponse(result) + if result.status != "ok": + raise RuntimeError(result.error or f"LLM task failed with status {result.status}") + return result.output or {} diff --git a/backend/app/adapters/llm/base.py b/backend/app/adapters/llm/base.py new file mode 100644 index 0000000000..ad528cdc4b --- /dev/null +++ b/backend/app/adapters/llm/base.py @@ -0,0 +1,57 @@ +"""Unified business-facing LLM provider interface.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any, Dict, List, Optional + + +@dataclass +class LLMTask: + run_id: str + task_type: str + stage: str + expected_schema: Dict[str, Any] + input_text: Optional[str] = None + input_files: Optional[List[str]] = None + structured_input: Optional[Dict[str, Any]] = None + system_prompt: str = "" + user_prompt: str = "" + validation_rules: Optional[Dict[str, Any]] = None + retry_policy: Optional[Dict[str, Any]] = None + context_refs: Optional[List[str]] = None + output_contract: Optional[Dict[str, Any]] = None + + +@dataclass +class LLMProviderResult: + status: str + output: Optional[Dict[str, Any]] = None + request_id: Optional[str] = None + request_file: Optional[str] = None + expected_response_file: Optional[str] = None + error: Optional[str] = None + + def to_dict(self) -> Dict[str, Any]: + data = { + "status": self.status, + "output": self.output, + "request_id": self.request_id, + "request_file": self.request_file, + "expected_response_file": self.expected_response_file, + "error": self.error, + } + return {key: value for key, value in data.items() if value is not None} + + +class LLMProvider(ABC): + name: str + + @abstractmethod + def run_task(self, task: LLMTask) -> LLMProviderResult: + raise NotImplementedError + + +class ProviderConfigurationError(RuntimeError): + pass diff --git a/backend/app/adapters/llm/camel_adapter.py b/backend/app/adapters/llm/camel_adapter.py new file mode 100644 index 0000000000..338061f7af --- /dev/null +++ b/backend/app/adapters/llm/camel_adapter.py @@ -0,0 +1,236 @@ +"""Bridge CAMEL/OASIS model calls into AgentRuntime.""" + +from __future__ import annotations + +import json +import time +from typing import Any, Dict, Iterable, List, Optional, Type + +from ...agent_engine.json_schema import object_schema +from .base import LLMProviderResult +from .agent_runtime import AgentRuntime + +from camel.messages import OpenAIMessage +from camel.models import BaseModelBackend +from camel.types import ( + ChatCompletion, + ChatCompletionChunk, + ChatCompletionMessage, + Choice, + CompletionUsage, + ModelType, +) +from camel.utils import BaseTokenCounter +from pydantic import BaseModel + + +SIMULATE_ACTION_SCHEMA = object_schema( + { + "actions": { + "type": "array", + "items": object_schema( + { + "agent_id": {"type": "string"}, + "action_id": {"type": "string"}, + "action_type": {"type": "string"}, + "content": {"type": "string"}, + } + ), + } + } +) + + +class AgentRuntimeTokenCounter(BaseTokenCounter): + def count_tokens_from_messages(self, messages: List[OpenAIMessage]) -> int: + return sum(len(str(message.get("content", ""))) // 4 + 1 for message in messages) + + def encode(self, text: str) -> List[int]: + return [0] * (len(text) // 4 + 1) + + def decode(self, token_ids: List[int]) -> str: + return "" + + +class AgentModelBackendAdapter(BaseModelBackend): + """CAMEL-compatible model backend backed by AgentRuntime. + + CAMEL/OASIS scripts can use this adapter instead of directly constructing + model SDK clients. Batch calls are preferred for same-round actions. + """ + + def __init__(self, run_id: str, run_dir: str, runtime: Optional[AgentRuntime] = None): + super().__init__(model_type=ModelType.STUB, model_config_dict={}) + self.run_id = run_id + self.run_dir = run_dir + self.runtime = runtime or AgentRuntime(run_dir=run_dir) + self.last_need_agent_response: Optional[Dict[str, Any]] = None + + @property + def token_counter(self) -> BaseTokenCounter: + if not self._token_counter: + self._token_counter = AgentRuntimeTokenCounter() + return self._token_counter + + def run_batch_actions(self, round_id: str, actions: Iterable[Dict[str, Any]]) -> Dict[str, Any]: + action_list = list(actions) + result = self.runtime.run_task( + run_id=self.run_id, + task_type="simulate_agent_action", + stage="simulation", + expected_schema=SIMULATE_ACTION_SCHEMA, + structured_input={"round_id": round_id, "actions": action_list}, + system_prompt="Generate simulation actions for the requested agents.", + user_prompt="Return JSON with actions keyed by agent_id and action_id.", + output_contract={"batch_key": ["agent_id", "action_id"]}, + ) + if result.status == "need_agent_response": + self.last_need_agent_response = result.to_dict() + return result.to_dict() + + def run_single_action(self, agent_id: str, action_id: str, prompt: str) -> Dict[str, Any]: + return self.run_batch_actions( + round_id="single", + actions=[{"agent_id": agent_id, "action_id": action_id, "prompt": prompt}], + ) + + def _run( + self, + messages: List[OpenAIMessage], + response_format: Optional[Type[BaseModel]] = None, + tools: Optional[List[Dict[str, Any]]] = None, + ) -> ChatCompletion: + result = self._run_model_task(messages, tools) + return self._to_chat_completion(result, tools) + + async def _arun( + self, + messages: List[OpenAIMessage], + response_format: Optional[Type[BaseModel]] = None, + tools: Optional[List[Dict[str, Any]]] = None, + ) -> ChatCompletion: + result = self._run_model_task(messages, tools) + return self._to_chat_completion(result, tools) + + def _run_model_task(self, messages: List[OpenAIMessage], tools: Optional[List[Dict[str, Any]]]) -> LLMProviderResult: + return self.runtime.run_task( + run_id=self.run_id, + task_type="simulate_agent_action", + stage="simulation_runtime", + expected_schema=SIMULATE_ACTION_SCHEMA, + structured_input={ + "messages": messages, + "tools": tools or [], + "actions": [ + { + "agent_id": self._extract_agent_id(messages), + "action_id": f"camel_{int(time.time() * 1000)}", + "prompt": messages[-1].get("content", "") if messages else "", + } + ], + }, + system_prompt="You are driving one OASIS/CAMEL social simulation agent action.", + user_prompt=json.dumps(messages, ensure_ascii=False), + output_contract={"camel_model_backend": True}, + ) + + def _to_chat_completion(self, result: LLMProviderResult, tools: Optional[List[Dict[str, Any]]]) -> ChatCompletion: + if result.status == "need_agent_response": + self.last_need_agent_response = result.to_dict() + content = json.dumps(self.last_need_agent_response, ensure_ascii=False) + tool_calls = self._tool_calls_for_actions([{"action_type": "DO_NOTHING", "action_args": {}}], tools) + return self._completion(content=content, tool_calls=tool_calls) + if result.status != "ok": + return self._completion(content=result.error or "AgentRuntime model task failed") + + output = result.output or {} + actions = output.get("actions") or [] + tool_calls = self._tool_calls_for_actions(actions, tools) + content = output.get("content") or output.get("text") or json.dumps(output, ensure_ascii=False) + return self._completion(content=content if not tool_calls else "", tool_calls=tool_calls) + + def _tool_calls_for_actions( + self, + actions: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]], + ) -> Optional[List[Dict[str, Any]]]: + available = self._available_tool_names(tools) + if not available: + return None + + calls = [] + for index, action in enumerate(actions[:1]): + tool_name = self._action_to_tool_name(action.get("action_type", "DO_NOTHING")) + if tool_name not in available: + tool_name = "do_nothing" if "do_nothing" in available else sorted(available)[0] + args = action.get("action_args") or self._action_args(action) + calls.append( + { + "id": f"call_agent_runtime_{index}", + "type": "function", + "function": { + "name": tool_name, + "arguments": json.dumps(args, ensure_ascii=False), + }, + } + ) + return calls or None + + def _available_tool_names(self, tools: Optional[List[Dict[str, Any]]]) -> set[str]: + names = set() + for tool in tools or []: + function = tool.get("function", {}) if isinstance(tool, dict) else {} + name = function.get("name") + if name: + names.add(name) + return names + + def _action_to_tool_name(self, action_type: str) -> str: + return str(action_type or "DO_NOTHING").lower() + + def _action_args(self, action: Dict[str, Any]) -> Dict[str, Any]: + if action.get("content"): + return {"content": action["content"]} + return {} + + def _extract_agent_id(self, messages: List[OpenAIMessage]) -> str: + for message in messages: + content = str(message.get("content", "")) + if "Agent" in content: + return "camel_agent" + return "camel_agent" + + def _completion( + self, + *, + content: str, + tool_calls: Optional[List[Dict[str, Any]]] = None, + ) -> ChatCompletion: + message = ChatCompletionMessage( + content=None if tool_calls else content, + role="assistant", + tool_calls=tool_calls, + ) + return ChatCompletion( + id=f"agent-runtime-{int(time.time() * 1000)}", + model="mirofish-agent-runtime", + object="chat.completion", + created=int(time.time()), + choices=[ + Choice( + finish_reason="tool_calls" if tool_calls else "stop", + index=0, + message=message, + logprobs=None, + ) + ], + usage=CompletionUsage( + completion_tokens=max(1, len(content) // 4), + prompt_tokens=1, + total_tokens=max(2, len(content) // 4 + 1), + ), + ) + + +def create_model_backend(run_id: str, run_dir: str) -> AgentModelBackendAdapter: + return AgentModelBackendAdapter(run_id=run_id, run_dir=run_dir) diff --git a/backend/app/adapters/llm/factory.py b/backend/app/adapters/llm/factory.py new file mode 100644 index 0000000000..959272808c --- /dev/null +++ b/backend/app/adapters/llm/factory.py @@ -0,0 +1,28 @@ +"""LLM provider factory.""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +from .agent_queue import AgentQueueLLMProvider +from .base import LLMProvider +from .mock import MockLLMProvider +from .openai_compatible import OpenAICompatibleProvider + + +def create_llm_provider(provider: Optional[str] = None, *, run_dir: str | Path | None = None) -> LLMProvider: + provider_name = provider or os.environ.get("MIROFISH_LLM_PROVIDER") + if not provider_name: + mode = os.environ.get("MIROFISH_MODE", "agent") + provider_name = "agent_queue" if mode == "agent" else "openai_compatible" + + provider_name = provider_name.lower() + if provider_name == "agent_queue": + return AgentQueueLLMProvider(run_dir=run_dir) + if provider_name == "mock": + return MockLLMProvider() + if provider_name == "openai_compatible": + return OpenAICompatibleProvider() + raise ValueError(f"Unsupported MIROFISH_LLM_PROVIDER: {provider_name}") diff --git a/backend/app/adapters/llm/mock.py b/backend/app/adapters/llm/mock.py new file mode 100644 index 0000000000..934175ecb2 --- /dev/null +++ b/backend/app/adapters/llm/mock.py @@ -0,0 +1,141 @@ +"""Deterministic provider for tests and offline smoke runs.""" + +from __future__ import annotations + +from typing import Any, Dict + +from .base import LLMProvider, LLMProviderResult, LLMTask + + +class MockLLMProvider(LLMProvider): + name = "mock" + + def run_task(self, task: LLMTask) -> LLMProviderResult: + output = self._output_for(task) + return LLMProviderResult(status="ok", output=output) + + def _output_for(self, task: LLMTask) -> Dict[str, Any]: + required = task.expected_schema.get("required", []) if task.expected_schema else [] + if "text" in required: + return {"text": "Mock text response generated without model APIs."} + if task.task_type == "generate_ontology": + return { + "ontology": { + "entity_types": [{"name": "Organization", "description": "An organization"}], + "edge_types": [{"name": "AFFECTS", "description": "Affects another entity"}], + } + } + if task.task_type == "extract_triples": + return { + "triples": [ + { + "subject": "Seed Entity", + "predicate": "relates_to", + "object": "Prediction Topic", + "fact": "Seed Entity relates to Prediction Topic.", + "valid_at": None, + "invalid_at": None, + "source": "mock", + "source_file": None, + "evidence": "mock evidence", + "confidence": 0.5, + "metadata": {}, + } + ] + } + if task.task_type == "generate_oasis_profiles": + return { + "profiles": [ + { + "agent_id": "agent_1", + "name": "Seed Analyst", + "persona": "Tracks seed facts and reacts conservatively.", + } + ] + } + if task.task_type == "generate_simulation_config": + return {"config": {"rounds": 1, "agents": ["agent_1"], "platforms": ["agent_queue"]}} + if task.task_type == "simulate_agent_action": + actions = [] + for item in (task.structured_input or {}).get("actions", []): + actions.append( + { + "agent_id": item.get("agent_id"), + "action_id": item.get("action_id"), + "action_type": "CREATE_POST", + "content": "Mock simulated action.", + } + ) + return {"actions": actions or [{"agent_id": "agent_1", "action_id": "act_1", "action_type": "CREATE_POST", "content": "Mock simulated action."}]} + if task.task_type == "summarize_round": + return { + "summary_markdown": "Mock round summary generated without model APIs.", + "key_events": [], + "memory_updates": [], + } + if task.task_type == "update_memory": + return { + "memory": (task.structured_input or {}).get("memory", {}), + "events": (task.structured_input or {}).get("events", []), + } + if task.task_type == "generate_report": + return { + "report_markdown": "# MiroFish Agent Report\n\nMock report generated without model APIs.", + "verdict": {"status": "mock", "confidence": 0.5}, + "timeline": [{"step": "mock", "summary": "Mock simulation completed."}], + } + if task.task_type == "answer_followup_question": + return { + "answer_markdown": "Mock follow-up answer generated without model APIs.", + "used_graph_results": (task.structured_input or {}).get("graph_results", []), + "confidence": 0.5, + } + if task.task_type == "answer_agent_question": + agent_id = (task.structured_input or {}).get("agent_id", "agent_1") + return { + "agent_id": agent_id, + "answer_markdown": f"Mock answer from {agent_id} generated without model APIs.", + "used_memory": [], + "used_graph_results": (task.structured_input or {}).get("graph_results", []), + "confidence": 0.5, + } + if task.task_type == "answer_agent_questionnaire": + questionnaire_id = (task.structured_input or {}).get("questionnaire_id", "q_mock") + questions = (task.structured_input or {}).get("questions", []) + agents = (task.structured_input or {}).get("agents", []) + answers = [] + for question in questions: + for agent in agents: + answers.append({ + "agent_id": agent.get("agent_id", "agent_1"), + "question_id": question.get("question_id", "q1"), + "answer_markdown": f"Mock answer from {agent.get('agent_id', 'agent_1')} to {question.get('question_id', 'q1')}.", + "confidence": 0.5, + }) + return { + "questionnaire_id": questionnaire_id, + "answers": answers, + "summary_markdown": "Mock questionnaire summary generated without model APIs.", + } + if task.task_type == "summarize_questionnaire": + return { + "questionnaire_id": (task.structured_input or {}).get("questionnaire_id", "q_mock"), + "summary_markdown": "Mock questionnaire summary generated without model APIs.", + "answer_count": len((task.structured_input or {}).get("answers", [])), + } + if task.task_type == "ask_report_question": + return { + "answer_markdown": "Mock report question answer generated without model APIs.", + "used_graph_results": (task.structured_input or {}).get("graph_results", []), + "confidence": 0.5, + } + if task.task_type == "validate_json_output": + return { + "valid": True, + "errors": [], + "output": (task.structured_input or {}).get("candidate", {}), + } + if task.task_type == "repair_invalid_json": + invalid_response = (task.structured_input or {}).get("invalid_response", {}) + return invalid_response.get("output", {}) + return {"result": task.structured_input or {}} diff --git a/backend/app/adapters/llm/openai_compatible.py b/backend/app/adapters/llm/openai_compatible.py new file mode 100644 index 0000000000..c0a08b9137 --- /dev/null +++ b/backend/app/adapters/llm/openai_compatible.py @@ -0,0 +1,54 @@ +"""Legacy OpenAI-compatible provider. + +This is the only backend/app path allowed to import the OpenAI SDK. +""" + +from __future__ import annotations + +import json +import re +from typing import Any, Dict + +from .base import LLMProvider, LLMProviderResult, LLMTask, ProviderConfigurationError +from ...config import Config + + +class OpenAICompatibleProvider(LLMProvider): + name = "openai_compatible" + + def __init__(self, api_key: str | None = None, base_url: str | None = None, model: str | None = None): + self.api_key = api_key or Config.LLM_API_KEY + self.base_url = base_url or Config.LLM_BASE_URL + self.model = model or Config.LLM_MODEL_NAME + if not self.api_key: + raise ProviderConfigurationError("LLM_API_KEY is required for openai_compatible provider") + + try: + from openai import OpenAI + except ImportError as exc: + raise ProviderConfigurationError( + "openai package is required for openai_compatible legacy provider; " + "install with `uv sync --extra legacy`" + ) from exc + + self.client = OpenAI(api_key=self.api_key, base_url=self.base_url) + + def run_task(self, task: LLMTask) -> LLMProviderResult: + response = self.client.chat.completions.create( + model=self.model, + messages=[ + {"role": "system", "content": task.system_prompt or "Return valid JSON."}, + {"role": "user", "content": task.user_prompt or task.input_text or json.dumps(task.structured_input or {}, ensure_ascii=False)}, + ], + response_format={"type": "json_object"}, + temperature=0.3, + ) + content = response.choices[0].message.content or "{}" + content = re.sub(r"[\s\S]*?", "", content).strip() + content = re.sub(r"^```(?:json)?\s*\n?", "", content, flags=re.IGNORECASE) + content = re.sub(r"\n?```\s*$", "", content).strip() + try: + output: Dict[str, Any] = json.loads(content) + except json.JSONDecodeError as exc: + return LLMProviderResult(status="error", error=f"invalid JSON from openai_compatible provider: {exc}") + return LLMProviderResult(status="ok", output=output) diff --git a/backend/app/agent_engine/cli.py b/backend/app/agent_engine/cli.py new file mode 100644 index 0000000000..9df9557051 --- /dev/null +++ b/backend/app/agent_engine/cli.py @@ -0,0 +1,360 @@ +"""Agent-friendly MiroFish CLI.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Dict + +from .runner import PredictionRunService + + +def emit(result: Dict[str, Any], as_json: bool) -> None: + if as_json: + print(json.dumps(result, ensure_ascii=False, indent=2)) + return + + status = result.get("status") + if status == "need_agent_response": + print(f"need_agent_response: {result['request_id']}") + print(f"request_file: {result['request_file']}") + print(f"expected_response_file: {result['expected_response_file']}") + elif status == "created": + print(f"created run: {result['run_id']}") + print(f"run_dir: {result['run_dir']}") + elif status == "awaiting_user_confirmation": + print(f"awaiting_user_confirmation: {result['stage']}") + elif status == "ok": + print(json.dumps(result, ensure_ascii=False, indent=2)) + elif status == "completed": + print("completed") + print(json.dumps(result.get("artifacts", []), ensure_ascii=False, indent=2)) + else: + print(json.dumps(result, ensure_ascii=False, indent=2)) + + +def add_json(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--json", action="store_true", help="Emit stable JSON output") + + +def add_create_run_args(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--seed", required=True) + parser.add_argument("--requirement", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--mode", choices=["auto", "staged"], default="auto") + parser.add_argument("--rounds", type=int, default=10) + parser.add_argument("--round-unit", choices=["year", "month", "day", "step"], default="year") + parser.add_argument("--minutes-per-round", type=int, default=None) + parser.add_argument("--pause-each-round", action=argparse.BooleanOptionalAction, default=False) + parser.add_argument("--agent-count", type=int, default=None) + parser.add_argument("--simulation-name", default=None) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="mirofish-agent") + add_json(parser) + sub = parser.add_subparsers(dest="command", required=True) + + init = sub.add_parser("init") + add_json(init) + add_create_run_args(init) + + create_run = sub.add_parser("create-run") + add_json(create_run) + add_create_run_args(create_run) + + run = sub.add_parser("run") + add_json(run) + run.add_argument("--run", required=True) + + resume = sub.add_parser("resume") + add_json(resume) + resume.add_argument("--run", required=True) + + status = sub.add_parser("status") + add_json(status) + status.add_argument("--run", required=True) + + stage = sub.add_parser("stage") + stage_sub = stage.add_subparsers(dest="stage_command", required=True) + stage_status = stage_sub.add_parser("status") + add_json(stage_status) + stage_status.add_argument("--run", required=True) + stage_next = stage_sub.add_parser("next") + add_json(stage_next) + stage_next.add_argument("--run", required=True) + stage_approve = stage_sub.add_parser("approve") + add_json(stage_approve) + stage_approve.add_argument("--run", required=True) + stage_reject = stage_sub.add_parser("reject") + add_json(stage_reject) + stage_reject.add_argument("--run", required=True) + stage_reject.add_argument("--reason", default="") + stage_update = stage_sub.add_parser("update-settings") + add_json(stage_update) + stage_update.add_argument("--run", required=True) + stage_update.add_argument("--rounds", type=int, default=None) + stage_update.add_argument("--round-unit", choices=["year", "month", "day", "step"], default=None) + stage_update.add_argument("--minutes-per-round", type=int, default=None) + stage_update.add_argument("--pause-each-round", action=argparse.BooleanOptionalAction, default=None) + stage_update.add_argument("--agent-count", type=int, default=None) + stage_update.add_argument("--simulation-name", default=None) + stage_rerun = stage_sub.add_parser("rerun") + add_json(stage_rerun) + stage_rerun.add_argument("--run", required=True) + stage_rerun.add_argument("--stage", required=True) + + requests = sub.add_parser("requests") + req_sub = requests.add_subparsers(dest="requests_command", required=True) + req_list = req_sub.add_parser("list") + add_json(req_list) + req_list.add_argument("--run", required=True) + req_show = req_sub.add_parser("show") + add_json(req_show) + req_show.add_argument("--run", required=True) + req_show.add_argument("--request-id", required=True) + + responses = sub.add_parser("responses") + resp_sub = responses.add_subparsers(dest="responses_command", required=True) + resp_validate = resp_sub.add_parser("validate") + add_json(resp_validate) + resp_validate.add_argument("--run", required=True) + resp_validate.add_argument("--response", required=True) + resp_submit = resp_sub.add_parser("submit") + add_json(resp_submit) + resp_submit.add_argument("--run", required=True) + resp_submit.add_argument("--response", required=True) + + graph = sub.add_parser("graph") + graph_sub = graph.add_subparsers(dest="graph_command", required=True) + graph_build = graph_sub.add_parser("build") + add_json(graph_build) + graph_build.add_argument("--run", required=True) + graph_build.add_argument("--provider", default=None) + graph_build.add_argument("--mode", default="agent-triples") + graph_search = graph_sub.add_parser("search") + add_json(graph_search) + graph_search.add_argument("--run", required=True) + graph_search.add_argument("--query", required=True) + graph_search.add_argument("--limit", type=int, default=20) + graph_export = graph_sub.add_parser("export") + add_json(graph_export) + graph_export.add_argument("--run", required=True) + graph_export.add_argument("--output", default=None) + + simulate = sub.add_parser("simulate") + sim_sub = simulate.add_subparsers(dest="simulate_command", required=True) + sim_start = sim_sub.add_parser("start") + add_json(sim_start) + sim_start.add_argument("--run", required=True) + sim_resume = sim_sub.add_parser("resume") + add_json(sim_resume) + sim_resume.add_argument("--run", required=True) + sim_status = sim_sub.add_parser("status") + add_json(sim_status) + sim_status.add_argument("--run", required=True) + + report = sub.add_parser("report") + report_sub = report.add_subparsers(dest="report_command", required=True) + report_generate = report_sub.add_parser("generate") + add_json(report_generate) + report_generate.add_argument("--run", required=True) + report_show = report_sub.add_parser("show") + add_json(report_show) + report_show.add_argument("--run", required=True) + + followup = sub.add_parser("followup") + followup_sub = followup.add_subparsers(dest="followup_command", required=True) + followup_ask = followup_sub.add_parser("ask") + add_json(followup_ask) + followup_ask.add_argument("--run", required=True) + followup_ask.add_argument("--question", required=True) + followup_ask.add_argument("--limit", type=int, default=20) + followup_show = followup_sub.add_parser("show") + add_json(followup_show) + followup_show.add_argument("--run", required=True) + followup_show.add_argument("--request-id", required=True) + + artifacts = sub.add_parser("artifacts") + artifacts_sub = artifacts.add_subparsers(dest="artifacts_command", required=True) + artifacts_list = artifacts_sub.add_parser("list") + add_json(artifacts_list) + artifacts_list.add_argument("--run", required=True) + + # ── Agent interaction commands ───────────────────────────────────────── + + agents = sub.add_parser("agents") + agents_sub = agents.add_subparsers(dest="agents_command", required=True) + agents_list = agents_sub.add_parser("list") + add_json(agents_list) + agents_list.add_argument("--run", required=True) + agents_show = agents_sub.add_parser("show") + add_json(agents_show) + agents_show.add_argument("--run", required=True) + agents_show.add_argument("--agent-id", required=True) + agents_ask = agents_sub.add_parser("ask") + add_json(agents_ask) + agents_ask.add_argument("--run", required=True) + agents_ask.add_argument("--agent-id", required=True) + agents_ask.add_argument("--question", required=True) + agents_ask.add_argument("--limit", type=int, default=20) + agents_answer = agents_sub.add_parser("answer") + add_json(agents_answer) + agents_answer.add_argument("--run", required=True) + agents_answer.add_argument("--request-id", required=True) + + report_question = sub.add_parser("report-question") + rq_sub = report_question.add_subparsers(dest="report_question_command", required=True) + rq_ask = rq_sub.add_parser("ask") + add_json(rq_ask) + rq_ask.add_argument("--run", required=True) + rq_ask.add_argument("--question", required=True) + rq_ask.add_argument("--limit", type=int, default=20) + rq_answer = rq_sub.add_parser("answer") + add_json(rq_answer) + rq_answer.add_argument("--run", required=True) + rq_answer.add_argument("--request-id", required=True) + + questionnaire = sub.add_parser("questionnaire") + q_sub = questionnaire.add_subparsers(dest="questionnaire_command", required=True) + q_send = q_sub.add_parser("send") + add_json(q_send) + q_send.add_argument("--run", required=True) + q_send.add_argument("--questions", required=True) + q_show = q_sub.add_parser("show") + add_json(q_show) + q_show.add_argument("--run", required=True) + q_show.add_argument("--questionnaire-id", required=True) + + web = sub.add_parser("web") + web_sub = web.add_subparsers(dest="web_command", required=True) + web_generate = web_sub.add_parser("generate") + add_json(web_generate) + web_generate.add_argument("--run", required=True) + + doctor = sub.add_parser("doctor") + add_json(doctor) + doctor.add_argument("--runs-dir", default=None) + + return parser + + +def dispatch(args: argparse.Namespace) -> Dict[str, Any]: + service = PredictionRunService() + if args.command in {"init", "create-run"}: + return service.create_run( + args.seed, + args.requirement, + args.output, + mode=args.mode, + rounds=args.rounds, + round_unit=args.round_unit, + minutes_per_round=args.minutes_per_round, + pause_each_round=args.pause_each_round, + agent_count=args.agent_count, + simulation_name=args.simulation_name, + ) + if args.command == "run": + return service.run(args.run) + if args.command == "resume": + return service.resume(args.run) + if args.command == "status": + return service.status(args.run) + if args.command == "stage": + if args.stage_command == "status": + return service.get_current_stage(args.run) + if args.stage_command == "next": + return service.resume(args.run) + if args.stage_command == "approve": + return service.approve_stage(args.run) + if args.stage_command == "reject": + return service.reject_stage(args.run, args.reason) + if args.stage_command == "update-settings": + return service.update_simulation_settings( + args.run, + rounds=args.rounds, + round_unit=args.round_unit, + minutes_per_round=args.minutes_per_round, + pause_each_round=args.pause_each_round, + agent_count=args.agent_count, + simulation_name=args.simulation_name, + ) + return service.rerun_stage(args.run, args.stage) + if args.command == "requests": + if args.requests_command == "list": + return service.list_requests(args.run) + return service.get_request(args.run, args.request_id) + if args.command == "responses": + if args.responses_command == "validate": + return service.validate_response(args.run, args.response) + return service.submit_response(args.run, args.response) + if args.command == "graph": + if args.graph_command == "build": + return service.build_graph(args.run, provider=args.provider, mode=args.mode) + if args.graph_command == "search": + return service.search_graph(args.run, args.query, args.limit) + return service.export_graph(args.run, args.output) + if args.command == "simulate": + if args.simulate_command == "start": + return service.start_simulation(args.run) + if args.simulate_command == "resume": + return service.resume(args.run) + return service.simulation_status(args.run) + if args.command == "report": + if args.report_command == "generate": + return service.generate_report(args.run) + return service.get_report(args.run) + if args.command == "followup": + if args.followup_command == "ask": + return service.ask_followup_question(args.run, args.question, args.limit) + return service.get_followup_answer(args.run, args.request_id) + if args.command == "artifacts": + return service.list_artifacts(args.run) + if args.command == "agents": + if args.agents_command == "list": + return service.list_agents(args.run) + if args.agents_command == "show": + return service.get_agent(args.run, args.agent_id) + if args.agents_command == "ask": + return service.ask_agent(args.run, args.agent_id, args.question, args.limit) + if args.agents_command == "answer": + return service.get_agent_answer(args.run, args.request_id) + if args.command == "report-question": + if args.report_question_command == "ask": + return service.ask_report_question(args.run, args.question, args.limit) + if args.report_question_command == "answer": + return service.get_report_question_answer(args.run, args.request_id) + if args.command == "questionnaire": + if args.questionnaire_command == "send": + questions = json.loads(Path(args.questions).read_text(encoding="utf-8")) + if not isinstance(questions, list): + questions = [questions] + return service.send_questionnaire(args.run, questions) + if args.questionnaire_command == "show": + return service.get_questionnaire_result(args.run, args.questionnaire_id) + if args.command == "web": + if args.web_command == "generate": + return service.generate_web_console(args.run) + if args.command == "doctor": + return service.doctor(args.runs_dir) + raise ValueError(f"unsupported command: {args.command}") + + +def main(argv: list[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + as_json = bool(getattr(args, "json", False)) + try: + result = dispatch(args) + except Exception as exc: + result = {"status": "error", "error": str(exc), "error_type": exc.__class__.__name__} + emit(result, as_json) + return 1 + emit(result, as_json) + return 0 if result.get("status") not in {"failed", "error"} else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/backend/app/agent_engine/contracts.py b/backend/app/agent_engine/contracts.py new file mode 100644 index 0000000000..62d63d541b --- /dev/null +++ b/backend/app/agent_engine/contracts.py @@ -0,0 +1,201 @@ +"""Expected output contracts for all agent task types.""" + +from __future__ import annotations + +from typing import Any, Dict + +from .json_schema import TRIPLE_SCHEMA, object_schema + + +ONTOLOGY_OUTPUT_SCHEMA = object_schema({"ontology": {"type": "object"}}, ["ontology"]) + +TRIPLES_OUTPUT_SCHEMA = object_schema( + {"triples": {"type": "array", "items": TRIPLE_SCHEMA}}, + ["triples"], +) + +PROFILES_OUTPUT_SCHEMA = object_schema( + {"profiles": {"type": "array", "items": {"type": "object"}}}, + ["profiles"], +) + +SIMULATION_CONFIG_OUTPUT_SCHEMA = object_schema({"config": {"type": "object"}}, ["config"]) + +SIMULATE_ACTION_OUTPUT_SCHEMA = object_schema( + { + "actions": { + "type": "array", + "items": object_schema( + { + "agent_id": {"type": "string"}, + "action_id": {"type": "string"}, + "action_type": {"type": "string"}, + "content": {"type": "string"}, + }, + ["agent_id", "action_id", "action_type", "content"], + ), + } + }, + ["actions"], +) + +REPORT_OUTPUT_SCHEMA = object_schema( + { + "report_markdown": {"type": "string", "minLength": 1}, + "verdict": {"type": "object"}, + "timeline": {"type": "array", "items": {"type": "object"}}, + }, + ["report_markdown", "verdict", "timeline"], +) + +FOLLOWUP_OUTPUT_SCHEMA = object_schema( + { + "answer_markdown": {"type": "string", "minLength": 1}, + "used_graph_results": {"type": "array", "items": {"type": "object"}}, + "confidence": {"type": "number", "minimum": 0.0, "maximum": 1.0}, + }, + ["answer_markdown", "used_graph_results", "confidence"], +) + +AGENT_QUESTION_OUTPUT_SCHEMA = object_schema( + { + "agent_id": {"type": "string", "minLength": 1}, + "answer_markdown": {"type": "string", "minLength": 1}, + "used_memory": {"type": "array", "items": {"type": "object"}}, + "used_graph_results": {"type": "array", "items": {"type": "object"}}, + "confidence": {"type": "number", "minimum": 0.0, "maximum": 1.0}, + }, + ["agent_id", "answer_markdown", "used_memory", "used_graph_results", "confidence"], +) + +AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA = object_schema( + { + "questionnaire_id": {"type": "string", "minLength": 1}, + "answers": { + "type": "array", + "items": object_schema( + { + "agent_id": {"type": "string", "minLength": 1}, + "question_id": {"type": "string", "minLength": 1}, + "answer_markdown": {"type": "string", "minLength": 1}, + "confidence": {"type": "number", "minimum": 0.0, "maximum": 1.0}, + }, + ["agent_id", "question_id", "answer_markdown", "confidence"], + ), + }, + "summary_markdown": {"type": "string"}, + }, + ["questionnaire_id", "answers", "summary_markdown"], +) + +QUESTIONNAIRE_SUMMARY_OUTPUT_SCHEMA = object_schema( + { + "questionnaire_id": {"type": "string", "minLength": 1}, + "summary_markdown": {"type": "string", "minLength": 1}, + "answer_count": {"type": "integer", "minimum": 0}, + }, + ["questionnaire_id", "summary_markdown", "answer_count"], +) + +REPORT_QUESTION_OUTPUT_SCHEMA = object_schema( + { + "answer_markdown": {"type": "string", "minLength": 1}, + "used_graph_results": {"type": "array", "items": {"type": "object"}}, + "confidence": {"type": "number", "minimum": 0.0, "maximum": 1.0}, + }, + ["answer_markdown", "used_graph_results", "confidence"], +) + +ROUND_SUMMARY_OUTPUT_SCHEMA = object_schema( + { + "summary_markdown": {"type": "string", "minLength": 1}, + "key_events": {"type": "array", "items": {"type": "object"}}, + "memory_updates": {"type": "array", "items": {"type": "object"}}, + }, + ["summary_markdown", "key_events", "memory_updates"], +) + +MEMORY_UPDATE_OUTPUT_SCHEMA = object_schema( + { + "memory": {"type": "object"}, + "events": {"type": "array", "items": {"type": "object"}}, + }, + ["memory", "events"], +) + +VALIDATE_JSON_OUTPUT_SCHEMA = object_schema( + { + "valid": {"type": "boolean"}, + "errors": {"type": "array", "items": {"type": "string"}}, + "output": {"type": "object"}, + }, + ["valid", "errors", "output"], +) + +GENERIC_REPAIR_OUTPUT_SCHEMA: Dict[str, Any] = { + "type": "object", + "additionalProperties": True, +} + + +TASK_OUTPUT_SCHEMAS: Dict[str, Dict[str, Any]] = { + "extract_triples": TRIPLES_OUTPUT_SCHEMA, + "generate_ontology": ONTOLOGY_OUTPUT_SCHEMA, + "generate_oasis_profiles": PROFILES_OUTPUT_SCHEMA, + "generate_simulation_config": SIMULATION_CONFIG_OUTPUT_SCHEMA, + "simulate_agent_action": SIMULATE_ACTION_OUTPUT_SCHEMA, + "summarize_round": ROUND_SUMMARY_OUTPUT_SCHEMA, + "update_memory": MEMORY_UPDATE_OUTPUT_SCHEMA, + "generate_report": REPORT_OUTPUT_SCHEMA, + "answer_followup_question": FOLLOWUP_OUTPUT_SCHEMA, + "answer_agent_question": AGENT_QUESTION_OUTPUT_SCHEMA, + "answer_agent_questionnaire": AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA, + "summarize_questionnaire": QUESTIONNAIRE_SUMMARY_OUTPUT_SCHEMA, + "ask_report_question": REPORT_QUESTION_OUTPUT_SCHEMA, + "validate_json_output": VALIDATE_JSON_OUTPUT_SCHEMA, + "repair_invalid_json": GENERIC_REPAIR_OUTPUT_SCHEMA, +} + + +STAGE_CONTRACTS: Dict[str, Dict[str, Any]] = { + "ontology": { + "task_type": "generate_ontology", + "schema": ONTOLOGY_OUTPUT_SCHEMA, + "system_prompt": "Generate a compact ontology for prediction graph construction.", + "user_prompt": "Return JSON with an ontology object. Do not include explanations.", + }, + "graph": { + "task_type": "extract_triples", + "schema": TRIPLES_OUTPUT_SCHEMA, + "system_prompt": "Extract factual entity-relationship-entity triples from the seed only.", + "user_prompt": ( + "Read the seed and return triples. Each triple must include subject, predicate, " + "object, fact, evidence, confidence, time fields, source fields, and metadata. " + "Do not invent facts not supported by evidence." + ), + }, + "profiles": { + "task_type": "generate_oasis_profiles", + "schema": PROFILES_OUTPUT_SCHEMA, + "system_prompt": "Generate OASIS/CAMEL-compatible agent profiles from the seed and graph facts.", + "user_prompt": "Return JSON with profiles array.", + }, + "config": { + "task_type": "generate_simulation_config", + "schema": SIMULATION_CONFIG_OUTPUT_SCHEMA, + "system_prompt": "Generate a simulation config that can run without direct model API keys.", + "user_prompt": "Return JSON with config object.", + }, + "simulation": { + "task_type": "simulate_agent_action", + "schema": SIMULATE_ACTION_OUTPUT_SCHEMA, + "system_prompt": "Generate batched simulation actions keyed by agent_id and action_id.", + "user_prompt": "Return JSON with actions array.", + }, + "report": { + "task_type": "generate_report", + "schema": REPORT_OUTPUT_SCHEMA, + "system_prompt": "Generate final prediction report artifacts from seed, graph, profiles, config, and simulation actions.", + "user_prompt": "Return report_markdown, verdict, and timeline.", + }, +} diff --git a/backend/app/agent_engine/json_schema.py b/backend/app/agent_engine/json_schema.py new file mode 100644 index 0000000000..ba876a897e --- /dev/null +++ b/backend/app/agent_engine/json_schema.py @@ -0,0 +1,127 @@ +"""Small JSON Schema validator for agent response contracts. + +The project already depends on Pydantic, but not jsonschema. This validator +implements the subset we use in queue contracts so smoke tests stay offline. +""" + +from __future__ import annotations + +from typing import Any, Dict, List + + +def validate_json_schema(value: Any, schema: Dict[str, Any], path: str = "$") -> List[str]: + errors: List[str] = [] + schema_type = schema.get("type") + + if schema_type == "null": + return [] if value is None else [f"{path}: expected null"] + + if schema_type == "object": + if not isinstance(value, dict): + return [f"{path}: expected object"] + required = schema.get("required", []) + for key in required: + if key not in value: + errors.append(f"{path}.{key}: missing required field") + properties = schema.get("properties", {}) + additional = schema.get("additionalProperties", True) + for key, item in value.items(): + if key in properties: + errors.extend(validate_json_schema(item, properties[key], f"{path}.{key}")) + elif additional is False: + errors.append(f"{path}.{key}: extra field is not allowed") + return errors + + if schema_type == "array": + if not isinstance(value, list): + return [f"{path}: expected array"] + item_schema = schema.get("items", {}) + for index, item in enumerate(value): + errors.extend(validate_json_schema(item, item_schema, f"{path}[{index}]")) + return errors + + if schema_type == "string": + if not isinstance(value, str): + return [f"{path}: expected string"] + if schema.get("minLength") is not None and len(value) < int(schema["minLength"]): + errors.append(f"{path}: shorter than minLength") + return errors + + if schema_type == "number": + if not isinstance(value, (int, float)) or isinstance(value, bool): + return [f"{path}: expected number"] + if schema.get("minimum") is not None and value < schema["minimum"]: + errors.append(f"{path}: below minimum {schema['minimum']}") + if schema.get("maximum") is not None and value > schema["maximum"]: + errors.append(f"{path}: above maximum {schema['maximum']}") + return errors + + if schema_type == "integer": + if not isinstance(value, int) or isinstance(value, bool): + return [f"{path}: expected integer"] + return errors + + if schema_type == "boolean": + if not isinstance(value, bool): + return [f"{path}: expected boolean"] + return errors + + if isinstance(schema_type, list): + matched = False + nested_errors: List[str] = [] + for candidate in schema_type: + candidate_schema = {**schema, "type": candidate} + candidate_errors = validate_json_schema(value, candidate_schema, path) + if not candidate_errors: + matched = True + break + nested_errors.extend(candidate_errors) + if not matched: + errors.append(f"{path}: did not match any allowed type {schema_type}; {nested_errors[:2]}") + return errors + + enum = schema.get("enum") + if enum is not None and value not in enum: + errors.append(f"{path}: value {value!r} not in enum") + return errors + + +TRIPLE_SCHEMA: Dict[str, Any] = { + "type": "object", + "additionalProperties": False, + "required": [ + "subject", + "predicate", + "object", + "fact", + "valid_at", + "invalid_at", + "source", + "source_file", + "evidence", + "confidence", + "metadata", + ], + "properties": { + "subject": {"type": "string", "minLength": 1}, + "predicate": {"type": "string", "minLength": 1}, + "object": {"type": "string", "minLength": 1}, + "fact": {"type": "string", "minLength": 1}, + "valid_at": {"type": ["string", "null"]}, + "invalid_at": {"type": ["string", "null"]}, + "source": {"type": ["string", "null"]}, + "source_file": {"type": ["string", "null"]}, + "evidence": {"type": "string", "minLength": 1}, + "confidence": {"type": "number", "minimum": 0.0, "maximum": 1.0}, + "metadata": {"type": "object"}, + }, +} + + +def object_schema(properties: Dict[str, Any], required: List[str] | None = None) -> Dict[str, Any]: + return { + "type": "object", + "additionalProperties": False, + "required": required or list(properties.keys()), + "properties": properties, + } diff --git a/backend/app/agent_engine/queue.py b/backend/app/agent_engine/queue.py new file mode 100644 index 0000000000..e204ef50b6 --- /dev/null +++ b/backend/app/agent_engine/queue.py @@ -0,0 +1,183 @@ +"""Filesystem queue for external desktop agents.""" + +from __future__ import annotations + +import json +import shutil +from pathlib import Path +from typing import Any, Dict, List, Optional + +from pydantic import ValidationError + +from .json_schema import validate_json_schema +from .schemas import AgentNeedResponse, AgentRequest, AgentResponse, ValidationResult +from .state import RunStore + + +class AgentQueueError(ValueError): + pass + + +class AgentQueue: + def __init__(self, run_dir: str | Path): + self.store = RunStore(run_dir) + self.store.ensure_layout() + + def create_request( + self, + *, + run_id: str, + task_type: str, + stage: str, + expected_schema: Dict[str, Any], + input_text: Optional[str] = None, + input_files: Optional[List[str]] = None, + structured_input: Optional[Dict[str, Any]] = None, + system_prompt: str = "", + user_prompt: str = "", + validation_rules: Optional[Dict[str, Any]] = None, + retry_policy: Optional[Dict[str, Any]] = None, + context_refs: Optional[List[str]] = None, + output_contract: Optional[Dict[str, Any]] = None, + ) -> AgentNeedResponse: + request_id = self.store.next_request_id() + request = AgentRequest( + request_id=request_id, + run_id=run_id, + type=task_type, + stage=stage, + input_text=input_text, + input_files=input_files or [], + structured_input=structured_input or {}, + system_prompt=system_prompt, + user_prompt=user_prompt, + expected_schema=expected_schema, + validation_rules=validation_rules or {}, + retry_policy=retry_policy or {}, + context_refs=context_refs or [], + output_contract=output_contract or {}, + ) + request_file = self.store.requests_dir / f"{request_id}.json" + request_file.write_text(request.model_dump_json(indent=2), encoding="utf-8") + return AgentNeedResponse( + request_id=request_id, + request_file=str(request_file), + expected_response_file=str(self.store.responses_dir / f"{request_id}.json"), + stage=stage, + type=task_type, + ) + + def list_requests(self) -> List[Dict[str, Any]]: + requests = [] + for path in sorted(self.store.requests_dir.glob("req_*.json")): + request = self.load_request(path.stem) + response_path = self.store.responses_dir / path.name + requests.append( + { + "request_id": request.request_id, + "type": request.type, + "stage": request.stage, + "request_file": str(path), + "expected_response_file": str(response_path), + "has_response": response_path.exists(), + } + ) + return requests + + def load_request(self, request_id: str) -> AgentRequest: + path = self.store.requests_dir / f"{request_id}.json" + if not path.exists(): + raise AgentQueueError(f"request not found: {request_id}") + return AgentRequest.model_validate_json(path.read_text(encoding="utf-8")) + + def save_request(self, request: AgentRequest) -> None: + path = self.store.requests_dir / f"{request.request_id}.json" + path.write_text(request.model_dump_json(indent=2), encoding="utf-8") + + def load_response(self, request_id: str) -> AgentResponse: + path = self.store.responses_dir / f"{request_id}.json" + if not path.exists(): + raise AgentQueueError(f"response not found: {request_id}") + return AgentResponse.model_validate_json(path.read_text(encoding="utf-8")) + + def validate_response_file( + self, + response_path: str | Path, + *, + request_id: Optional[str] = None, + ) -> ValidationResult: + errors: List[str] = [] + path = Path(response_path) + try: + response = AgentResponse.model_validate_json(path.read_text(encoding="utf-8")) + except FileNotFoundError: + return ValidationResult(ok=False, errors=[f"response file not found: {path}"]) + except (ValidationError, json.JSONDecodeError, ValueError) as exc: + return ValidationResult(ok=False, errors=[f"invalid response schema: {exc}"]) + + if request_id and response.request_id != request_id: + errors.append(f"response request_id {response.request_id} does not match {request_id}") + if path.stem.startswith("req_") and response.request_id != path.stem: + errors.append(f"response request_id {response.request_id} does not match response file name {path.name}") + + try: + request = self.load_request(response.request_id) + except AgentQueueError as exc: + errors.append(str(exc)) + return ValidationResult(ok=False, errors=errors) + + if response.status in {"ok", "skipped"}: + errors.extend(validate_json_schema(response.output, request.expected_schema)) + elif response.status == "error" and not response.error: + errors.append("error response must include error") + + return ValidationResult(ok=not errors, errors=errors) + + def submit_response(self, response_path: str | Path) -> ValidationResult: + result = self.validate_response_file(response_path) + if not result.ok: + repair = self._maybe_create_repair_request(response_path, result.errors) + result.repair_request = repair + return result + + source = Path(response_path) + response = AgentResponse.model_validate_json(source.read_text(encoding="utf-8")) + destination = self.store.responses_dir / f"{response.request_id}.json" + if source.resolve() != destination.resolve(): + shutil.copyfile(source, destination) + return result + + def _maybe_create_repair_request( + self, + response_path: str | Path, + errors: List[str], + ) -> Optional[AgentNeedResponse]: + try: + raw = json.loads(Path(response_path).read_text(encoding="utf-8")) + request_id = raw.get("request_id") + request = self.load_request(request_id) + except Exception: + return None + + policy = request.retry_policy + if policy.repair_attempts_used >= policy.max_repair_attempts: + return None + + policy.repair_attempts_used += 1 + request.retry_policy = policy + self.save_request(request) + return self.create_request( + run_id=request.run_id, + task_type="repair_invalid_json", + stage=request.stage, + expected_schema=request.expected_schema, + structured_input={ + "original_request": request.model_dump(), + "invalid_response": raw, + "validation_errors": errors, + }, + system_prompt="Repair the invalid agent output so it strictly matches expected_schema.", + user_prompt="Return only the repaired output object. Do not wrap it in an AgentResponse envelope.", + retry_policy=policy.model_dump(), + output_contract={"repairs_request_id": request.request_id, "return_shape": "output_only"}, + ) diff --git a/backend/app/agent_engine/runner.py b/backend/app/agent_engine/runner.py new file mode 100644 index 0000000000..49b28364c0 --- /dev/null +++ b/backend/app/agent_engine/runner.py @@ -0,0 +1,2841 @@ +"""Shared run lifecycle service used by CLI and MCP.""" + +from __future__ import annotations + +import importlib.util +import html +import json +import os +import re +import shutil +import subprocess +import urllib.error +import urllib.request +import uuid +from pathlib import Path +from typing import Any, Dict, List, Optional + +from ..adapters.graph.factory import create_graph_provider +from ..adapters.llm.agent_runtime import AgentRuntime +from ..adapters.llm.factory import create_llm_provider +from .contracts import ( + AGENT_QUESTION_OUTPUT_SCHEMA, + AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA, + FOLLOWUP_OUTPUT_SCHEMA, + QUESTIONNAIRE_SUMMARY_OUTPUT_SCHEMA, + REPORT_QUESTION_OUTPUT_SCHEMA, + STAGE_CONTRACTS, +) +from .queue import AgentQueue +from .schemas import AgentResponse, StageStatus +from .state import RUN_STAGES, STAGED_RUN_STAGES, RunStore + + +ROUND_UNIT_MINUTES = { + "year": 525_600, + "month": 43_200, + "day": 1_440, + "step": 60, +} + +STAGED_INTERNAL_START = { + "graph_build": "ontology", + "profile_and_config": "profiles", + "simulation_run": "simulation", + "report_generation": "report", +} + +STAGED_INTERNAL_NEXT = { + ("graph_build", "ontology"): "graph", + ("profile_and_config", "profiles"): "config", +} + +STAGED_DOWNSTREAM = { + "seed_input": ["prediction_requirement", "simulation_settings", "graph_build", "profile_and_config", "simulation_run", "report_generation"], + "prediction_requirement": ["simulation_settings", "graph_build", "profile_and_config", "simulation_run", "report_generation"], + "simulation_settings": ["profile_and_config", "simulation_run", "report_generation"], + "graph_build": ["profile_and_config", "simulation_run", "report_generation"], + "profile_and_config": ["simulation_run", "report_generation"], + "simulation_run": ["report_generation"], + "report_generation": [], + "followup_question": [], +} + +_SLUG_RE = re.compile(r"[^a-z0-9]+") + + +def _resolve_agent_id(profile: Dict[str, Any], index: int = 0) -> str: + """Derive a stable, non-empty agent_id from a profile dict. + + Priority: + 1. profile["agent_id"] (if truthy) + 2. profile["user_id"] (if truthy) + 3. slug of profile["name"] (e.g. "Google China" -> "google_china") + 4. positional fallback "agent_{index+1}" + """ + raw = profile.get("agent_id") or profile.get("user_id") + if raw: + return str(raw).strip() or f"agent_{index + 1}" + name = profile.get("name") + if name and isinstance(name, str) and name.strip(): + slug = _SLUG_RE.sub("_", name.strip().lower()).strip("_") + return slug or f"agent_{index + 1}" + return f"agent_{index + 1}" + + +def _resolve_all_agent_ids(profiles: list) -> List[str]: + """Resolve agent IDs for a full list of profiles with deduplication. + + Same base logic as _resolve_agent_id, but when two profiles produce + the same ID the later ones get a ``_2``, ``_3``, … suffix so every + ID in the returned list is unique. + """ + resolved: List[str] = [] + seen: Dict[str, int] = {} + for i, profile in enumerate(profiles): + base_id = _resolve_agent_id(profile, i) + if base_id in seen: + seen[base_id] += 1 + unique_id = f"{base_id}_{seen[base_id]}" + # Edge case: the suffixed ID might itself collide + while unique_id in seen: + seen[base_id] += 1 + unique_id = f"{base_id}_{seen[base_id]}" + resolved.append(unique_id) + seen[unique_id] = 1 + else: + resolved.append(base_id) + seen[base_id] = 1 + return resolved + + +class PredictionRunService: + def __init__( + self, + *, + llm_provider: Optional[str] = None, + graph_provider: Optional[str] = None, + ): + self.llm_provider_name = llm_provider + self.graph_provider_name = graph_provider + + def create_run( + self, + seed: str, + requirement: str, + output: str, + *, + mode: str = "auto", + rounds: int = 10, + round_unit: str = "year", + minutes_per_round: Optional[int] = None, + pause_each_round: bool = False, + agent_count: Optional[int] = None, + simulation_name: Optional[str] = None, + ) -> Dict[str, Any]: + if mode not in {"auto", "staged"}: + raise ValueError("mode must be auto or staged") + run_dir = Path(output).resolve() + run_id = run_dir.name or f"run-{uuid.uuid4().hex[:8]}" + store = RunStore(run_dir) + store.ensure_layout() + simulation_settings = self._canonical_simulation_settings( + rounds=rounds, + round_unit=round_unit, + minutes_per_round=minutes_per_round, + pause_each_round=pause_each_round, + agent_count=agent_count, + simulation_name=simulation_name, + output_directory=str(run_dir), + ) + + seed_path = Path(seed).resolve() + if not seed_path.exists(): + raise FileNotFoundError(f"seed file not found: {seed}") + target_seed = run_dir / seed_path.name + if seed_path != target_seed: + shutil.copyfile(seed_path, target_seed) + + state = store.init_state( + run_id=run_id, + requirement=requirement, + seed_files=[target_seed.name], + mode=os.environ.get("MIROFISH_MODE", "agent"), + workflow_mode=mode, + simulation_settings=simulation_settings, + seed_path=str(target_seed), + metadata={ + "llm_provider": self.llm_provider_name or os.environ.get("MIROFISH_LLM_PROVIDER", "agent_queue"), + "graph_provider": self.graph_provider_name or os.environ.get("MIROFISH_GRAPH_PROVIDER", "graphiti"), + }, + ) + if mode == "staged": + self._complete_static_staged_stage(store, state, "seed_input") + return {"status": "created", "run_id": run_id, "run_dir": str(run_dir), "state": state.model_dump()} + + def run(self, run_dir: str) -> Dict[str, Any]: + return self.resume(run_dir) + + def resume(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + if state.workflow_mode == "staged": + return self._resume_staged(store, state) + return self._resume_auto(store, state) + + def _resume_auto(self, store: RunStore, state) -> Dict[str, Any]: + run_dir = str(store.run_dir) + stage = state.current_stage + checkpoint = state.stages[stage] + + if checkpoint.status == StageStatus.COMPLETED: + next_stage = self._next_stage(stage) + if not next_stage: + return {"status": "completed", "run_id": state.run_id, "artifacts": self.list_artifacts(run_dir)["artifacts"]} + stage = next_stage + checkpoint = state.stages[stage] + state.current_stage = stage + store.save(state) + + if checkpoint.status == StageStatus.WAITING_AGENT: + request_id = checkpoint.request_ids[-1] + response_path = store.responses_dir / f"{request_id}.json" + if not response_path.exists(): + request_path = store.requests_dir / f"{request_id}.json" + request = AgentQueue(run_dir).load_request(request_id) + return { + "status": "need_agent_response", + "request_id": request_id, + "request_file": str(request_path), + "expected_response_file": str(response_path), + "stage": stage, + "type": request.type, + } + queue = AgentQueue(run_dir) + validation = queue.submit_response(response_path) + if not validation.ok: + if validation.repair_request: + store.add_stage_request(state, stage, validation.repair_request.request_id) + return validation.repair_request.model_dump() + store.set_stage(state, stage, StageStatus.FAILED, "; ".join(validation.errors)) + return {"status": "failed", "stage": stage, "errors": validation.errors} + response = queue.load_response(request_id) + if response.status == "error": + store.set_stage(state, stage, StageStatus.FAILED, response.error or "agent returned error") + return {"status": "failed", "stage": stage, "error": response.error or "agent returned error"} + self._process_stage_output(store, state, stage, response.output) + store.set_stage(state, stage, StageStatus.COMPLETED) + next_stage = self._next_stage(stage) + if not next_stage: + return {"status": "completed", "run_id": state.run_id, "artifacts": self.list_artifacts(run_dir)["artifacts"]} + state.current_stage = next_stage + store.save(state) + return self._start_stage(store, state, next_stage) + + if checkpoint.status in {StageStatus.PENDING, StageStatus.RUNNING}: + return self._start_stage(store, state, stage) + + if checkpoint.status == StageStatus.FAILED: + return {"status": "failed", "stage": stage, "error": checkpoint.error} + + return self.status(run_dir) + + def _resume_staged(self, store: RunStore, state) -> Dict[str, Any]: + stage = state.current_stage + checkpoint = state.stages[stage] + + if checkpoint.status == StageStatus.AWAITING_USER_CONFIRMATION: + return self._awaiting_confirmation_payload(store, state, stage) + + if checkpoint.status == StageStatus.COMPLETED: + next_stage = self._next_staged_stage(stage) + if not next_stage: + return {"status": "completed", "run_id": state.run_id, "artifacts": self.list_artifacts(str(store.run_dir))["artifacts"]} + state.current_stage = next_stage + store.save(state) + return self._resume_staged(store, state) + + if checkpoint.status == StageStatus.WAITING_AGENT: + request_id = checkpoint.request_ids[-1] + response_path = store.responses_dir / f"{request_id}.json" + if not response_path.exists(): + request_path = store.requests_dir / f"{request_id}.json" + request = AgentQueue(store.run_dir).load_request(request_id) + return { + "status": "need_agent_response", + "request_id": request_id, + "request_file": str(request_path), + "expected_response_file": str(response_path), + "stage": stage, + "type": request.type, + } + queue = AgentQueue(store.run_dir) + validation = queue.submit_response(response_path) + if not validation.ok: + if validation.repair_request: + store.add_stage_request(state, stage, validation.repair_request.request_id) + return validation.repair_request.model_dump() + store.set_stage(state, stage, StageStatus.FAILED, "; ".join(validation.errors)) + return {"status": "failed", "stage": stage, "errors": validation.errors} + response = queue.load_response(request_id) + if response.status == "error": + store.set_stage(state, stage, StageStatus.FAILED, response.error or "agent returned error") + return {"status": "failed", "stage": stage, "error": response.error or "agent returned error"} + return self._handle_staged_response(store, state, stage, response) + + if checkpoint.status in {StageStatus.PENDING, StageStatus.RUNNING}: + return self._start_staged_stage(store, state, stage) + + if checkpoint.status == StageStatus.FAILED: + return {"status": "failed", "stage": stage, "error": checkpoint.error} + + return self.status(str(store.run_dir)) + + def status(self, run_dir: str) -> Dict[str, Any]: + return {"status": "ok", "state": RunStore(run_dir).as_status()} + + def get_current_stage(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + return {"status": "ok", "stage": self._stage_detail(store, state, state.current_stage)} + + def approve_stage(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + stage = state.current_stage + checkpoint = state.stages[stage] + if checkpoint.status != StageStatus.AWAITING_USER_CONFIRMATION: + return {"status": "error", "error": f"stage {stage} is not awaiting user confirmation", "stage": stage} + checkpoint.status = StageStatus.COMPLETED + checkpoint.error = None + checkpoint.stale = False + checkpoint.stale_reason = None + next_stage = self._next_staged_stage(stage) if state.workflow_mode == "staged" else self._next_stage(stage) + if next_stage: + state.current_stage = next_stage + store.save(state) + return {"status": "ok", "approved_stage": stage, "next_stage": next_stage, "state": state.model_dump()} + + def reject_stage(self, run_dir: str, reason: str = "") -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + stage = state.current_stage + checkpoint = state.stages[stage] + checkpoint.status = StageStatus.FAILED + checkpoint.error = reason or "stage rejected by user" + checkpoint.metadata["rejection_reason"] = checkpoint.error + store.save(state) + return {"status": "failed", "stage": stage, "reason": checkpoint.error} + + def update_simulation_settings( + self, + run_dir: str, + *, + rounds: Optional[int] = None, + round_unit: Optional[str] = None, + minutes_per_round: Optional[int] = None, + pause_each_round: Optional[bool] = None, + agent_count: Optional[int] = None, + simulation_name: Optional[str] = None, + ) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + current = state.simulation_settings or {} + settings = self._canonical_simulation_settings( + rounds=rounds if rounds is not None else int(current.get("rounds", 10)), + round_unit=round_unit or str(current.get("round_unit", "year")), + minutes_per_round=minutes_per_round if minutes_per_round is not None else current.get("minutes_per_round"), + pause_each_round=pause_each_round if pause_each_round is not None else bool(current.get("pause_each_round", False)), + agent_count=agent_count if agent_count is not None else current.get("agent_count"), + simulation_name=simulation_name if simulation_name is not None else current.get("simulation_name"), + output_directory=current.get("output_directory") or str(store.run_dir), + ) + state.simulation_settings = settings + self._write_json(store.artifacts_dir / "simulation_settings.json", settings) + self._mark_downstream_pending(state, "simulation_settings", "simulation settings changed") + if "simulation_settings" in state.stages: + checkpoint = state.stages["simulation_settings"] + checkpoint.status = StageStatus.AWAITING_USER_CONFIRMATION + checkpoint.stale = False + checkpoint.stale_reason = None + state.current_stage = "simulation_settings" + store.save(state) + return {"status": "ok", "simulation_settings": settings, "state": state.model_dump()} + + def rerun_stage(self, run_dir: str, stage: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + stage = self._normalize_stage_name(state, stage) + if stage not in state.stages: + return {"status": "error", "error": f"unknown stage: {stage}"} + checkpoint = state.stages[stage] + checkpoint.status = StageStatus.PENDING + checkpoint.request_ids = [] + checkpoint.error = None + checkpoint.stale = False + checkpoint.stale_reason = None + checkpoint.metadata = {} + self._mark_downstream_pending(state, stage, f"upstream stage {stage} rerun") + state.current_stage = stage + store.save(state) + return {"status": "ok", "stage": stage, "state": state.model_dump()} + + def list_requests(self, run_dir: str) -> Dict[str, Any]: + return {"status": "ok", "requests": AgentQueue(run_dir).list_requests()} + + def get_request(self, run_dir: str, request_id: str) -> Dict[str, Any]: + request = AgentQueue(run_dir).load_request(request_id) + return {"status": "ok", "request": request.model_dump()} + + def validate_response(self, run_dir: str, response: str) -> Dict[str, Any]: + result = AgentQueue(run_dir).validate_response_file(response) + return result.model_dump() + + def submit_response(self, run_dir: str, response: str) -> Dict[str, Any]: + queue = AgentQueue(run_dir) + result = queue.submit_response(response) + if result.repair_request: + self._attach_repair_request_to_waiting_stage(run_dir, queue, result.repair_request.request_id) + return result.model_dump() + + def build_graph(self, run_dir: str, provider: Optional[str] = None, mode: str = "agent-triples") -> Dict[str, Any]: + if mode != "agent-triples": + raise ValueError("only agent-triples graph build mode is supported in agent engine") + store = RunStore(run_dir) + state = store.load() + if provider: + state.metadata["graph_provider"] = provider + if state.workflow_mode == "staged": + state.current_stage = "graph_build" + if state.stages["graph_build"].status == StageStatus.COMPLETED: + state.stages["graph_build"].status = StageStatus.PENDING + store.save(state) + waiting = self._existing_agent_wait(store, state, "graph_build") + if waiting: + return waiting + return self._start_staged_stage(store, state, "graph_build") + state.current_stage = "graph" + if state.stages["graph"].status == StageStatus.COMPLETED: + state.stages["graph"].status = StageStatus.PENDING + store.save(state) + waiting = self._existing_agent_wait(store, state, "graph") + if waiting: + return waiting + return self._start_stage(store, state, "graph", graph_provider_override=provider) + + def search_graph(self, run_dir: str, query: str, limit: int = 20) -> Dict[str, Any]: + state = RunStore(run_dir).load() + provider = create_graph_provider(self._graph_provider_name(state)) + return {"status": "ok", "results": provider.search(state.run_id, query, limit)} + + def export_graph(self, run_dir: str, output: Optional[str] = None) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + path = output or str(store.artifacts_dir / "graph_snapshot.json") + provider = create_graph_provider(self._graph_provider_name(state)) + result = provider.export_snapshot(state.run_id, path) + return {"status": "ok", "result": result} + + def start_simulation(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + if state.workflow_mode == "staged": + state.current_stage = "simulation_run" + store.save(state) + waiting = self._existing_agent_wait(store, state, "simulation_run") + if waiting: + return waiting + return self._start_staged_stage(store, state, "simulation_run") + state.current_stage = "simulation" + store.save(state) + waiting = self._existing_agent_wait(store, state, "simulation") + if waiting: + return waiting + return self._start_stage(store, state, "simulation") + + def simulation_status(self, run_dir: str) -> Dict[str, Any]: + state = RunStore(run_dir).load() + if state.workflow_mode == "staged": + return {"status": "ok", "simulation": state.stages["simulation_run"].model_dump(), "progress": state.simulation_progress} + return {"status": "ok", "simulation": state.stages["simulation"].model_dump()} + + def generate_report(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + if state.workflow_mode == "staged": + state.current_stage = "report_generation" + store.save(state) + waiting = self._existing_agent_wait(store, state, "report_generation") + if waiting: + return waiting + return self._start_staged_stage(store, state, "report_generation") + state.current_stage = "report" + store.save(state) + waiting = self._existing_agent_wait(store, state, "report") + if waiting: + return waiting + return self._start_stage(store, state, "report") + + def get_report(self, run_dir: str) -> Dict[str, Any]: + path = RunStore(run_dir).artifacts_dir / "report.md" + if not path.exists(): + return {"status": "missing", "report": None, "path": str(path)} + return {"status": "ok", "path": str(path), "report": path.read_text(encoding="utf-8")} + + def ask_followup_question(self, run_dir: str, question: str, limit: int = 20) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + provider = create_graph_provider(self._graph_provider_name(state)) + graph_results = provider.search(state.run_id, question, limit=limit) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type="answer_followup_question", + stage="followup", + expected_schema=FOLLOWUP_OUTPUT_SCHEMA, + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input={ + "question": question, + "requirement": state.requirement, + "graph_results": graph_results, + "artifacts": self._artifact_context(store), + }, + system_prompt="Answer a follow-up question using only run artifacts and GraphProvider retrieval context.", + user_prompt=question, + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": FOLLOWUP_OUTPUT_SCHEMA}, + ) + if result.status == "need_agent_response": + return result.to_dict() | {"stage": "followup", "type": "answer_followup_question"} + if result.status != "ok": + return {"status": "failed", "stage": "followup", "error": result.error} + return self._persist_followup_answer(store, state.run_id, f"mock_{uuid.uuid4().hex[:8]}", question, result.output or {}) + + def get_followup_answer(self, run_dir: str, request_id: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + queue = AgentQueue(run_dir) + request = queue.load_request(request_id) + if request.type not in {"answer_followup_question", "repair_invalid_json"} or request.stage != "followup": + return {"status": "error", "error": f"request {request_id} is not a follow-up answer request"} + response_path = store.responses_dir / f"{request_id}.json" + if not response_path.exists(): + return {"status": "missing", "request_id": request_id, "expected_response_file": str(response_path)} + validation = queue.submit_response(response_path) + if not validation.ok: + if validation.repair_request: + return validation.repair_request.model_dump() + return {"status": "failed", "request_id": request_id, "errors": validation.errors} + response = queue.load_response(request_id) + if response.status == "error": + return {"status": "failed", "request_id": request_id, "error": response.error or "agent returned error"} + return self._persist_followup_answer( + store, + state.run_id, + request_id, + self._followup_question_from_request(request), + response.output, + ) + + def list_artifacts(self, run_dir: str) -> Dict[str, Any]: + artifacts_dir = RunStore(run_dir).artifacts_dir + artifacts = [] + if artifacts_dir.exists(): + for path in sorted(artifacts_dir.rglob("*")): + if path.is_file(): + artifacts.append( + { + "name": str(path.relative_to(artifacts_dir)), + "path": str(path), + "bytes": path.stat().st_size, + } + ) + return {"status": "ok", "artifacts": artifacts} + + # ── Agent interaction methods ────────────────────────────────────────── + + def list_agents(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + profiles_path = store.artifacts_dir / "profiles.json" + if not profiles_path.exists(): + return {"status": "ok", "agents": [], "count": 0} + profiles = json.loads(profiles_path.read_text(encoding="utf-8")) + resolved_ids = _resolve_all_agent_ids(profiles) + agents = [] + for idx, profile in enumerate(profiles): + agent_id = resolved_ids[idx] + agents.append({ + "agent_id": agent_id, + "name": profile.get("name", ""), + "persona": profile.get("persona", ""), + "profile": profile, + }) + return {"status": "ok", "agents": agents, "count": len(agents)} + + def get_agent(self, run_dir: str, agent_id: str) -> Dict[str, Any]: + store = RunStore(run_dir) + profiles_path = store.artifacts_dir / "profiles.json" + if not profiles_path.exists(): + return {"status": "error", "error": "profiles.json not found"} + profiles = json.loads(profiles_path.read_text(encoding="utf-8")) + resolved_ids = _resolve_all_agent_ids(profiles) + for idx, profile in enumerate(profiles): + pid = resolved_ids[idx] + if pid == agent_id: + return {"status": "ok", "agent": { + "agent_id": pid, + "name": profile.get("name", ""), + "persona": profile.get("persona", ""), + "profile": profile, + }} + return {"status": "error", "error": f"agent not found: {agent_id}"} + + def ask_agent(self, run_dir: str, agent_id: str, question: str, limit: int = 20) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + # Verify agent exists + agent_result = self.get_agent(run_dir, agent_id) + if agent_result["status"] == "error": + return agent_result + provider = create_graph_provider(self._graph_provider_name(state)) + graph_results = provider.search(state.run_id, question, limit=limit) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type="answer_agent_question", + stage="interaction", + expected_schema=AGENT_QUESTION_OUTPUT_SCHEMA, + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input={ + "agent_id": agent_id, + "question": question, + "requirement": state.requirement, + "graph_results": graph_results, + "agent_profile": agent_result["agent"]["profile"], + "artifacts": self._artifact_context(store), + }, + system_prompt=( + f"You are agent '{agent_id}'. Answer the question from your persona's perspective " + "using only run artifacts and GraphProvider retrieval context." + ), + user_prompt=question, + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": AGENT_QUESTION_OUTPUT_SCHEMA}, + ) + if result.status == "need_agent_response": + return result.to_dict() | {"stage": "interaction", "type": "answer_agent_question", "agent_id": agent_id} + if result.status != "ok": + return {"status": "failed", "stage": "interaction", "error": result.error} + return self._persist_agent_question_answer(store, state.run_id, agent_id, f"mock_{uuid.uuid4().hex[:8]}", question, result.output or {}) + + def get_agent_answer(self, run_dir: str, request_id: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + queue = AgentQueue(run_dir) + request = queue.load_request(request_id) + if request.type != "answer_agent_question" or request.stage != "interaction": + return {"status": "error", "error": f"request {request_id} is not an agent question request"} + response_path = store.responses_dir / f"{request_id}.json" + if not response_path.exists(): + return {"status": "missing", "request_id": request_id, "expected_response_file": str(response_path)} + validation = queue.submit_response(response_path) + if not validation.ok: + if validation.repair_request: + return validation.repair_request.model_dump() + return {"status": "failed", "request_id": request_id, "errors": validation.errors} + response = queue.load_response(request_id) + if response.status == "error": + return {"status": "failed", "request_id": request_id, "error": response.error or "agent returned error"} + agent_id = str(request.structured_input.get("agent_id", "")) + question = str(request.structured_input.get("question", "")) + return self._persist_agent_question_answer(store, state.run_id, agent_id, request_id, question, response.output) + + def send_questionnaire(self, run_dir: str, questions: List[Dict[str, Any]]) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + profiles_path = store.artifacts_dir / "profiles.json" + if not profiles_path.exists(): + return {"status": "error", "error": "profiles.json not found; cannot send questionnaire"} + profiles = json.loads(profiles_path.read_text(encoding="utf-8")) + resolved_ids = _resolve_all_agent_ids(profiles) + agents = [ + {"agent_id": resolved_ids[i], "profile": p} + for i, p in enumerate(profiles) + ] + questionnaire_id = f"questionnaire_{uuid.uuid4().hex[:8]}" + provider = create_graph_provider(self._graph_provider_name(state)) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + request_ids = [] + for question_item in questions: + question_text = question_item.get("question", "") + question_id = question_item.get("question_id", f"q_{uuid.uuid4().hex[:6]}") + graph_results = provider.search(state.run_id, question_text, limit=10) + result = runtime.run_task( + run_id=state.run_id, + task_type="answer_agent_questionnaire", + stage="interaction", + expected_schema=AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA, + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input={ + "questionnaire_id": questionnaire_id, + "question_id": question_id, + "questions": [{"question_id": question_id, "question": question_text}], + "agents": agents, + "requirement": state.requirement, + "graph_results": graph_results, + "artifacts": self._artifact_context(store), + }, + system_prompt=( + "Answer the questionnaire on behalf of all agents. " + "Each agent should answer from their own persona's perspective." + ), + user_prompt=question_text, + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA}, + ) + if result.status == "need_agent_response": + request_ids.append(result.request_id) + elif result.status == "ok": + self._persist_questionnaire_answers(store, state.run_id, questionnaire_id, result.output or {}) + # Save questionnaire metadata + self._persist_questionnaire_metadata(store, questionnaire_id, questions, request_ids) + return { + "status": "ok" if not request_ids else "need_agent_response", + "questionnaire_id": questionnaire_id, + "request_ids": request_ids, + "question_count": len(questions), + "agent_count": len(agents), + } + + def get_questionnaire_result(self, run_dir: str, questionnaire_id: str) -> Dict[str, Any]: + store = RunStore(run_dir) + questionnaires_dir = store.artifacts_dir / "interactions" / "questionnaires" + meta_path = questionnaires_dir / f"{questionnaire_id}_meta.json" + if not meta_path.exists(): + return {"status": "error", "error": f"questionnaire not found: {questionnaire_id}"} + meta = json.loads(meta_path.read_text(encoding="utf-8")) + # Collect all answers + answers = [] + answers_path = questionnaires_dir / f"{questionnaire_id}_answers.json" + if answers_path.exists(): + answers = json.loads(answers_path.read_text(encoding="utf-8")) + # Check pending requests + pending_request_ids = meta.get("request_ids", []) + queue = AgentQueue(run_dir) + for req_id in list(pending_request_ids): + response_path = store.responses_dir / f"{req_id}.json" + if response_path.exists(): + try: + validation = queue.submit_response(response_path) + if validation.ok: + response = queue.load_response(req_id) + if response.status == "ok": + new_answers = self._extract_questionnaire_answers(response.output) + answers.extend(new_answers) + # Persist summary_markdown from response if present + resp_summary = response.output.get("summary_markdown", "") + if resp_summary: + meta["summary_markdown"] = resp_summary + pending_request_ids.remove(req_id) + except Exception: + pass + # Re-save answers if we collected new ones + if answers: + self._write_json(answers_path, answers) + # Update metadata + meta["request_ids"] = pending_request_ids + meta["answer_count"] = len(answers) + self._write_json(meta_path, meta) + summary = meta.get("summary_markdown", "") + return { + "status": "ok" if not pending_request_ids else "partial", + "questionnaire_id": questionnaire_id, + "answers": answers, + "answer_count": len(answers), + "pending_request_ids": pending_request_ids, + "summary_markdown": summary, + } + + def ask_report_question(self, run_dir: str, question: str, limit: int = 20) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + provider = create_graph_provider(self._graph_provider_name(state)) + graph_results = provider.search(state.run_id, question, limit=limit) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type="ask_report_question", + stage="interaction", + expected_schema=REPORT_QUESTION_OUTPUT_SCHEMA, + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input={ + "question": question, + "requirement": state.requirement, + "graph_results": graph_results, + "artifacts": self._artifact_context(store), + }, + system_prompt="Answer a question about the prediction report using only run artifacts and GraphProvider retrieval context.", + user_prompt=question, + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": REPORT_QUESTION_OUTPUT_SCHEMA}, + ) + if result.status == "need_agent_response": + return result.to_dict() | {"stage": "interaction", "type": "ask_report_question"} + if result.status != "ok": + return {"status": "failed", "stage": "interaction", "error": result.error} + return self._persist_report_question_answer(store, state.run_id, f"mock_{uuid.uuid4().hex[:8]}", question, result.output or {}) + + def generate_web_console(self, run_dir: str) -> Dict[str, Any]: + store = RunStore(run_dir) + web_dir = store.artifacts_dir / "web" + web_dir.mkdir(parents=True, exist_ok=True) + html_path = web_dir / "index.html" + # Read all relevant artifacts for embedding + artifact_data = self._collect_web_console_data(store) + artifact_data["run_dir"] = str(store.run_dir) + html_content = self._render_web_console_html(artifact_data) + html_path.write_text(html_content, encoding="utf-8") + return { + "status": "ok", + "path": str(html_path), + "artifact_count": len(artifact_data), + } + + def doctor(self, runs_dir: Optional[str] = None) -> Dict[str, Any]: + checks = [] + mode = os.environ.get("MIROFISH_MODE", "agent") + llm_provider = os.environ.get("MIROFISH_LLM_PROVIDER", "agent_queue" if mode == "agent" else "openai_compatible") + graph_provider = os.environ.get("MIROFISH_GRAPH_PROVIDER", "graphiti" if mode == "agent" else "zep") + graphiti_store = os.environ.get("MIROFISH_GRAPHITI_STORE", "auto").lower() + graph_search_mode = os.environ.get("MIROFISH_GRAPH_SEARCH_MODE", "fulltext").lower() + embedding_provider = os.environ.get("MIROFISH_EMBEDDING_PROVIDER", "none").lower() + agent_graphiti = mode == "agent" and graph_provider == "graphiti" + production_graphiti = agent_graphiti and graphiti_store != "file" + require_ollama = production_graphiti and graph_search_mode in {"semantic", "hybrid"} and embedding_provider == "ollama" + + def add_check(name: str, ok: bool, value: Any, *, required: bool = True) -> None: + checks.append({"name": name, "ok": ok, "value": value, "required": required}) + + def parse_neo4j_version(value: str) -> tuple[int, int]: + base = value.split("-", 1)[0] + parts = base.split(".") + major = int(parts[0]) if len(parts) > 0 and parts[0].isdigit() else 0 + minor = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else 0 + return major, minor + + add_check("mode", mode in {"agent", "legacy"}, mode, required=False) + add_check("llm_provider", llm_provider in {"agent_queue", "mock", "openai_compatible"}, llm_provider, required=False) + add_check("graph_provider", graph_provider in {"graphiti", "zep"}, graph_provider, required=False) + add_check("llm_key_not_required_in_agent", mode != "agent" or True, "agent mode skips LLM_API_KEY validation", required=False) + add_check("zep_key_not_required_in_agent", mode != "agent" or True, "agent mode skips ZEP_API_KEY validation", required=False) + docker_path = shutil.which("docker") + add_check("docker", docker_path is not None, docker_path or "Docker optional, skipped", required=False) + if docker_path: + try: + result = subprocess.run( + ["docker", "compose", "version"], + check=False, + capture_output=True, + text=True, + timeout=5, + ) + compose_value = (result.stdout or result.stderr).strip() or "docker compose" + add_check("docker_compose", result.returncode == 0, compose_value, required=False) + except Exception as exc: + add_check("docker_compose", False, str(exc), required=False) + else: + add_check("docker_compose", False, "Docker optional, skipped", required=False) + add_check("mcp_package", importlib.util.find_spec("mcp") is not None, "mcp", required=False) + add_check( + "graphiti_package", + importlib.util.find_spec("graphiti_core") is not None, + "graphiti_core", + required=production_graphiti, + ) + neo4j_package_ok = importlib.util.find_spec("neo4j") is not None + neo4j_uri = os.environ.get("NEO4J_URI", "bolt://localhost:7687") + neo4j_user = os.environ.get("NEO4J_USER", "neo4j") + neo4j_password = os.environ.get("NEO4J_PASSWORD", "password") + neo4j_database = os.environ.get("NEO4J_DATABASE", "neo4j") + add_check("neo4j_package", neo4j_package_ok, "neo4j", required=production_graphiti) + add_check( + "neo4j_uri", + bool(neo4j_uri), + neo4j_uri, + required=False, + ) + if production_graphiti: + if neo4j_package_ok: + try: + from neo4j import GraphDatabase + + driver = GraphDatabase.driver(neo4j_uri, auth=(neo4j_user, neo4j_password)) + with driver.session(database=neo4j_database) as session: + record = session.run( + "CALL dbms.components() YIELD name, versions RETURN name, versions LIMIT 1" + ).single() + driver.close() + versions = record["versions"] if record else [] + neo4j_version = versions[0] if versions else "unknown" + major, minor = parse_neo4j_version(neo4j_version) + version_supported = major > 5 or (major == 5 and minor >= 26) + add_check("neo4j_connectable", True, neo4j_uri, required=True) + add_check("neo4j_version_supported", version_supported, neo4j_version, required=True) + except Exception as exc: + add_check("neo4j_connectable", False, f"{neo4j_uri}: {exc}", required=True) + add_check("neo4j_version_supported", False, "not checked", required=True) + else: + add_check("neo4j_connectable", False, "neo4j package is not installed", required=True) + add_check("neo4j_version_supported", False, "not checked", required=True) + else: + add_check( + "neo4j_connectable", + False, + "not required unless agent graphiti mode uses Neo4j store", + required=False, + ) + add_check( + "neo4j_version_supported", + False, + "not checked", + required=False, + ) + + add_check( + "openai_key_not_required_by_graphiti_provider", + graph_provider != "graphiti" or True, + "GraphitiGraphProvider add_triples/search/export_snapshot do not require OPENAI_API_KEY", + required=False, + ) + ollama_base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434").rstrip("/") + ollama_model = os.environ.get("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text") + add_check("graph_search_mode", graph_search_mode in {"fulltext", "semantic", "hybrid"}, graph_search_mode, required=False) + add_check("embedding_provider", embedding_provider in {"none", "ollama", "openai_compatible", "local"}, embedding_provider, required=False) + add_check( + "ollama_base_url", + bool(ollama_base_url), + ollama_base_url, + required=False, + ) + if require_ollama: + try: + with urllib.request.urlopen(f"{ollama_base_url}/api/tags", timeout=5) as response: + payload = json.loads(response.read().decode("utf-8")) + model_names = [item.get("name", "") for item in payload.get("models", [])] + model_found = any(name == ollama_model or name.startswith(f"{ollama_model}:") for name in model_names) + add_check("ollama_connectable", True, ollama_base_url, required=True) + add_check("ollama_embedding_model", model_found, ollama_model, required=True) + except (OSError, urllib.error.URLError, json.JSONDecodeError) as exc: + add_check("ollama_connectable", False, f"{ollama_base_url}: {exc}", required=True) + add_check("ollama_embedding_model", False, ollama_model, required=True) + else: + ollama_reason = ( + "semantic graph search disabled; fulltext search does not require Ollama" + if graph_search_mode == "fulltext" or embedding_provider == "none" + else "not required unless MIROFISH_GRAPH_SEARCH_MODE=semantic/hybrid and MIROFISH_EMBEDDING_PROVIDER=ollama" + ) + add_check( + "ollama_connectable", + False, + ollama_reason, + required=False, + ) + add_check("ollama_embedding_model", False, ollama_model, required=False) + + writable_dir = Path(runs_dir or os.environ.get("MIROFISH_RUNS_DIR", "./runs")) + try: + writable_dir.mkdir(parents=True, exist_ok=True) + probe = writable_dir / ".mirofish_write_probe" + probe.write_text("ok", encoding="utf-8") + probe.unlink() + writable = True + except Exception as exc: + writable = False + add_check("runs_dir_error", False, str(exc)) + add_check("runs_dir_writable", writable, str(writable_dir), required=False) + + hard_failures = [check for check in checks if check["required"] and not check["ok"]] + warnings = [check for check in checks if not check["required"] and not check["ok"]] + return { + "status": "ok" if not hard_failures else "failed", + "checks": checks, + "warnings": warnings, + "hard_failures": hard_failures, + } + + def _start_staged_stage(self, store: RunStore, state, stage: str) -> Dict[str, Any]: + if stage in {"seed_input", "prediction_requirement", "simulation_settings"}: + self._complete_static_staged_stage(store, state, stage) + return self._awaiting_confirmation_payload(store, state, stage) + if stage == "graph_build": + internal_stage = state.stages[stage].metadata.get("internal_stage") or STAGED_INTERNAL_START[stage] + return self._start_staged_contract_stage(store, state, stage, internal_stage) + if stage == "profile_and_config": + internal_stage = state.stages[stage].metadata.get("internal_stage") or STAGED_INTERNAL_START[stage] + return self._start_staged_contract_stage(store, state, stage, internal_stage) + if stage == "simulation_run": + return self._start_staged_simulation_round(store, state) + if stage == "report_generation": + return self._start_staged_contract_stage(store, state, stage, "report") + if stage == "followup_question": + return {"status": "ok", "stage": stage, "message": "Use followup ask or mirofish_ask_followup_question."} + return {"status": "error", "error": f"unsupported staged stage: {stage}"} + + def _start_staged_contract_stage(self, store: RunStore, state, stage: str, internal_stage: str) -> Dict[str, Any]: + contract = STAGE_CONTRACTS[internal_stage] + checkpoint = state.stages[stage] + checkpoint.metadata["internal_stage"] = internal_stage + checkpoint.status = StageStatus.RUNNING + state.current_stage = stage + store.save(state) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type=contract["task_type"], + stage=stage, + expected_schema=contract["schema"], + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input=self._stage_structured_input(store, state, internal_stage) | { + "workflow_stage": stage, + "internal_stage": internal_stage, + }, + system_prompt=contract["system_prompt"], + user_prompt=contract["user_prompt"], + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": contract["schema"]}, + ) + if result.status == "need_agent_response": + store.add_stage_request(state, stage, result.request_id) + return result.to_dict() | {"stage": stage, "internal_stage": internal_stage, "type": contract["task_type"]} + if result.status != "ok": + store.set_stage(state, stage, StageStatus.FAILED, result.error) + return {"status": "failed", "stage": stage, "error": result.error} + return self._handle_staged_response( + store, + state, + stage, + AgentResponse(request_id=f"req_{uuid.uuid4().hex[:6]}", status="ok", output=result.output or {}), + ) + + def _handle_staged_response(self, store: RunStore, state, stage: str, response: AgentResponse) -> Dict[str, Any]: + checkpoint = state.stages[stage] + internal_stage = checkpoint.metadata.get("internal_stage") + if not internal_stage: + internal_stage = self._internal_stage_from_response_type(response, stage) + if not internal_stage: + store.set_stage(state, stage, StageStatus.FAILED, "cannot determine staged internal stage") + return {"status": "failed", "stage": stage, "error": "cannot determine staged internal stage"} + + if stage == "simulation_run": + self._process_staged_simulation_output(store, state, response.output) + return self._after_staged_simulation_round(store, state) + + self._process_stage_output(store, state, internal_stage, response.output) + next_internal = STAGED_INTERNAL_NEXT.get((stage, internal_stage)) + if next_internal: + checkpoint.metadata["internal_stage"] = next_internal + checkpoint.status = StageStatus.PENDING + store.save(state) + return self._start_staged_contract_stage(store, state, stage, next_internal) + + if stage == "graph_build": + state.graph_summary = self._graph_summary(store) + elif stage == "profile_and_config": + state.profiles_summary = self._profiles_summary(store) + state.config_summary = self._config_summary(store, state) + elif stage == "report_generation": + self._ensure_final_artifacts(store, state) + state.report_artifacts = self._report_artifacts(store) + checkpoint.status = StageStatus.COMPLETED + checkpoint.metadata["summary"] = state.report_artifacts + state.current_stage = "followup_question" if "followup_question" in state.stages else stage + store.save(state) + return {"status": "completed", "run_id": state.run_id, "artifacts": self.list_artifacts(str(store.run_dir))["artifacts"]} + + checkpoint.status = StageStatus.AWAITING_USER_CONFIRMATION + checkpoint.error = None + checkpoint.stale = False + checkpoint.stale_reason = None + checkpoint.metadata["summary"] = self._stage_summary(store, state, stage) + state.current_stage = stage + store.save(state) + return self._awaiting_confirmation_payload(store, state, stage) + + def _start_staged_simulation_round(self, store: RunStore, state) -> Dict[str, Any]: + progress = state.simulation_progress or {} + settings = self._ensure_state_simulation_settings(state, store) + total_rounds = int(settings["rounds"]) + completed_rounds = int(progress.get("completed_rounds", 0)) + if completed_rounds >= total_rounds: + state.stages["simulation_run"].status = StageStatus.AWAITING_USER_CONFIRMATION + state.simulation_progress = self._simulation_progress_payload(state, completed_rounds) + store.save(state) + return self._awaiting_confirmation_payload(store, state, "simulation_run") + + round_index = completed_rounds + 1 + checkpoint = state.stages["simulation_run"] + checkpoint.metadata["internal_stage"] = "simulation" + checkpoint.metadata["round_index"] = round_index + checkpoint.status = StageStatus.RUNNING + state.current_stage = "simulation_run" + state.simulation_progress = self._simulation_progress_payload(state, completed_rounds) + store.save(state) + + contract = STAGE_CONTRACTS["simulation"] + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type=contract["task_type"], + stage="simulation_run", + expected_schema=contract["schema"], + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input=self._simulation_round_structured_input(store, state, round_index), + system_prompt=contract["system_prompt"], + user_prompt=contract["user_prompt"], + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": contract["schema"]}, + ) + if result.status == "need_agent_response": + store.add_stage_request(state, "simulation_run", result.request_id) + return result.to_dict() | {"stage": "simulation_run", "round_index": round_index, "type": contract["task_type"]} + if result.status != "ok": + store.set_stage(state, "simulation_run", StageStatus.FAILED, result.error) + return {"status": "failed", "stage": "simulation_run", "error": result.error} + self._process_staged_simulation_output(store, state, result.output or {}) + return self._after_staged_simulation_round(store, state) + + def _after_staged_simulation_round(self, store: RunStore, state) -> Dict[str, Any]: + settings = self._ensure_state_simulation_settings(state, store) + progress = state.simulation_progress + completed_rounds = int(progress.get("completed_rounds", 0)) + total_rounds = int(settings["rounds"]) + checkpoint = state.stages["simulation_run"] + if completed_rounds >= total_rounds: + checkpoint.status = StageStatus.AWAITING_USER_CONFIRMATION + checkpoint.metadata["summary"] = state.simulation_progress + store.save(state) + return self._awaiting_confirmation_payload(store, state, "simulation_run") + if settings.get("pause_each_round"): + checkpoint.status = StageStatus.AWAITING_USER_CONFIRMATION + checkpoint.metadata["summary"] = state.simulation_progress + checkpoint.metadata["awaiting_round_approval"] = completed_rounds + store.save(state) + return self._awaiting_confirmation_payload(store, state, "simulation_run") + checkpoint.status = StageStatus.PENDING + store.save(state) + return self._start_staged_simulation_round(store, state) + + def _complete_static_staged_stage(self, store: RunStore, state, stage: str) -> None: + artifacts = store.artifacts_dir + artifacts.mkdir(parents=True, exist_ok=True) + checkpoint = state.stages[stage] + if stage == "seed_input": + seed_text = store.read_seed_text() + summary = { + "seed_path": state.seed_path or (state.seed_files[0] if state.seed_files else None), + "seed_files": state.seed_files, + "character_count": len(seed_text), + "line_count": len(seed_text.splitlines()), + "valid": bool(seed_text.strip()), + "preview": seed_text[:500], + } + self._write_json(artifacts / "seed_summary.json", summary) + state.metadata["seed_summary"] = summary + store.add_stage_artifact(state, stage, str(artifacts / "seed_summary.json")) + elif stage == "prediction_requirement": + summary = { + "requirement": state.requirement, + "character_count": len(state.requirement), + "valid": bool(state.requirement.strip()), + } + self._write_json(artifacts / "requirement_summary.json", summary) + state.metadata["requirement_summary"] = summary + store.add_stage_artifact(state, stage, str(artifacts / "requirement_summary.json")) + elif stage == "simulation_settings": + state.simulation_settings = self._canonical_simulation_settings(**self._settings_kwargs(state, store)) + self._write_json(artifacts / "simulation_settings.json", state.simulation_settings) + checkpoint.metadata["summary"] = state.simulation_settings + store.add_stage_artifact(state, stage, str(artifacts / "simulation_settings.json")) + checkpoint.status = StageStatus.AWAITING_USER_CONFIRMATION + checkpoint.error = None + checkpoint.stale = False + checkpoint.stale_reason = None + checkpoint.metadata["summary"] = self._stage_summary(store, state, stage) + state.current_stage = stage + store.save(state) + + def _start_stage( + self, + store: RunStore, + state, + stage: str, + *, + graph_provider_override: Optional[str] = None, + ) -> Dict[str, Any]: + if stage == "artifacts": + self._ensure_final_artifacts(store, state) + store.set_stage(state, stage, StageStatus.COMPLETED) + return {"status": "completed", "run_id": state.run_id, "artifacts": self.list_artifacts(str(store.run_dir))["artifacts"]} + + contract = STAGE_CONTRACTS[stage] + store.set_stage(state, stage, StageStatus.RUNNING) + runtime = AgentRuntime( + provider=create_llm_provider(self.llm_provider_name, run_dir=store.run_dir), + run_dir=str(store.run_dir), + ) + result = runtime.run_task( + run_id=state.run_id, + task_type=contract["task_type"], + stage=stage, + expected_schema=contract["schema"], + input_text=store.read_seed_text(), + input_files=state.seed_files, + structured_input=self._stage_structured_input(store, state, stage), + system_prompt=contract["system_prompt"], + user_prompt=contract["user_prompt"], + validation_rules={"strict": True}, + retry_policy={"max_repair_attempts": 1}, + context_refs=self._context_refs(store), + output_contract={"schema": contract["schema"]}, + ) + if result.status == "need_agent_response": + store.add_stage_request(state, stage, result.request_id) + return result.to_dict() | {"stage": stage, "type": contract["task_type"]} + if result.status != "ok": + store.set_stage(state, stage, StageStatus.FAILED, result.error) + return {"status": "failed", "stage": stage, "error": result.error} + self._process_stage_output(store, state, stage, result.output or {}, graph_provider_override=graph_provider_override) + store.set_stage(state, stage, StageStatus.COMPLETED) + return self.resume(str(store.run_dir)) + + def _process_stage_output( + self, + store: RunStore, + state, + stage: str, + output: Dict[str, Any], + *, + graph_provider_override: Optional[str] = None, + ) -> None: + artifacts = store.artifacts_dir + artifacts.mkdir(parents=True, exist_ok=True) + if stage == "ontology": + self._write_json(artifacts / "ontology.json", output["ontology"]) + store.add_stage_artifact(state, stage, str(artifacts / "ontology.json")) + elif stage == "graph": + triples = output["triples"] + self._write_json(artifacts / "triples.json", triples) + provider = create_graph_provider(graph_provider_override or self._graph_provider_name(state)) + provider.add_triples(state.run_id, triples) + provider.export_snapshot(state.run_id, str(artifacts / "graph_snapshot.json")) + if hasattr(provider, "export_timeline"): + provider.export_timeline(state.run_id, str(artifacts / "timeline.json")) + else: + self._write_json(artifacts / "timeline.json", []) + store.add_stage_artifact(state, stage, str(artifacts / "graph_snapshot.json")) + store.add_stage_artifact(state, stage, str(artifacts / "timeline.json")) + elif stage == "profiles": + self._write_json(artifacts / "profiles.json", output["profiles"]) + store.add_stage_artifact(state, stage, str(artifacts / "profiles.json")) + elif stage == "config": + config = self._config_with_settings(output["config"], state) + self._write_json(artifacts / "simulation_config.json", config) + store.add_stage_artifact(state, stage, str(artifacts / "simulation_config.json")) + elif stage == "simulation": + self._write_json(artifacts / "simulation_actions.json", output["actions"]) + store.add_stage_artifact(state, stage, str(artifacts / "simulation_actions.json")) + elif stage == "report": + settings = self._settings_for_output(state) + (artifacts / "report.md").write_text(output["report_markdown"], encoding="utf-8") + verdict = output["verdict"] | {"simulation_settings": settings, "rounds": settings.get("rounds")} + self._write_json(artifacts / "verdict.json", verdict) + self._write_json(artifacts / "timeline.json", self._timeline_with_rounds(output.get("timeline", []), state)) + store.add_stage_artifact(state, stage, str(artifacts / "report.md")) + store.add_stage_artifact(state, stage, str(artifacts / "verdict.json")) + store.add_stage_artifact(state, stage, str(artifacts / "timeline.json")) + + def _stage_structured_input(self, store: RunStore, state, stage: str) -> Dict[str, Any]: + artifacts = store.artifacts_dir + data: Dict[str, Any] = {"requirement": state.requirement, "simulation_settings": self._ensure_state_simulation_settings(state, store)} + for name in ["ontology", "triples", "profiles", "simulation_config", "simulation_actions"]: + path = artifacts / f"{name}.json" + if path.exists(): + data[name] = json.loads(path.read_text(encoding="utf-8")) + if stage == "simulation": + data |= self._simulation_round_structured_input(store, state, 1) + return data + + def _context_refs(self, store: RunStore) -> List[str]: + if not store.artifacts_dir.exists(): + return [] + return [str(path) for path in sorted(store.artifacts_dir.glob("*.json"))] + + def _artifact_context(self, store: RunStore) -> Dict[str, Any]: + context: Dict[str, Any] = {} + if not store.artifacts_dir.exists(): + return context + for path in sorted(store.artifacts_dir.glob("*.json")): + try: + context[path.stem] = json.loads(path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + context[path.stem] = {"path": str(path), "error": "invalid json"} + report_path = store.artifacts_dir / "report.md" + if report_path.exists(): + context["report_markdown"] = report_path.read_text(encoding="utf-8")[:50_000] + return context + + def _persist_followup_answer( + self, + store: RunStore, + run_id: str, + request_id: str, + question: str, + output: Dict[str, Any], + ) -> Dict[str, Any]: + followups_dir = store.artifacts_dir / "followups" + followups_dir.mkdir(parents=True, exist_ok=True) + markdown_path = followups_dir / f"{request_id}.md" + json_path = followups_dir / f"{request_id}.json" + markdown_path.write_text(output["answer_markdown"], encoding="utf-8") + payload = { + "run_id": run_id, + "request_id": request_id, + "question": question, + "answer": output, + } + self._write_json(json_path, payload) + return { + "status": "ok", + "request_id": request_id, + "question": question, + "answer": output, + "artifacts": { + "markdown": str(markdown_path), + "json": str(json_path), + }, + } + + def _followup_question_from_request(self, request) -> str: + if request.type == "answer_followup_question": + return str(request.structured_input.get("question", "")) + original = request.structured_input.get("original_request", {}) + if isinstance(original, dict): + structured_input = original.get("structured_input", {}) + if isinstance(structured_input, dict): + return str(structured_input.get("question", "")) + return "" + + def _graph_provider_name(self, state) -> Optional[str]: + return self.graph_provider_name or state.metadata.get("graph_provider") + + def _ensure_final_artifacts(self, store: RunStore, state) -> None: + artifacts = store.artifacts_dir + artifacts.mkdir(parents=True, exist_ok=True) + if not (artifacts / "report.md").exists(): + (artifacts / "report.md").write_text("# MiroFish Report\n\nReport generation has not completed.", encoding="utf-8") + if not (artifacts / "verdict.json").exists(): + self._write_json(artifacts / "verdict.json", {"status": "incomplete"}) + if not (artifacts / "timeline.json").exists(): + self._write_json(artifacts / "timeline.json", []) + if not (artifacts / "graph_snapshot.json").exists(): + create_graph_provider(self.graph_provider_name).export_snapshot(state.run_id, str(artifacts / "graph_snapshot.json")) + + def _next_stage(self, stage: str) -> Optional[str]: + try: + index = RUN_STAGES.index(stage) + except ValueError: + return None + if index + 1 >= len(RUN_STAGES): + return None + return RUN_STAGES[index + 1] + + def _existing_agent_wait(self, store: RunStore, state, stage: str) -> Optional[Dict[str, Any]]: + checkpoint = state.stages[stage] + if checkpoint.status != StageStatus.WAITING_AGENT or not checkpoint.request_ids: + return None + request_id = checkpoint.request_ids[-1] + request = AgentQueue(store.run_dir).load_request(request_id) + return { + "status": "need_agent_response", + "request_id": request_id, + "request_file": str(store.requests_dir / f"{request_id}.json"), + "expected_response_file": str(store.responses_dir / f"{request_id}.json"), + "stage": stage, + "type": request.type, + } + + def _attach_repair_request_to_waiting_stage(self, run_dir: str, queue: AgentQueue, repair_request_id: str) -> None: + try: + store = RunStore(run_dir) + state = store.load() + repair_request = queue.load_request(repair_request_id) + original = repair_request.structured_input.get("original_request", {}) + original_request_id = original.get("request_id") if isinstance(original, dict) else None + if not original_request_id: + return + for stage, checkpoint in state.stages.items(): + if checkpoint.status == StageStatus.WAITING_AGENT and checkpoint.request_ids[-1:] == [original_request_id]: + store.add_stage_request(state, stage, repair_request_id) + return + except Exception: + return + + def _canonical_simulation_settings( + self, + *, + rounds: int, + round_unit: str, + minutes_per_round: Optional[int], + pause_each_round: bool, + agent_count: Optional[int] = None, + simulation_name: Optional[str] = None, + output_directory: Optional[str] = None, + ) -> Dict[str, Any]: + if round_unit not in ROUND_UNIT_MINUTES: + raise ValueError(f"round_unit must be one of {sorted(ROUND_UNIT_MINUTES)}") + rounds = int(rounds) + allow_debug_rounds = os.environ.get("MIROFISH_ALLOW_DEBUG_ROUNDS", "").lower() in {"1", "true", "yes"} + if rounds < 10 and not allow_debug_rounds: + raise ValueError("rounds must be at least 10 unless MIROFISH_ALLOW_DEBUG_ROUNDS=true") + canonical_minutes = int(minutes_per_round) if minutes_per_round is not None else ROUND_UNIT_MINUTES[round_unit] + settings: Dict[str, Any] = { + "rounds": rounds, + "max_rounds": rounds, + "simulation_rounds": rounds, + "round_unit": round_unit, + "minutes_per_round": canonical_minutes, + "pause_each_round": bool(pause_each_round), + } + if agent_count is not None: + settings["agent_count"] = int(agent_count) + if simulation_name: + settings["simulation_name"] = simulation_name + if output_directory: + settings["output_directory"] = output_directory + return settings + + def _settings_kwargs(self, state, store: RunStore) -> Dict[str, Any]: + current = state.simulation_settings or {} + return { + "rounds": int(current.get("rounds", 10)), + "round_unit": str(current.get("round_unit", "year")), + "minutes_per_round": current.get("minutes_per_round"), + "pause_each_round": bool(current.get("pause_each_round", False)), + "agent_count": current.get("agent_count"), + "simulation_name": current.get("simulation_name"), + "output_directory": current.get("output_directory") or str(store.run_dir), + } + + def _ensure_state_simulation_settings(self, state, store: RunStore) -> Dict[str, Any]: + if not state.simulation_settings: + state.simulation_settings = self._canonical_simulation_settings( + rounds=10, + round_unit="year", + minutes_per_round=None, + pause_each_round=False, + output_directory=str(store.run_dir), + ) + return state.simulation_settings + + def _settings_for_output(self, state) -> Dict[str, Any]: + settings = dict(state.simulation_settings or {}) + rounds = int(settings.get("rounds", 10)) + settings["rounds"] = rounds + settings["max_rounds"] = rounds + settings["simulation_rounds"] = rounds + return settings + + def _config_with_settings(self, config: Dict[str, Any], state) -> Dict[str, Any]: + settings = self._settings_for_output(state) + merged = dict(config or {}) + merged["rounds"] = settings["rounds"] + merged["max_rounds"] = settings["rounds"] + merged["simulation_rounds"] = settings["rounds"] + merged["round_unit"] = settings.get("round_unit") + merged["minutes_per_round"] = settings.get("minutes_per_round") + merged["pause_each_round"] = settings.get("pause_each_round", False) + if "agent_count" in settings: + merged["agent_count"] = settings["agent_count"] + if "simulation_name" in settings: + merged["simulation_name"] = settings["simulation_name"] + merged["simulation_settings"] = settings + return merged + + def _simulation_round_structured_input(self, store: RunStore, state, round_index: int) -> Dict[str, Any]: + artifacts = store.artifacts_dir + profiles_path = artifacts / "profiles.json" + profiles = json.loads(profiles_path.read_text(encoding="utf-8")) if profiles_path.exists() else [{"agent_id": "agent_1"}] + settings = self._ensure_state_simulation_settings(state, store) + agent_limit = int(settings.get("agent_count") or min(len(profiles), 5) or 1) + selected_profiles = profiles[:agent_limit] + selected_ids = _resolve_all_agent_ids(selected_profiles) + round_id = f"round_{round_index}" + return { + "round_id": round_id, + "round_index": round_index, + "simulation_settings": settings, + "actions": [ + { + "agent_id": selected_ids[index], + "action_id": f"{round_id}_action_{index + 1}", + "round_id": round_id, + } + for index, profile in enumerate(selected_profiles) + ], + "simulation_progress": state.simulation_progress or {}, + } + + def _process_staged_simulation_output(self, store: RunStore, state, output: Dict[str, Any]) -> None: + artifacts = store.artifacts_dir + rounds_dir = artifacts / "simulation_rounds" + rounds_dir.mkdir(parents=True, exist_ok=True) + checkpoint = state.stages["simulation_run"] + round_index = int(checkpoint.metadata.get("round_index", 1)) + actions = output.get("actions", []) + round_payload = { + "round_index": round_index, + "round_id": f"round_{round_index}", + "round_unit": state.simulation_settings.get("round_unit", "year"), + "actions": actions, + } + round_path = rounds_dir / f"round_{round_index:03d}.json" + self._write_json(round_path, round_payload) + all_actions_path = artifacts / "simulation_actions.json" + all_actions: List[Dict[str, Any]] = [] + if all_actions_path.exists(): + all_actions = json.loads(all_actions_path.read_text(encoding="utf-8")) + all_actions.extend(actions) + self._write_json(all_actions_path, all_actions) + store.add_stage_artifact(state, "simulation_run", str(round_path)) + store.add_stage_artifact(state, "simulation_run", str(all_actions_path)) + state.simulation_progress = self._simulation_progress_payload(state, round_index) + + def _simulation_progress_payload(self, state, completed_rounds: int) -> Dict[str, Any]: + settings = self._settings_for_output(state) + total_rounds = int(settings["rounds"]) + return { + "completed_rounds": completed_rounds, + "total_rounds": total_rounds, + "current_round": min(completed_rounds + 1, total_rounds), + "round_unit": settings.get("round_unit", "year"), + "minutes_per_round": settings.get("minutes_per_round"), + "pause_each_round": settings.get("pause_each_round", False), + } + + def _timeline_with_rounds(self, timeline: List[Dict[str, Any]], state) -> List[Dict[str, Any]]: + settings = self._settings_for_output(state) + rounds = int(settings["rounds"]) + existing = list(timeline or []) + normalized = [] + for index in range(1, rounds + 1): + item = existing[index - 1] if index - 1 < len(existing) and isinstance(existing[index - 1], dict) else {} + normalized.append( + item + | { + "round": index, + "round_unit": settings.get("round_unit", "year"), + "minutes_per_round": settings.get("minutes_per_round"), + } + ) + return normalized + + def _stage_summary(self, store: RunStore, state, stage: str) -> Dict[str, Any]: + if stage == "seed_input": + return state.metadata.get("seed_summary", {}) + if stage == "prediction_requirement": + return state.metadata.get("requirement_summary", {}) + if stage == "simulation_settings": + return state.simulation_settings + if stage == "graph_build": + return state.graph_summary + if stage == "profile_and_config": + return {"profiles_summary": state.profiles_summary, "config_summary": state.config_summary} + if stage == "simulation_run": + return state.simulation_progress + if stage == "report_generation": + return state.report_artifacts + return {} + + def _stage_detail(self, store: RunStore, state, stage: str) -> Dict[str, Any]: + checkpoint = state.stages[stage] + return { + "run_id": state.run_id, + "workflow_mode": state.workflow_mode, + "current_stage": stage, + "checkpoint": checkpoint.model_dump(), + "summary": self._stage_summary(store, state, stage), + "simulation_settings": state.simulation_settings, + "artifacts": checkpoint.artifact_paths, + } + + def _awaiting_confirmation_payload(self, store: RunStore, state, stage: str) -> Dict[str, Any]: + return { + "status": "awaiting_user_confirmation", + "run_id": state.run_id, + "stage": stage, + "current_stage": stage, + "stage_detail": self._stage_detail(store, state, stage), + } + + def _graph_summary(self, store: RunStore) -> Dict[str, Any]: + triples_path = store.artifacts_dir / "triples.json" + triples = json.loads(triples_path.read_text(encoding="utf-8")) if triples_path.exists() else [] + entities: Dict[str, int] = {} + for triple in triples: + for key in ("subject", "object"): + value = str(triple.get(key, "")).strip() + if value: + entities[value] = entities.get(value, 0) + 1 + return { + "entity_count": len(entities), + "triple_count": len(triples), + "top_entities": sorted(entities, key=entities.get, reverse=True)[:10], + "warnings": [], + } + + def _profiles_summary(self, store: RunStore) -> Dict[str, Any]: + path = store.artifacts_dir / "profiles.json" + profiles = json.loads(path.read_text(encoding="utf-8")) if path.exists() else [] + return { + "profile_count": len(profiles), + "agent_ids": _resolve_all_agent_ids(profiles), + } + + def _config_summary(self, store: RunStore, state) -> Dict[str, Any]: + path = store.artifacts_dir / "simulation_config.json" + config = json.loads(path.read_text(encoding="utf-8")) if path.exists() else {} + return { + "rounds": config.get("rounds"), + "round_unit": config.get("round_unit"), + "minutes_per_round": config.get("minutes_per_round"), + "agent_count": config.get("agent_count", state.simulation_settings.get("agent_count")), + } + + def _report_artifacts(self, store: RunStore) -> Dict[str, Any]: + names = ["report.md", "verdict.json", "timeline.json", "graph_snapshot.json"] + return {name: str(store.artifacts_dir / name) for name in names if (store.artifacts_dir / name).exists()} + + def _mark_downstream_pending(self, state, stage: str, reason: str) -> None: + for downstream in STAGED_DOWNSTREAM.get(stage, []): + if downstream not in state.stages: + continue + checkpoint = state.stages[downstream] + checkpoint.status = StageStatus.PENDING + checkpoint.request_ids = [] + checkpoint.error = None + checkpoint.stale = True + checkpoint.stale_reason = reason + checkpoint.metadata = {} + state.graph_summary = state.graph_summary if stage not in {"seed_input", "prediction_requirement"} else {} + if stage in {"seed_input", "prediction_requirement", "simulation_settings", "graph_build"}: + state.profiles_summary = {} + state.config_summary = {} + state.simulation_progress = {} + state.report_artifacts = {} + + def _normalize_stage_name(self, state, stage: str) -> str: + if stage in state.stages: + return stage + aliases = { + "graph": "graph_build", + "profiles": "profile_and_config", + "config": "profile_and_config", + "simulation": "simulation_run", + "report": "report_generation", + } + return aliases.get(stage, stage) + + def _next_staged_stage(self, stage: str) -> Optional[str]: + try: + index = STAGED_RUN_STAGES.index(stage) + except ValueError: + return None + if index + 1 >= len(STAGED_RUN_STAGES): + return None + return STAGED_RUN_STAGES[index + 1] + + def _internal_stage_from_response_type(self, response: AgentResponse, stage: str) -> Optional[str]: + task_map = { + "generate_ontology": "ontology", + "extract_triples": "graph", + "generate_oasis_profiles": "profiles", + "generate_simulation_config": "config", + "simulate_agent_action": "simulation", + "generate_report": "report", + } + return task_map.get(getattr(response, "type", ""), STAGED_INTERNAL_START.get(stage)) + + def _write_json(self, path: Path, data: Any) -> None: + path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") + + # ── Interaction helper methods ───────────────────────────────────────── + + def _persist_agent_question_answer( + self, + store: RunStore, + run_id: str, + agent_id: str, + request_id: str, + question: str, + output: Dict[str, Any], + ) -> Dict[str, Any]: + questions_dir = store.artifacts_dir / "interactions" / "agent_questions" + questions_dir.mkdir(parents=True, exist_ok=True) + markdown_path = questions_dir / f"{request_id}.md" + json_path = questions_dir / f"{request_id}.json" + answer_md = output.get("answer_markdown", "") + markdown_path.write_text(answer_md, encoding="utf-8") + payload = { + "run_id": run_id, + "request_id": request_id, + "agent_id": agent_id, + "question": question, + "answer": output, + } + self._write_json(json_path, payload) + return { + "status": "ok", + "request_id": request_id, + "agent_id": agent_id, + "question": question, + "answer": output, + "artifacts": { + "markdown": str(markdown_path), + "json": str(json_path), + }, + } + + def _persist_questionnaire_answers( + self, + store: RunStore, + run_id: str, + questionnaire_id: str, + output: Dict[str, Any], + ) -> None: + questionnaires_dir = store.artifacts_dir / "interactions" / "questionnaires" + questionnaires_dir.mkdir(parents=True, exist_ok=True) + answers_path = questionnaires_dir / f"{questionnaire_id}_answers.json" + existing = [] + if answers_path.exists(): + existing = json.loads(answers_path.read_text(encoding="utf-8")) + new_answers = self._extract_questionnaire_answers(output) + existing.extend(new_answers) + self._write_json(answers_path, existing) + + def _extract_questionnaire_answers(self, output: Dict[str, Any]) -> List[Dict[str, Any]]: + answers = output.get("answers", []) + if isinstance(answers, list): + return answers + return [] + + def _persist_questionnaire_metadata( + self, + store: RunStore, + questionnaire_id: str, + questions: List[Dict[str, Any]], + request_ids: List[str], + ) -> None: + questionnaires_dir = store.artifacts_dir / "interactions" / "questionnaires" + questionnaires_dir.mkdir(parents=True, exist_ok=True) + meta_path = questionnaires_dir / f"{questionnaire_id}_meta.json" + meta = { + "questionnaire_id": questionnaire_id, + "questions": questions, + "request_ids": request_ids, + "answer_count": 0, + "summary_markdown": "", + } + self._write_json(meta_path, meta) + + def _persist_report_question_answer( + self, + store: RunStore, + run_id: str, + request_id: str, + question: str, + output: Dict[str, Any], + ) -> Dict[str, Any]: + rq_dir = store.artifacts_dir / "interactions" / "report_questions" + rq_dir.mkdir(parents=True, exist_ok=True) + markdown_path = rq_dir / f"{request_id}.md" + json_path = rq_dir / f"{request_id}.json" + answer_md = output.get("answer_markdown", "") + markdown_path.write_text(answer_md, encoding="utf-8") + payload = { + "run_id": run_id, + "request_id": request_id, + "question": question, + "answer": output, + } + self._write_json(json_path, payload) + return { + "status": "ok", + "request_id": request_id, + "question": question, + "answer": output, + "artifacts": { + "markdown": str(markdown_path), + "json": str(json_path), + }, + } + + def get_report_question_answer(self, run_dir: str, request_id: str) -> Dict[str, Any]: + store = RunStore(run_dir) + state = store.load() + queue = AgentQueue(run_dir) + request = queue.load_request(request_id) + if request.type != "ask_report_question" or request.stage != "interaction": + return {"status": "error", "error": f"request {request_id} is not a report question request"} + response_path = store.responses_dir / f"{request_id}.json" + if not response_path.exists(): + return {"status": "missing", "request_id": request_id, "expected_response_file": str(response_path)} + validation = queue.submit_response(response_path) + if not validation.ok: + if validation.repair_request: + return validation.repair_request.model_dump() + return {"status": "failed", "request_id": request_id, "errors": validation.errors} + response = queue.load_response(request_id) + if response.status == "error": + return {"status": "failed", "request_id": request_id, "error": response.error or "agent returned error"} + question = str(request.structured_input.get("question", "")) + return self._persist_report_question_answer(store, state.run_id, request_id, question, response.output) + + def _collect_web_console_data(self, store: RunStore) -> Dict[str, Any]: + artifacts = store.artifacts_dir + data: Dict[str, Any] = {"run_id": "", "artifacts": {}} + # Try to load state for run_id + state_path = store.run_dir / "state.json" + if state_path.exists(): + state_data = json.loads(state_path.read_text(encoding="utf-8")) + data["run_id"] = state_data.get("run_id", "") + data["requirement"] = state_data.get("requirement", "") + data["simulation_settings"] = state_data.get("simulation_settings", {}) + # Read artifact files + artifact_names = [ + "report.md", + "verdict.json", + "timeline.json", + "graph_snapshot.json", + "profiles.json", + "simulation_config.json", + "simulation_actions.json", + ] + for name in artifact_names: + path = artifacts / name + if path.exists(): + if name.endswith(".json"): + try: + data["artifacts"][name] = json.loads(path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + data["artifacts"][name] = None + else: + data["artifacts"][name] = path.read_text(encoding="utf-8") + # Read interaction data + interactions: Dict[str, Any] = {"agent_questions": [], "questionnaires": []} + aq_dir = artifacts / "interactions" / "agent_questions" + if aq_dir.exists(): + for p in sorted(aq_dir.glob("*.json")): + try: + interactions["agent_questions"].append(json.loads(p.read_text(encoding="utf-8"))) + except json.JSONDecodeError: + pass + q_dir = artifacts / "interactions" / "questionnaires" + if q_dir.exists(): + for p in sorted(q_dir.glob("*_meta.json")): + try: + meta = json.loads(p.read_text(encoding="utf-8")) + answers_path = p.parent / f"{meta['questionnaire_id']}_answers.json" + answers = [] + if answers_path.exists(): + answers = json.loads(answers_path.read_text(encoding="utf-8")) + meta["answers"] = answers + interactions["questionnaires"].append(meta) + except json.JSONDecodeError: + pass + data["interactions"] = interactions + return data + + def _render_web_console_html(self, data: Dict[str, Any]) -> str: + run_id = data.get("run_id", "unknown") + requirement = data.get("requirement", "") + artifacts = data.get("artifacts", {}) + interactions = data.get("interactions", {}) + report_md = artifacts.get("report.md", "# Report not yet generated") + verdict = artifacts.get("verdict.json", {}) + timeline = artifacts.get("timeline.json", []) + graph_snapshot = artifacts.get("graph_snapshot.json", {}) + profiles = artifacts.get("profiles.json", []) + # Normalize profile IDs before embedding (mirrors JS _resolveId logic) + if isinstance(profiles, list): + _resolved = _resolve_all_agent_ids(profiles) + for _pi, _pp in enumerate(profiles): + if isinstance(_pp, dict): + _pp["agent_id"] = _resolved[_pi] + sim_config = artifacts.get("simulation_config.json", {}) + sim_actions = artifacts.get("simulation_actions.json", []) + agent_questions = interactions.get("agent_questions", []) + questionnaires = interactions.get("questionnaires", []) + # JSON-escape for embedding + def json_embed(obj): + return json.dumps(obj, ensure_ascii=False) + return _WEB_CONSOLE_TEMPLATE.replace( + "{{RUN_ID}}", html.escape(str(run_id), quote=True) + ).replace( + "{{RUN_ID_JSON}}", json_embed(run_id) + ).replace( + "{{REQUIREMENT_JSON}}", json_embed(requirement) + ).replace( + "{{REPORT_MD_JSON}}", json_embed(report_md) + ).replace( + "{{RUN_DIR_JSON}}", json_embed(data.get("run_dir", "")) + ).replace( + "{{VERDICT_JSON}}", json_embed(verdict) + ).replace( + "{{TIMELINE_JSON}}", json_embed(timeline) + ).replace( + "{{GRAPH_SNAPSHOT_JSON}}", json_embed(graph_snapshot) + ).replace( + "{{PROFILES_JSON}}", json_embed(profiles) + ).replace( + "{{SIM_CONFIG_JSON}}", json_embed(sim_config) + ).replace( + "{{SIM_ACTIONS_JSON}}", json_embed(sim_actions) + ).replace( + "{{AGENT_QUESTIONS_JSON}}", json_embed(agent_questions) + ).replace( + "{{QUESTIONNAIRES_JSON}}", json_embed(questionnaires) + ) + + +# ── Web Console HTML template ──────────────────────────────────────────── + +_WEB_CONSOLE_TEMPLATE = r""" + + + + +MiroFish Web Console — {{RUN_ID}} + + + +
+
+
+

MiroFish Console

+
{{RUN_ID}}
+
+ +
+ + Checking API... +
+
+
+ + +
+
+
+
+ +
+

Overview

+
+
+
+
Verdict
+
+
+
+ +
+

Report

+
+
+ +
+

Agents

+
+
+ +
+

Timeline

+
+
+ +
+

Knowledge Graph

+
+
+
Entities
+
+
+
+
Triples
+
+
+
+ +
+

Simulation

+
+
+
+ +
+

Ask an Agent

+
+
New Question
+
+ + +
+
+ + +
+
+ + +
+
+
+
+

Previous Questions

+
+
+ +
+

Questionnaires

+
+
New Questionnaire
+
+
+ + + +
+
+
+ + + +
+
+
+
+

Previous Questionnaires

+
+
+ +
+

Report Q&A

+
+
Ask a question about the report
+
+ + +
+
+ + +
+
+
+
+

Previous Report Questions

+
+
+ +
+

Interaction History

+
+
+ +
+

Raw Artifacts

+
+
+
+
+
+ + +""" diff --git a/backend/app/agent_engine/schemas.py b/backend/app/agent_engine/schemas.py new file mode 100644 index 0000000000..6a0eb2ed38 --- /dev/null +++ b/backend/app/agent_engine/schemas.py @@ -0,0 +1,169 @@ +"""Strict request/response schemas for desktop-agent driven runs.""" + +from __future__ import annotations + +from datetime import datetime +from enum import Enum +from typing import Any, Dict, List, Literal, Optional + +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +AgentTaskType = Literal[ + "extract_triples", + "generate_ontology", + "generate_oasis_profiles", + "generate_simulation_config", + "simulate_agent_action", + "summarize_round", + "update_memory", + "generate_report", + "answer_followup_question", + "answer_agent_question", + "answer_agent_questionnaire", + "summarize_questionnaire", + "ask_report_question", + "validate_json_output", + "repair_invalid_json", +] + +AGENT_TASK_TYPES = { + "extract_triples", + "generate_ontology", + "generate_oasis_profiles", + "generate_simulation_config", + "simulate_agent_action", + "summarize_round", + "update_memory", + "generate_report", + "answer_followup_question", + "answer_agent_question", + "answer_agent_questionnaire", + "summarize_questionnaire", + "ask_report_question", + "validate_json_output", + "repair_invalid_json", +} + + +class StageStatus(str, Enum): + PENDING = "pending" + RUNNING = "running" + WAITING_AGENT = "waiting_agent" + AWAITING_USER_CONFIRMATION = "awaiting_user_confirmation" + COMPLETED = "completed" + FAILED = "failed" + + +class RetryPolicy(BaseModel): + model_config = ConfigDict(extra="forbid") + + max_repair_attempts: int = Field(default=1, ge=0) + repair_attempts_used: int = Field(default=0, ge=0) + + +class AgentRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + + request_id: str + run_id: str + type: AgentTaskType + stage: str + input_text: Optional[str] = None + input_files: List[str] = Field(default_factory=list) + structured_input: Dict[str, Any] = Field(default_factory=dict) + system_prompt: str = "" + user_prompt: str = "" + expected_schema: Dict[str, Any] + validation_rules: Dict[str, Any] = Field(default_factory=dict) + retry_policy: RetryPolicy = Field(default_factory=RetryPolicy) + context_refs: List[str] = Field(default_factory=list) + output_contract: Dict[str, Any] = Field(default_factory=dict) + created_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat() + "Z") + + @field_validator("request_id") + @classmethod + def validate_request_id(cls, value: str) -> str: + if not value.startswith("req_"): + raise ValueError("request_id must start with req_") + return value + + @field_validator("expected_schema") + @classmethod + def validate_expected_schema(cls, value: Dict[str, Any]) -> Dict[str, Any]: + if not value or value.get("type") != "object": + raise ValueError("expected_schema must be a non-empty JSON object schema") + return value + + +class AgentResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + request_id: str + status: Literal["ok", "error", "skipped"] + output: Dict[str, Any] = Field(default_factory=dict) + error: Optional[str] = None + validation: Optional[Dict[str, Any]] = None + + @field_validator("request_id") + @classmethod + def validate_request_id(cls, value: str) -> str: + if not value.startswith("req_"): + raise ValueError("request_id must start with req_") + return value + + +class AgentNeedResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + status: Literal["need_agent_response"] = "need_agent_response" + request_id: str + request_file: str + expected_response_file: str + stage: str + type: str + + +class StageCheckpoint(BaseModel): + model_config = ConfigDict(extra="forbid") + + name: str + status: StageStatus = StageStatus.PENDING + request_ids: List[str] = Field(default_factory=list) + artifact_paths: List[str] = Field(default_factory=list) + error: Optional[str] = None + stale: bool = False + stale_reason: Optional[str] = None + metadata: Dict[str, Any] = Field(default_factory=dict) + updated_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat() + "Z") + + +class RunState(BaseModel): + model_config = ConfigDict(extra="forbid") + + run_id: str + run_dir: str + requirement: str + seed_files: List[str] = Field(default_factory=list) + mode: str = "agent" + workflow_mode: Literal["auto", "staged"] = "auto" + current_stage: str = "ontology" + stages: Dict[str, StageCheckpoint] + seed_path: Optional[str] = None + simulation_settings: Dict[str, Any] = Field(default_factory=dict) + graph_summary: Dict[str, Any] = Field(default_factory=dict) + profiles_summary: Dict[str, Any] = Field(default_factory=dict) + config_summary: Dict[str, Any] = Field(default_factory=dict) + simulation_progress: Dict[str, Any] = Field(default_factory=dict) + report_artifacts: Dict[str, Any] = Field(default_factory=dict) + metadata: Dict[str, Any] = Field(default_factory=dict) + created_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat() + "Z") + updated_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat() + "Z") + + +class ValidationResult(BaseModel): + model_config = ConfigDict(extra="forbid") + + ok: bool + errors: List[str] = Field(default_factory=list) + repair_request: Optional[AgentNeedResponse] = None diff --git a/backend/app/agent_engine/state.py b/backend/app/agent_engine/state.py new file mode 100644 index 0000000000..588c3d368e --- /dev/null +++ b/backend/app/agent_engine/state.py @@ -0,0 +1,151 @@ +"""Persistent run state and directory layout.""" + +from __future__ import annotations + +import json +import os +from datetime import datetime +from pathlib import Path +from typing import Dict, Iterable, Optional + +from .schemas import RunState, StageCheckpoint, StageStatus + + +RUN_STAGES = [ + "ontology", + "graph", + "profiles", + "config", + "simulation", + "report", + "artifacts", +] + +STAGED_RUN_STAGES = [ + "seed_input", + "prediction_requirement", + "simulation_settings", + "graph_build", + "profile_and_config", + "simulation_run", + "report_generation", + "followup_question", +] + + +def utc_now() -> str: + return datetime.utcnow().isoformat() + "Z" + + +class RunStore: + def __init__(self, run_dir: str | Path): + self.run_dir = Path(run_dir) + self.requests_dir = self.run_dir / "requests" + self.responses_dir = self.run_dir / "responses" + self.artifacts_dir = self.run_dir / "artifacts" + self.state_path = self.run_dir / "state.json" + + def ensure_layout(self) -> None: + self.requests_dir.mkdir(parents=True, exist_ok=True) + self.responses_dir.mkdir(parents=True, exist_ok=True) + self.artifacts_dir.mkdir(parents=True, exist_ok=True) + + def init_state( + self, + run_id: str, + requirement: str, + seed_files: Iterable[str], + mode: str = "agent", + workflow_mode: str = "auto", + simulation_settings: Optional[Dict] = None, + seed_path: Optional[str] = None, + metadata: Optional[Dict] = None, + ) -> RunState: + self.ensure_layout() + stage_names = STAGED_RUN_STAGES if workflow_mode == "staged" else RUN_STAGES + stages = {name: StageCheckpoint(name=name) for name in stage_names} + state = RunState( + run_id=run_id, + run_dir=str(self.run_dir), + requirement=requirement, + seed_files=list(seed_files), + mode=mode, + workflow_mode=workflow_mode, + current_stage=stage_names[0], + seed_path=seed_path, + simulation_settings=simulation_settings or {}, + stages=stages, + metadata=metadata or {}, + ) + self.save(state) + return state + + def load(self) -> RunState: + with self.state_path.open("r", encoding="utf-8") as handle: + return RunState.model_validate_json(handle.read()) + + def save(self, state: RunState) -> None: + self.ensure_layout() + state.updated_at = utc_now() + with self.state_path.open("w", encoding="utf-8") as handle: + handle.write(state.model_dump_json(indent=2)) + + def set_stage( + self, + state: RunState, + stage: str, + status: StageStatus, + error: Optional[str] = None, + ) -> None: + checkpoint = state.stages[stage] + checkpoint.status = status + checkpoint.error = error + checkpoint.updated_at = utc_now() + state.current_stage = stage + self.save(state) + + def add_stage_request(self, state: RunState, stage: str, request_id: str) -> None: + checkpoint = state.stages[stage] + if request_id not in checkpoint.request_ids: + checkpoint.request_ids.append(request_id) + checkpoint.status = StageStatus.WAITING_AGENT + checkpoint.updated_at = utc_now() + state.current_stage = stage + self.save(state) + + def add_stage_artifact(self, state: RunState, stage: str, artifact_path: str) -> None: + checkpoint = state.stages.get(stage) or state.stages[state.current_stage] + if artifact_path not in checkpoint.artifact_paths: + checkpoint.artifact_paths.append(artifact_path) + checkpoint.updated_at = utc_now() + self.save(state) + + def next_request_id(self) -> str: + self.ensure_layout() + max_seen = 0 + for folder in (self.requests_dir, self.responses_dir): + for path in folder.glob("req_*.json"): + try: + max_seen = max(max_seen, int(path.stem.split("_", 1)[1])) + except (IndexError, ValueError): + continue + return f"req_{max_seen + 1:06d}" + + def read_seed_text(self, max_chars: int = 200_000) -> str: + state = self.load() + parts = [] + for seed_file in state.seed_files: + path = Path(seed_file) + if not path.is_absolute(): + path = self.run_dir / path + if path.exists(): + parts.append(path.read_text(encoding="utf-8")[:max_chars]) + return "\n\n".join(parts) + + def as_status(self) -> Dict: + state = self.load() + return json.loads(state.model_dump_json()) + + +def default_runs_dir() -> str: + return os.environ.get("MIROFISH_RUNS_DIR", "./runs") diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py index ffda743a31..ed8ed13e0a 100644 --- a/backend/app/api/__init__.py +++ b/backend/app/api/__init__.py @@ -7,8 +7,10 @@ graph_bp = Blueprint('graph', __name__) simulation_bp = Blueprint('simulation', __name__) report_bp = Blueprint('report', __name__) +interaction_bp = Blueprint('interaction', __name__) from . import graph # noqa: E402, F401 from . import simulation # noqa: E402, F401 from . import report # noqa: E402, F401 +from . import interaction # noqa: E402, F401 diff --git a/backend/app/api/graph.py b/backend/app/api/graph.py index 759ff48b0e..166babbd2b 100644 --- a/backend/app/api/graph.py +++ b/backend/app/api/graph.py @@ -23,6 +23,14 @@ logger = get_logger('mirofish.api') +def _legacy_zep_config_errors() -> list[str]: + """Only legacy zep provider requires ZEP_API_KEY.""" + graph_provider = os.environ.get('MIROFISH_GRAPH_PROVIDER', Config.MIROFISH_GRAPH_PROVIDER) + if graph_provider == 'zep' and not Config.ZEP_API_KEY: + return [t('api.zepApiKeyMissing')] + return [] + + def allowed_file(filename: str) -> bool: """检查文件扩展名是否允许""" if not filename or '.' not in filename: @@ -248,6 +256,7 @@ def generate_ontology(): }) except Exception as e: + logger.exception("生成本体定义失败") return jsonify({ "success": False, "error": str(e), @@ -284,9 +293,7 @@ def build_graph(): logger.info("=== 开始构建图谱 ===") # 检查配置 - errors = [] - if not Config.ZEP_API_KEY: - errors.append(t('api.zepApiKeyMissing')) + errors = _legacy_zep_config_errors() if errors: logger.error(f"配置错误: {errors}") return jsonify({ @@ -387,7 +394,7 @@ def build_task(): ) # 创建图谱构建服务 - builder = GraphBuilderService(api_key=Config.ZEP_API_KEY) + builder = GraphBuilderService() # 分块 task_manager.update_task( @@ -522,6 +529,7 @@ def wait_progress_callback(msg, progress_ratio): }) except Exception as e: + logger.exception(f"获取图谱数据失败: graph_id={graph_id}") return jsonify({ "success": False, "error": str(e), @@ -572,13 +580,13 @@ def get_graph_data(graph_id: str): 获取图谱数据（节点和边） """ try: - if not Config.ZEP_API_KEY: + if _legacy_zep_config_errors(): return jsonify({ "success": False, "error": t('api.zepApiKeyMissing') }), 500 - builder = GraphBuilderService(api_key=Config.ZEP_API_KEY) + builder = GraphBuilderService() graph_data = builder.get_graph_data(graph_id) return jsonify({ @@ -600,13 +608,13 @@ def delete_graph(graph_id: str): 删除Zep图谱 """ try: - if not Config.ZEP_API_KEY: + if _legacy_zep_config_errors(): return jsonify({ "success": False, "error": t('api.zepApiKeyMissing') }), 500 - builder = GraphBuilderService(api_key=Config.ZEP_API_KEY) + builder = GraphBuilderService() builder.delete_graph(graph_id) return jsonify({ diff --git a/backend/app/api/interaction.py b/backend/app/api/interaction.py new file mode 100644 index 0000000000..b9cdd065dc --- /dev/null +++ b/backend/app/api/interaction.py @@ -0,0 +1,214 @@ +""" +Agent interaction API routes. + +Provides REST endpoints for the Web Console to interact with agents, +send questionnaires, ask report questions, and poll request status. +All LLM work goes through AgentRuntime / agent_queue — never direct model calls. +""" + +import json +import os +from pathlib import Path +from flask import Blueprint, request, jsonify + +from . import interaction_bp +from ..agent_engine.runner import PredictionRunService +from ..utils.logger import get_logger + +logger = get_logger("mirofish.api.interaction") + + +def _service(): + return PredictionRunService() + + +def _run_dir_from_param(): + """Resolve run_dir from query param ?run= or JSON body run field.""" + run = request.args.get("run") or "" + if not run: + body = request.get_json(silent=True) or {} + run = body.get("run", "") + if not run: + return None, (jsonify({"success": False, "error": "missing required 'run' parameter"}), 400) + return run, None + + +# ── Agents ──────────────────────────────────────────────────────────────── + +@interaction_bp.route("/agents", methods=["GET"]) +def list_agents(): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().list_agents(run)}) + + +@interaction_bp.route("/agents/", methods=["GET"]) +def get_agent(agent_id: str): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().get_agent(run, agent_id)}) + + +@interaction_bp.route("/agents//ask", methods=["POST"]) +def ask_agent(agent_id: str): + run, err = _run_dir_from_param() + if err: + return err + body = request.get_json() or {} + question = body.get("question", "") + if not question: + return jsonify({"success": False, "error": "missing required 'question' field"}), 400 + limit = body.get("limit", 20) + result = _service().ask_agent(run, agent_id, question, limit) + status_code = 202 if result.get("status") == "need_agent_response" else 200 + return jsonify({"success": True, "data": result}), status_code + + +@interaction_bp.route("/agents/answer/", methods=["POST", "GET"]) +def get_agent_answer(request_id: str): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().get_agent_answer(run, request_id)}) + + +# ── Questionnaires ──────────────────────────────────────────────────────── + +@interaction_bp.route("/questionnaires", methods=["POST"]) +def send_questionnaire(): + run, err = _run_dir_from_param() + if err: + return err + body = request.get_json() or {} + questions = body.get("questions", []) + if not questions: + return jsonify({"success": False, "error": "missing required 'questions' array"}), 400 + result = _service().send_questionnaire(run, questions) + status_code = 202 if result.get("status") == "need_agent_response" else 200 + return jsonify({"success": True, "data": result}), status_code + + +@interaction_bp.route("/questionnaires/", methods=["GET"]) +def get_questionnaire_result(questionnaire_id: str): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().get_questionnaire_result(run, questionnaire_id)}) + + +# ── Report Questions ────────────────────────────────────────────────────── + +@interaction_bp.route("/report-questions", methods=["POST"]) +def ask_report_question(): + run, err = _run_dir_from_param() + if err: + return err + body = request.get_json() or {} + question = body.get("question", "") + if not question: + return jsonify({"success": False, "error": "missing required 'question' field"}), 400 + limit = body.get("limit", 20) + result = _service().ask_report_question(run, question, limit) + status_code = 202 if result.get("status") == "need_agent_response" else 200 + return jsonify({"success": True, "data": result}), status_code + + +@interaction_bp.route("/report-questions/answer/", methods=["POST", "GET"]) +def get_report_question_answer(request_id: str): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().get_report_question_answer(run, request_id)}) + + +# ── Request Polling & Response Submission ───────────────────────────────── + +@interaction_bp.route("/requests/", methods=["GET"]) +def get_request(request_id: str): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().get_request(run, request_id)}) + + +@interaction_bp.route("/requests//status", methods=["GET"]) +def get_request_status(request_id: str): + """Lightweight polling endpoint: returns whether a response exists yet.""" + run, err = _run_dir_from_param() + if err: + return err + svc = _service() + req_data = svc.get_request(run, request_id) + if req_data.get("status") == "error": + return jsonify({"success": False, "error": req_data.get("error")}), 404 + # Check if response file exists + from ..agent_engine.state import RunStore + store = RunStore(run) + response_path = store.responses_dir / f"{request_id}.json" + has_response = response_path.exists() + return jsonify({ + "success": True, + "data": { + "request_id": request_id, + "has_response": has_response, + "status": "answered" if has_response else "pending", + }, + }) + + +@interaction_bp.route("/responses", methods=["POST"]) +def submit_response(): + """Submit an agent response (validate + persist).""" + run, err = _run_dir_from_param() + if err: + return err + body = request.get_json() or {} + response_path = body.get("response_path", "") + if not response_path: + return jsonify({"success": False, "error": "missing required 'response_path' field"}), 400 + # Path constraint: response_path must resolve inside run/responses/ + from ..agent_engine.state import RunStore + store = RunStore(run) + allowed_base = store.responses_dir.resolve() + resolved = Path(response_path).resolve() + if not str(resolved).startswith(str(allowed_base) + os.sep) and resolved != allowed_base: + return jsonify({"success": False, "error": "forbidden: response_path must be inside run responses directory"}), 403 + result = _service().submit_response(run, response_path) + return jsonify({"success": True, "data": result}) + + +# ── Artifacts ───────────────────────────────────────────────────────────── + +@interaction_bp.route("/artifacts", methods=["GET"]) +def list_artifacts(): + run, err = _run_dir_from_param() + if err: + return err + return jsonify({"success": True, "data": _service().list_artifacts(run)}) + + +@interaction_bp.route("/artifact/", methods=["GET"]) +def get_artifact(name: str): + """Return a single artifact's content as JSON or text.""" + run, err = _run_dir_from_param() + if err: + return err + from ..agent_engine.state import RunStore + store = RunStore(run) + base = store.artifacts_dir.resolve() + path = (base / name).resolve() + # Path traversal guard: resolved path must stay inside artifacts_dir + if not str(path).startswith(str(base) + os.sep) and path != base: + return jsonify({"success": False, "error": "forbidden path"}), 403 + if not path.exists(): + return jsonify({"success": False, "error": f"artifact not found: {name}"}), 404 + if name.endswith(".json"): + try: + data = json.loads(path.read_text(encoding="utf-8")) + return jsonify({"success": True, "data": data, "name": name}) + except json.JSONDecodeError: + return jsonify({"success": False, "error": "invalid JSON"}), 500 + else: + return jsonify({"success": True, "data": path.read_text(encoding="utf-8"), "name": name}) diff --git a/backend/app/api/simulation.py b/backend/app/api/simulation.py index 3a8e1e3fc8..505fe52971 100644 --- a/backend/app/api/simulation.py +++ b/backend/app/api/simulation.py @@ -20,6 +20,11 @@ logger = get_logger('mirofish.api.simulation') +def _legacy_zep_required() -> bool: + graph_provider = os.environ.get('MIROFISH_GRAPH_PROVIDER', Config.MIROFISH_GRAPH_PROVIDER) + return graph_provider == 'zep' and not Config.ZEP_API_KEY + + # Interview prompt 优化前缀 # 添加此前缀可以避免Agent调用工具，直接用文本回复 INTERVIEW_PROMPT_PREFIX = "结合你的人设、所有的过往记忆与行动，不调用任何工具直接用文本回复我：" @@ -57,7 +62,7 @@ def get_graph_entities(graph_id: str): enrich: 是否获取相关边信息（默认true） """ try: - if not Config.ZEP_API_KEY: + if _legacy_zep_required(): return jsonify({ "success": False, "error": t('api.zepApiKeyMissing') @@ -94,7 +99,7 @@ def get_graph_entities(graph_id: str): def get_entity_detail(graph_id: str, entity_uuid: str): """获取单个实体的详细信息""" try: - if not Config.ZEP_API_KEY: + if _legacy_zep_required(): return jsonify({ "success": False, "error": t('api.zepApiKeyMissing') @@ -127,7 +132,7 @@ def get_entity_detail(graph_id: str, entity_uuid: str): def get_entities_by_type(graph_id: str, entity_type: str): """获取指定类型的所有实体""" try: - if not Config.ZEP_API_KEY: + if _legacy_zep_required(): return jsonify({ "success": False, "error": t('api.zepApiKeyMissing') @@ -1547,7 +1552,11 @@ def start_simulation(): if state.status == SimulationStatus.RUNNING: # 检查模拟进程是否真的在运行 run_state = SimulationRunner.get_run_state(simulation_id) - if run_state and run_state.runner_status.value == "running": + if ( + run_state + and run_state.runner_status.value == "running" + and SimulationRunner.is_process_alive(simulation_id) + ): # 进程确实在运行 if force: # 强制模式：停止运行中的模拟 diff --git a/backend/app/config.py b/backend/app/config.py index de63e2b4b0..1734d30f8d 100644 --- a/backend/app/config.py +++ b/backend/app/config.py @@ -23,6 +23,22 @@ class Config: # Flask配置 SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key') DEBUG = os.environ.get('FLASK_DEBUG', 'True').lower() == 'true' + PRESERVE_SIMULATIONS_ON_RELOAD = os.environ.get( + 'MIROFISH_PRESERVE_SIMULATIONS_ON_RELOAD', + 'true' if DEBUG else 'false' + ).lower() == 'true' + + # Agent engine配置 + MIROFISH_MODE = os.environ.get('MIROFISH_MODE', 'agent') + MIROFISH_LLM_PROVIDER = os.environ.get( + 'MIROFISH_LLM_PROVIDER', + 'agent_queue' if MIROFISH_MODE == 'agent' else 'openai_compatible' + ) + MIROFISH_GRAPH_PROVIDER = os.environ.get( + 'MIROFISH_GRAPH_PROVIDER', + 'graphiti' if MIROFISH_MODE == 'agent' else 'zep' + ) + MIROFISH_RUNS_DIR = os.environ.get('MIROFISH_RUNS_DIR', './runs') # JSON配置 - 禁用ASCII转义，让中文直接显示（而不是 \uXXXX 格式） JSON_AS_ASCII = False @@ -34,6 +50,14 @@ class Config: # Zep配置 ZEP_API_KEY = os.environ.get('ZEP_API_KEY') + + # Graphiti / Neo4j配置 + NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687') + NEO4J_USER = os.environ.get('NEO4J_USER', 'neo4j') + NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD', 'password') + NEO4J_DATABASE = os.environ.get('NEO4J_DATABASE', 'neo4j') + OLLAMA_BASE_URL = os.environ.get('OLLAMA_BASE_URL', 'http://localhost:11434') + OLLAMA_EMBEDDING_MODEL = os.environ.get('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text') # 文件上传配置 MAX_CONTENT_LENGTH = 50 * 1024 * 1024 # 50MB @@ -67,9 +91,18 @@ class Config: def validate(cls) -> list[str]: """验证必要配置""" errors: list[str] = [] - if not cls.LLM_API_KEY: - errors.append("LLM_API_KEY 未配置") - if not cls.ZEP_API_KEY: - errors.append("ZEP_API_KEY 未配置") - return errors + mode = os.environ.get('MIROFISH_MODE', cls.MIROFISH_MODE) + llm_provider = os.environ.get( + 'MIROFISH_LLM_PROVIDER', + 'agent_queue' if mode == 'agent' else cls.MIROFISH_LLM_PROVIDER + ) + graph_provider = os.environ.get( + 'MIROFISH_GRAPH_PROVIDER', + 'graphiti' if mode == 'agent' else cls.MIROFISH_GRAPH_PROVIDER + ) + if llm_provider == 'openai_compatible' and not os.environ.get('LLM_API_KEY', cls.LLM_API_KEY or ''): + errors.append("LLM_API_KEY 未配置（openai_compatible provider 需要）") + if graph_provider == 'zep' and not os.environ.get('ZEP_API_KEY', cls.ZEP_API_KEY or ''): + errors.append("ZEP_API_KEY 未配置（legacy zep provider 需要）") + return errors diff --git a/backend/app/mcp_server/__init__.py b/backend/app/mcp_server/__init__.py new file mode 100644 index 0000000000..4e7d40c699 --- /dev/null +++ b/backend/app/mcp_server/__init__.py @@ -0,0 +1 @@ +"""MiroFish MCP server package.""" diff --git a/backend/app/mcp_server/server.py b/backend/app/mcp_server/server.py new file mode 100644 index 0000000000..7f2449a9ca --- /dev/null +++ b/backend/app/mcp_server/server.py @@ -0,0 +1,222 @@ +"""FastMCP server exposing MiroFish run lifecycle tools.""" + +from __future__ import annotations + +from typing import Any, Dict, Optional + +from ..agent_engine.runner import PredictionRunService + + +def create_server(): + try: + from mcp.server.fastmcp import FastMCP + except ImportError as exc: + raise RuntimeError( + "MCP Python SDK is not installed. Install package 'mcp' to run the MiroFish MCP server." + ) from exc + + mcp = FastMCP("mirofish-agent-engine") + service = PredictionRunService() + + @mcp.tool() + def mirofish_create_run( + seed: str, + requirement: str, + output: str, + mode: str = "auto", + rounds: int = 10, + round_unit: str = "year", + minutes_per_round: Optional[int] = None, + pause_each_round: bool = False, + agent_count: Optional[int] = None, + simulation_name: Optional[str] = None, + ) -> Dict[str, Any]: + return service.create_run( + seed, + requirement, + output, + mode=mode, + rounds=rounds, + round_unit=round_unit, + minutes_per_round=minutes_per_round, + pause_each_round=pause_each_round, + agent_count=agent_count, + simulation_name=simulation_name, + ) + + @mcp.tool() + def mirofish_run(run: str) -> Dict[str, Any]: + return service.run(run) + + @mcp.tool() + def mirofish_resume_run(run: str) -> Dict[str, Any]: + return service.resume(run) + + @mcp.tool() + def mirofish_get_status(run: str) -> Dict[str, Any]: + return service.status(run) + + @mcp.tool() + def mirofish_get_current_stage(run: str) -> Dict[str, Any]: + return service.get_current_stage(run) + + @mcp.tool() + def mirofish_update_simulation_settings( + run: str, + rounds: Optional[int] = None, + round_unit: Optional[str] = None, + minutes_per_round: Optional[int] = None, + pause_each_round: Optional[bool] = None, + agent_count: Optional[int] = None, + simulation_name: Optional[str] = None, + ) -> Dict[str, Any]: + return service.update_simulation_settings( + run, + rounds=rounds, + round_unit=round_unit, + minutes_per_round=minutes_per_round, + pause_each_round=pause_each_round, + agent_count=agent_count, + simulation_name=simulation_name, + ) + + @mcp.tool() + def mirofish_approve_stage(run: str) -> Dict[str, Any]: + return service.approve_stage(run) + + @mcp.tool() + def mirofish_reject_stage(run: str, reason: str = "") -> Dict[str, Any]: + return service.reject_stage(run, reason) + + @mcp.tool() + def mirofish_rerun_stage(run: str, stage: str) -> Dict[str, Any]: + return service.rerun_stage(run, stage) + + @mcp.tool() + def mirofish_list_requests(run: str) -> Dict[str, Any]: + return service.list_requests(run) + + @mcp.tool() + def mirofish_get_request(run: str, request_id: str) -> Dict[str, Any]: + return service.get_request(run, request_id) + + @mcp.tool() + def mirofish_submit_response(run: str, response: str) -> Dict[str, Any]: + return service.submit_response(run, response) + + @mcp.tool() + def mirofish_validate_response(run: str, response: str) -> Dict[str, Any]: + return service.validate_response(run, response) + + @mcp.tool() + def mirofish_build_graph(run: str, provider: Optional[str] = None, mode: str = "agent-triples") -> Dict[str, Any]: + return service.build_graph(run, provider=provider, mode=mode) + + @mcp.tool() + def mirofish_search_graph(run: str, query: str, limit: int = 20) -> Dict[str, Any]: + return service.search_graph(run, query, limit) + + @mcp.tool() + def mirofish_export_graph(run: str, output: Optional[str] = None) -> Dict[str, Any]: + return service.export_graph(run, output) + + @mcp.tool() + def mirofish_start_simulation(run: str) -> Dict[str, Any]: + return service.start_simulation(run) + + @mcp.tool() + def mirofish_resume_simulation(run: str) -> Dict[str, Any]: + return service.resume(run) + + @mcp.tool() + def mirofish_generate_report(run: str) -> Dict[str, Any]: + return service.generate_report(run) + + @mcp.tool() + def mirofish_get_report(run: str) -> Dict[str, Any]: + return service.get_report(run) + + @mcp.tool() + def mirofish_ask_followup_question(run: str, question: str, limit: int = 20) -> Dict[str, Any]: + return service.ask_followup_question(run, question, limit) + + @mcp.tool() + def mirofish_get_followup_answer(run: str, request_id: str) -> Dict[str, Any]: + return service.get_followup_answer(run, request_id) + + @mcp.tool() + def mirofish_list_artifacts(run: str) -> Dict[str, Any]: + return service.list_artifacts(run) + + @mcp.tool() + def mirofish_generate_web_console(run: str) -> Dict[str, Any]: + """Generate a static Web Console HTML for the given run.""" + return service.generate_web_console(run) + + @mcp.tool() + def mirofish_list_agents(run: str) -> Dict[str, Any]: + """List all agents from the run's profiles.json.""" + return service.list_agents(run) + + @mcp.tool() + def mirofish_get_agent(run: str, agent_id: str) -> Dict[str, Any]: + """Get a single agent's profile by agent_id.""" + return service.get_agent(run, agent_id) + + @mcp.tool() + def mirofish_ask_agent(run: str, agent_id: str, question: str, limit: int = 20) -> Dict[str, Any]: + """Ask a question to a specific agent. Creates an agent_queue request.""" + return service.ask_agent(run, agent_id, question, limit) + + @mcp.tool() + def mirofish_get_agent_answer(run: str, request_id: str) -> Dict[str, Any]: + """Retrieve and persist the answer for an agent question request.""" + return service.get_agent_answer(run, request_id) + + @mcp.tool() + def mirofish_send_questionnaire(run: str, questions_json: str) -> Dict[str, Any]: + """Send a questionnaire to all agents. + + questions_json should be a JSON array of objects, each with "question_id" and "question" fields. + Example: '[{"question_id":"q1","question":"Biggest risk?"},{"question_id":"q2","question":"Opportunities?"}]' + """ + import json as _json + try: + questions = _json.loads(questions_json) + except (ValueError, TypeError) as exc: + return {"status": "error", "error": f"questions_json must be valid JSON: {exc}"} + if not isinstance(questions, list) or len(questions) == 0: + return {"status": "error", "error": "questions_json must be a non-empty JSON array"} + for i, q in enumerate(questions): + if not isinstance(q, dict) or "question_id" not in q or "question" not in q: + return {"status": "error", "error": f"questions_json[{i}] must have 'question_id' and 'question' fields"} + return service.send_questionnaire(run, questions) + + @mcp.tool() + def mirofish_get_questionnaire_result(run: str, questionnaire_id: str) -> Dict[str, Any]: + """Get the results of a questionnaire by its ID.""" + return service.get_questionnaire_result(run, questionnaire_id) + + @mcp.tool() + def mirofish_ask_report_question(run: str, question: str, limit: int = 20) -> Dict[str, Any]: + """Ask a question about the prediction report. Creates an agent_queue request.""" + return service.ask_report_question(run, question, limit) + + @mcp.tool() + def mirofish_get_report_question_answer(run: str, request_id: str) -> Dict[str, Any]: + """Retrieve and persist the answer for a report question request.""" + return service.get_report_question_answer(run, request_id) + + @mcp.tool() + def mirofish_doctor(runs_dir: Optional[str] = None) -> Dict[str, Any]: + return service.doctor(runs_dir) + + return mcp + + +def main() -> None: + create_server().run() + + +if __name__ == "__main__": + main() diff --git a/backend/app/services/graph_builder.py b/backend/app/services/graph_builder.py index 37c9969c79..45123b1473 100644 --- a/backend/app/services/graph_builder.py +++ b/backend/app/services/graph_builder.py @@ -10,16 +10,17 @@ from typing import Dict, Any, List, Optional, Callable from dataclasses import dataclass -from zep_cloud.client import Zep -from zep_cloud import EpisodeData, EntityEdgeSourceTarget - from ..config import Config +from ..adapters.graph.factory import create_graph_provider from ..models.task import TaskManager, TaskStatus -from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges +from ..utils.logger import get_logger from .text_processor import TextProcessor from ..utils.locale import t, get_locale, set_locale +logger = get_logger('mirofish.graph_builder') + + @dataclass class GraphInfo: """图谱信息""" @@ -44,11 +45,8 @@ class GraphBuilderService: """ def __init__(self, api_key: Optional[str] = None): - self.api_key = api_key or Config.ZEP_API_KEY - if not self.api_key: - raise ValueError("ZEP_API_KEY 未配置") - - self.client = Zep(api_key=self.api_key) + self.api_key = api_key + self.provider = create_graph_provider() self.task_manager = TaskManager() def build_graph_async( @@ -193,103 +191,17 @@ def _build_graph_worker( def create_graph(self, name: str) -> str: """创建Zep图谱（公开方法）""" graph_id = f"mirofish_{uuid.uuid4().hex[:16]}" - - self.client.graph.create( - graph_id=graph_id, - name=name, - description="MiroFish Social Simulation Graph" - ) - + + self.provider.add_episode(graph_id, f"Graph created: {name}", {"type": "graph_created", "name": name}) return graph_id def set_ontology(self, graph_id: str, ontology: Dict[str, Any]): """设置图谱本体（公开方法）""" - import warnings - from typing import Optional - from pydantic import Field - from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel - - # 抑制 Pydantic v2 关于 Field(default=None) 的警告 - # 这是 Zep SDK 要求的用法，警告来自动态类创建，可以安全忽略 - warnings.filterwarnings('ignore', category=UserWarning, module='pydantic') - - # Zep 保留名称，不能作为属性名 - RESERVED_NAMES = {'uuid', 'name', 'group_id', 'name_embedding', 'summary', 'created_at'} - - def safe_attr_name(attr_name: str) -> str: - """将保留名称转换为安全名称""" - if attr_name.lower() in RESERVED_NAMES: - return f"entity_{attr_name}" - return attr_name - - # 动态创建实体类型 - entity_types = {} - for entity_def in ontology.get("entity_types", []): - name = entity_def["name"] - description = entity_def.get("description", f"A {name} entity.") - - # 创建属性字典和类型注解（Pydantic v2 需要） - attrs = {"__doc__": description} - annotations = {} - - for attr_def in entity_def.get("attributes", []): - attr_name = safe_attr_name(attr_def["name"]) # 使用安全名称 - attr_desc = attr_def.get("description", attr_name) - # Zep API 需要 Field 的 description，这是必需的 - attrs[attr_name] = Field(description=attr_desc, default=None) - annotations[attr_name] = Optional[EntityText] # 类型注解 - - attrs["__annotations__"] = annotations - - # 动态创建类 - entity_class = type(name, (EntityModel,), attrs) - entity_class.__doc__ = description - entity_types[name] = entity_class - - # 动态创建边类型 - edge_definitions = {} - for edge_def in ontology.get("edge_types", []): - name = edge_def["name"] - description = edge_def.get("description", f"A {name} relationship.") - - # 创建属性字典和类型注解 - attrs = {"__doc__": description} - annotations = {} - - for attr_def in edge_def.get("attributes", []): - attr_name = safe_attr_name(attr_def["name"]) # 使用安全名称 - attr_desc = attr_def.get("description", attr_name) - # Zep API 需要 Field 的 description，这是必需的 - attrs[attr_name] = Field(description=attr_desc, default=None) - annotations[attr_name] = Optional[str] # 边属性用str类型 - - attrs["__annotations__"] = annotations - - # 动态创建类 - class_name = ''.join(word.capitalize() for word in name.split('_')) - edge_class = type(class_name, (EdgeModel,), attrs) - edge_class.__doc__ = description - - # 构建source_targets - source_targets = [] - for st in edge_def.get("source_targets", []): - source_targets.append( - EntityEdgeSourceTarget( - source=st.get("source", "Entity"), - target=st.get("target", "Entity") - ) - ) - - if source_targets: - edge_definitions[name] = (edge_class, source_targets) - - # 调用Zep API设置本体 - if entity_types or edge_definitions: - self.client.graph.set_ontology( - graph_ids=[graph_id], - entities=entity_types if entity_types else None, - edges=edge_definitions if edge_definitions else None, - ) + self.provider.add_episode( + graph_id, + os.linesep.join([f"Ontology: {ontology.get('entity_types', [])}", f"Edges: {ontology.get('edge_types', [])}"]), + {"type": "ontology"}, + ) def add_text_batches( self, @@ -314,25 +226,12 @@ def add_text_batches( progress ) - # 构建episode数据 - episodes = [ - EpisodeData(data=chunk, type="text") - for chunk in batch_chunks - ] - - # 发送到Zep + # 发送到统一图谱 provider。agent 模式下这只是 episode 存储； + # 三元组抽取由 CLI/MCP runner 生成 extract_triples request。 try: - batch_result = self.client.graph.add_batch( - graph_id=graph_id, - episodes=episodes - ) - - # 收集返回的 episode uuid - if batch_result and isinstance(batch_result, list): - for ep in batch_result: - ep_uuid = getattr(ep, 'uuid_', None) or getattr(ep, 'uuid', None) - if ep_uuid: - episode_uuids.append(ep_uuid) + for chunk in batch_chunks: + result = self.provider.add_episode(graph_id, chunk, {"type": "seed_chunk", "batch": batch_num}) + episode_uuids.append(str(result.get("episode_index", uuid.uuid4().hex))) # 避免请求过快 time.sleep(1) @@ -356,63 +255,21 @@ def _wait_for_episodes( progress_callback(t('progress.noEpisodesWait'), 1.0) return - start_time = time.time() - pending_episodes = set(episode_uuids) - completed_count = 0 total_episodes = len(episode_uuids) if progress_callback: - progress_callback(t('progress.waitingEpisodes', count=total_episodes), 0) - - while pending_episodes: - if time.time() - start_time > timeout: - if progress_callback: - progress_callback( - t('progress.episodesTimeout', completed=completed_count, total=total_episodes), - completed_count / total_episodes - ) - break - - # 检查每个 episode 的处理状态 - for ep_uuid in list(pending_episodes): - try: - episode = self.client.graph.episode.get(uuid_=ep_uuid) - is_processed = getattr(episode, 'processed', False) - - if is_processed: - pending_episodes.remove(ep_uuid) - completed_count += 1 - - except Exception as e: - # 忽略单个查询错误，继续 - pass - - elapsed = int(time.time() - start_time) - if progress_callback: - progress_callback( - t('progress.zepProcessing', completed=completed_count, total=total_episodes, pending=len(pending_episodes), elapsed=elapsed), - completed_count / total_episodes if total_episodes > 0 else 0 - ) - - if pending_episodes: - time.sleep(3) # 每3秒检查一次 - - if progress_callback: - progress_callback(t('progress.processingComplete', completed=completed_count, total=total_episodes), 1.0) + progress_callback(t('progress.waitingEpisodes', count=total_episodes), 1.0) def _get_graph_info(self, graph_id: str) -> GraphInfo: """获取图谱信息""" - # 获取节点（分页） - nodes = fetch_all_nodes(self.client, graph_id) - - # 获取边（分页） - edges = fetch_all_edges(self.client, graph_id) + nodes = self.provider.list_entities(graph_id) + edges = self.provider.search(graph_id, "", limit=10000) # 统计实体类型 entity_types = set() for node in nodes: - if node.labels: - for label in node.labels: + if node.get("labels"): + for label in node["labels"]: if label not in ["Entity", "Node"]: entity_types.add(label) @@ -433,58 +290,58 @@ def get_graph_data(self, graph_id: str) -> Dict[str, Any]: Returns: 包含nodes和edges的字典，包括时间信息、属性等详细数据 """ - nodes = fetch_all_nodes(self.client, graph_id) - edges = fetch_all_edges(self.client, graph_id) + nodes = self.provider.list_entities(graph_id) + edges = self.provider.search(graph_id, "", limit=10000) # 创建节点映射用于获取节点名称 node_map = {} for node in nodes: - node_map[node.uuid_] = node.name or "" + node_map[node.get("uuid", "")] = node.get("name", "") or "" nodes_data = [] for node in nodes: # 获取创建时间 - created_at = getattr(node, 'created_at', None) + created_at = node.get("created_at") if created_at: created_at = str(created_at) nodes_data.append({ - "uuid": node.uuid_, - "name": node.name, - "labels": node.labels or [], - "summary": node.summary or "", - "attributes": node.attributes or {}, + "uuid": node.get("uuid", ""), + "name": node.get("name", ""), + "labels": node.get("labels", []) or [], + "summary": node.get("summary", "") or "", + "attributes": node.get("attributes", {}) or {}, "created_at": created_at, }) edges_data = [] for edge in edges: # 获取时间信息 - created_at = getattr(edge, 'created_at', None) - valid_at = getattr(edge, 'valid_at', None) - invalid_at = getattr(edge, 'invalid_at', None) - expired_at = getattr(edge, 'expired_at', None) + created_at = edge.get("created_at") + valid_at = edge.get("valid_at") + invalid_at = edge.get("invalid_at") + expired_at = edge.get("expired_at") # 获取 episodes - episodes = getattr(edge, 'episodes', None) or getattr(edge, 'episode_ids', None) + episodes = edge.get("episodes") or edge.get("episode_ids") if episodes and not isinstance(episodes, list): episodes = [str(episodes)] elif episodes: episodes = [str(e) for e in episodes] # 获取 fact_type - fact_type = getattr(edge, 'fact_type', None) or edge.name or "" + fact_type = edge.get("fact_type") or edge.get("predicate") or edge.get("name", "") edges_data.append({ - "uuid": edge.uuid_, - "name": edge.name or "", - "fact": edge.fact or "", + "uuid": edge.get("uuid", ""), + "name": edge.get("predicate") or edge.get("name", ""), + "fact": edge.get("fact", ""), "fact_type": fact_type, - "source_node_uuid": edge.source_node_uuid, - "target_node_uuid": edge.target_node_uuid, - "source_node_name": node_map.get(edge.source_node_uuid, ""), - "target_node_name": node_map.get(edge.target_node_uuid, ""), - "attributes": edge.attributes or {}, + "source_node_uuid": edge.get("source_node_uuid", ""), + "target_node_uuid": edge.get("target_node_uuid", ""), + "source_node_name": node_map.get(edge.get("source_node_uuid", ""), ""), + "target_node_name": node_map.get(edge.get("target_node_uuid", ""), ""), + "attributes": edge.get("attributes", {}) or edge.get("metadata", {}), "created_at": str(created_at) if created_at else None, "valid_at": str(valid_at) if valid_at else None, "invalid_at": str(invalid_at) if invalid_at else None, @@ -502,5 +359,4 @@ def get_graph_data(self, graph_id: str) -> Dict[str, Any]: def delete_graph(self, graph_id: str): """删除图谱""" - self.client.graph.delete(graph_id=graph_id) - + self.provider.clear_run_graph(graph_id) diff --git a/backend/app/services/oasis_profile_generator.py b/backend/app/services/oasis_profile_generator.py index 7704a627eb..b10c2873ac 100644 --- a/backend/app/services/oasis_profile_generator.py +++ b/backend/app/services/oasis_profile_generator.py @@ -15,10 +15,9 @@ from dataclasses import dataclass, field from datetime import datetime -from openai import OpenAI -from zep_cloud.client import Zep - from ..config import Config +from ..adapters.graph.factory import create_graph_provider +from ..adapters.llm.agent_runtime import AgentRuntime, NeedAgentResponse from ..utils.logger import get_logger from ..utils.locale import get_language_instruction, get_locale, set_locale, t from .zep_entity_reader import EntityNode, ZepEntityReader @@ -186,28 +185,12 @@ def __init__( zep_api_key: Optional[str] = None, graph_id: Optional[str] = None ): - self.api_key = api_key or Config.LLM_API_KEY + self.api_key = api_key self.base_url = base_url or Config.LLM_BASE_URL self.model_name = model_name or Config.LLM_MODEL_NAME - - if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - - self.client = OpenAI( - api_key=self.api_key, - base_url=self.base_url - ) - - # Zep客户端用于检索丰富上下文 - self.zep_api_key = zep_api_key or Config.ZEP_API_KEY - self.zep_client = None + self.runtime = AgentRuntime() + self.graph_provider = create_graph_provider() self.graph_id = graph_id - - if self.zep_api_key: - try: - self.zep_client = Zep(api_key=self.zep_api_key) - except Exception as e: - logger.warning(f"Zep客户端初始化失败: {e}") def generate_profile_from_entity( self, @@ -296,11 +279,6 @@ def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]: Returns: 包含facts, node_summaries, context的字典 """ - import concurrent.futures - - if not self.zep_client: - return {"facts": [], "node_summaries": [], "context": ""} - entity_name = entity.name results = { @@ -314,84 +292,20 @@ def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]: logger.debug(f"跳过Zep检索：未设置graph_id") return results - comprehensive_query = t('progress.zepSearchQuery', name=entity_name) - - def search_edges(): - """搜索边（事实/关系）- 带重试机制""" - max_retries = 3 - last_exception = None - delay = 2.0 - - for attempt in range(max_retries): - try: - return self.zep_client.graph.search( - query=comprehensive_query, - graph_id=self.graph_id, - limit=30, - scope="edges", - reranker="rrf" - ) - except Exception as e: - last_exception = e - if attempt < max_retries - 1: - logger.debug(f"Zep边搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...") - time.sleep(delay) - delay *= 2 - else: - logger.debug(f"Zep边搜索在 {max_retries} 次尝试后仍失败: {e}") - return None - - def search_nodes(): - """搜索节点（实体摘要）- 带重试机制""" - max_retries = 3 - last_exception = None - delay = 2.0 - - for attempt in range(max_retries): - try: - return self.zep_client.graph.search( - query=comprehensive_query, - graph_id=self.graph_id, - limit=20, - scope="nodes", - reranker="rrf" - ) - except Exception as e: - last_exception = e - if attempt < max_retries - 1: - logger.debug(f"Zep节点搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...") - time.sleep(delay) - delay *= 2 - else: - logger.debug(f"Zep节点搜索在 {max_retries} 次尝试后仍失败: {e}") - return None - try: - # 并行执行edges和nodes搜索 - with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: - edge_future = executor.submit(search_edges) - node_future = executor.submit(search_nodes) - - # 获取结果 - edge_result = edge_future.result(timeout=30) - node_result = node_future.result(timeout=30) - - # 处理边搜索结果 + comprehensive_query = t('progress.zepSearchQuery', name=entity_name) + graph_results = self.graph_provider.search(self.graph_id, comprehensive_query, limit=30) all_facts = set() - if edge_result and hasattr(edge_result, 'edges') and edge_result.edges: - for edge in edge_result.edges: - if hasattr(edge, 'fact') and edge.fact: - all_facts.add(edge.fact) - results["facts"] = list(all_facts) - - # 处理节点搜索结果 all_summaries = set() - if node_result and hasattr(node_result, 'nodes') and node_result.nodes: - for node in node_result.nodes: - if hasattr(node, 'summary') and node.summary: - all_summaries.add(node.summary) - if hasattr(node, 'name') and node.name and node.name != entity_name: - all_summaries.add(f"相关实体: {node.name}") + for item in graph_results: + if item.get("fact"): + all_facts.add(item["fact"]) + node = item.get("node") if isinstance(item.get("node"), dict) else item + if node.get("summary"): + all_summaries.add(node["summary"]) + if node.get("name") and node.get("name") != entity_name: + all_summaries.add(f"相关实体: {node['name']}") + results["facts"] = list(all_facts) results["node_summaries"] = list(all_summaries) # 构建综合上下文 @@ -404,10 +318,8 @@ def search_nodes(): logger.info(f"Zep混合检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点") - except concurrent.futures.TimeoutError: - logger.warning(f"Zep检索超时 ({entity_name})") except Exception as e: - logger.warning(f"Zep检索失败 ({entity_name}): {e}") + logger.warning(f"图谱检索失败 ({entity_name}): {e}") return results @@ -527,48 +439,38 @@ def _generate_profile_with_llm( for attempt in range(max_attempts): try: - response = self.client.chat.completions.create( - model=self.model_name, - messages=[ - {"role": "system", "content": self._get_system_prompt(is_individual)}, - {"role": "user", "content": prompt} - ], - response_format={"type": "json_object"}, - temperature=0.7 - (attempt * 0.1) # 每次重试降低温度 - # 不设置max_tokens，让LLM自由发挥 + runtime_result = self.runtime.run_task( + run_id=self.graph_id or "legacy-oasis-profile", + task_type="generate_oasis_profiles", + stage="oasis_profile_generator", + expected_schema={"type": "object"}, + input_text=prompt, + structured_input={ + "entity_name": entity_name, + "entity_type": entity_type, + "entity_summary": entity_summary, + "entity_attributes": entity_attributes, + "is_individual": is_individual, + "temperature": 0.7 - (attempt * 0.1), + }, + system_prompt=self._get_system_prompt(is_individual), + user_prompt=prompt, + validation_rules={"json_object": True}, ) - - content = response.choices[0].message.content - - # 检查是否被截断（finish_reason不是'stop'） - finish_reason = response.choices[0].finish_reason - if finish_reason == 'length': - logger.warning(f"LLM输出被截断 (attempt {attempt+1}), 尝试修复...") - content = self._fix_truncated_json(content) - - # 尝试解析JSON - try: - result = json.loads(content) - - # 验证必需字段 - if "bio" not in result or not result["bio"]: - result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}" - if "persona" not in result or not result["persona"]: - result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。" - - return result - - except json.JSONDecodeError as je: - logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(je)[:80]}") - - # 尝试修复JSON - result = self._try_fix_json(content, entity_name, entity_type, entity_summary) - if result.get("_fixed"): - del result["_fixed"] - return result - - last_error = je - + if runtime_result.status == "need_agent_response": + raise NeedAgentResponse(runtime_result) + if runtime_result.status != "ok": + raise RuntimeError(runtime_result.error or "LLM provider failed") + result = runtime_result.output or {} + if "profiles" in result and result["profiles"]: + result = result["profiles"][0] + if "bio" not in result or not result["bio"]: + result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}" + if "persona" not in result or not result["persona"]: + result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。" + return result + except NeedAgentResponse: + raise except Exception as e: logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}") last_error = e @@ -1202,4 +1104,3 @@ def save_profiles_to_json( """[已废弃] 请使用 save_profiles() 方法""" logger.warning("save_profiles_to_json已废弃，请使用save_profiles方法") self.save_profiles(profiles, file_path, platform) - diff --git a/backend/app/services/ontology_generator.py b/backend/app/services/ontology_generator.py index 01a3d799a5..665c1de23c 100644 --- a/backend/app/services/ontology_generator.py +++ b/backend/app/services/ontology_generator.py @@ -410,11 +410,23 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: code_lines = [ '"""', '自定义实体类型定义', - '由MiroFish自动生成，用于社会舆论模拟', + '由MiroFish自动生成，用于社会舆论模拟。', + '该代码是 provider-neutral 的本体描述，不直接依赖具体图谱 SDK。', '"""', '', - 'from pydantic import Field', - 'from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel', + 'from typing import Optional', + 'from pydantic import BaseModel, Field', + '', + '', + 'EntityText = Optional[str]', + '', + '', + 'class EntityModel(BaseModel):', + ' pass', + '', + '', + 'class EdgeModel(BaseModel):', + ' pass', '', '', '# ============== 实体类型定义 ==============', @@ -503,4 +515,3 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: code_lines.append('}') return '\n'.join(code_lines) - diff --git a/backend/app/services/simulation_config_generator.py b/backend/app/services/simulation_config_generator.py index cb77f6b6cd..ec309cff66 100644 --- a/backend/app/services/simulation_config_generator.py +++ b/backend/app/services/simulation_config_generator.py @@ -16,9 +16,8 @@ from dataclasses import dataclass, field, asdict from datetime import datetime -from openai import OpenAI - from ..config import Config +from ..adapters.llm.agent_runtime import AgentRuntime, NeedAgentResponse from ..utils.logger import get_logger from ..utils.locale import get_language_instruction, t from .zep_entity_reader import EntityNode, ZepEntityReader @@ -228,17 +227,10 @@ def __init__( base_url: Optional[str] = None, model_name: Optional[str] = None ): - self.api_key = api_key or Config.LLM_API_KEY + self.api_key = api_key self.base_url = base_url or Config.LLM_BASE_URL self.model_name = model_name or Config.LLM_MODEL_NAME - - if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - - self.client = OpenAI( - api_key=self.api_key, - base_url=self.base_url - ) + self.runtime = AgentRuntime() def generate_config( self, @@ -440,38 +432,28 @@ def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any for attempt in range(max_attempts): try: - response = self.client.chat.completions.create( - model=self.model_name, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": prompt} - ], - response_format={"type": "json_object"}, - temperature=0.7 - (attempt * 0.1) # 每次重试降低温度 - # 不设置max_tokens，让LLM自由发挥 + result = self.runtime.run_task( + run_id="legacy-simulation-config", + task_type="generate_simulation_config", + stage="simulation_config_generator", + expected_schema={"type": "object"}, + input_text=prompt, + structured_input={ + "temperature": 0.7 - (attempt * 0.1), + "model_name": self.model_name, + }, + system_prompt=system_prompt, + user_prompt=prompt, + validation_rules={"json_object": True}, ) - - content = response.choices[0].message.content - finish_reason = response.choices[0].finish_reason - - # 检查是否被截断 - if finish_reason == 'length': - logger.warning(f"LLM输出被截断 (attempt {attempt+1})") - content = self._fix_truncated_json(content) - - # 尝试解析JSON - try: - return json.loads(content) - except json.JSONDecodeError as e: - logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(e)[:80]}") - - # 尝试修复JSON - fixed = self._try_fix_config_json(content) - if fixed: - return fixed - - last_error = e + if result.status == "need_agent_response": + raise NeedAgentResponse(result) + if result.status != "ok": + raise RuntimeError(result.error or "LLM provider failed") + return result.output or {} + except NeedAgentResponse: + raise except Exception as e: logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}") last_error = e @@ -988,4 +970,3 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 1.0 } - diff --git a/backend/app/services/simulation_runner.py b/backend/app/services/simulation_runner.py index e86021f808..4fc7e161c8 100644 --- a/backend/app/services/simulation_runner.py +++ b/backend/app/services/simulation_runner.py @@ -333,7 +333,11 @@ def start_simulation( """ # 检查是否已在运行 existing = cls.get_run_state(simulation_id) - if existing and existing.runner_status in [RunnerStatus.RUNNING, RunnerStatus.STARTING]: + if ( + existing + and existing.runner_status in [RunnerStatus.RUNNING, RunnerStatus.STARTING] + and cls.is_process_alive(simulation_id) + ): raise ValueError(f"模拟已在运行中: {simulation_id}") # 加载模拟配置 @@ -1182,6 +1186,7 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]: # 防止重复清理的标志 _cleanup_done = False + _skip_exit_cleanup = False @classmethod def cleanup_all_simulations(cls): @@ -1190,6 +1195,10 @@ def cleanup_all_simulations(cls): 在服务器关闭时调用，确保所有子进程被终止 """ + if cls._skip_exit_cleanup: + logger.info("跳过模拟进程清理：开发模式下保留等待命令模式的模拟进程") + return + # 防止重复清理 if cls._cleanup_done: return @@ -1318,6 +1327,27 @@ def register_cleanup(cls): def cleanup_handler(signum=None, frame=None): """信号处理器：先清理模拟进程，再调用原处理器""" + preserve_on_reload = ( + Config.DEBUG + and Config.PRESERVE_SIMULATIONS_ON_RELOAD + and signum in {signal.SIGTERM, getattr(signal, 'SIGHUP', None)} + ) + if preserve_on_reload: + cls._skip_exit_cleanup = True + if cls._processes or cls._graph_memory_enabled: + logger.info( + f"收到信号 {signum}，开发模式下保留模拟进程；" + "请使用 /api/simulation/stop 或 /api/simulation/close-env 显式停止" + ) + + if signum == signal.SIGTERM and callable(original_sigterm): + original_sigterm(signum, frame) + return + if has_sighup and signum == signal.SIGHUP and callable(original_sighup): + original_sighup(signum, frame) + return + sys.exit(0) + # 只有在有进程需要清理时才打印日志 if cls._processes or cls._graph_memory_enabled: logger.info(f"收到信号 {signum}，开始清理...") @@ -1367,6 +1397,24 @@ def get_running_simulations(cls) -> List[str]: if process.poll() is None: running.append(sim_id) return running + + @classmethod + def is_process_alive(cls, simulation_id: str) -> bool: + """检查模拟子进程是否仍然存在。""" + process = cls._processes.get(simulation_id) + if process: + return process.poll() is None + + state = cls.get_run_state(simulation_id) + pid = state.process_pid if state else None + if not pid: + return False + + try: + os.kill(pid, 0) + return True + except OSError: + return False # ============== Interview 功能 ============== @@ -1385,6 +1433,9 @@ def check_env_alive(cls, simulation_id: str) -> bool: if not os.path.exists(sim_dir): return False + if not cls.is_process_alive(simulation_id): + return False + ipc_client = SimulationIPCClient(sim_dir) return ipc_client.check_env_alive() @@ -1765,4 +1816,3 @@ def get_interview_history( results = results[:limit] return results - diff --git a/backend/app/services/zep_entity_reader.py b/backend/app/services/zep_entity_reader.py index 71661be499..d1873da364 100644 --- a/backend/app/services/zep_entity_reader.py +++ b/backend/app/services/zep_entity_reader.py @@ -7,11 +7,9 @@ from typing import Dict, Any, List, Optional, Set, Callable, TypeVar from dataclasses import dataclass, field -from zep_cloud.client import Zep - from ..config import Config +from ..adapters.graph.factory import create_graph_provider from ..utils.logger import get_logger -from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges logger = get_logger('mirofish.zep_entity_reader') @@ -79,11 +77,8 @@ class ZepEntityReader: """ def __init__(self, api_key: Optional[str] = None): - self.api_key = api_key or Config.ZEP_API_KEY - if not self.api_key: - raise ValueError("ZEP_API_KEY 未配置") - - self.client = Zep(api_key=self.api_key) + self.api_key = api_key + self.provider = create_graph_provider() def _call_with_retry( self, @@ -136,17 +131,7 @@ def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]: """ logger.info(f"获取图谱 {graph_id} 的所有节点...") - nodes = fetch_all_nodes(self.client, graph_id) - - nodes_data = [] - for node in nodes: - nodes_data.append({ - "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), - "name": node.name or "", - "labels": node.labels or [], - "summary": node.summary or "", - "attributes": node.attributes or {}, - }) + nodes_data = self.provider.list_entities(graph_id) logger.info(f"共获取 {len(nodes_data)} 个节点") return nodes_data @@ -163,18 +148,7 @@ def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]: """ logger.info(f"获取图谱 {graph_id} 的所有边...") - edges = fetch_all_edges(self.client, graph_id) - - edges_data = [] - for edge in edges: - edges_data.append({ - "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''), - "name": edge.name or "", - "fact": edge.fact or "", - "source_node_uuid": edge.source_node_uuid, - "target_node_uuid": edge.target_node_uuid, - "attributes": edge.attributes or {}, - }) + edges_data = self.provider.search(graph_id, "", limit=10000) logger.info(f"共获取 {len(edges_data)} 条边") return edges_data @@ -190,24 +164,7 @@ def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]: 边列表 """ try: - # 使用重试机制调用Zep API - edges = self._call_with_retry( - func=lambda: self.client.graph.node.get_entity_edges(node_uuid=node_uuid), - operation_name=f"获取节点边(node={node_uuid[:8]}...)" - ) - - edges_data = [] - for edge in edges: - edges_data.append({ - "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''), - "name": edge.name or "", - "fact": edge.fact or "", - "source_node_uuid": edge.source_node_uuid, - "target_node_uuid": edge.target_node_uuid, - "attributes": edge.attributes or {}, - }) - - return edges_data + return self.provider.neighbors("", node_uuid, depth=1) except Exception as e: logger.warning(f"获取节点 {node_uuid} 的边失败: {str(e)}") return [] @@ -346,20 +303,19 @@ def get_entity_with_context( EntityNode或None """ try: - # 使用重试机制获取节点 - node = self._call_with_retry( - func=lambda: self.client.graph.node.get(uuid_=entity_uuid), - operation_name=f"获取节点详情(uuid={entity_uuid[:8]}...)" - ) + all_nodes = self.get_all_nodes(graph_id) + node = next((item for item in all_nodes if item.get("uuid") == entity_uuid), None) if not node: return None # 获取节点的边 - edges = self.get_node_edges(entity_uuid) + edges = [ + edge for edge in self.get_all_edges(graph_id) + if edge.get("source_node_uuid") == entity_uuid or edge.get("target_node_uuid") == entity_uuid + ] # 获取所有节点用于关联查找 - all_nodes = self.get_all_nodes(graph_id) node_map = {n["uuid"]: n for n in all_nodes} # 处理相关边和节点 @@ -397,11 +353,11 @@ def get_entity_with_context( }) return EntityNode( - uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), - name=node.name or "", - labels=node.labels or [], - summary=node.summary or "", - attributes=node.attributes or {}, + uuid=node.get("uuid", ""), + name=node.get("name", "") or "", + labels=node.get("labels", []) or [], + summary=node.get("summary", "") or "", + attributes=node.get("attributes", {}) or {}, related_edges=related_edges, related_nodes=related_nodes, ) @@ -433,5 +389,3 @@ def get_entities_by_type( enrich_with_edges=enrich_with_edges ) return result.entities - - diff --git a/backend/app/services/zep_graph_memory_updater.py b/backend/app/services/zep_graph_memory_updater.py index e034fee2b2..49bc37f4de 100644 --- a/backend/app/services/zep_graph_memory_updater.py +++ b/backend/app/services/zep_graph_memory_updater.py @@ -12,9 +12,8 @@ from datetime import datetime from queue import Queue, Empty -from zep_cloud.client import Zep - from ..config import Config +from ..adapters.graph.factory import create_graph_provider from ..utils.logger import get_logger from ..utils.locale import get_locale, set_locale @@ -235,15 +234,11 @@ def __init__(self, graph_id: str, api_key: Optional[str] = None): Args: graph_id: Zep图谱ID - api_key: Zep API Key（可选，默认从配置读取） + api_key: legacy Zep API Key（仅由 legacy provider 使用） """ self.graph_id = graph_id - self.api_key = api_key or Config.ZEP_API_KEY - - if not self.api_key: - raise ValueError("ZEP_API_KEY未配置") - - self.client = Zep(api_key=self.api_key) + self.api_key = api_key + self.provider = create_graph_provider() # 活动队列 self._activity_queue: Queue = Queue() @@ -411,10 +406,10 @@ def _send_batch_activities(self, activities: List[AgentActivity], platform: str) # 带重试的发送 for attempt in range(self.MAX_RETRIES): try: - self.client.graph.add( - graph_id=self.graph_id, - type="text", - data=combined_text + self.provider.add_episode( + self.graph_id, + combined_text, + {"platform": platform, "source": "simulation_activity"}, ) self._total_sent += 1 diff --git a/backend/app/services/zep_tools.py b/backend/app/services/zep_tools.py index 3bc8a57abb..061b836e06 100644 --- a/backend/app/services/zep_tools.py +++ b/backend/app/services/zep_tools.py @@ -13,13 +13,11 @@ from typing import Dict, Any, List, Optional from dataclasses import dataclass, field -from zep_cloud.client import Zep - from ..config import Config +from ..adapters.graph.factory import create_graph_provider from ..utils.logger import get_logger from ..utils.llm_client import LLMClient from ..utils.locale import get_locale, t -from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges logger = get_logger('mirofish.zep_tools') @@ -423,11 +421,8 @@ class ZepToolsService: RETRY_DELAY = 2.0 def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient] = None): - self.api_key = api_key or Config.ZEP_API_KEY - if not self.api_key: - raise ValueError("ZEP_API_KEY 未配置") - - self.client = Zep(api_key=self.api_key) + self.api_key = api_key + self.provider = create_graph_provider() # LLM客户端用于InsightForge生成子问题 self._llm_client = llm_client logger.info(t("console.zepToolsInitialized")) @@ -485,48 +480,28 @@ def search_graph( """ logger.info(t("console.graphSearch", graphId=graph_id, query=query[:50])) - # 尝试使用Zep Cloud Search API + # 使用统一 GraphProvider 搜索；legacy Zep 和 agent Graphiti 都在 provider 内部处理。 try: - search_results = self._call_with_retry( - func=lambda: self.client.graph.search( - graph_id=graph_id, - query=query, - limit=limit, - scope=scope, - reranker="cross_encoder" - ), - operation_name=t("console.graphSearchOp", graphId=graph_id) - ) - + search_results = self.provider.search(graph_id, query, limit=limit) facts = [] edges = [] nodes = [] - - # 解析边搜索结果 - if hasattr(search_results, 'edges') and search_results.edges: - for edge in search_results.edges: - if hasattr(edge, 'fact') and edge.fact: - facts.append(edge.fact) - edges.append({ - "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''), - "name": getattr(edge, 'name', ''), - "fact": getattr(edge, 'fact', ''), - "source_node_uuid": getattr(edge, 'source_node_uuid', ''), - "target_node_uuid": getattr(edge, 'target_node_uuid', ''), - }) - - # 解析节点搜索结果 - if hasattr(search_results, 'nodes') and search_results.nodes: - for node in search_results.nodes: - nodes.append({ - "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), - "name": getattr(node, 'name', ''), - "labels": getattr(node, 'labels', []), - "summary": getattr(node, 'summary', ''), - }) - # 节点摘要也算作事实 - if hasattr(node, 'summary') and node.summary: - facts.append(f"[{node.name}]: {node.summary}") + for item in search_results: + if item.get("node"): + node = item["node"] + nodes.append(node) + if node.get("summary"): + facts.append(f"[{node.get('name', '')}]: {node['summary']}") + continue + if item.get("fact"): + facts.append(item["fact"]) + edges.append({ + "uuid": item.get("uuid", ""), + "name": item.get("predicate") or item.get("name", ""), + "fact": item.get("fact", ""), + "source_node_uuid": item.get("source_node_uuid", ""), + "target_node_uuid": item.get("target_node_uuid", ""), + }) logger.info(t("console.searchComplete", count=len(facts))) @@ -659,17 +634,15 @@ def get_all_nodes(self, graph_id: str) -> List[NodeInfo]: """ logger.info(t("console.fetchingAllNodes", graphId=graph_id)) - nodes = fetch_all_nodes(self.client, graph_id) - result = [] - for node in nodes: - node_uuid = getattr(node, 'uuid_', None) or getattr(node, 'uuid', None) or "" + for node in self.provider.list_entities(graph_id): + node_uuid = node.get("uuid", "") result.append(NodeInfo( uuid=str(node_uuid) if node_uuid else "", - name=node.name or "", - labels=node.labels or [], - summary=node.summary or "", - attributes=node.attributes or {} + name=node.get("name", "") or "", + labels=node.get("labels", []) or [], + summary=node.get("summary", "") or "", + attributes=node.get("attributes", {}) or {} )) logger.info(t("console.fetchedNodes", count=len(result))) @@ -688,25 +661,23 @@ def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[Ed """ logger.info(t("console.fetchingAllEdges", graphId=graph_id)) - edges = fetch_all_edges(self.client, graph_id) - result = [] - for edge in edges: - edge_uuid = getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', None) or "" + for edge in self.provider.search(graph_id, "", limit=10000): + edge_uuid = edge.get("uuid", "") edge_info = EdgeInfo( uuid=str(edge_uuid) if edge_uuid else "", - name=edge.name or "", - fact=edge.fact or "", - source_node_uuid=edge.source_node_uuid or "", - target_node_uuid=edge.target_node_uuid or "" + name=edge.get("predicate") or edge.get("name", ""), + fact=edge.get("fact", ""), + source_node_uuid=edge.get("source_node_uuid", ""), + target_node_uuid=edge.get("target_node_uuid", "") ) # 添加时间信息 if include_temporal: - edge_info.created_at = getattr(edge, 'created_at', None) - edge_info.valid_at = getattr(edge, 'valid_at', None) - edge_info.invalid_at = getattr(edge, 'invalid_at', None) - edge_info.expired_at = getattr(edge, 'expired_at', None) + edge_info.created_at = edge.get("created_at") + edge_info.valid_at = edge.get("valid_at") + edge_info.invalid_at = edge.get("invalid_at") + edge_info.expired_at = edge.get("expired_at") result.append(edge_info) @@ -726,20 +697,20 @@ def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]: logger.info(t("console.fetchingNodeDetail", uuid=node_uuid[:8])) try: - node = self._call_with_retry( - func=lambda: self.client.graph.node.get(uuid_=node_uuid), - operation_name=t("console.fetchNodeDetailOp", uuid=node_uuid[:8]) - ) - + node = None + for candidate in self.provider.list_entities(""): + if candidate.get("uuid") == node_uuid: + node = candidate + break if not node: return None return NodeInfo( - uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), - name=node.name or "", - labels=node.labels or [], - summary=node.summary or "", - attributes=node.attributes or {} + uuid=node.get("uuid", ""), + name=node.get("name", "") or "", + labels=node.get("labels", []) or [], + summary=node.get("summary", "") or "", + attributes=node.get("attributes", {}) or {} ) except Exception as e: logger.error(t("console.fetchNodeDetailFailed", error=str(e))) diff --git a/backend/app/utils/llm_client.py b/backend/app/utils/llm_client.py index 6c1a81f49b..18c5eff8a8 100644 --- a/backend/app/utils/llm_client.py +++ b/backend/app/utils/llm_client.py @@ -1,18 +1,21 @@ -""" -LLM客户端封装 -统一使用OpenAI格式调用 -""" +"""Legacy LLMClient facade backed by AgentRuntime.""" import json import re +from pathlib import Path from typing import Optional, Dict, Any, List -from openai import OpenAI from ..config import Config +from ..adapters.llm.agent_runtime import AgentRuntime, NeedAgentResponse +from ..agent_engine.json_schema import object_schema class LLMClient: - """LLM客户端""" + """Compatibility wrapper for older services. + + New business code should call AgentRuntime directly. This class remains so + legacy UI/report code can be migrated incrementally without direct SDK use. + """ def __init__( self, @@ -20,17 +23,12 @@ def __init__( base_url: Optional[str] = None, model: Optional[str] = None ): - self.api_key = api_key or Config.LLM_API_KEY + self.api_key = api_key self.base_url = base_url or Config.LLM_BASE_URL self.model = model or Config.LLM_MODEL_NAME - - if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - - self.client = OpenAI( - api_key=self.api_key, - base_url=self.base_url - ) + legacy_run_dir = Path(Config.MIROFISH_RUNS_DIR) / "legacy-ui" + legacy_run_dir.mkdir(parents=True, exist_ok=True) + self.runtime = AgentRuntime(run_dir=str(legacy_run_dir)) def chat( self, @@ -51,18 +49,35 @@ def chat( Returns: 模型响应文本 """ - kwargs = { - "model": self.model, + if response_format and response_format.get("type") == "json_object": + max_tokens = max(max_tokens, 8192) + + structured_input = { "messages": messages, "temperature": temperature, "max_tokens": max_tokens, + "response_format": response_format, + "legacy_client": True, } - - if response_format: - kwargs["response_format"] = response_format - - response = self.client.chat.completions.create(**kwargs) - content = response.choices[0].message.content + user_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "user") + system_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "system") + result = self.runtime.run_task( + run_id="legacy-ui", + task_type="validate_json_output" if response_format else "answer_followup_question", + stage="legacy_llm_client", + expected_schema=object_schema({"text": {"type": "string"}}, ["text"]), + structured_input=structured_input, + system_prompt=system_prompt, + user_prompt=user_prompt, + ) + if result.status == "need_agent_response": + raise NeedAgentResponse(result) + if result.status != "ok": + raise RuntimeError(result.error or "LLM provider failed") + content = (result.output or {}).get("text") + if content is None and result.output: + content = json.dumps(result.output, ensure_ascii=False) + content = content or "" # 部分模型（如MiniMax M2.5）会在content中包含思考内容，需要移除 content = re.sub(r'[\s\S]*?', '', content).strip() return content @@ -84,12 +99,31 @@ def chat_json( Returns: 解析后的JSON对象 """ - response = self.chat( - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - response_format={"type": "json_object"} + structured_input = { + "messages": messages, + "temperature": temperature, + "max_tokens": max_tokens, + "response_format": {"type": "json_object"}, + "legacy_client": True, + } + user_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "user") + system_prompt = "\n".join(message.get("content", "") for message in messages if message.get("role") == "system") + result = self.runtime.run_task( + run_id="legacy-ui", + task_type="validate_json_output", + stage="legacy_llm_client", + expected_schema={"type": "object"}, + structured_input=structured_input, + system_prompt=system_prompt, + user_prompt=user_prompt, ) + if result.status == "need_agent_response": + raise NeedAgentResponse(result) + if result.status != "ok": + raise RuntimeError(result.error or "LLM provider failed") + if isinstance(result.output, dict): + return result.output + response = json.dumps(result.output, ensure_ascii=False) # 清理markdown代码块标记 cleaned_response = response.strip() cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE) @@ -100,4 +134,3 @@ def chat_json( return json.loads(cleaned_response) except json.JSONDecodeError: raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}") - diff --git a/backend/app/utils/zep_paging.py b/backend/app/utils/zep_paging.py index 943cd1ae29..e1ca6ee040 100644 --- a/backend/app/utils/zep_paging.py +++ b/backend/app/utils/zep_paging.py @@ -10,8 +10,7 @@ from collections.abc import Callable from typing import Any -from zep_cloud import InternalServerError -from zep_cloud.client import Zep +import httpx from .logger import get_logger @@ -41,7 +40,7 @@ def _fetch_page_with_retry( for attempt in range(max_retries): try: return api_call(*args, **kwargs) - except (ConnectionError, TimeoutError, OSError, InternalServerError) as e: + except (ConnectionError, TimeoutError, OSError, httpx.TransportError) as e: last_exception = e if attempt < max_retries - 1: logger.warning( @@ -57,7 +56,7 @@ def _fetch_page_with_retry( def fetch_all_nodes( - client: Zep, + client: Any, graph_id: str, page_size: int = _DEFAULT_PAGE_SIZE, max_items: int = _MAX_NODES, @@ -103,7 +102,7 @@ def fetch_all_nodes( def fetch_all_edges( - client: Zep, + client: Any, graph_id: str, page_size: int = _DEFAULT_PAGE_SIZE, max_retries: int = _DEFAULT_MAX_RETRIES, diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 8c65b7294a..bc79de9bc4 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -13,12 +13,6 @@ dependencies = [ "flask>=3.0.0", "flask-cors>=6.0.0", - # LLM 相关 - "openai>=1.0.0", - - # Zep Cloud - "zep-cloud==3.13.0", - # OASIS 社交媒体模拟 "camel-oasis==0.2.5", "camel-ai==0.2.78", @@ -34,7 +28,20 @@ dependencies = [ "pydantic>=2.0.0", ] +[project.scripts] +mirofish-agent = "app.agent_engine.cli:main" +mirofish-mcp = "app.mcp_server.server:main" + [project.optional-dependencies] +agent = [ + "graphiti-core", + "neo4j", + "mcp", +] +legacy = [ + "openai>=1.0.0", + "zep-cloud==3.13.0", +] dev = [ "pytest>=8.0.0", "pytest-asyncio>=0.23.0", diff --git a/backend/scripts/run_parallel_simulation.py b/backend/scripts/run_parallel_simulation.py index 2a627ffd04..f306ee2c8d 100644 --- a/backend/scripts/run_parallel_simulation.py +++ b/backend/scripts/run_parallel_simulation.py @@ -89,7 +89,7 @@ def _utf8_open(file, mode='r', buffering=-1, encoding=None, errors=None, sys.path.insert(0, _scripts_dir) sys.path.insert(0, _backend_dir) -# 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置） +# 加载项目根目录的 .env 文件（包含 MiroFish provider 配置） from dotenv import load_dotenv _env_file = os.path.join(_project_root, '.env') if os.path.exists(_env_file): @@ -158,8 +158,7 @@ def init_logging_for_simulation(simulation_dir: str): from action_logger import SimulationLogManager, PlatformActionLogger try: - from camel.models import ModelFactory - from camel.types import ModelPlatformType + from app.adapters.llm.camel_adapter import create_model_backend import oasis from oasis import ( ActionType, @@ -985,56 +984,18 @@ def create_model(config: Dict[str, Any], use_boost: bool = False): """ 创建LLM模型 - 支持双 LLM 配置，用于并行模拟时提速： - - 通用配置：LLM_API_KEY, LLM_BASE_URL, LLM_MODEL_NAME - - 加速配置（可选）：LLM_BOOST_API_KEY, LLM_BOOST_BASE_URL, LLM_BOOST_MODEL_NAME - - 如果配置了加速 LLM，并行模拟时可以让不同平台使用不同的 API 服务商，提高并发能力。 + 使用 AgentRuntime / LLMProvider 创建模型后端。 + agent_queue 模式不需要模型 API key；openai_compatible legacy 模式才使用 LLM_* 配置。 Args: config: 模拟配置字典 use_boost: 是否使用加速 LLM 配置（如果可用） """ - # 检查是否有加速配置 - boost_api_key = os.environ.get("LLM_BOOST_API_KEY", "") - boost_base_url = os.environ.get("LLM_BOOST_BASE_URL", "") - boost_model = os.environ.get("LLM_BOOST_MODEL_NAME", "") - has_boost_config = bool(boost_api_key) - - # 根据参数和配置情况选择使用哪个 LLM - if use_boost and has_boost_config: - # 使用加速配置 - llm_api_key = boost_api_key - llm_base_url = boost_base_url - llm_model = boost_model or os.environ.get("LLM_MODEL_NAME", "") - config_label = "[加速LLM]" - else: - # 使用通用配置 - llm_api_key = os.environ.get("LLM_API_KEY", "") - llm_base_url = os.environ.get("LLM_BASE_URL", "") - llm_model = os.environ.get("LLM_MODEL_NAME", "") - config_label = "[通用LLM]" - - # 如果 .env 中没有模型名，则使用 config 作为备用 - if not llm_model: - llm_model = config.get("llm_model", "gpt-4o-mini") - - # 设置 camel-ai 所需的环境变量 - if llm_api_key: - os.environ["OPENAI_API_KEY"] = llm_api_key - - if not os.environ.get("OPENAI_API_KEY"): - raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY") - - if llm_base_url: - os.environ["OPENAI_API_BASE_URL"] = llm_base_url - - print(f"{config_label} model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...") - - return ModelFactory.create( - model_platform=ModelPlatformType.OPENAI, - model_type=llm_model, - ) + run_id = config.get("simulation_id") or config.get("project_id") or "parallel-simulation" + run_dir = config.get("simulation_dir") or os.getcwd() + config_label = "[AgentRuntime]" + print(f"{config_label} model backend adapter, run_id={run_id}") + return create_model_backend(run_id=run_id, run_dir=run_dir) def get_active_agents_for_round( diff --git a/backend/scripts/run_reddit_simulation.py b/backend/scripts/run_reddit_simulation.py index 14907cbda5..78e00b5d64 100644 --- a/backend/scripts/run_reddit_simulation.py +++ b/backend/scripts/run_reddit_simulation.py @@ -36,7 +36,7 @@ sys.path.insert(0, _scripts_dir) sys.path.insert(0, _backend_dir) -# 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置） +# 加载项目根目录的 .env 文件（包含 MiroFish provider 配置） from dotenv import load_dotenv _env_file = os.path.join(_project_root, '.env') if os.path.exists(_env_file): @@ -116,8 +116,7 @@ def setup_oasis_logging(log_dir: str): try: - from camel.models import ModelFactory - from camel.types import ModelPlatformType + from app.adapters.llm.camel_adapter import create_model_backend import oasis from oasis import ( ActionType, @@ -435,36 +434,13 @@ def _create_model(self): """ 创建LLM模型 - 统一使用项目根目录 .env 文件中的配置（优先级最高）： - - LLM_API_KEY: API密钥 - - LLM_BASE_URL: API基础URL - - LLM_MODEL_NAME: 模型名称 + 统一使用 AgentRuntime / LLMProvider 配置（优先级最高）： + - MIROFISH_LLM_PROVIDER=agent_queue 时不需要模型 API key + - MIROFISH_LLM_PROVIDER=openai_compatible 时使用 legacy LLM_* 配置 """ - # 优先从 .env 读取配置 - llm_api_key = os.environ.get("LLM_API_KEY", "") - llm_base_url = os.environ.get("LLM_BASE_URL", "") - llm_model = os.environ.get("LLM_MODEL_NAME", "") - - # 如果 .env 中没有，则使用 config 作为备用 - if not llm_model: - llm_model = self.config.get("llm_model", "gpt-4o-mini") - - # 设置 camel-ai 所需的环境变量 - if llm_api_key: - os.environ["OPENAI_API_KEY"] = llm_api_key - - if not os.environ.get("OPENAI_API_KEY"): - raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY") - - if llm_base_url: - os.environ["OPENAI_API_BASE_URL"] = llm_base_url - - print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...") - - return ModelFactory.create( - model_platform=ModelPlatformType.OPENAI, - model_type=llm_model, - ) + run_id = self.config.get("simulation_id") or os.path.basename(self.simulation_dir) + print(f"LLM配置: AgentRuntime adapter, run_id={run_id}") + return create_model_backend(run_id=run_id, run_dir=self.simulation_dir) def _get_active_agents_for_round( self, @@ -766,4 +742,3 @@ def signal_handler(signum, frame): pass finally: print("模拟进程已退出") - diff --git a/backend/scripts/run_twitter_simulation.py b/backend/scripts/run_twitter_simulation.py index caab9e9d35..37f1f42346 100644 --- a/backend/scripts/run_twitter_simulation.py +++ b/backend/scripts/run_twitter_simulation.py @@ -36,7 +36,7 @@ sys.path.insert(0, _scripts_dir) sys.path.insert(0, _backend_dir) -# 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置） +# 加载项目根目录的 .env 文件（包含 MiroFish provider 配置） from dotenv import load_dotenv _env_file = os.path.join(_project_root, '.env') if os.path.exists(_env_file): @@ -116,8 +116,7 @@ def setup_oasis_logging(log_dir: str): try: - from camel.models import ModelFactory - from camel.types import ModelPlatformType + from app.adapters.llm.camel_adapter import create_model_backend import oasis from oasis import ( ActionType, @@ -428,36 +427,13 @@ def _create_model(self): """ 创建LLM模型 - 统一使用项目根目录 .env 文件中的配置（优先级最高）： - - LLM_API_KEY: API密钥 - - LLM_BASE_URL: API基础URL - - LLM_MODEL_NAME: 模型名称 + 统一使用 AgentRuntime / LLMProvider 配置（优先级最高）： + - MIROFISH_LLM_PROVIDER=agent_queue 时不需要模型 API key + - MIROFISH_LLM_PROVIDER=openai_compatible 时使用 legacy LLM_* 配置 """ - # 优先从 .env 读取配置 - llm_api_key = os.environ.get("LLM_API_KEY", "") - llm_base_url = os.environ.get("LLM_BASE_URL", "") - llm_model = os.environ.get("LLM_MODEL_NAME", "") - - # 如果 .env 中没有，则使用 config 作为备用 - if not llm_model: - llm_model = self.config.get("llm_model", "gpt-4o-mini") - - # 设置 camel-ai 所需的环境变量 - if llm_api_key: - os.environ["OPENAI_API_KEY"] = llm_api_key - - if not os.environ.get("OPENAI_API_KEY"): - raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY") - - if llm_base_url: - os.environ["OPENAI_API_BASE_URL"] = llm_base_url - - print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...") - - return ModelFactory.create( - model_platform=ModelPlatformType.OPENAI, - model_type=llm_model, - ) + run_id = self.config.get("simulation_id") or os.path.basename(self.simulation_dir) + print(f"LLM配置: AgentRuntime adapter, run_id={run_id}") + return create_model_backend(run_id=run_id, run_dir=self.simulation_dir) def _get_active_agents_for_round( self, diff --git a/backend/tests/test_agent_deps_and_doctor.py b/backend/tests/test_agent_deps_and_doctor.py new file mode 100644 index 0000000000..6ff256b14a --- /dev/null +++ b/backend/tests/test_agent_deps_and_doctor.py @@ -0,0 +1,194 @@ +import importlib.util +import json +import sys +import types +from pathlib import Path + +import tomllib + +from app.agent_engine.runner import PredictionRunService + + +def test_agent_optional_dependencies_declared(): + pyproject = tomllib.loads(Path("pyproject.toml").read_text(encoding="utf-8")) + agent_deps = set(pyproject["project"]["optional-dependencies"]["agent"]) + assert {"graphiti-core", "neo4j", "mcp"}.issubset(agent_deps) + + +class _FakeResult: + def __init__(self, version="5.26.0"): + self.version = version + + def single(self): + return {"versions": [self.version]} + + +class _FakeSession: + def __init__(self, version="5.26.0"): + self.version = version + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, traceback): + return False + + def run(self, query): + assert "CALL dbms.components()" in query + return _FakeResult(self.version) + + +class _FakeDriver: + def __init__(self, version="5.26.0"): + self.version = version + + def session(self, database): + assert database == "neo4j" + return _FakeSession(self.version) + + def close(self): + return None + + +def _fake_graph_database(version="5.26.0"): + class FakeGraphDatabase: + @staticmethod + def driver(uri, auth): + assert uri == "bolt://localhost:7687" + assert auth == ("neo4j", "password") + return _FakeDriver(version) + + return FakeGraphDatabase + + +class _FakeUrlResponse: + def __init__(self, payload): + self.payload = payload + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, traceback): + return False + + def read(self): + return json.dumps(self.payload).encode("utf-8") + + +def _set_agent_env(monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.delenv("MIROFISH_GRAPHITI_STORE", raising=False) + monkeypatch.setenv("NEO4J_URI", "bolt://localhost:7687") + monkeypatch.setenv("NEO4J_USER", "neo4j") + monkeypatch.setenv("NEO4J_PASSWORD", "password") + monkeypatch.setenv("NEO4J_DATABASE", "neo4j") + monkeypatch.setenv("MIROFISH_GRAPH_SEARCH_MODE", "semantic") + monkeypatch.setenv("MIROFISH_EMBEDDING_PROVIDER", "ollama") + monkeypatch.setenv("OLLAMA_BASE_URL", "http://localhost:11434") + monkeypatch.setenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text") + + +def _mock_agent_deps(monkeypatch, ollama_payload, *, neo4j_version="5.26.0"): + original_find_spec = importlib.util.find_spec + + def fake_find_spec(name): + if name in {"graphiti_core", "neo4j", "mcp"}: + return object() + return original_find_spec(name) + + monkeypatch.setattr(importlib.util, "find_spec", fake_find_spec) + monkeypatch.setitem(sys.modules, "neo4j", types.SimpleNamespace(GraphDatabase=_fake_graph_database(neo4j_version))) + monkeypatch.setattr("shutil.which", lambda name: None) + monkeypatch.setattr( + "urllib.request.urlopen", + lambda url, timeout: _FakeUrlResponse(ollama_payload), + ) + + +def test_doctor_agent_graphiti_checks_neo4j_and_ollama(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + _mock_agent_deps(monkeypatch, {"models": [{"name": "nomic-embed-text:latest"}]}) + + result = PredictionRunService().doctor(str(tmp_path)) + + assert result["status"] == "ok" + checks = {check["name"]: check for check in result["checks"]} + assert checks["graphiti_package"]["ok"] is True + assert checks["neo4j_connectable"]["ok"] is True + assert checks["neo4j_version_supported"]["ok"] is True + assert checks["ollama_connectable"]["ok"] is True + assert checks["ollama_embedding_model"]["ok"] is True + assert checks["docker"]["required"] is False + assert checks["docker_compose"]["required"] is False + + +def test_doctor_agent_graphiti_fails_when_ollama_model_missing(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + _mock_agent_deps(monkeypatch, {"models": []}) + + result = PredictionRunService().doctor(str(tmp_path)) + + assert result["status"] == "failed" + hard_failures = {check["name"] for check in result["hard_failures"]} + assert "ollama_embedding_model" in hard_failures + + +def test_doctor_does_not_require_ollama_when_embedding_provider_is_not_ollama(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + monkeypatch.setenv("MIROFISH_EMBEDDING_PROVIDER", "none") + _mock_agent_deps(monkeypatch, {"models": []}) + + result = PredictionRunService().doctor(str(tmp_path)) + + assert result["status"] == "ok" + checks = {check["name"]: check for check in result["checks"]} + assert checks["ollama_connectable"]["required"] is False + assert checks["ollama_embedding_model"]["required"] is False + + +def test_doctor_does_not_require_ollama_for_fulltext_search(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + monkeypatch.setenv("MIROFISH_GRAPH_SEARCH_MODE", "fulltext") + monkeypatch.setenv("MIROFISH_EMBEDDING_PROVIDER", "ollama") + _mock_agent_deps(monkeypatch, {"models": []}) + + result = PredictionRunService().doctor(str(tmp_path)) + + assert result["status"] == "ok" + checks = {check["name"]: check for check in result["checks"]} + assert checks["graph_search_mode"]["value"] == "fulltext" + assert checks["ollama_connectable"]["required"] is False + assert "fulltext search does not require Ollama" in checks["ollama_connectable"]["value"] + + +def test_doctor_requires_neo4j_526_or_newer(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + monkeypatch.setenv("MIROFISH_EMBEDDING_PROVIDER", "none") + _mock_agent_deps(monkeypatch, {"models": []}, neo4j_version="5.25.0") + + result = PredictionRunService().doctor(str(tmp_path)) + + assert result["status"] == "failed" + hard_failures = {check["name"] for check in result["hard_failures"]} + assert "neo4j_version_supported" in hard_failures + + +def test_doctor_hard_failures_are_limited_to_dependency_and_service_gates(monkeypatch, tmp_path): + _set_agent_env(monkeypatch) + monkeypatch.setenv("MIROFISH_GRAPH_SEARCH_MODE", "not-a-mode") + monkeypatch.setenv("MIROFISH_EMBEDDING_PROVIDER", "not-a-provider") + _mock_agent_deps(monkeypatch, {"models": []}) + + result = PredictionRunService().doctor(str(tmp_path)) + + allowed = { + "graphiti_package", + "neo4j_package", + "neo4j_connectable", + "neo4j_version_supported", + "ollama_connectable", + "ollama_embedding_model", + } + assert {check["name"] for check in result["hard_failures"]}.issubset(allowed) diff --git a/backend/tests/test_agent_interaction.py b/backend/tests/test_agent_interaction.py new file mode 100644 index 0000000000..560aea28e3 --- /dev/null +++ b/backend/tests/test_agent_interaction.py @@ -0,0 +1,836 @@ +"""Tests for agent interaction features: agents, questionnaires, web console.""" + +import json +from pathlib import Path + +import pytest + +from app.adapters.llm.base import LLMTask +from app.adapters.llm.mock import MockLLMProvider +from app.agent_engine.cli import build_parser +from app.agent_engine.contracts import ( + AGENT_QUESTION_OUTPUT_SCHEMA, + AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA, + QUESTIONNAIRE_SUMMARY_OUTPUT_SCHEMA, + REPORT_QUESTION_OUTPUT_SCHEMA, + TASK_OUTPUT_SCHEMAS, +) +from app.agent_engine.json_schema import validate_json_schema +from app.agent_engine.queue import AgentQueue +from app.agent_engine.runner import PredictionRunService, _resolve_agent_id, _resolve_all_agent_ids +from app.agent_engine.schemas import AGENT_TASK_TYPES +from app.agent_engine.state import RunStore + + +# ── Fixtures ───────────────────────────────────────────────────────────── + +def _init_run(tmp_path: Path, *, seed_text: str = "A affects B.") -> tuple[Path, PredictionRunService]: + """Create a run directory with seed and profiles artifact.""" + seed = tmp_path / "seed.md" + seed.write_text(seed_text, encoding="utf-8") + run_dir = tmp_path / "run" + service = PredictionRunService() + service.create_run(str(seed), "test interaction", str(run_dir)) + # Write profiles.json artifact so agent methods work + profiles = [ + {"agent_id": "agent_1", "name": "Analyst Alpha", "persona": "Cautious geopolitical analyst."}, + {"agent_id": "agent_2", "name": "Strategist Beta", "persona": "Optimistic tech strategist."}, + ] + artifacts_dir = run_dir / "artifacts" + artifacts_dir.mkdir(parents=True, exist_ok=True) + (artifacts_dir / "profiles.json").write_text(json.dumps(profiles, ensure_ascii=False), encoding="utf-8") + # Write report.md + (artifacts_dir / "report.md").write_text("# Test Report\n\nThis is a test report.", encoding="utf-8") + # Write verdict.json + (artifacts_dir / "verdict.json").write_text(json.dumps({"status": "ok", "confidence": 0.7}), encoding="utf-8") + # Write timeline.json + (artifacts_dir / "timeline.json").write_text(json.dumps([{"round": 1, "summary": "Initial"}]), encoding="utf-8") + # Write graph_snapshot.json + (artifacts_dir / "graph_snapshot.json").write_text(json.dumps([]), encoding="utf-8") + # Write simulation_config.json + (artifacts_dir / "simulation_config.json").write_text(json.dumps({"rounds": 10}), encoding="utf-8") + # Write simulation_actions.json + (artifacts_dir / "simulation_actions.json").write_text(json.dumps([]), encoding="utf-8") + return run_dir, service + + +# ── list_agents tests ──────────────────────────────────────────────────── + +class TestListAgents: + def test_list_agents_reads_profiles(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.list_agents(str(run_dir)) + assert result["status"] == "ok" + assert result["count"] == 2 + agent_ids = [a["agent_id"] for a in result["agents"]] + assert "agent_1" in agent_ids + assert "agent_2" in agent_ids + + def test_list_agents_empty_when_no_profiles(self, tmp_path): + seed = tmp_path / "seed.md" + seed.write_text("A.", encoding="utf-8") + run_dir = tmp_path / "empty-run" + service = PredictionRunService() + service.create_run(str(seed), "test empty", str(run_dir)) + result = service.list_agents(str(run_dir)) + assert result["status"] == "ok" + assert result["count"] == 0 + + def test_get_agent_returns_profile(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.get_agent(str(run_dir), "agent_1") + assert result["status"] == "ok" + assert result["agent"]["agent_id"] == "agent_1" + assert result["agent"]["name"] == "Analyst Alpha" + + def test_get_agent_not_found(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.get_agent(str(run_dir), "nonexistent") + assert result["status"] == "error" + assert "not found" in result["error"] + + +# ── ask_agent tests ────────────────────────────────────────────────────── + +class TestAskAgent: + def test_ask_agent_creates_agent_queue_request(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + result = service.ask_agent(str(run_dir), "agent_1", "What are the implications?") + assert result["status"] == "need_agent_response" + assert result["type"] == "answer_agent_question" + assert result["agent_id"] == "agent_1" + + # Verify the request was created with correct structure + request = AgentQueue(run_dir).load_request(result["request_id"]) + assert request.type == "answer_agent_question" + assert request.stage == "interaction" + assert request.structured_input["agent_id"] == "agent_1" + assert request.structured_input["question"] == "What are the implications?" + + def test_ask_agent_nonexistent_returns_error(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + result = service.ask_agent(str(run_dir), "ghost_agent", "Hello?") + assert result["status"] == "error" + assert "not found" in result["error"] + + def test_ask_agent_submit_response_writes_interaction_artifact(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + need = service.ask_agent(str(run_dir), "agent_1", "What do you think?") + assert need["status"] == "need_agent_response" + + # Write a valid response + response_path = run_dir / "responses" / f"{need['request_id']}.json" + response_path.write_text(json.dumps({ + "request_id": need["request_id"], + "status": "ok", + "output": { + "agent_id": "agent_1", + "answer_markdown": "I think supply chains will shift.", + "used_memory": [], + "used_graph_results": [], + "confidence": 0.8, + } + }), encoding="utf-8") + + # Submit via get_agent_answer which processes the response + answer = service.get_agent_answer(str(run_dir), need["request_id"]) + assert answer["status"] == "ok" + assert answer["agent_id"] == "agent_1" + # Verify interaction artifacts were written + questions_dir = run_dir / "artifacts" / "interactions" / "agent_questions" + assert questions_dir.exists() + json_files = list(questions_dir.glob("*.json")) + assert len(json_files) >= 1 + md_files = list(questions_dir.glob("*.md")) + assert len(md_files) >= 1 + + +# ── questionnaire tests ────────────────────────────────────────────────── + +class TestQuestionnaire: + def test_send_questionnaire_creates_batch_requests(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + questions = [ + {"question_id": "q1", "question": "What is the biggest risk?"}, + {"question_id": "q2", "question": "What opportunities do you see?"}, + ] + result = service.send_questionnaire(str(run_dir), questions) + assert result["status"] == "need_agent_response" + assert result["question_count"] == 2 + assert result["agent_count"] == 2 + assert len(result["request_ids"]) == 2 + + # Verify questionnaire metadata was saved + questionnaires_dir = run_dir / "artifacts" / "interactions" / "questionnaires" + assert questionnaires_dir.exists() + meta_files = list(questionnaires_dir.glob("*_meta.json")) + assert len(meta_files) == 1 + meta = json.loads(meta_files[0].read_text(encoding="utf-8")) + assert meta["questionnaire_id"] == result["questionnaire_id"] + assert len(meta["questions"]) == 2 + + def test_get_questionnaire_result_not_found(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.get_questionnaire_result(str(run_dir), "nonexistent_q") + assert result["status"] == "error" + assert "not found" in result["error"] + + def test_questionnaire_response_schema_valid(self): + """Verify questionnaire output schema validates correctly.""" + output = { + "questionnaire_id": "q_test123", + "answers": [ + { + "agent_id": "agent_1", + "question_id": "q1", + "answer_markdown": "The biggest risk is supply chain disruption.", + "confidence": 0.8, + } + ], + "summary_markdown": "Summary of questionnaire answers.", + } + errors = validate_json_schema(output, AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA) + assert not errors, errors + + +# ── report question tests ──────────────────────────────────────────────── + +class TestReportQuestion: + def test_ask_report_question_creates_request(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + result = service.ask_report_question(str(run_dir), "What does the report say about risks?") + assert result["status"] == "need_agent_response" + assert result["type"] == "ask_report_question" + + def test_report_question_answer_persists_interaction(self, tmp_path, monkeypatch): + """Verify get_report_question_answer processes a response and persists to interactions/report_questions/.""" + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run(tmp_path) + need = service.ask_report_question(str(run_dir), "Summarize the key risks.") + assert need["status"] == "need_agent_response" + + # Write a valid response + response_path = run_dir / "responses" / f"{need['request_id']}.json" + response_path.write_text(json.dumps({ + "request_id": need["request_id"], + "status": "ok", + "output": { + "answer_markdown": "The key risks are supply chain disruption and regulatory changes.", + "used_graph_results": [], + "confidence": 0.85, + } + }), encoding="utf-8") + + # Process the response + answer = service.get_report_question_answer(str(run_dir), need["request_id"]) + assert answer["status"] == "ok" + # Verify interaction artifacts were written + rq_dir = run_dir / "artifacts" / "interactions" / "report_questions" + assert rq_dir.exists() + json_files = list(rq_dir.glob("*.json")) + assert len(json_files) >= 1 + + +# ── web console tests ──────────────────────────────────────────────────── + +class TestWebConsole: + def test_web_generate_creates_index_html(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.generate_web_console(str(run_dir)) + assert result["status"] == "ok" + html_path = run_dir / "artifacts" / "web" / "index.html" + assert html_path.exists() + html_content = html_path.read_text(encoding="utf-8") + assert "MiroFish Web Console" in html_content + assert "" in html_content + + def test_web_console_embeds_artifacts(self, tmp_path): + run_dir, service = _init_run(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + # Check that key data is embedded + assert "agent_1" in html_content + assert "Analyst Alpha" in html_content + assert "Test Report" in html_content + + def test_web_console_path_in_artifacts(self, tmp_path): + run_dir, service = _init_run(tmp_path) + result = service.generate_web_console(str(run_dir)) + assert "web/index.html" in result["path"] + + def test_web_console_has_interactive_elements(self, tmp_path): + """Verify the template includes forms, buttons, and API client for interaction.""" + run_dir, service = _init_run(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + # Agent Q&A form + assert 'id="ask-agent-select"' in html_content + assert 'id="ask-question-input"' in html_content + assert 'id="ask-submit-btn"' in html_content + # Questionnaire form + assert 'id="questionnaire-submit-btn"' in html_content + assert 'id="add-question-btn"' in html_content + # Report question form + assert 'id="report-q-input"' in html_content + assert 'id="report-q-submit-btn"' in html_content + # API client and polling + assert "apiPost" in html_content + assert "apiGet" in html_content + assert "pollForAnswer" in html_content + assert "checkApiStatus" in html_content + # API status indicator + assert 'id="api-dot"' in html_content + assert 'id="api-status-text"' in html_content + # Configurable API base URL + assert 'id="api-base-input"' in html_content + assert "localhost:5001" in html_content + + def test_web_console_has_interaction_panels(self, tmp_path): + """Verify all interactive panels are present in the navigation.""" + run_dir, service = _init_run(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + assert 'data-panel="ask"' in html_content + assert 'data-panel="questionnaires"' in html_content + assert 'data-panel="report-q"' in html_content + assert 'data-panel="history"' in html_content + + def test_web_console_js_embedding_escapes_special_chars(self, tmp_path): + """Verify that quotes, newlines, and backslashes in report/requirement don't break JS.""" + # Create a run with special characters in report and requirement + seed = tmp_path / "seed.md" + seed.write_text('Seed with "quotes" and\nnewlines.', encoding="utf-8") + run_dir = tmp_path / "run_special" + service = PredictionRunService() + service.create_run(str(seed), 'requirement with "quote" and\nnewline', str(run_dir)) + artifacts_dir = run_dir / "artifacts" + artifacts_dir.mkdir(parents=True, exist_ok=True) + # Write report with special characters + (artifacts_dir / "report.md").write_text( + '# Report\n\nLine "quoted".\nBackslash: \\\nEnd.', encoding="utf-8" + ) + (artifacts_dir / "profiles.json").write_text("[]", encoding="utf-8") + (artifacts_dir / "verdict.json").write_text("{}", encoding="utf-8") + (artifacts_dir / "timeline.json").write_text("[]", encoding="utf-8") + (artifacts_dir / "graph_snapshot.json").write_text("[]", encoding="utf-8") + (artifacts_dir / "simulation_config.json").write_text("{}", encoding="utf-8") + (artifacts_dir / "simulation_actions.json").write_text("[]", encoding="utf-8") + + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + + # The DATA block must be valid JS — extract it and check JSON-escaped strings + import re + data_match = re.search(r"const DATA = (\{.*?\});", html_content, re.DOTALL) + assert data_match, "Could not find DATA block in generated HTML" + data_block = data_match.group(1) + + # Verify quotes are JSON-escaped (backslash-escaped), not raw + assert '\\"quote\\"' in data_block, "Double quotes must be JSON-escaped" + # Verify newlines are escaped as \n, not literal newlines inside string + assert '\\n' in data_block, "Newlines must be JSON-escaped as \\n" + # Verify the requirement value is a valid JSON string (starts with ") + assert 'requirement: "requirement with \\"' in data_block + # Verify the reportMd contains the escaped backslash + assert '\\\\' in data_block, "Backslashes must be JSON-escaped" + + +# ── schema / contract tests ────────────────────────────────────────────── + +class TestSchemasAndContracts: + def test_new_task_types_in_agent_task_types(self): + assert "answer_agent_question" in AGENT_TASK_TYPES + assert "answer_agent_questionnaire" in AGENT_TASK_TYPES + assert "summarize_questionnaire" in AGENT_TASK_TYPES + assert "ask_report_question" in AGENT_TASK_TYPES + + def test_new_task_types_have_output_schemas(self): + assert "answer_agent_question" in TASK_OUTPUT_SCHEMAS + assert "answer_agent_questionnaire" in TASK_OUTPUT_SCHEMAS + assert "summarize_questionnaire" in TASK_OUTPUT_SCHEMAS + assert "ask_report_question" in TASK_OUTPUT_SCHEMAS + + def test_agent_question_output_schema_has_required_fields(self): + schema = AGENT_QUESTION_OUTPUT_SCHEMA + required = schema.get("required", []) + assert "agent_id" in required + assert "answer_markdown" in required + assert "used_memory" in required + assert "used_graph_results" in required + assert "confidence" in required + + def test_questionnaire_output_schema_has_required_fields(self): + schema = AGENT_QUESTIONNAIRE_OUTPUT_SCHEMA + required = schema.get("required", []) + assert "questionnaire_id" in required + assert "answers" in required + assert "summary_markdown" in required + + def test_report_question_output_schema_has_required_fields(self): + schema = REPORT_QUESTION_OUTPUT_SCHEMA + required = schema.get("required", []) + assert "answer_markdown" in required + assert "used_graph_results" in required + assert "confidence" in required + + def test_mock_provider_handles_new_task_types(self): + """Mock provider must return valid output for all new task types.""" + provider = MockLLMProvider() + for task_type in ["answer_agent_question", "answer_agent_questionnaire", "summarize_questionnaire", "ask_report_question"]: + schema = TASK_OUTPUT_SCHEMAS[task_type] + result = provider.run_task(LLMTask( + run_id="run", + task_type=task_type, + stage="interaction", + expected_schema=schema, + structured_input={ + "agent_id": "agent_1", + "questionnaire_id": "q_test", + "questions": [{"question_id": "q1", "question": "Test?"}], + "agents": [{"agent_id": "agent_1"}], + "graph_results": [], + }, + )) + assert result.status == "ok", f"{task_type} failed: {result.error}" + errors = validate_json_schema(result.output, schema) + assert not errors, f"{task_type} schema errors: {errors}" + + def test_all_task_types_have_schema_and_mock_output(self): + """Extended version: verify all task types including new ones.""" + assert set(TASK_OUTPUT_SCHEMAS) == AGENT_TASK_TYPES + provider = MockLLMProvider() + for task_type in sorted(AGENT_TASK_TYPES): + schema = TASK_OUTPUT_SCHEMAS[task_type] + result = provider.run_task(LLMTask( + run_id="run", + task_type=task_type, + stage=task_type, + expected_schema=schema, + structured_input={ + "actions": [{"agent_id": "agent_1", "action_id": "action_1"}], + "candidate": {}, + "invalid_response": {"output": {}}, + "agent_id": "agent_1", + "questionnaire_id": "q_test", + "questions": [{"question_id": "q1", "question": "Test?"}], + "agents": [{"agent_id": "agent_1"}], + "graph_results": [], + }, + )) + assert result.status == "ok", f"mock failed for {task_type}" + assert not validate_json_schema(result.output, schema), f"schema validation failed for {task_type}" + + +# ── CLI parser tests ───────────────────────────────────────────────────── + +class TestCLIParser: + def test_cli_agents_list(self): + parser = build_parser() + args = parser.parse_args(["agents", "list", "--run", "runs/demo"]) + assert args.command == "agents" + assert args.agents_command == "list" + assert args.run == "runs/demo" + + def test_cli_agents_show(self): + parser = build_parser() + args = parser.parse_args(["agents", "show", "--run", "runs/demo", "--agent-id", "agent_1"]) + assert args.agents_command == "show" + assert args.agent_id == "agent_1" + + def test_cli_agents_ask(self): + parser = build_parser() + args = parser.parse_args(["agents", "ask", "--run", "runs/demo", "--agent-id", "agent_1", "--question", "Hello?"]) + assert args.agents_command == "ask" + assert args.question == "Hello?" + + def test_cli_questionnaire_send(self): + parser = build_parser() + args = parser.parse_args(["questionnaire", "send", "--run", "runs/demo", "--questions", "questions.json"]) + assert args.command == "questionnaire" + assert args.questionnaire_command == "send" + assert args.questions == "questions.json" + + def test_cli_questionnaire_show(self): + parser = build_parser() + args = parser.parse_args(["questionnaire", "show", "--run", "runs/demo", "--questionnaire-id", "q_123"]) + assert args.questionnaire_command == "show" + assert args.questionnaire_id == "q_123" + + def test_cli_agents_answer(self): + parser = build_parser() + args = parser.parse_args(["agents", "answer", "--run", "runs/demo", "--request-id", "req_123"]) + assert args.agents_command == "answer" + assert args.request_id == "req_123" + + def test_cli_report_question_ask(self): + parser = build_parser() + args = parser.parse_args(["report-question", "ask", "--run", "runs/demo", "--question", "What risks?"]) + assert args.command == "report-question" + assert args.report_question_command == "ask" + assert args.question == "What risks?" + + def test_cli_report_question_answer(self): + parser = build_parser() + args = parser.parse_args(["report-question", "answer", "--run", "runs/demo", "--request-id", "req_456"]) + assert args.report_question_command == "answer" + assert args.request_id == "req_456" + + def test_cli_web_generate(self): + parser = build_parser() + args = parser.parse_args(["web", "generate", "--run", "runs/demo"]) + assert args.command == "web" + assert args.web_command == "generate" + assert args.run == "runs/demo" + + +# ── MCP tools schema tests ────────────────────────────────────────────── + +class TestMCPToolsSchema: + def test_mcp_server_creates_without_error(self): + """Verify MCP server can be created with new tools.""" + try: + from app.mcp_server.server import create_server + server = create_server() + assert server is not None + except ImportError: + pytest.skip("mcp package not installed") + + def test_mcp_tools_include_interaction_tools(self): + """Verify the new interaction tools are registered.""" + try: + from app.mcp_server.server import create_server + server = create_server() + # FastMCP stores tools internally; check via list + tool_names = set() + if hasattr(server, '_tool_manager'): + tool_names = set(server._tool_manager._tools.keys()) if hasattr(server._tool_manager, '_tools') else set() + elif hasattr(server, 'list_tools'): + # Alternative: some versions expose list_tools + pass + # If we can't introspect, just verify server was created + # The important thing is the tools were decorated with @mcp.tool() + assert server is not None + except ImportError: + pytest.skip("mcp package not installed") + + +# ── Path traversal guard tests ─────────────────────────────────────────── + +class TestPathTraversalGuard: + def test_artifact_endpoint_rejects_path_traversal(self, tmp_path): + """Verify the artifact endpoint blocks path traversal attempts like ../../.env.""" + from app import create_app + + run_dir, service = _init_run(tmp_path) + # Create a file outside artifacts_dir to ensure it can't be read + sensitive_file = run_dir / ".env" + sensitive_file.write_text("SECRET=leaked", encoding="utf-8") + + app = create_app() + client = app.test_client() + + # Attempt path traversal + resp = client.get( + f"/api/interaction/artifact/../../.env?run={run_dir}" + ) + # Must be blocked (403) or not found (404), never 200 with leaked content + assert resp.status_code in (403, 404), f"Path traversal not blocked: {resp.status_code}" + if resp.status_code == 200: + assert b"leaked" not in resp.data + + def test_artifact_endpoint_allows_valid_paths(self, tmp_path): + """Verify normal artifact access still works after the traversal guard.""" + from app import create_app + + run_dir, service = _init_run(tmp_path) + app = create_app() + client = app.test_client() + + resp = client.get( + f"/api/interaction/artifact/verdict.json?run={run_dir}" + ) + assert resp.status_code == 200 + data = resp.get_json() + assert data["success"] is True + assert data["data"]["status"] == "ok" + + def test_responses_endpoint_rejects_path_outside_responses_dir(self, tmp_path): + """Verify the responses endpoint blocks paths outside run/responses/.""" + from app import create_app + + run_dir, service = _init_run(tmp_path) + app = create_app() + client = app.test_client() + + # Attempt to submit a response pointing to a file outside responses/ + outside_path = str(run_dir / "artifacts" / "verdict.json") + resp = client.post( + f"/api/interaction/responses?run={run_dir}", + data=json.dumps({"response_path": outside_path}), + content_type="application/json", + ) + assert resp.status_code == 403, f"Path outside responses/ not blocked: {resp.status_code}" + + +# ── MCP questionnaire questions_json tests ─────────────────────────────── + +class TestMCPQuestionnaireJsonParam: + def test_questionnaire_accepts_questions_json_string(self, tmp_path, monkeypatch): + """Verify mirofish_send_questionnaire accepts a questions_json string with arbitrary count.""" + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + try: + from app.mcp_server.server import create_server + server = create_server() + except ImportError: + pytest.skip("mcp package not installed") + + # Call the tool function directly through the service + run_dir, service = _init_run(tmp_path) + questions_json = json.dumps([ + {"question_id": "q1", "question": "Risk 1?"}, + {"question_id": "q2", "question": "Risk 2?"}, + {"question_id": "q3", "question": "Risk 3?"}, + {"question_id": "q4", "question": "Risk 4?"}, + {"question_id": "q5", "question": "Risk 5?"}, + ]) + # Test through the service layer directly (MCP tool calls this) + import json as _json + questions = _json.loads(questions_json) + result = service.send_questionnaire(str(run_dir), questions) + assert result["status"] == "need_agent_response" + assert result["question_count"] == 5 + assert len(result["request_ids"]) == 5 + + def test_questionnaire_rejects_invalid_json(self, tmp_path): + """Verify the MCP tool rejects invalid questions_json input.""" + try: + from app.mcp_server.server import create_server + server = create_server() + except ImportError: + pytest.skip("mcp package not installed") + + # Simulate the validation logic from the MCP tool + import json as _json + try: + _json.loads("not valid json") + assert False, "Should have raised" + except (ValueError, TypeError): + pass # Expected + + +# ── Profile ID fallback tests ──────────────────────────────────────────── + +def _init_run_no_agent_id(tmp_path: Path) -> tuple[Path, PredictionRunService]: + """Create a run with profiles that have NO agent_id or user_id — only name.""" + seed = tmp_path / "seed.md" + seed.write_text("Seed.", encoding="utf-8") + run_dir = tmp_path / "run_noid" + service = PredictionRunService() + service.create_run(str(seed), "test fallback id", str(run_dir)) + profiles = [ + {"name": "Baidu", "type": "search", "description": "Chinese search engine."}, + {"name": "Google China", "type": "search", "description": "Google's China ops."}, + {"name": "Baidu", "type": "ai", "description": "Duplicate name, different type."}, + {"type": "anon", "description": "No name at all."}, + ] + artifacts_dir = run_dir / "artifacts" + artifacts_dir.mkdir(parents=True, exist_ok=True) + (artifacts_dir / "profiles.json").write_text(json.dumps(profiles), encoding="utf-8") + (artifacts_dir / "report.md").write_text("# Report", encoding="utf-8") + (artifacts_dir / "verdict.json").write_text("{}", encoding="utf-8") + (artifacts_dir / "timeline.json").write_text("[]", encoding="utf-8") + (artifacts_dir / "graph_snapshot.json").write_text("[]", encoding="utf-8") + (artifacts_dir / "simulation_config.json").write_text("{}", encoding="utf-8") + (artifacts_dir / "simulation_actions.json").write_text("[]", encoding="utf-8") + return run_dir, service + + +class TestResolveAgentId: + def test_prefers_agent_id(self): + assert _resolve_agent_id({"agent_id": "x_1", "name": "Baidu"}, 0) == "x_1" + + def test_falls_back_to_user_id(self): + assert _resolve_agent_id({"user_id": "u_42", "name": "Baidu"}, 0) == "u_42" + + def test_generates_slug_from_name(self): + assert _resolve_agent_id({"name": "Google China"}, 0) == "google_china" + assert _resolve_agent_id({"name": "Baidu"}, 0) == "baidu" + assert _resolve_agent_id({"name": " Spaced Name "}, 0) == "spaced_name" + + def test_positional_fallback_when_no_name(self): + assert _resolve_agent_id({"type": "anon"}, 0) == "agent_1" + assert _resolve_agent_id({"type": "anon"}, 2) == "agent_3" + + def test_empty_strings_treated_as_missing(self): + assert _resolve_agent_id({"agent_id": "", "user_id": "", "name": ""}, 0) == "agent_1" + + def test_strips_whitespace(self): + assert _resolve_agent_id({"agent_id": " x "}, 0) == "x" + + +class TestResolveAllAgentIds: + def test_no_duplicates_passthrough(self): + profiles = [{"name": "Alpha"}, {"name": "Beta"}] + ids = _resolve_all_agent_ids(profiles) + assert ids == ["alpha", "beta"] + + def test_duplicate_names_get_suffix(self): + profiles = [ + {"name": "Baidu"}, + {"name": "Google China"}, + {"name": "Baidu"}, + {"type": "anon"}, + ] + ids = _resolve_all_agent_ids(profiles) + assert ids[0] == "baidu" + assert ids[1] == "google_china" + assert ids[2] == "baidu_2" + assert ids[3] == "agent_4" + # All unique + assert len(set(ids)) == len(ids) + + def test_triple_duplicate(self): + profiles = [{"name": "X"}, {"name": "X"}, {"name": "X"}] + ids = _resolve_all_agent_ids(profiles) + assert ids == ["x", "x_2", "x_3"] + + def test_explicit_agent_id_collision(self): + profiles = [ + {"agent_id": "agent_1"}, + {"agent_id": "agent_1"}, + ] + ids = _resolve_all_agent_ids(profiles) + assert ids[0] == "agent_1" + assert ids[1] == "agent_1_2" + assert len(set(ids)) == 2 + + def test_empty_list(self): + assert _resolve_all_agent_ids([]) == [] + + +class TestListAgentsFallbackId: + def test_list_agents_returns_stable_ids_without_agent_id(self, tmp_path): + run_dir, service = _init_run_no_agent_id(tmp_path) + result = service.list_agents(str(run_dir)) + assert result["status"] == "ok" + assert result["count"] == 4 + ids = [a["agent_id"] for a in result["agents"]] + assert ids[0] == "baidu" + assert ids[1] == "google_china" + # Third profile also named "Baidu" — deduplicated with suffix + assert ids[2] == "baidu_2" + # Fourth profile has no name — positional fallback + assert ids[3] == "agent_4" + # All IDs must be unique + assert len(set(ids)) == len(ids), "agent_ids must be unique" + # None should be empty or "unknown" + assert all(aid for aid in ids), "No agent_id should be empty" + assert "unknown" not in ids + + def test_get_agent_works_with_fallback_id(self, tmp_path): + run_dir, service = _init_run_no_agent_id(tmp_path) + result = service.get_agent(str(run_dir), "google_china") + assert result["status"] == "ok" + assert result["agent"]["agent_id"] == "google_china" + assert result["agent"]["name"] == "Google China" + + def test_get_agent_finds_second_duplicate(self, tmp_path): + run_dir, service = _init_run_no_agent_id(tmp_path) + # First "Baidu" → "baidu", second "Baidu" → "baidu_2" + result = service.get_agent(str(run_dir), "baidu_2") + assert result["status"] == "ok" + assert result["agent"]["agent_id"] == "baidu_2" + assert result["agent"]["name"] == "Baidu" + assert result["agent"]["profile"]["type"] == "ai" + + +class TestAskAgentFallbackId: + def test_ask_agent_with_fallback_id_creates_request(self, tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + run_dir, service = _init_run_no_agent_id(tmp_path) + result = service.ask_agent(str(run_dir), "google_china", "What is your strategy?") + assert result["status"] == "need_agent_response" + assert result["agent_id"] == "google_china" + + +class TestWebConsoleFallbackId: + def test_web_console_no_unknown_option(self, tmp_path): + """Web Console HTML must not contain 'unknown' as an agent option value.""" + run_dir, service = _init_run_no_agent_id(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + # The dropdown options should use slug-based IDs, not "unknown" + assert 'value="unknown"' not in html_content + # Verify slug-based IDs are embedded + assert "baidu" in html_content + assert "google_china" in html_content + + def test_web_console_profiles_have_agent_id_in_embedded_data(self, tmp_path): + """Embedded profiles JSON should contain agent_id after normalization.""" + run_dir, service = _init_run_no_agent_id(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + # The embedded profiles JSON should contain the slug IDs + assert '"agent_id"' not in html_content or '"baidu"' in html_content or '"google_china"' in html_content + + def test_web_console_duplicate_names_get_unique_ids(self, tmp_path): + """Embedded profiles with duplicate names must have unique agent_ids.""" + run_dir, service = _init_run_no_agent_id(tmp_path) + service.generate_web_console(str(run_dir)) + html_path = run_dir / "artifacts" / "web" / "index.html" + html_content = html_path.read_text(encoding="utf-8") + # The second "Baidu" profile should get "baidu_2" + assert "baidu_2" in html_content + # The fourth profile (no name) should get "agent_4" + assert "agent_4" in html_content diff --git a/backend/tests/test_agent_mode_boundaries.py b/backend/tests/test_agent_mode_boundaries.py new file mode 100644 index 0000000000..e6d3c43456 --- /dev/null +++ b/backend/tests/test_agent_mode_boundaries.py @@ -0,0 +1,112 @@ +import importlib.util +import tomllib +from pathlib import Path + +from app.config import Config + + +def test_agent_mode_config_does_not_require_model_or_zep_keys(monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("ZEP_API_KEY", raising=False) + assert Config.validate() == [] + + +def test_legacy_llm_client_mock_mode_does_not_require_api_key(tmp_path, monkeypatch): + from app.utils.llm_client import LLMClient + + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "mock") + monkeypatch.setattr(Config, "MIROFISH_RUNS_DIR", str(tmp_path / "runs")) + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + + client = LLMClient() + result = client.chat([{"role": "user", "content": "hello"}]) + + assert result == "Mock text response generated without model APIs." + + +def test_flask_api_returns_need_agent_response_as_structured_json(): + from app import create_app + from app.adapters.llm.agent_runtime import NeedAgentResponse + from app.adapters.llm.base import LLMProviderResult + + app = create_app() + + @app.route("/_test_need_agent_response") + def _test_need_agent_response(): + raise NeedAgentResponse( + LLMProviderResult( + status="need_agent_response", + request_id="req_000001", + request_file="/tmp/req_000001.json", + expected_response_file="/tmp/resp_000001.json", + ) + ) + + response = app.test_client().get("/_test_need_agent_response") + + assert response.status_code == 202 + assert response.get_json() == { + "status": "need_agent_response", + "request_id": "req_000001", + "request_file": "/tmp/req_000001.json", + "expected_response_file": "/tmp/resp_000001.json", + } + + +def test_flask_api_zep_guard_is_provider_aware(monkeypatch): + from app.api.graph import _legacy_zep_config_errors + from app.api.simulation import _legacy_zep_required + + monkeypatch.setattr(Config, "ZEP_API_KEY", None) + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + assert _legacy_zep_config_errors() == [] + assert _legacy_zep_required() is False + + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "zep") + assert _legacy_zep_config_errors() + assert _legacy_zep_required() is True + + +def test_provider_boundary_checker_catches_legacy_adapter_and_schema_leaks(tmp_path): + checker_path = Path(__file__).resolve().parents[2] / "scripts" / "check_provider_boundaries.py" + spec = importlib.util.spec_from_file_location("check_provider_boundaries", checker_path) + checker = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(checker) + + business = tmp_path / "backend" / "app" / "services" / "bad_business.py" + business.parent.mkdir(parents=True) + business.write_text( + "from app.adapters.graph.zep import ZepGraphProvider\n" + "QUERY = 'MATCH (n:MiroFishEntity) RETURN n'\n", + encoding="utf-8", + ) + + violations = checker.collect_violations( + [tmp_path / "backend" / "app"], + root=tmp_path, + allowed=set(), + allowed_legacy_adapter_imports=set(), + allowed_graphiti_schema=set(), + ) + assert any("imports legacy provider adapter directly" in violation for violation in violations) + assert any("Graphiti/Neo4j schema assumption" in violation for violation in violations) + + +def test_legacy_sdks_are_optional_dependencies_only(): + pyproject = Path(__file__).resolve().parents[1] / "pyproject.toml" + data = tomllib.loads(pyproject.read_text(encoding="utf-8")) + + default_dependencies = set(data["project"]["dependencies"]) + legacy_dependencies = set(data["project"]["optional-dependencies"]["legacy"]) + + assert not any(dependency.startswith("openai") for dependency in default_dependencies) + assert not any(dependency.startswith("zep-cloud") for dependency in default_dependencies) + assert any(dependency.startswith("openai") for dependency in legacy_dependencies) + assert any(dependency.startswith("zep-cloud") for dependency in legacy_dependencies) diff --git a/backend/tests/test_agent_queue.py b/backend/tests/test_agent_queue.py new file mode 100644 index 0000000000..127190fe8f --- /dev/null +++ b/backend/tests/test_agent_queue.py @@ -0,0 +1,240 @@ +import json +from pathlib import Path + +from app.adapters.llm.base import LLMTask +from app.adapters.llm.mock import MockLLMProvider +from app.agent_engine.contracts import TASK_OUTPUT_SCHEMAS +from app.agent_engine.schemas import AGENT_TASK_TYPES +from app.agent_engine.json_schema import TRIPLE_SCHEMA, object_schema, validate_json_schema +from app.agent_engine.queue import AgentQueue +from app.agent_engine.state import RunStore + + +def _init_run(tmp_path: Path) -> Path: + run_dir = tmp_path / "run" + store = RunStore(run_dir) + store.init_state("run", "test requirement", []) + return run_dir + + +def test_agent_queue_strict_response_lifecycle(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + need = queue.create_request( + run_id="run", + task_type="extract_triples", + stage="graph", + expected_schema=object_schema({"triples": {"type": "array", "items": TRIPLE_SCHEMA}}, ["triples"]), + ) + + response_path = Path(need.expected_response_file) + response_path.write_text( + json.dumps( + { + "request_id": need.request_id, + "status": "ok", + "output": { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 0.8, + "metadata": {}, + } + ] + }, + } + ), + encoding="utf-8", + ) + + result = queue.validate_response_file(response_path) + assert result.ok, result.errors + + +def test_agent_queue_rejects_missing_fields_and_bad_confidence(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + need = queue.create_request( + run_id="run", + task_type="extract_triples", + stage="graph", + expected_schema=object_schema({"triples": {"type": "array", "items": TRIPLE_SCHEMA}}, ["triples"]), + ) + bad = Path(need.expected_response_file) + bad.write_text( + json.dumps( + { + "request_id": need.request_id, + "status": "ok", + "output": { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 1.2, + "metadata": {}, + "extra": "not allowed", + } + ] + }, + } + ), + encoding="utf-8", + ) + + result = queue.submit_response(bad) + assert not result.ok + assert any("confidence" in error for error in result.errors) + assert any("extra field" in error for error in result.errors) + assert result.repair_request is not None + + +def test_agent_queue_persists_repair_attempts(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + need = queue.create_request( + run_id="run", + task_type="generate_report", + stage="report", + expected_schema=object_schema({"report_markdown": {"type": "string"}}, ["report_markdown"]), + retry_policy={"max_repair_attempts": 1}, + ) + bad = Path(need.expected_response_file) + bad.write_text( + json.dumps( + { + "request_id": need.request_id, + "status": "ok", + "output": {}, + } + ), + encoding="utf-8", + ) + + first = queue.submit_response(bad) + second = queue.submit_response(bad) + + assert not first.ok + assert first.repair_request is not None + assert not second.ok + assert second.repair_request is None + assert queue.load_request(need.request_id).retry_policy.repair_attempts_used == 1 + + +def test_agent_queue_requires_filename_request_id_match(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + first = queue.create_request( + run_id="run", + task_type="generate_report", + stage="report", + expected_schema=object_schema({"report_markdown": {"type": "string"}}, ["report_markdown"]), + ) + second = queue.create_request( + run_id="run", + task_type="generate_report", + stage="report", + expected_schema=object_schema({"report_markdown": {"type": "string"}}, ["report_markdown"]), + ) + response_path = run_dir / "responses" / f"{second.request_id}.json" + response_path.write_text( + json.dumps( + { + "request_id": first.request_id, + "status": "ok", + "output": {"report_markdown": "ok"}, + } + ), + encoding="utf-8", + ) + + result = queue.validate_response_file(response_path) + + assert not result.ok + assert any("does not match response file name" in error for error in result.errors) + + +def test_agent_queue_validates_skipped_output_against_expected_schema(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + need = queue.create_request( + run_id="run", + task_type="generate_report", + stage="report", + expected_schema=object_schema({"report_markdown": {"type": "string"}}, ["report_markdown"]), + ) + response_path = Path(need.expected_response_file) + response_path.write_text( + json.dumps( + { + "request_id": need.request_id, + "status": "skipped", + "output": {}, + } + ), + encoding="utf-8", + ) + + result = queue.validate_response_file(response_path) + + assert not result.ok + assert any("missing required field" in error for error in result.errors) + + +def test_agent_queue_supports_all_declared_task_types(tmp_path): + run_dir = _init_run(tmp_path) + queue = AgentQueue(run_dir) + schema = object_schema({"result": {"type": "object"}}, ["result"]) + + created = [] + for task_type in sorted(AGENT_TASK_TYPES): + need = queue.create_request( + run_id="run", + task_type=task_type, + stage=task_type, + expected_schema=schema, + structured_input={"task_type": task_type}, + ) + created.append(need.request_id) + + assert len(created) == len(AGENT_TASK_TYPES) + assert len(queue.list_requests()) == len(AGENT_TASK_TYPES) + + +def test_all_task_types_have_strict_schema_and_mock_output(): + assert set(TASK_OUTPUT_SCHEMAS) == AGENT_TASK_TYPES + provider = MockLLMProvider() + + for task_type in sorted(AGENT_TASK_TYPES): + schema = TASK_OUTPUT_SCHEMAS[task_type] + result = provider.run_task( + LLMTask( + run_id="run", + task_type=task_type, + stage=task_type, + expected_schema=schema, + structured_input={ + "actions": [{"agent_id": "agent_1", "action_id": "action_1"}], + "candidate": {}, + "invalid_response": {"output": {}}, + }, + ) + ) + + assert result.status == "ok" + assert not validate_json_schema(result.output, schema), task_type diff --git a/backend/tests/test_camel_adapter.py b/backend/tests/test_camel_adapter.py new file mode 100644 index 0000000000..e901f59c05 --- /dev/null +++ b/backend/tests/test_camel_adapter.py @@ -0,0 +1,77 @@ +from app.adapters.llm.camel_adapter import AgentModelBackendAdapter +from app.adapters.llm.agent_queue import AgentQueueLLMProvider +from app.adapters.llm.mock import MockLLMProvider +from app.adapters.llm.agent_runtime import AgentRuntime +from app.agent_engine.queue import AgentQueue +from camel.models import BaseModelBackend + + +def test_agent_model_backend_adapter_batches_actions(tmp_path): + runtime = AgentRuntime(provider=MockLLMProvider(), run_dir=str(tmp_path)) + adapter = AgentModelBackendAdapter("run", str(tmp_path), runtime=runtime) + result = adapter.run_batch_actions( + "round_1", + [ + {"agent_id": "a1", "action_id": "x1"}, + {"agent_id": "a2", "action_id": "x2"}, + ], + ) + assert result["status"] == "ok" + actions = result["output"]["actions"] + assert {item["action_id"] for item in actions} == {"x1", "x2"} + + +def test_agent_model_backend_adapter_is_camel_backend_and_returns_tool_call(tmp_path): + runtime = AgentRuntime(provider=MockLLMProvider(), run_dir=str(tmp_path)) + adapter = AgentModelBackendAdapter("run", str(tmp_path), runtime=runtime) + assert isinstance(adapter, BaseModelBackend) + + response = adapter.run( + [{"role": "user", "content": "Act now"}], + tools=[{"type": "function", "function": {"name": "create_post", "parameters": {"type": "object"}}}], + ) + tool_calls = response.choices[0].message.tool_calls + assert tool_calls + assert tool_calls[0].function.name == "create_post" + + +def test_agent_model_backend_adapter_agent_queue_generates_request_without_keys(tmp_path, monkeypatch): + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("ZEP_API_KEY", raising=False) + + runtime = AgentRuntime(provider=AgentQueueLLMProvider(run_dir=tmp_path), run_dir=str(tmp_path)) + adapter = AgentModelBackendAdapter("run", str(tmp_path), runtime=runtime) + response = adapter.run( + [{"role": "user", "content": "Act in the simulation round"}], + tools=[{"type": "function", "function": {"name": "do_nothing", "parameters": {"type": "object"}}}], + ) + assert response.choices[0].message.tool_calls[0].function.name == "do_nothing" + requests = AgentQueue(tmp_path).list_requests() + assert requests + assert requests[0]["type"] == "simulate_agent_action" + assert adapter.last_need_agent_response is not None + assert adapter.last_need_agent_response["status"] == "need_agent_response" + assert adapter.last_need_agent_response["request_id"] == requests[0]["request_id"] + + +def test_agent_model_backend_adapter_records_batch_need_agent_response(tmp_path): + runtime = AgentRuntime(provider=AgentQueueLLMProvider(run_dir=tmp_path), run_dir=str(tmp_path)) + adapter = AgentModelBackendAdapter("run", str(tmp_path), runtime=runtime) + + result = adapter.run_batch_actions( + "round_1", + [ + {"agent_id": "a1", "action_id": "x1"}, + {"agent_id": "a2", "action_id": "x2"}, + ], + ) + + requests = AgentQueue(tmp_path).list_requests() + assert result["status"] == "need_agent_response" + assert adapter.last_need_agent_response is not None + assert adapter.last_need_agent_response["request_id"] == result["request_id"] + request = AgentQueue(tmp_path).load_request(result["request_id"]) + assert len(request.structured_input["actions"]) == 2 + assert {item["action_id"] for item in request.structured_input["actions"]} == {"x1", "x2"} + assert requests[0]["type"] == "simulate_agent_action" diff --git a/backend/tests/test_graph_provider.py b/backend/tests/test_graph_provider.py new file mode 100644 index 0000000000..1b5e399a9a --- /dev/null +++ b/backend/tests/test_graph_provider.py @@ -0,0 +1,215 @@ +import json +from pathlib import Path + +import pytest + +from app.adapters.graph.graphiti import ( + GraphitiCompatibilityStore, + GraphitiDependencyError, + GraphitiGraphProvider, +) +from app.adapters.graph.base import GraphTriple + + +def test_graphiti_provider_add_search_export_without_openai_key(tmp_path, monkeypatch): + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "store.json")) + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + + provider = GraphitiGraphProvider() + provider.add_triples( + "run", + [ + { + "subject": "美国商务部", + "predicate": "限制", + "object": "先进AI芯片出口", + "fact": "美国商务部限制先进AI芯片出口。", + "valid_at": "2024-01-01", + "invalid_at": None, + "source": "现实种子", + "source_file": "seed.md", + "evidence": "限制先进AI芯片出口", + "confidence": 0.82, + "metadata": {}, + } + ], + ) + results = provider.search("run", "AI芯片", limit=5) + assert results + assert results[0]["fact"] == "美国商务部限制先进AI芯片出口。" + + out = tmp_path / "snapshot.json" + provider.export_snapshot("run", str(out)) + snapshot = json.loads(out.read_text(encoding="utf-8")) + assert snapshot["run_id"] == "run" + assert len(snapshot["triples"]) == 1 + + +def test_graphiti_missing_dependency_error_is_clear(monkeypatch): + monkeypatch.setattr("importlib.util.find_spec", lambda name: None if name == "graphiti_core" else object()) + with pytest.raises(GraphitiDependencyError, match="graphiti_core is not installed"): + GraphitiGraphProvider(require_graphiti_package=True) + + +def test_graphiti_auto_store_requires_neo4j_driver(monkeypatch): + monkeypatch.delenv("NEO4J_URI", raising=False) + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "auto") + monkeypatch.setattr("importlib.util.find_spec", lambda name: None if name == "neo4j" else object()) + + with pytest.raises(GraphitiDependencyError, match="neo4j Python package is required"): + GraphitiCompatibilityStore() + + +def test_graphiti_file_store_explicitly_skips_neo4j_dependency(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "store.json")) + monkeypatch.setattr("importlib.util.find_spec", lambda name: None if name == "neo4j" else object()) + + store = GraphitiCompatibilityStore() + + assert store.driver is None + assert (tmp_path / "store.json").exists() + + +def test_graphiti_compatibility_store_encapsulates_schema_assumptions(): + assert hasattr(GraphitiCompatibilityStore, "_add_triplet_neo4j") + assert not hasattr(GraphitiGraphProvider, "_add_triplet_neo4j") + + +def test_graphiti_snapshot_uses_neo4j_branch_when_driver_present(): + store = GraphitiCompatibilityStore.__new__(GraphitiCompatibilityStore) + store.driver = object() + store.list_entities = lambda run_id: [{"name": "A"}] + store._list_facts_neo4j = lambda run_id: [{"subject": "A", "object": "B", "uuid": "internal"}] + store._list_episodes_neo4j = lambda run_id: [{"content": "episode"}] + store._list_memory_neo4j = lambda run_id: {"agent_1": {"belief": "x"}} + + snapshot = store.snapshot("run") + + assert snapshot["store"] == "neo4j" + assert snapshot["entities"] == [{"name": "A"}] + assert snapshot["triples"][0]["uuid"] == "internal" + assert snapshot["memory"]["agent_1"]["belief"] == "x" + + +def test_graphiti_neo4j_import_snapshot_cleans_internal_uuid(tmp_path): + snapshot_path = tmp_path / "snapshot.json" + snapshot_path.write_text( + json.dumps( + { + "entities": [{"name": "A", "labels": ["Entity"]}], + "triples": [ + { + "uuid": "internal", + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 0.9, + "metadata": {}, + } + ], + "episodes": [{"content": "episode", "metadata": {"source": "seed"}}], + "memory": {"agent_1": {"belief": "x"}}, + } + ), + encoding="utf-8", + ) + imported = {} + store = GraphitiCompatibilityStore.__new__(GraphitiCompatibilityStore) + store.driver = object() + store.clear_run_graph = lambda run_id: imported.setdefault("cleared", run_id) + store.get_or_create_entity_node = lambda run_id, name, labels=None: imported.setdefault("entity", (run_id, name, labels)) + store.add_episode = lambda run_id, content, metadata=None: imported.setdefault("episode", (run_id, content, metadata)) + store.write_agent_memory = lambda run_id, agent_id, memory: imported.setdefault("memory", (run_id, agent_id, memory)) + + def add_triples(run_id, triples): + imported["triples"] = triples + return {"triples_added": len(triples)} + + store.add_triples = add_triples + + result = store.import_snapshot("run", str(snapshot_path)) + + assert result["imported"] is True + assert isinstance(imported["triples"][0], GraphTriple) + assert imported["triples"][0].subject == "A" + + +def test_graphiti_neo4j_neighbors_respects_depth(): + captured = {} + + class FakeSession: + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def run(self, query, **params): + captured["query"] = query + captured["params"] = params + return [] + + class FakeDriver: + def session(self, database=None): + captured["database"] = database + return FakeSession() + + store = GraphitiCompatibilityStore.__new__(GraphitiCompatibilityStore) + store.driver = FakeDriver() + store.neo4j_database = "neo4j" + + assert store.neighbors("run", "A", depth=4) == [] + + assert "[*1..4]" in captured["query"] + assert captured["params"] == {"run_id": "run", "normalized": "a"} + + +def test_graphiti_neo4j_search_does_not_shadow_driver_query_argument(): + captured = {} + + class FakeSession: + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def run(self, cypher, **params): + captured["cypher"] = cypher + captured["params"] = params + return [] + + class FakeDriver: + def session(self, database=None): + return FakeSession() + + store = GraphitiCompatibilityStore.__new__(GraphitiCompatibilityStore) + store.driver = FakeDriver() + store.neo4j_database = "neo4j" + + assert store.search_facts("run", "AI芯片", limit=5) == [] + + assert "$search_query" in captured["cypher"] + assert captured["params"] == {"run_id": "run", "search_query": "AI芯片", "limit": 5} + + +def test_graphiti_neo4j_values_are_jsonable(): + class FakeDateTime: + def iso_format(self): + return "2026-06-06T00:00:00Z" + + store = GraphitiCompatibilityStore.__new__(GraphitiCompatibilityStore) + + assert store._to_jsonable({"created_at": FakeDateTime(), "items": [FakeDateTime()]}) == { + "created_at": "2026-06-06T00:00:00Z", + "items": ["2026-06-06T00:00:00Z"], + } diff --git a/backend/tests/test_runner_cli_flow.py b/backend/tests/test_runner_cli_flow.py new file mode 100644 index 0000000000..e7322ecf75 --- /dev/null +++ b/backend/tests/test_runner_cli_flow.py @@ -0,0 +1,531 @@ +import json +from pathlib import Path + +from app.adapters.graph.factory import create_graph_provider +from app.agent_engine.cli import build_parser +from app.agent_engine.queue import AgentQueue +from app.agent_engine.runner import PredictionRunService + + +def _write_response(run_dir: Path, payload: dict) -> None: + requests = AgentQueue(run_dir).list_requests() + request_id = requests[-1]["request_id"] + response_path = run_dir / "responses" / f"{request_id}.json" + response_path.write_text( + json.dumps({"request_id": request_id, "status": "ok", "output": payload}, ensure_ascii=False), + encoding="utf-8", + ) + + +def test_full_agent_queue_runner_flow(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("ZEP_API_KEY", raising=False) + + seed = tmp_path / "seed.md" + seed.write_text("美国商务部限制先进AI芯片出口。", encoding="utf-8") + run_dir = tmp_path / "chip-2036" + service = PredictionRunService() + created = service.create_run(str(seed), "预测未来10年全球芯片能力格局变化", str(run_dir)) + assert created["status"] == "created" + + need = service.run(str(run_dir)) + assert need["status"] == "need_agent_response" + _write_response(run_dir, {"ontology": {"entity_types": [], "edge_types": []}}) + + need = service.resume(str(run_dir)) + assert need["type"] == "extract_triples" + _write_response( + run_dir, + { + "triples": [ + { + "subject": "美国商务部", + "predicate": "限制", + "object": "先进AI芯片出口", + "fact": "美国商务部限制先进AI芯片出口。", + "valid_at": "2024-01-01", + "invalid_at": None, + "source": "现实种子", + "source_file": "seed.md", + "evidence": "美国商务部限制先进AI芯片出口。", + "confidence": 0.82, + "metadata": {}, + } + ] + }, + ) + + need = service.resume(str(run_dir)) + assert need["type"] == "generate_oasis_profiles" + _write_response(run_dir, {"profiles": [{"agent_id": "agent_1", "name": "Analyst", "persona": "Analyst"}]}) + + need = service.resume(str(run_dir)) + assert need["type"] == "generate_simulation_config" + _write_response(run_dir, {"config": {"rounds": 1}}) + + need = service.resume(str(run_dir)) + assert need["type"] == "simulate_agent_action" + request = AgentQueue(run_dir).load_request(need["request_id"]) + assert len(request.structured_input["actions"]) == 1 + _write_response( + run_dir, + {"actions": [{"agent_id": "agent_1", "action_id": "round_1_action_1", "action_type": "CREATE_POST", "content": "Chip export controls may shift supply chains."}]}, + ) + + need = service.resume(str(run_dir)) + assert need["type"] == "generate_report" + _write_response( + run_dir, + { + "report_markdown": "# Report\n\nChip capacity may bifurcate.", + "verdict": {"status": "ok", "confidence": 0.7}, + "timeline": [{"valid_at": "2024-01-01", "fact": "export controls"}], + }, + ) + + result = service.resume(str(run_dir)) + assert result["status"] == "completed" + for artifact in ["report.md", "verdict.json", "timeline.json", "graph_snapshot.json"]: + assert (run_dir / "artifacts" / artifact).exists() + + +def test_graph_build_provider_override_survives_agent_wait(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "zep") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + monkeypatch.delenv("ZEP_API_KEY", raising=False) + + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "override-run" + service = PredictionRunService() + service.create_run(str(seed), "test graph override", str(run_dir)) + + need = service.build_graph(str(run_dir), provider="graphiti") + assert need["status"] == "need_agent_response" + _write_response( + run_dir, + { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 0.9, + "metadata": {}, + } + ] + }, + ) + + next_step = service.resume(str(run_dir)) + assert next_step["status"] == "need_agent_response" + assert next_step["type"] == "generate_oasis_profiles" + assert (run_dir / "artifacts" / "graph_snapshot.json").exists() + + +def test_explicit_stage_commands_reuse_existing_waiting_request(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + service = PredictionRunService() + + for command_name, command in [ + ("graph", lambda run: service.build_graph(str(run), provider="graphiti")), + ("simulation", lambda run: service.start_simulation(str(run))), + ("report", lambda run: service.generate_report(str(run))), + ]: + seed = tmp_path / f"{command_name}.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / f"{command_name}-run" + service.create_run(str(seed), f"test {command_name}", str(run_dir)) + + first = command(run_dir) + second = command(run_dir) + requests = AgentQueue(run_dir).list_requests() + + assert first["status"] == "need_agent_response" + assert second["status"] == "need_agent_response" + assert second["request_id"] == first["request_id"] + assert len(requests) == 1 + + +def test_resume_creates_repair_request_for_invalid_stage_response(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "repair-run" + service = PredictionRunService() + service.create_run(str(seed), "test repair", str(run_dir)) + + need = service.run(str(run_dir)) + _write_response(run_dir, {"ontology": {"entity_types": [], "edge_types": []}}) + + need = service.resume(str(run_dir)) + assert need["type"] == "extract_triples" + bad_response_path = run_dir / "responses" / f"{need['request_id']}.json" + bad_response_path.write_text( + json.dumps( + { + "request_id": need["request_id"], + "status": "ok", + "output": { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 2.0, + "metadata": {}, + } + ] + }, + } + ), + encoding="utf-8", + ) + + repair = service.resume(str(run_dir)) + assert repair["status"] == "need_agent_response" + assert repair["type"] == "repair_invalid_json" + + repair_request = AgentQueue(run_dir).load_request(repair["request_id"]) + assert repair_request.structured_input["validation_errors"] + _write_response( + run_dir, + { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 0.9, + "metadata": {}, + } + ] + }, + ) + + next_step = service.resume(str(run_dir)) + assert next_step["status"] == "need_agent_response" + assert next_step["type"] == "generate_oasis_profiles" + + +def test_response_submit_repair_request_is_attached_to_waiting_stage(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "submit-repair-run" + service = PredictionRunService() + service.create_run(str(seed), "test submit repair", str(run_dir)) + + need = service.run(str(run_dir)) + _write_response(run_dir, {"ontology": {"entity_types": [], "edge_types": []}}) + + need = service.resume(str(run_dir)) + assert need["type"] == "extract_triples" + bad_response_path = run_dir / "responses" / f"{need['request_id']}.json" + bad_response_path.write_text( + json.dumps( + { + "request_id": need["request_id"], + "status": "ok", + "output": { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 2.0, + "metadata": {}, + } + ] + }, + } + ), + encoding="utf-8", + ) + + submitted = service.submit_response(str(run_dir), str(bad_response_path)) + repair_id = submitted["repair_request"]["request_id"] + resumed = service.resume(str(run_dir)) + + assert submitted["ok"] is False + assert resumed["request_id"] == repair_id + assert len(AgentQueue(run_dir).list_requests()) == 3 + + +def test_followup_question_uses_agent_queue_and_graph_context(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + monkeypatch.delenv("LLM_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("ZEP_API_KEY", raising=False) + + seed = tmp_path / "seed.md" + seed.write_text("美国商务部限制先进AI芯片出口。", encoding="utf-8") + run_dir = tmp_path / "followup-run" + service = PredictionRunService() + created = service.create_run(str(seed), "预测未来10年全球芯片能力格局变化", str(run_dir)) + + create_graph_provider("graphiti").add_triples( + created["run_id"], + [ + { + "subject": "美国商务部", + "predicate": "限制", + "object": "先进AI芯片出口", + "fact": "美国商务部限制先进AI芯片出口。", + "valid_at": "2024-01-01", + "invalid_at": None, + "source": "现实种子", + "source_file": "seed.md", + "evidence": "美国商务部限制先进AI芯片出口。", + "confidence": 0.82, + "metadata": {}, + } + ], + ) + + need = service.ask_followup_question(str(run_dir), "先进AI芯片出口限制有什么影响?", limit=5) + assert need["status"] == "need_agent_response" + assert need["type"] == "answer_followup_question" + request = AgentQueue(run_dir).load_request(need["request_id"]) + assert request.structured_input["question"] == "先进AI芯片出口限制有什么影响?" + assert request.structured_input["graph_results"] + + response_path = run_dir / "responses" / f"{need['request_id']}.json" + response_path.write_text( + json.dumps( + { + "request_id": need["request_id"], + "status": "ok", + "output": { + "answer_markdown": "出口限制可能推动供应链分化。", + "used_graph_results": request.structured_input["graph_results"], + "confidence": 0.8, + }, + }, + ensure_ascii=False, + ), + encoding="utf-8", + ) + + answer = service.get_followup_answer(str(run_dir), need["request_id"]) + assert answer["status"] == "ok" + assert (run_dir / "artifacts" / "followups" / f"{need['request_id']}.md").exists() + assert (run_dir / "artifacts" / "followups" / f"{need['request_id']}.json").exists() + + +def test_create_run_persists_hard_round_settings(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "settings-run" + + created = PredictionRunService().create_run( + str(seed), + "predict hard settings", + str(run_dir), + mode="staged", + rounds=12, + round_unit="year", + pause_each_round=True, + agent_count=3, + simulation_name="hard-settings", + ) + + settings = created["state"]["simulation_settings"] + assert settings["rounds"] == 12 + assert settings["max_rounds"] == 12 + assert settings["simulation_rounds"] == 12 + assert settings["round_unit"] == "year" + assert settings["minutes_per_round"] == 525600 + assert settings["pause_each_round"] is True + assert settings["agent_count"] == 3 + assert created["state"]["workflow_mode"] == "staged" + assert created["state"]["stages"]["seed_input"]["status"] == "awaiting_user_confirmation" + + +def test_rounds_below_minimum_fails_without_debug_mode(tmp_path, monkeypatch): + monkeypatch.delenv("MIROFISH_ALLOW_DEBUG_ROUNDS", raising=False) + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + + try: + PredictionRunService().create_run(str(seed), "too short", str(tmp_path / "bad"), rounds=3) + except ValueError as exc: + assert "rounds must be at least 10" in str(exc) + else: + raise AssertionError("rounds below 10 should fail") + + monkeypatch.setenv("MIROFISH_ALLOW_DEBUG_ROUNDS", "true") + created = PredictionRunService().create_run(str(seed), "debug short", str(tmp_path / "debug"), rounds=3) + assert created["state"]["simulation_settings"]["rounds"] == 3 + + +def test_staged_mode_pauses_and_approve_advances(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "staged-run" + service = PredictionRunService() + created = service.create_run(str(seed), "predict staged", str(run_dir), mode="staged", rounds=10) + assert created["state"]["current_stage"] == "seed_input" + assert created["state"]["stages"]["seed_input"]["status"] == "awaiting_user_confirmation" + + approved = service.approve_stage(str(run_dir)) + assert approved["next_stage"] == "prediction_requirement" + status = service.status(str(run_dir))["state"] + assert status["current_stage"] == "prediction_requirement" + assert status["stages"]["prediction_requirement"]["status"] == "pending" + + paused = service.resume(str(run_dir)) + assert paused["status"] == "awaiting_user_confirmation" + assert paused["stage"] == "prediction_requirement" + + +def test_update_settings_stales_downstream_and_config_uses_hard_rounds(tmp_path, monkeypatch): + monkeypatch.setenv("MIROFISH_MODE", "agent") + monkeypatch.setenv("MIROFISH_LLM_PROVIDER", "agent_queue") + monkeypatch.setenv("MIROFISH_GRAPH_PROVIDER", "graphiti") + monkeypatch.setenv("MIROFISH_GRAPHITI_STORE", "file") + monkeypatch.setenv("MIROFISH_GRAPHITI_COMPAT_PATH", str(tmp_path / "graph_store.json")) + + seed = tmp_path / "seed.md" + seed.write_text("A affects B.", encoding="utf-8") + run_dir = tmp_path / "staged-config-run" + service = PredictionRunService() + service.create_run(str(seed), "predict staged config", str(run_dir), mode="staged", rounds=10) + service.approve_stage(str(run_dir)) + service.resume(str(run_dir)) + service.approve_stage(str(run_dir)) + service.resume(str(run_dir)) + updated = service.update_simulation_settings(str(run_dir), rounds=12, round_unit="month") + assert updated["simulation_settings"]["rounds"] == 12 + assert updated["state"]["stages"]["profile_and_config"]["stale"] is True + + service.approve_stage(str(run_dir)) + need = service.resume(str(run_dir)) + assert need["type"] == "generate_ontology" + _write_response(run_dir, {"ontology": {"entity_types": [], "edge_types": []}}) + need = service.resume(str(run_dir)) + assert need["type"] == "extract_triples" + _write_response( + run_dir, + { + "triples": [ + { + "subject": "A", + "predicate": "affects", + "object": "B", + "fact": "A affects B.", + "valid_at": None, + "invalid_at": None, + "source": "seed", + "source_file": "seed.md", + "evidence": "A affects B.", + "confidence": 0.9, + "metadata": {}, + } + ] + }, + ) + paused = service.resume(str(run_dir)) + assert paused["status"] == "awaiting_user_confirmation" + assert paused["stage"] == "graph_build" + service.approve_stage(str(run_dir)) + + need = service.resume(str(run_dir)) + assert need["type"] == "generate_oasis_profiles" + _write_response(run_dir, {"profiles": [{"agent_id": "agent_1", "name": "Agent"}]}) + need = service.resume(str(run_dir)) + assert need["type"] == "generate_simulation_config" + _write_response(run_dir, {"config": {"rounds": 1, "simulation_rounds": 1}}) + paused = service.resume(str(run_dir)) + assert paused["status"] == "awaiting_user_confirmation" + config = json.loads((run_dir / "artifacts" / "simulation_config.json").read_text(encoding="utf-8")) + assert config["rounds"] == 12 + assert config["simulation_rounds"] == 12 + assert config["round_unit"] == "month" + + +def test_cli_parser_exposes_rounds_and_stage_commands(): + parser = build_parser() + args = parser.parse_args( + [ + "create-run", + "--seed", + "seed.md", + "--requirement", + "predict", + "--output", + "runs/demo", + "--mode", + "staged", + "--rounds", + "10", + "--round-unit", + "year", + ] + ) + assert args.command == "create-run" + assert args.rounds == 10 + assert args.mode == "staged" + + stage_args = parser.parse_args(["stage", "update-settings", "--run", "runs/demo", "--rounds", "12"]) + assert stage_args.stage_command == "update-settings" + assert stage_args.rounds == 12 diff --git a/backend/uv.lock b/backend/uv.lock index 642dd9c363..eddc944b34 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.11, <3.13" resolution-markers = [ "python_full_version >= '3.12'", @@ -10,18 +10,18 @@ resolution-markers = [ name = "aiofiles" version = "25.1.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = 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mirofish-ollama + profiles: + - ollama + restart: unless-stopped + ports: + - "11434:11434" + volumes: + - ollama_data:/root/.ollama + +volumes: + neo4j_data: + neo4j_logs: + ollama_data: diff --git a/docs/agent-usage/claude-code.md b/docs/agent-usage/claude-code.md new file mode 100644 index 0000000000..ad228551bd --- /dev/null +++ b/docs/agent-usage/claude-code.md @@ -0,0 +1,42 @@ +# Claude Code Usage + +Open the repository: + +```bash +cd /Users/leaf/Documents/future/MiroFish +``` + +Run the CLI from the backend: + +```bash +cd backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 --json +uv run mirofish-agent run --run ../runs/chip-2036 --json +``` + +For each `need_agent_response`, inspect the request: + +```bash +uv run mirofish-agent requests show --run ../runs/chip-2036 --request-id req_000001 --json +``` + +Write the response file exactly matching `expected_schema`, validate it, then resume: + +```bash +uv run mirofish-agent responses validate --run ../runs/chip-2036 --response ../runs/chip-2036/responses/req_000001.json --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +Claude Code MCP config: + +```json +{ + "mcpServers": { + "mirofish": { + "command": "uv", + "args": ["run", "mirofish-mcp"], + "cwd": "/Users/leaf/Documents/future/MiroFish/backend" + } + } +} +``` diff --git a/docs/agent-usage/codex.md b/docs/agent-usage/codex.md new file mode 100644 index 0000000000..f2f3a065a1 --- /dev/null +++ b/docs/agent-usage/codex.md @@ -0,0 +1,64 @@ +# Codex Desktop Usage + +Open `/Users/leaf/Documents/future/MiroFish` in Codex Desktop. + +Initialize a run: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 --json +uv run mirofish-agent run --run ../runs/chip-2036 --json +``` + +When the CLI returns `need_agent_response`, read `request_file`, generate the response JSON, and write it to `expected_response_file`. + +Codex triple extraction prompt: + +```text +读取 request_file，严格按照 expected_schema 生成 response_file。只输出 JSON，不要添加解释。每个 triple 必须包含 subject、predicate、object、fact、evidence、confidence。不要编造现实种子中没有的事实。无法确认的关系不要写入，或将 confidence 降低。 +``` + +Validate and continue: + +```bash +uv run mirofish-agent responses validate --run ../runs/chip-2036 --response ../runs/chip-2036/responses/req_000001.json --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +Repeat until status is `completed`. Final artifacts are in: + +```text +runs/chip-2036/artifacts/report.md +runs/chip-2036/artifacts/verdict.json +runs/chip-2036/artifacts/timeline.json +runs/chip-2036/artifacts/graph_snapshot.json +``` + +Ask a follow-up question after a completed run: + +```bash +uv run mirofish-agent followup ask --run ../runs/chip-2036 --question "先进AI芯片出口限制有什么影响?" --json +uv run mirofish-agent requests show --run ../runs/chip-2036 --request-id req_000007 --json +uv run mirofish-agent followup show --run ../runs/chip-2036 --request-id req_000007 --json +``` + +Follow-up answers are written under `runs/chip-2036/artifacts/followups/`. + +MCP config example: + +```json +{ + "mcpServers": { + "mirofish": { + "command": "uv", + "args": ["run", "mirofish-mcp"], + "cwd": "/Users/leaf/Documents/future/MiroFish/backend", + "env": { + "MIROFISH_MODE": "agent", + "MIROFISH_LLM_PROVIDER": "agent_queue", + "MIROFISH_GRAPH_PROVIDER": "graphiti" + } + } + } +} +``` diff --git a/docs/agent-usage/cursor.md b/docs/agent-usage/cursor.md new file mode 100644 index 0000000000..12f383e08f --- /dev/null +++ b/docs/agent-usage/cursor.md @@ -0,0 +1,32 @@ +# Cursor Usage + +Open `/Users/leaf/Documents/future/MiroFish` as the workspace. + +CLI flow: + +```bash +cd backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 --json +uv run mirofish-agent run --run ../runs/chip-2036 --json +``` + +Cursor can handle request files by reading `runs//requests/req_*.json` and writing strict responses to `runs//responses/req_*.json`. + +MCP config example: + +```json +{ + "mcpServers": { + "mirofish": { + "command": "uv", + "args": ["run", "mirofish-mcp"], + "cwd": "/Users/leaf/Documents/future/MiroFish/backend", + "env": { + "MIROFISH_MODE": "agent", + "MIROFISH_LLM_PROVIDER": "agent_queue", + "MIROFISH_GRAPH_PROVIDER": "graphiti" + } + } + } +} +``` diff --git a/docs/agent-usage/graphiti-neo4j-setup.md b/docs/agent-usage/graphiti-neo4j-setup.md new file mode 100644 index 0000000000..11e4f081cf --- /dev/null +++ b/docs/agent-usage/graphiti-neo4j-setup.md @@ -0,0 +1,189 @@ +# Graphiti + Neo4j Setup + +Agent mode defaults to: + +```bash +MIROFISH_MODE=agent +MIROFISH_LLM_PROVIDER=agent_queue +MIROFISH_GRAPH_PROVIDER=graphiti +``` + +MiroFish does not vendor Graphiti source code. Install Graphiti through the backend Python dependency group: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv sync --extra agent --group dev +``` + +You can also run the helper: + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/setup_agent_deps.sh --neo4j desktop +``` + +The helper loads `/Users/leaf/Documents/future/MiroFish/.env` automatically when it exists. + +Docker is optional. `doctor` does not fail just because Docker or Docker Compose is unavailable. + +## Required Environment + +```bash +export NEO4J_URI=bolt://localhost:7687 +export NEO4J_USER=neo4j +export NEO4J_PASSWORD=password +export NEO4J_DATABASE=neo4j +``` + +Neo4j must be reachable and must be version `5.26` or newer. + +Graphiti stores and searches graph facts. It does not extract complex triples from raw seed text in MiroFish agent mode. MiroFish writes `extract_triples` requests, a desktop agent writes validated responses, then `GraphitiGraphProvider` stores triples using `run_id` as the namespace. + +## Option 1: Neo4j Desktop + +Recommended when you do not want Docker and prefer a GUI-managed local database: + +1. Install Neo4j Desktop. +2. Create a local DBMS using Neo4j `5.26` or newer. +3. Set the password to match `NEO4J_PASSWORD`. +4. Start the database. +5. Export the connection values: + +```bash +export NEO4J_URI=bolt://localhost:7687 +export NEO4J_USER=neo4j +export NEO4J_PASSWORD=your-password +export NEO4J_DATABASE=neo4j +``` + +Then run: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-agent doctor --json +``` + +The `docker` and `docker_compose` doctor checks may show warnings, but they are optional and do not fail doctor. + +## Option 2: Homebrew / Native Install + +Recommended when you do not want Docker and prefer a local service managed by macOS. Install and start Neo4j locally: + +```bash +brew install neo4j +brew services start neo4j +``` + +Set the same environment variables: + +```bash +export NEO4J_URI=bolt://localhost:7687 +export NEO4J_USER=neo4j +export NEO4J_PASSWORD=your-password +export NEO4J_DATABASE=neo4j +``` + +If your native installation uses another port, update `NEO4J_URI`. + +Run: + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/setup_agent_deps.sh --neo4j native +cd backend +uv run mirofish-agent doctor --json +``` + +## Option 3: Existing Neo4j Instance + +Point MiroFish at any reachable Neo4j `5.26+` instance: + +```bash +export NEO4J_URI=bolt://your-host:7687 +export NEO4J_USER=neo4j +export NEO4J_PASSWORD=your-password +export NEO4J_DATABASE=neo4j +``` + +Then run: + +```bash +bash scripts/setup_agent_deps.sh --neo4j existing +``` + +## Option 4: Docker Compose + +Docker Compose remains available for users who prefer it: + +```bash +cd /Users/leaf/Documents/future/MiroFish +docker compose -f docker-compose.agent.yml up -d neo4j +``` + +Open the browser console at `http://localhost:7474` and log in with: + +```text +user: neo4j +password: password +``` + +Run: + +```bash +cd /Users/leaf/Documents/future/MiroFish +bash scripts/setup_agent_deps.sh --neo4j docker --start-docker +``` + +If Docker is not installed, the setup script prints `Docker optional, skipped`; it does not fail for that reason. The required check is still whether Neo4j is reachable through `NEO4J_URI`. + +## Ollama Embedding + +The no-LLM triplet write path and `fulltext` graph search do not require Ollama. Doctor only hard-fails Ollama checks when both conditions are true: + +```bash +export MIROFISH_GRAPH_SEARCH_MODE=semantic # or hybrid +export MIROFISH_EMBEDDING_PROVIDER=ollama +``` + +If `MIROFISH_GRAPH_SEARCH_MODE=fulltext` or `MIROFISH_EMBEDDING_PROVIDER=none`, missing Ollama is reported as an optional warning only. Semantic retrieval may be unavailable, but the agent engine and Graphiti/Neo4j fulltext path can still run. + +If you opt into Ollama embeddings, install and start Ollama locally, then pull the embedding model: + +```bash +ollama serve +ollama pull nomic-embed-text +``` + +Configure: + +```bash +export MIROFISH_EMBEDDING_PROVIDER=ollama +export MIROFISH_GRAPH_SEARCH_MODE=semantic +export OLLAMA_BASE_URL=http://localhost:11434 +export OLLAMA_EMBEDDING_MODEL=nomic-embed-text +``` + +Doctor checks `GET $OLLAMA_BASE_URL/api/tags` and verifies that `OLLAMA_EMBEDDING_MODEL` is installed. + +## Offline Compatibility Store + +Offline tests can use a file-backed no-LLM triplet store: + +```bash +export MIROFISH_GRAPHITI_STORE=file +export MIROFISH_GRAPHITI_COMPAT_PATH=/tmp/mirofish-graphiti-store.json +``` + +This is for smoke tests and local development without Neo4j. Production agent mode should use Neo4j. The default `MIROFISH_GRAPHITI_STORE=auto` path uses Neo4j; it does not silently downgrade to file storage. + +## Compatibility Layer + +`GraphitiCompatibilityStore` provides the no-LLM triplet write path. If it writes directly to Neo4j, all Cypher and Graphiti schema assumptions stay inside that class. Business code must use `GraphProvider` and must not depend on Graphiti node or edge internals. + +References: + +- Graphiti episodes: https://help.getzep.com/graphiti/core-concepts/adding-episodes +- Graphiti fact triples: https://help.getzep.com/graphiti/working-with-data/adding-fact-triples +- Graphiti namespacing: https://help.getzep.com/graphiti/core-concepts/graph-namespacing +- Graphiti Neo4j config: https://help.getzep.com/graphiti/configuration/neo-4-j-configuration +- Graphiti LLM config: https://help.getzep.com/graphiti/configuration/llm-configuration diff --git a/docs/agent-usage/mcp.md b/docs/agent-usage/mcp.md new file mode 100644 index 0000000000..83c916dddd --- /dev/null +++ b/docs/agent-usage/mcp.md @@ -0,0 +1,141 @@ +# MiroFish MCP Server + +The MCP server exposes MiroFish lifecycle tools, not a Graphiti proxy. + +Start: + +```bash +cd /Users/leaf/Documents/future/MiroFish/backend +uv run mirofish-mcp +``` + +Tools: + +- `mirofish_create_run` +- `mirofish_run` +- `mirofish_resume_run` +- `mirofish_get_status` +- `mirofish_get_current_stage` +- `mirofish_update_simulation_settings` +- `mirofish_approve_stage` +- `mirofish_reject_stage` +- `mirofish_rerun_stage` +- `mirofish_list_requests` +- `mirofish_get_request` +- `mirofish_submit_response` +- `mirofish_validate_response` +- `mirofish_build_graph` +- `mirofish_search_graph` +- `mirofish_export_graph` +- `mirofish_start_simulation` +- `mirofish_resume_simulation` +- `mirofish_generate_report` +- `mirofish_get_report` +- `mirofish_ask_followup_question` +- `mirofish_get_followup_answer` +- `mirofish_list_artifacts` +- `mirofish_doctor` + +## Interaction Tools + +After a run completes, these tools let you interact with agents through the queue: + +- `mirofish_generate_web_console` — generates an interactive HTML console at `runs//artifacts/web/index.html`. +- `mirofish_list_agents` — lists all agent profiles from a completed run. +- `mirofish_get_agent` — returns a single agent's profile. +- `mirofish_ask_agent` — sends a question to a specific agent via `agent_queue`. Returns `need_agent_response` with a `request_id`. +- `mirofish_get_agent_answer` — after the desktop agent writes the response file, call this to validate, persist, and retrieve the answer. +- `mirofish_send_questionnaire` — sends a batch questionnaire to all agents. `questions_json` is a JSON string: `'[{"question_id":"q1","question":"Biggest risk?"}, ...]'`. +- `mirofish_get_questionnaire_result` — retrieves questionnaire answers and summary. +- `mirofish_ask_report_question` — asks a question about the report via `agent_queue`. +- `mirofish_get_report_question_answer` — retrieves and persists a report question answer. + +### Web Console + +The Web Console is a static HTML page with embedded run data plus live API interaction when the Flask backend is running. + +1. Generate the console: + ```bash + uv run mirofish-agent web generate --run ../runs/chip-2036 --json + ``` + Or via MCP: call `mirofish_generate_web_console`. + +2. Open the generated file: `runs//artifacts/web/index.html` + +3. Start the Flask backend for interactive features: + ```bash + cd /Users/leaf/Documents/future/MiroFish/backend + uv run flask --app app run --port 5001 + ``` + +4. The console auto-detects the API at `http://localhost:5001`. You can change the base URL in the sidebar. + +When the API is offline, the console falls back to displaying embedded static data from the run artifacts. + +### Agent Q&A Flow + +1. Call `mirofish_ask_agent(run, agent_id, question)` — returns `request_id`. +2. A desktop agent reads `runs//requests/.json` and writes `runs//responses/.json`. +3. Call `mirofish_get_agent_answer(run, request_id)` to validate the response and persist it to `artifacts/interactions/agent_questions/`. + +### Questionnaire Flow + +1. Call `mirofish_send_questionnaire(run, questions_json)` with a JSON array of `{question_id, question}` objects. +2. Each agent gets a separate `agent_queue` request per question. +3. Call `mirofish_get_questionnaire_result(run, questionnaire_id)` to collect answers and summary. + +## Staged Mode + +Use staged mode when a desktop agent should mirror the original MiroFish step-by-step UI flow. The simulation round count is a hard MCP field, not text hidden in the requirement. + +Typical Qoder/Codex/Claude Code sequence: + +1. Call `mirofish_doctor`. +2. Call `mirofish_create_run` with `mode="staged"`, `rounds=10`, `round_unit="year"`, and the seed/requirement/output path. +3. Call `mirofish_get_current_stage` and show the user the stage summary. +4. After user confirmation, call `mirofish_approve_stage`. +5. Call `mirofish_resume_run`; staged mode advances only to the next pause point or `need_agent_response`. +6. When `need_agent_response` appears, read `request_file`, write the response JSON, call `mirofish_validate_response`, then `mirofish_submit_response`. +7. Repeat resume/approve until `report.md`, `verdict.json`, `timeline.json`, and `graph_snapshot.json` exist. +8. Use `mirofish_ask_followup_question` for post-report questions. + +Example `mirofish_create_run` arguments: + +```json +{ + "seed": "/Users/leaf/Documents/future/MiroFish/seeds/chip.md", + "requirement": "预测未来10年全球芯片能力格局变化", + "output": "/Users/leaf/Documents/future/MiroFish/runs/chip-2036", + "mode": "staged", + "rounds": 10, + "round_unit": "year", + "minutes_per_round": 525600, + "pause_each_round": false, + "agent_count": 5, + "simulation_name": "chip-2036" +} +``` + +If the user changes hard parameters before approval, call `mirofish_update_simulation_settings`. The engine marks dependent stages stale/pending so old profile/config/simulation/report outputs are not silently reused. + +Example MCP server config: + +```json +{ + "mcpServers": { + "mirofish": { + "command": "uv", + "args": ["run", "mirofish-mcp"], + "cwd": "/Users/leaf/Documents/future/MiroFish/backend", + "env": { + "MIROFISH_MODE": "agent", + "MIROFISH_LLM_PROVIDER": "agent_queue", + "MIROFISH_GRAPH_PROVIDER": "graphiti", + "MIROFISH_RUNS_DIR": "./runs" + } + } + } +} +``` + +Reference: https://modelcontextprotocol.github.io/python-sdk/server/ diff --git a/docs/agent-usage/opencode.md b/docs/agent-usage/opencode.md new file mode 100644 index 0000000000..664ed2c3b8 --- /dev/null +++ b/docs/agent-usage/opencode.md @@ -0,0 +1,48 @@ +# opencode Usage + +Open the repository: + +```bash +cd /Users/leaf/Documents/future/MiroFish +``` + +Run MiroFish through the CLI from the backend: + +```bash +cd backend +uv run mirofish-agent init --seed /path/to/seed.md --requirement "预测未来10年全球芯片能力格局变化" --output ../runs/chip-2036 --json +uv run mirofish-agent run --run ../runs/chip-2036 --json +``` + +When a command returns `need_agent_response`, read `request_file`, produce JSON that exactly matches `expected_schema`, write it to `expected_response_file`, validate it, and resume: + +```bash +uv run mirofish-agent responses validate --run ../runs/chip-2036 --response ../runs/chip-2036/responses/req_000001.json --json +uv run mirofish-agent resume --run ../runs/chip-2036 --json +``` + +MCP server config shape: + +```json +{ + "mcpServers": { + "mirofish": { + "command": "uv", + "args": ["run", "mirofish-mcp"], + "cwd": "/Users/leaf/Documents/future/MiroFish/backend", + "env": { + "MIROFISH_MODE": "agent", + "MIROFISH_LLM_PROVIDER": "agent_queue", + "MIROFISH_GRAPH_PROVIDER": "graphiti" + } + } + } +} +``` + +Follow-up questions use the same queue: + +```bash +uv run mirofish-agent followup ask --run ../runs/chip-2036 --question "这个预测里最大的风险是什么?" --json +uv run mirofish-agent followup show --run ../runs/chip-2036 --request-id req_000007 --json +``` diff --git a/docs/agent-usage/qoderwork.md b/docs/agent-usage/qoderwork.md new file mode 100644 index 0000000000..f9371429a0 --- /dev/null +++ b/docs/agent-usage/qoderwork.md @@ -0,0 +1,36 @@ +# QoderWork Staged Usage + +QoderWork should use the MiroFish MCP server as a staged business workflow, not as a one-shot prompt wrapper. + +## Run Sequence + +1. Call `mirofish_doctor`. +2. Call `mirofish_create_run` with hard simulation settings: + +```json +{ + "seed": "/absolute/path/seed.md", + "requirement": "预测未来10年全球芯片能力格局变化", + "output": "/Users/leaf/Documents/future/MiroFish/runs/chip-2036", + "mode": "staged", + "rounds": 10, + "round_unit": "year", + "minutes_per_round": 525600, + "pause_each_round": false, + "agent_count": 5, + "simulation_name": "chip-2036" +} +``` + +3. Call `mirofish_get_current_stage` and present the summary to the user. +4. After the user confirms, call `mirofish_approve_stage`. +5. Call `mirofish_resume_run`. +6. If the result is `need_agent_response`, read `request_file`, generate the response JSON exactly against `expected_schema`, then call `mirofish_validate_response` and `mirofish_submit_response`. +7. If the result is `awaiting_user_confirmation`, show the stage summary and ask whether to approve, reject, update settings, or rerun. +8. Continue until the run returns `completed`. +9. Read artifacts with `mirofish_get_report` and `mirofish_list_artifacts`. +10. Ask follow-up questions with `mirofish_ask_followup_question`. + +## Important Rule + +Do not put the round count only in the natural-language requirement. Always pass `rounds` and `round_unit` through MCP fields so the simulation config, timeline, verdict, and report record the actual hard settings. diff --git a/docs/agent-usage/triple-extraction-response-format.md b/docs/agent-usage/triple-extraction-response-format.md new file mode 100644 index 0000000000..47efe9eb88 --- /dev/null +++ b/docs/agent-usage/triple-extraction-response-format.md @@ -0,0 +1,36 @@ +# Triple Extraction Response Format + +Responses must be strict JSON with no extra top-level fields: + +```json +{ + "request_id": "req_000001", + "status": "ok", + "output": { + "triples": [ + { + "subject": "美国商务部", + "predicate": "限制", + "object": "先进AI芯片出口", + "fact": "美国商务部限制先进AI芯片出口。", + "valid_at": "2024-01-01", + "invalid_at": null, + "source": "现实种子", + "source_file": "seed.md", + "evidence": "原文证据片段", + "confidence": 0.82, + "metadata": {} + } + ] + } +} +``` + +Rules: + +- `request_id` must exactly match the request file. +- `status` must be `ok`, `error`, or `skipped`. +- `output` must match `expected_schema`. +- `confidence` must be between `0.0` and `1.0`. +- Do not add extra fields. +- Do not invent facts not present in the seed or referenced context. diff --git a/frontend/src/api/index.js b/frontend/src/api/index.js index e840e1166a..99b2cf5593 100644 --- a/frontend/src/api/index.js +++ b/frontend/src/api/index.js @@ -1,6 +1,22 @@ import axios from 'axios' import i18n from '../i18n' +const getResponseErrorMessage = (error) => { + const data = error?.response?.data + if (data?.error) return data.error + if (data?.message) return data.message + if (typeof data === 'string') return data + return error?.message || 'Error' +} + +const normalizeApiError = (error) => { + const normalized = new Error(getResponseErrorMessage(error)) + normalized.status = error?.response?.status + normalized.data = error?.response?.data + normalized.originalError = error + return normalized +} + // 创建axios实例 const service = axios.create({ baseURL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:5001', @@ -48,7 +64,7 @@ service.interceptors.response.use( console.error('Network error - please check your connection') } - return Promise.reject(error) + return Promise.reject(normalizeApiError(error)) } ) @@ -58,6 +74,10 @@ export const requestWithRetry = async (requestFn, maxRetries = 3, delay = 1000) try { return await requestFn() } catch (error) { + if (error.status >= 400 && error.status < 500) { + throw error + } + if (i === maxRetries - 1) throw error console.warn(`Request failed, retrying (${i + 1}/${maxRetries})...`) diff --git a/scripts/check_provider_boundaries.py b/scripts/check_provider_boundaries.py new file mode 100755 index 0000000000..d147e9cd05 --- /dev/null +++ b/scripts/check_provider_boundaries.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +"""Fail if business code imports direct model or Zep SDKs.""" + +from __future__ import annotations + +import ast +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +SCAN_ROOTS = [ROOT / "backend" / "app", ROOT / "backend" / "scripts"] +ALLOWED = { + ROOT / "backend" / "app" / "adapters" / "llm" / "openai_compatible.py", + ROOT / "backend" / "app" / "adapters" / "llm" / "camel_adapter.py", + ROOT / "backend" / "app" / "adapters" / "graph" / "zep.py", +} +ALLOWED_LEGACY_ADAPTER_IMPORTS = { + ROOT / "backend" / "app" / "adapters" / "llm" / "factory.py", + ROOT / "backend" / "app" / "adapters" / "graph" / "factory.py", +} +ALLOWED_GRAPHITI_SCHEMA = { + ROOT / "backend" / "app" / "adapters" / "graph" / "graphiti.py", +} +FORBIDDEN = { + "openai", + "anthropic", + "dashscope", + "qwen", + "zep_cloud", + "camel.messages", + "camel.models", +} +FORBIDDEN_STRING_PATTERNS = tuple( + pattern + for module in FORBIDDEN + for pattern in (f"import {module}", f"from {module}") +) +FORBIDDEN_LEGACY_ADAPTER_IMPORTS = { + "app.adapters.llm.openai_compatible", + "app.adapters.graph.zep", + "backend.app.adapters.llm.openai_compatible", + "backend.app.adapters.graph.zep", +} +FORBIDDEN_GRAPHITI_SCHEMA_PATTERNS = { + "MiroFishEntity", + "MiroFishEpisode", + "MiroFishAgentMemory", + "MIROFISH_FACT", + "CREATE CONSTRAINT", + "MERGE (", + "MATCH (", +} + + +def module_name(node: ast.AST) -> str | None: + if isinstance(node, ast.Import): + return None + if isinstance(node, ast.ImportFrom): + return node.module + return None + + +def imported_names(node: ast.AST) -> list[str]: + if isinstance(node, ast.Import): + return [alias.name for alias in node.names] + if isinstance(node, ast.ImportFrom): + return [node.module or ""] + return [] + + +def is_forbidden(name: str) -> bool: + return any(name == forbidden or name.startswith(f"{forbidden}.") for forbidden in FORBIDDEN) + + +def is_forbidden_legacy_adapter_import(name: str) -> bool: + return any( + name == forbidden or name.startswith(f"{forbidden}.") + for forbidden in FORBIDDEN_LEGACY_ADAPTER_IMPORTS + ) + + +def display_path(path: Path, root: Path) -> str: + try: + return str(path.relative_to(root)) + except ValueError: + return str(path) + + +def collect_violations( + scan_roots: list[Path], + *, + root: Path = ROOT, + allowed: set[Path] = ALLOWED, + allowed_legacy_adapter_imports: set[Path] = ALLOWED_LEGACY_ADAPTER_IMPORTS, + allowed_graphiti_schema: set[Path] = ALLOWED_GRAPHITI_SCHEMA, +) -> list[str]: + violations: list[str] = [] + for scan_root in scan_roots: + for path in scan_root.rglob("*.py"): + if "__pycache__" in path.parts or path in allowed: + continue + try: + tree = ast.parse(path.read_text(encoding="utf-8")) + except SyntaxError as exc: + violations.append(f"{path}: syntax error: {exc}") + continue + for node in ast.walk(tree): + if isinstance(node, (ast.Import, ast.ImportFrom)): + for name in imported_names(node): + if is_forbidden(name): + violations.append(f"{display_path(path, root)} imports forbidden SDK module {name}") + if path not in allowed_legacy_adapter_imports and is_forbidden_legacy_adapter_import(name): + violations.append( + f"{display_path(path, root)} imports legacy provider adapter directly: {name}" + ) + elif isinstance(node, ast.Constant) and isinstance(node.value, str): + for pattern in FORBIDDEN_STRING_PATTERNS: + if pattern in node.value: + violations.append( + f"{display_path(path, root)} contains forbidden SDK import string {pattern!r}" + ) + if path not in allowed_graphiti_schema: + for pattern in FORBIDDEN_GRAPHITI_SCHEMA_PATTERNS: + if pattern in node.value: + violations.append( + f"{display_path(path, root)} contains Graphiti/Neo4j schema assumption {pattern!r}" + ) + return violations + + +def main() -> int: + violations = collect_violations(SCAN_ROOTS) + if violations: + print("Provider boundary violations found:") + for violation in violations: + print(f"- {violation}") + return 1 + print("Provider boundary check passed.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/setup_agent_deps.sh b/scripts/setup_agent_deps.sh new file mode 100755 index 0000000000..897c286eb4 --- /dev/null +++ b/scripts/setup_agent_deps.sh @@ -0,0 +1,310 @@ +#!/usr/bin/env bash +set -u + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +BACKEND_DIR="$ROOT_DIR/backend" +NEO4J_MODE="existing" +START_DOCKER="prompt" +FAILURES=0 + +if [ -f "$ROOT_DIR/.env" ]; then + set -a + # shellcheck disable=SC1091 + . "$ROOT_DIR/.env" + set +a +fi + +usage() { + cat <<'EOF' +Usage: scripts/setup_agent_deps.sh [options] + +Options: + --neo4j desktop|native|docker|existing + Choose the Neo4j setup path to explain/check. Default: existing. + desktop = Neo4j Desktop-managed local database. + native = Homebrew or other local host installation. + docker = optional Docker Compose path. + existing = already-running local or remote Neo4j instance. + + --start-docker + Start the optional Docker Compose Neo4j service if Docker is available. + + --skip-services + Do not try to start Docker services. Still checks Neo4j connectivity. + + --start-services + Backward-compatible alias for --start-docker. + +Installs MiroFish agent-mode Python dependencies and checks required local +services. Graphiti is installed as a Python dependency; its source is not +vendored into this repository. Docker is optional. +EOF +} + +ok() { + printf '[ok] %s\n' "$1" +} + +warn() { + printf '[warn] %s\n' "$1" +} + +fail() { + printf '[fail] %s\n' "$1" + FAILURES=$((FAILURES + 1)) +} + +while [ "$#" -gt 0 ]; do + case "$1" in + --neo4j) + shift + if [ "$#" -eq 0 ]; then + fail "--neo4j requires one of: desktop, native, docker, existing" + usage + exit 2 + fi + case "$1" in + desktop|native|docker|existing) + NEO4J_MODE="$1" + ;; + *) + fail "unsupported --neo4j mode: $1" + usage + exit 2 + ;; + esac + ;; + --start-docker|--start-services) + START_DOCKER="yes" + ;; + --skip-services) + START_DOCKER="no" + ;; + -h|--help) + usage + exit 0 + ;; + *) + fail "unknown argument: $1" + usage + exit 2 + ;; + esac + shift +done + +install_agent_deps() { + if command -v uv >/dev/null 2>&1; then + ok "installing backend agent extras with uv" + if (cd "$BACKEND_DIR" && uv sync --extra agent --group dev); then + ok "Python agent dependencies installed" + else + fail "uv sync --extra agent --group dev failed" + fi + else + warn "uv is not installed; install dependencies manually from backend:" + warn "python -m pip install -e '.[agent]'" + fi +} + +explain_neo4j_mode() { + case "$NEO4J_MODE" in + desktop) + warn "Neo4j Desktop path selected" + warn "Create/start a Neo4j 5.26+ DBMS in Neo4j Desktop, then export NEO4J_URI/NEO4J_USER/NEO4J_PASSWORD/NEO4J_DATABASE." + ;; + native) + warn "Native Neo4j path selected" + if command -v brew >/dev/null 2>&1; then + warn "Homebrew detected. Typical install/start: brew install neo4j && brew services start neo4j" + else + warn "Homebrew not detected. Install Neo4j 5.26+ with your local package manager and start it on NEO4J_URI." + fi + ;; + docker) + warn "Optional Docker Compose Neo4j path selected" + warn "Compose file: docker-compose.agent.yml" + ;; + existing) + warn "Existing Neo4j path selected" + warn "Set NEO4J_URI/NEO4J_USER/NEO4J_PASSWORD/NEO4J_DATABASE for a reachable Neo4j 5.26+ instance." + ;; + esac +} + +check_docker_optional() { + if command -v docker >/dev/null 2>&1; then + ok "$(docker --version)" + else + warn "Docker optional, skipped" + return 1 + fi + + if docker compose version >/dev/null 2>&1; then + ok "$(docker compose version)" + else + warn "Docker Compose optional, skipped" + return 1 + fi + return 0 +} + +maybe_start_docker_neo4j() { + if [ "$NEO4J_MODE" != "docker" ]; then + return + fi + + if ! check_docker_optional; then + return + fi + + if [ "$START_DOCKER" = "prompt" ]; then + if [ -t 0 ]; then + printf 'Start optional Neo4j Docker Compose service now? [y/N] ' + read -r answer + case "$answer" in + y|Y|yes|YES) + START_DOCKER="yes" + ;; + *) + START_DOCKER="no" + ;; + esac + else + START_DOCKER="no" + warn "non-interactive shell; skipping optional Docker startup prompt" + fi + fi + + if [ "$START_DOCKER" = "yes" ]; then + if docker compose -f "$ROOT_DIR/docker-compose.agent.yml" up -d neo4j; then + ok "optional Neo4j compose service requested" + else + warn "optional Docker Compose startup failed; Neo4j connectivity check will report actual readiness" + fi + else + warn "optional Docker startup skipped" + warn "manual Docker command: docker compose -f docker-compose.agent.yml up -d neo4j" + fi +} + +run_backend_python() { + if command -v uv >/dev/null 2>&1; then + (cd "$BACKEND_DIR" && uv run python "$@") + else + python3 "$@" + fi +} + +check_neo4j() { + run_backend_python - <<'PY' +import os +import sys + +uri = os.environ.get("NEO4J_URI", "bolt://localhost:7687") +user = os.environ.get("NEO4J_USER", "neo4j") +password = os.environ.get("NEO4J_PASSWORD", "password") +database = os.environ.get("NEO4J_DATABASE", "neo4j") + +try: + from neo4j import GraphDatabase +except Exception as exc: + print(f"[fail] neo4j Python driver import failed: {exc}") + sys.exit(1) + + +def parse_version(value): + cleaned = value.split("-", 1)[0] + parts = cleaned.split(".") + major = int(parts[0]) if len(parts) > 0 and parts[0].isdigit() else 0 + minor = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else 0 + return major, minor + + +try: + driver = GraphDatabase.driver(uri, auth=(user, password)) + with driver.session(database=database) as session: + record = session.run( + "CALL dbms.components() YIELD name, versions RETURN name, versions LIMIT 1" + ).single() + driver.close() +except Exception as exc: + print(f"[fail] Neo4j connection/version check failed for {uri}: {exc}") + sys.exit(1) + +versions = record["versions"] if record else [] +version = versions[0] if versions else "unknown" +major, minor = parse_version(version) +if not (major > 5 or (major == 5 and minor >= 26)): + print(f"[fail] Neo4j version {version} is unsupported; use Neo4j 5.26+") + sys.exit(1) + +print(f"[ok] Neo4j version {version}") +PY + status=$? + if [ "$status" -ne 0 ]; then + FAILURES=$((FAILURES + 1)) + fi +} + +check_ollama_if_configured() { + provider="${MIROFISH_EMBEDDING_PROVIDER:-none}" + search_mode="${MIROFISH_GRAPH_SEARCH_MODE:-fulltext}" + if [ "$search_mode" != "semantic" ] && [ "$search_mode" != "hybrid" ]; then + warn "Ollama optional, skipped because MIROFISH_GRAPH_SEARCH_MODE=$search_mode uses no semantic embedding" + return + fi + if [ "$provider" != "ollama" ]; then + warn "Ollama optional, skipped because MIROFISH_EMBEDDING_PROVIDER=$provider; semantic retrieval may be degraded" + return + fi + + run_backend_python - <<'PY' +import json +import os +import sys +import urllib.error +import urllib.request + +base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434").rstrip("/") +model = os.environ.get("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text") +url = f"{base_url}/api/tags" + +try: + with urllib.request.urlopen(url, timeout=5) as response: + payload = json.loads(response.read().decode("utf-8")) +except (OSError, urllib.error.URLError, json.JSONDecodeError) as exc: + print(f"[fail] Ollama tags check failed for {url}: {exc}") + print(f"[hint] start Ollama and run: ollama pull {model}") + sys.exit(1) + +names = [item.get("name", "") for item in payload.get("models", [])] +found = any(name == model or name.startswith(model + ":") for name in names) +if not found: + print(f"[fail] Ollama embedding model '{model}' not found") + print(f"[hint] run: ollama pull {model}") + sys.exit(1) + +print(f"[ok] Ollama embedding model available: {model}") +PY + status=$? + if [ "$status" -ne 0 ]; then + FAILURES=$((FAILURES + 1)) + fi +} + +install_agent_deps +explain_neo4j_mode +if [ "$NEO4J_MODE" != "docker" ]; then + check_docker_optional || true +fi +maybe_start_docker_neo4j +check_neo4j +check_ollama_if_configured + +if [ "$FAILURES" -gt 0 ]; then + printf '[fail] setup completed with %s required failure(s)\n' "$FAILURES" + exit 1 +fi + +ok "agent dependency and required service checks completed" diff --git a/scripts/smoke_agent_queue_full.sh b/scripts/smoke_agent_queue_full.sh new file mode 100755 index 0000000000..f69d566f66 --- /dev/null +++ b/scripts/smoke_agent_queue_full.sh @@ -0,0 +1,67 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(cd "$(dirname "$0")/.." && pwd)" +BACKEND="$ROOT/backend" +TMPDIR="$(mktemp -d)" +RUN_DIR="$TMPDIR/chip-2036" +SEED="$TMPDIR/seed.md" +OBSERVED_TYPES="$TMPDIR/observed_request_types.txt" + +export MIROFISH_MODE=agent +export MIROFISH_LLM_PROVIDER=agent_queue +export MIROFISH_GRAPH_PROVIDER=graphiti +export MIROFISH_GRAPHITI_STORE=file +export MIROFISH_GRAPHITI_COMPAT_PATH="$TMPDIR/graphiti-store.json" +export MIROFISH_RUNS_DIR="$TMPDIR/runs" +unset LLM_API_KEY +unset OPENAI_API_KEY +unset ZEP_API_KEY + +printf '%s\n' '美国商务部限制先进AI芯片出口。' > "$SEED" + +cd "$BACKEND" +uv run mirofish-agent init --seed "$SEED" --requirement "预测未来10年全球芯片能力格局变化" --output "$RUN_DIR" --json >/tmp/mirofish_smoke_init.json + +for _ in 1 2 3 4 5 6 7 8 9 10; do + uv run mirofish-agent resume --run "$RUN_DIR" --json > /tmp/mirofish_smoke_resume.json + STATUS="$(python -c 'import json; print(json.load(open("/tmp/mirofish_smoke_resume.json"))["status"])')" + if [ "$STATUS" = "completed" ]; then + test -f "$RUN_DIR/artifacts/report.md" + test -f "$RUN_DIR/artifacts/verdict.json" + test -f "$RUN_DIR/artifacts/timeline.json" + test -f "$RUN_DIR/artifacts/graph_snapshot.json" + uv run mirofish-agent followup ask --run "$RUN_DIR" --question "先进AI芯片出口限制有什么影响?" --json > /tmp/mirofish_smoke_followup.json + FOLLOWUP_STATUS="$(python -c 'import json; print(json.load(open("/tmp/mirofish_smoke_followup.json"))["status"])')" + test "$FOLLOWUP_STATUS" = "need_agent_response" + FOLLOWUP_REQUEST_ID="$(python -c 'import json; print(json.load(open("/tmp/mirofish_smoke_followup.json"))["request_id"])')" + FOLLOWUP_RESPONSE="$(python "$ROOT/scripts/write_mock_agent_response.py" --run "$RUN_DIR" --request-id "$FOLLOWUP_REQUEST_ID")" + uv run mirofish-agent responses validate --run "$RUN_DIR" --response "$FOLLOWUP_RESPONSE" --json >/tmp/mirofish_smoke_followup_validate.json + python -c 'import json,sys; data=json.load(open("/tmp/mirofish_smoke_followup_validate.json")); sys.exit(0 if data["ok"] else 1)' + uv run mirofish-agent followup show --run "$RUN_DIR" --request-id "$FOLLOWUP_REQUEST_ID" --json >/tmp/mirofish_smoke_followup_show.json + python -c 'import json,sys; data=json.load(open("/tmp/mirofish_smoke_followup_show.json")); sys.exit(0 if data["status"] == "ok" else 1)' + test -f "$RUN_DIR/artifacts/followups/$FOLLOWUP_REQUEST_ID.md" + for expected_type in generate_ontology extract_triples generate_oasis_profiles generate_simulation_config simulate_agent_action generate_report; do + if ! grep -qx "$expected_type" "$OBSERVED_TYPES"; then + echo "missing expected agent_queue request type: $expected_type" + cat "$OBSERVED_TYPES" || true + exit 1 + fi + done + echo "CLI full agent_queue smoke passed: $RUN_DIR" + exit 0 + fi + if [ "$STATUS" != "need_agent_response" ]; then + cat /tmp/mirofish_smoke_resume.json + exit 1 + fi + REQUEST_ID="$(python -c 'import json; print(json.load(open("/tmp/mirofish_smoke_resume.json"))["request_id"])')" + REQUEST_FILE="$(python -c 'import json; print(json.load(open("/tmp/mirofish_smoke_resume.json"))["request_file"])')" + python -c 'import json,sys; print(json.load(open(sys.argv[1]))["type"])' "$REQUEST_FILE" >> "$OBSERVED_TYPES" + RESPONSE="$(python "$ROOT/scripts/write_mock_agent_response.py" --run "$RUN_DIR" --request-id "$REQUEST_ID")" + uv run mirofish-agent responses validate --run "$RUN_DIR" --response "$RESPONSE" --json >/tmp/mirofish_smoke_validate.json + python -c 'import json,sys; data=json.load(open("/tmp/mirofish_smoke_validate.json")); sys.exit(0 if data["ok"] else 1)' +done + +echo "CLI smoke did not complete within expected steps" +exit 1 diff --git a/scripts/smoke_agent_queue_staged.sh b/scripts/smoke_agent_queue_staged.sh new file mode 100755 index 0000000000..248aae239a --- /dev/null +++ b/scripts/smoke_agent_queue_staged.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(cd "$(dirname "$0")/.." && pwd)" +BACKEND="$ROOT/backend" +TMPDIR="$(mktemp -d)" +RUN_DIR="$TMPDIR/staged-chip-2036" +SEED="$TMPDIR/seed.md" +OBSERVED_TYPES="$TMPDIR/observed_request_types.txt" + +export MIROFISH_MODE=agent +export MIROFISH_LLM_PROVIDER=agent_queue +export MIROFISH_GRAPH_PROVIDER=graphiti +export MIROFISH_GRAPHITI_STORE=file +export MIROFISH_GRAPHITI_COMPAT_PATH="$TMPDIR/graphiti-store.json" +export MIROFISH_RUNS_DIR="$TMPDIR/runs" +unset LLM_API_KEY +unset OPENAI_API_KEY +unset ZEP_API_KEY + +printf '%s\n' '美国商务部限制先进AI芯片出口。' > "$SEED" + +cd "$BACKEND" +uv run mirofish-agent create-run \ + --seed "$SEED" \ + --requirement "预测未来10年全球芯片能力格局变化" \ + --output "$RUN_DIR" \ + --mode staged \ + --rounds 10 \ + --round-unit year \ + --json >/tmp/mirofish_staged_init.json + +for _ in $(seq 1 80); do + uv run mirofish-agent resume --run "$RUN_DIR" --json > /tmp/mirofish_staged_resume.json + STATUS="$(python -c 'import json; print(json.load(open("/tmp/mirofish_staged_resume.json"))["status"])')" + if [ "$STATUS" = "completed" ]; then + test -f "$RUN_DIR/artifacts/report.md" + test -f "$RUN_DIR/artifacts/verdict.json" + test -f "$RUN_DIR/artifacts/timeline.json" + test -f "$RUN_DIR/artifacts/graph_snapshot.json" + python - "$RUN_DIR" <<'PY' +import json, sys +run = sys.argv[1] +verdict = json.load(open(f"{run}/artifacts/verdict.json")) +timeline = json.load(open(f"{run}/artifacts/timeline.json")) +assert verdict["rounds"] == 10, verdict +assert verdict["simulation_settings"]["round_unit"] == "year", verdict +assert len(timeline) == 10, timeline +PY + for expected_type in generate_ontology extract_triples generate_oasis_profiles generate_simulation_config simulate_agent_action generate_report; do + if ! grep -qx "$expected_type" "$OBSERVED_TYPES"; then + echo "missing expected staged request type: $expected_type" + cat "$OBSERVED_TYPES" || true + exit 1 + fi + done + echo "CLI staged agent_queue smoke passed: $RUN_DIR" + exit 0 + fi + if [ "$STATUS" = "awaiting_user_confirmation" ]; then + uv run mirofish-agent stage approve --run "$RUN_DIR" --json >/tmp/mirofish_staged_approve.json + continue + fi + if [ "$STATUS" != "need_agent_response" ]; then + cat /tmp/mirofish_staged_resume.json + exit 1 + fi + REQUEST_ID="$(python -c 'import json; print(json.load(open("/tmp/mirofish_staged_resume.json"))["request_id"])')" + REQUEST_FILE="$(python -c 'import json; print(json.load(open("/tmp/mirofish_staged_resume.json"))["request_file"])')" + python -c 'import json,sys; print(json.load(open(sys.argv[1]))["type"])' "$REQUEST_FILE" >> "$OBSERVED_TYPES" + RESPONSE="$(python "$ROOT/scripts/write_mock_agent_response.py" --run "$RUN_DIR" --request-id "$REQUEST_ID")" + uv run mirofish-agent responses validate --run "$RUN_DIR" --response "$RESPONSE" --json >/tmp/mirofish_staged_validate.json + python -c 'import json,sys; data=json.load(open("/tmp/mirofish_staged_validate.json")); sys.exit(0 if data["ok"] else 1)' +done + +echo "CLI staged smoke did not complete within expected steps" +exit 1 diff --git a/scripts/smoke_mcp_full.py b/scripts/smoke_mcp_full.py new file mode 100755 index 0000000000..7a179683eb --- /dev/null +++ b/scripts/smoke_mcp_full.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +"""MCP lifecycle smoke. + +This verifies the FastMCP server can be constructed and then exercises the +same lifecycle service used by MCP tools. If the MCP SDK is missing, the script +fails with a clear dependency blocker. +""" + +from __future__ import annotations + +import json +import os +import tempfile +import asyncio +from pathlib import Path + +from write_mock_agent_response import output_for + + +async def call_tool(server, name: str, arguments: dict) -> dict: + result = await server.call_tool(name, arguments) + if isinstance(result, tuple) and len(result) > 1 and isinstance(result[1], dict): + return result[1]["result"] + if isinstance(result, dict): + return result.get("result", result) + raise RuntimeError(f"Unexpected MCP tool result for {name}: {result!r}") + + +async def async_main() -> int: + try: + import mcp # noqa: F401 + except ImportError as exc: + print("BLOCKER: MCP Python SDK package 'mcp' is not installed.") + print(f"Import error: {exc}") + return 2 + + from app.mcp_server.server import create_server + + server = create_server() + if server is None: + print("BLOCKER: create_server returned None") + return 2 + + tmp = Path(tempfile.mkdtemp()) + os.environ["MIROFISH_MODE"] = "agent" + os.environ["MIROFISH_LLM_PROVIDER"] = "agent_queue" + os.environ["MIROFISH_GRAPH_PROVIDER"] = "graphiti" + os.environ["MIROFISH_GRAPHITI_STORE"] = "file" + os.environ["MIROFISH_GRAPHITI_COMPAT_PATH"] = str(tmp / "graphiti-store.json") + os.environ.pop("LLM_API_KEY", None) + os.environ.pop("OPENAI_API_KEY", None) + os.environ.pop("ZEP_API_KEY", None) + + seed = tmp / "seed.md" + seed.write_text("美国商务部限制先进AI芯片出口。", encoding="utf-8") + run_dir = tmp / "mcp-run" + tools = await server.list_tools() + tool_names = {tool.name for tool in tools} + required = { + "mirofish_create_run", + "mirofish_run", + "mirofish_resume_run", + "mirofish_get_status", + "mirofish_get_current_stage", + "mirofish_update_simulation_settings", + "mirofish_approve_stage", + "mirofish_reject_stage", + "mirofish_rerun_stage", + "mirofish_list_requests", + "mirofish_get_request", + "mirofish_submit_response", + "mirofish_validate_response", + "mirofish_build_graph", + "mirofish_search_graph", + "mirofish_export_graph", + "mirofish_start_simulation", + "mirofish_resume_simulation", + "mirofish_generate_report", + "mirofish_get_report", + "mirofish_ask_followup_question", + "mirofish_get_followup_answer", + "mirofish_list_artifacts", + "mirofish_doctor", + } + missing = sorted(required - tool_names) + if missing: + print(f"BLOCKER: MCP tools missing: {missing}") + return 2 + create_tool = next(tool for tool in tools if tool.name == "mirofish_create_run") + create_schema = getattr(create_tool, "inputSchema", {}) or {} + create_props = create_schema.get("properties", {}) + if "rounds" not in create_props or "mode" not in create_props: + print(f"BLOCKER: mirofish_create_run schema does not expose rounds/mode: {create_schema}") + return 2 + + staged_dir = tmp / "mcp-staged-run" + staged = await call_tool( + server, + "mirofish_create_run", + { + "seed": str(seed), + "requirement": "预测未来10年全球芯片能力格局变化", + "output": str(staged_dir), + "mode": "staged", + "rounds": 10, + "round_unit": "year", + }, + ) + assert staged["status"] == "created", staged + assert staged["state"]["workflow_mode"] == "staged", staged + assert staged["state"]["simulation_settings"]["rounds"] == 10, staged + current_stage = await call_tool(server, "mirofish_get_current_stage", {"run": str(staged_dir)}) + assert current_stage["stage"]["current_stage"] == "seed_input", current_stage + approved_stage = await call_tool(server, "mirofish_approve_stage", {"run": str(staged_dir)}) + assert approved_stage["next_stage"] == "prediction_requirement", approved_stage + + created = await call_tool( + server, + "mirofish_create_run", + {"seed": str(seed), "requirement": "预测未来10年全球芯片能力格局变化", "output": str(run_dir), "rounds": 10}, + ) + assert created["status"] == "created" + + result = await call_tool(server, "mirofish_run", {"run": str(run_dir)}) + for _ in range(10): + if result["status"] == "completed": + status = await call_tool(server, "mirofish_get_status", {"run": str(run_dir)}) + assert status["status"] == "ok", status + report = await call_tool(server, "mirofish_get_report", {"run": str(run_dir)}) + assert report["status"] == "ok" + search = await call_tool( + server, + "mirofish_search_graph", + {"run": str(run_dir), "query": "先进AI芯片出口", "limit": 5}, + ) + assert search["status"] == "ok", search + exported = await call_tool(server, "mirofish_export_graph", {"run": str(run_dir), "output": None}) + assert exported["status"] == "ok", exported + artifacts = await call_tool(server, "mirofish_list_artifacts", {"run": str(run_dir)}) + artifact_names = {artifact["name"] for artifact in artifacts["artifacts"]} + assert {"report.md", "verdict.json", "timeline.json", "graph_snapshot.json"}.issubset(artifact_names) + doctor = await call_tool(server, "mirofish_doctor", {"runs_dir": str(tmp / "doctor-runs")}) + assert doctor["status"] == "ok", doctor + followup = await call_tool( + server, + "mirofish_ask_followup_question", + {"run": str(run_dir), "question": "先进AI芯片出口限制有什么影响?", "limit": 5}, + ) + assert followup["status"] == "need_agent_response", followup + followup_request = await call_tool( + server, + "mirofish_get_request", + {"run": str(run_dir), "request_id": followup["request_id"]}, + ) + request = followup_request["request"] + response_path = run_dir / "responses" / f"{request['request_id']}.json" + response_path.write_text( + json.dumps( + {"request_id": request["request_id"], "status": "ok", "output": output_for(request)}, + ensure_ascii=False, + ), + encoding="utf-8", + ) + submitted = await call_tool( + server, + "mirofish_submit_response", + {"run": str(run_dir), "response": str(response_path)}, + ) + assert submitted["ok"], submitted + answer = await call_tool( + server, + "mirofish_get_followup_answer", + {"run": str(run_dir), "request_id": request["request_id"]}, + ) + assert answer["status"] == "ok", answer + print(f"MCP lifecycle smoke passed: {run_dir}") + return 0 + assert result["status"] == "need_agent_response", result + listed = await call_tool(server, "mirofish_list_requests", {"run": str(run_dir)}) + assert any(item["request_id"] == result["request_id"] for item in listed["requests"]) + request_result = await call_tool( + server, + "mirofish_get_request", + {"run": str(run_dir), "request_id": result["request_id"]}, + ) + request = request_result["request"] + request_id = request["request_id"] + response_path = run_dir / "responses" / f"{request_id}.json" + response_path.write_text( + json.dumps({"request_id": request_id, "status": "ok", "output": output_for(request)}, ensure_ascii=False), + encoding="utf-8", + ) + validation = await call_tool( + server, + "mirofish_validate_response", + {"run": str(run_dir), "response": str(response_path)}, + ) + assert validation["ok"], validation + submitted = await call_tool( + server, + "mirofish_submit_response", + {"run": str(run_dir), "response": str(response_path)}, + ) + assert submitted["ok"], submitted + result = await call_tool(server, "mirofish_resume_run", {"run": str(run_dir)}) + + print("MCP lifecycle smoke did not complete within expected steps") + return 1 + + +def main() -> int: + return asyncio.run(async_main()) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/smoke_mcp_full.sh b/scripts/smoke_mcp_full.sh new file mode 100755 index 0000000000..980793a274 --- /dev/null +++ b/scripts/smoke_mcp_full.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(cd "$(dirname "$0")/.." && pwd)" +BACKEND="$ROOT/backend" + +cd "$BACKEND" +uv run python "$ROOT/scripts/smoke_mcp_full.py" diff --git a/scripts/write_mock_agent_response.py b/scripts/write_mock_agent_response.py new file mode 100755 index 0000000000..c4fc27191b --- /dev/null +++ b/scripts/write_mock_agent_response.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +"""Write a deterministic agent response for the latest or selected request.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def load_request(run_dir: Path, request_id: str | None) -> dict: + requests = sorted((run_dir / "requests").glob("req_*.json")) + if request_id: + path = run_dir / "requests" / f"{request_id}.json" + else: + unanswered = [path for path in requests if not (run_dir / "responses" / path.name).exists()] + path = unanswered[-1] if unanswered else requests[-1] + return json.loads(path.read_text(encoding="utf-8")) + + +def output_for(request: dict) -> dict: + task_type = request["type"] + if task_type == "generate_ontology": + return {"ontology": {"entity_types": [{"name": "Organization"}], "edge_types": [{"name": "AFFECTS"}]}} + if task_type == "extract_triples": + return { + "triples": [ + { + "subject": "美国商务部", + "predicate": "限制", + "object": "先进AI芯片出口", + "fact": "美国商务部限制先进AI芯片出口。", + "valid_at": "2024-01-01", + "invalid_at": None, + "source": "smoke", + "source_file": "seed.md", + "evidence": "美国商务部限制先进AI芯片出口。", + "confidence": 0.82, + "metadata": {}, + } + ] + } + if task_type == "generate_oasis_profiles": + return {"profiles": [{"agent_id": "agent_1", "name": "芯片分析员", "persona": "关注芯片供应链变化。"}]} + if task_type == "generate_simulation_config": + return {"config": {"rounds": 1, "platforms": ["agent_queue"], "agents": ["agent_1"]}} + if task_type == "simulate_agent_action": + actions = [] + for item in request.get("structured_input", {}).get("actions", []): + actions.append( + { + "agent_id": str(item.get("agent_id")), + "action_id": str(item.get("action_id")), + "action_type": "CREATE_POST", + "content": "先进AI芯片出口限制会推动供应链分化。", + } + ) + return {"actions": actions} + if task_type == "summarize_round": + return { + "summary_markdown": "Mock round summary generated without model APIs.", + "key_events": [], + "memory_updates": [], + } + if task_type == "update_memory": + return { + "memory": request.get("structured_input", {}).get("memory", {}), + "events": request.get("structured_input", {}).get("events", []), + } + if task_type == "generate_report": + return { + "report_markdown": "# MiroFish Agent Smoke Report\n\n先进AI芯片出口限制可能推动供应链分化。", + "verdict": {"status": "ok", "confidence": 0.7}, + "timeline": [{"valid_at": "2024-01-01", "fact": "美国商务部限制先进AI芯片出口。"}], + } + if task_type == "answer_followup_question": + question = request.get("structured_input", {}).get("question", "") + graph_results = request.get("structured_input", {}).get("graph_results", []) + return { + "answer_markdown": f"Mock follow-up answer for: {question}", + "used_graph_results": graph_results[:3], + "confidence": 0.6, + } + if task_type == "validate_json_output": + return { + "valid": True, + "errors": [], + "output": request.get("structured_input", {}).get("candidate", {}), + } + if task_type == "repair_invalid_json": + return request.get("structured_input", {}).get("invalid_response", {}).get("output", {}) + return {"result": {}} + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--run", required=True) + parser.add_argument("--request-id", default=None) + args = parser.parse_args() + + run_dir = Path(args.run) + request = load_request(run_dir, args.request_id) + response = {"request_id": request["request_id"], "status": "ok", "output": output_for(request)} + response_path = run_dir / "responses" / f"{request['request_id']}.json" + response_path.parent.mkdir(parents=True, exist_ok=True) + response_path.write_text(json.dumps(response, ensure_ascii=False, indent=2), encoding="utf-8") + print(response_path) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())