fedacq‑rag‑chatbot

Federal Acquisition Regulation Retrieval‑Augmented Generation (RAG) Chatbot

A production‑ready Retrieval‑Augmented Generation (RAG) system that provides fast, accurate, citation‑backed answers to questions about the Federal Acquisition Regulation (FAR) and Defense Federal Acquisition Regulation Supplement (DFARS). Designed for federal contractors, acquisition professionals, and businesses navigating the federal market.

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Background

Federal contracting regulations are complex, distributed across thousands of pages of FAR/DFARS text, and updated frequently. Professionals need fast, reliable, context‑aware answers to support:

• Capture strategy
• Proposal development
• Compliance reviews
• Contract administration
• Market entry decisions

This project automates that research using a modern RAG pipeline.

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User Story

As a federal contractor, federal employee, or business entering the federal market, I need quick, accurate answers to questions about the regulatory landscape so that I can make informed business strategy decisions.

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Acceptance Criteria

• Natural‑language question interface
• Retrieval of relevant FAR/DFARS sections
• Accurate, citation‑backed responses
• Up‑to‑date regulatory text
• Reproducible end‑to‑end pipeline
• Deployable locally or via Docker

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Technical Approach

Data Source

• FAR and DFARS pulled from official .dita XML repositories
• Parsed into structured documents
• Metadata normalized for retrieval

Embeddings + Vector Store

• HuggingFace Embeddings (BGE‑small)
• ChromaDB persistent vector store
• Chunking via LlamaIndex SentenceSplitter

Retrieval‑Augmented Generation

• LlamaIndex orchestration
• Custom ONNX Runtime GenAI LLM (Phi‑4‑mini‑instruct‑onnx)
• ChromaDB for retrieval
• BGE-small embeddings
• Query engine configured with top‑k similarity search

Why ONNX Runtime GenAI?

• Fast inference on CPU (no GPU required)
• No PyTorch dependency
• No external API calls (fully local)
• Smaller memory footprint
• Production-ready kernels optimized by Microsoft
• Works well with smaller models like Phi-4-mini-instruct-onnx

Application Layer

• Flask application (src.app)
• Served as an ASGI app via Hypercorn
• /chat_stream endpoint with token streaming (Server‑Sent Events)
• Lightweight HTML/JS/CSS UI served from src/app/static/

Deployment

• Local Python environment
• Docker container (Hypercorn ASGI server)
• GitHub Actions CI pipeline (optional)

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Architecture Overview

User → Flask (ASGI via Hypercorn) → Query Engine → LlamaIndex → ChromaDB → FAR/DFARS DITA Source

Pipeline:

1. Clone FAR/DFARS repos
2. Parse .dita XML
3. Chunk + embed
4. Store in ChromaDB
5. Serve via Flask API (ASGI)
6. LLM generates answers with citations

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Repository Structure

fedacq-rag-chatbot/
│
├── src/
│   ├── app/
│   │   ├── __init__.py
│   │   ├── api.py
│   │   ├── config.py
│   │   ├── asgi.py
│   │   └── static/
│   │       ├── index.html
│   │       ├── app.js
│   │       └── styles.css
│   │
│   ├── rag/
│   │   ├── __init__.py
│   │   │
│   │   ├── indexing/
│   │   │   ├── __init__.py
│   │   │   ├── builder.py
│   │   │   └── loader.py
│   │   │
│   │   ├── llm/
│   │   │   ├── __init__.py
│   │   │   └── models.py
│   │   │
│   │   └── retrieval/
│   │       ├── __init__.py
│   │       ├── metadata.py
│   │       ├── parser_dita.py
│   │       └── query_engine.py
│   │
│   └── scripts/
│       └── build_index.py
│
├── data/
│   ├── chroma/      # Persistent ChromaDB index (Git LFS)
│   └── regs/        # FAR/DFARS cloned repositories (Git LFS)
│
├── tests/
│   ├── test_indexing.py
│   ├── test_llm.py
│   ├── test_metadata.py
│   ├── test_parser.py
│   └── test_query_engine.py
│
├── docker/
│   ├── docker-compose.yml
│   └── local.env
│
├── .dockerignore
├── .gitattributes
├── .gitignore
├── Dockerfile
├── Makefile
├── pyproject.toml
├── pytest.ini
├── requirements.txt
└── requirements.lock

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Setup & Installation

1. Clone the repository

git clone https://github.com/PWDevens/fedacq-rag-chatbot.git
cd fedacq-rag-chatbot

2. Install Git LFS (required for Chroma index)

git lfs install
git lfs pull

3. Create & activate environment

python -m venv .venv
source .venv/bin/activate

python -m venv .venv
.\.venv\Scripts\Activate.ps1

4. Install project + dependencies

pip install -e .
pip install -r requirements.txt

huggingface-cli download microsoft/Phi-4-mini-instruct-onnx \
  --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* \
  --local-dir .

This creates cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/ with the ONNX model files.

5. Verify the prebuilt RAG index

Ensure these folders contain data:

data/chroma/
data/regs/

If empty:

git lfs pull

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Building the RAG Index (Local Only)

The RAG index is not built in CI due to the size of FAR/DFARS and the cost of embedding.

To rebuild locally:

First, edit .gitignore and comment out 'data/chroma/chroma.sqlite3'; then,

python -m scripts.build_index

This will:

• Clone FAR + DFARS into data/regs/
• Parse .dita XML files
• Chunk and embed text
• Write a new ChromaDB index into data/chroma/

After rebuilding, commit the updated index using Git LFS:

git add data/chroma 
git commit -m "Rebuild RAG index"
git push

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Running the Application

Flask Development Server (local)

python -m flask --app src.app run --host=0.0.0.0 --port=7860

Hypercorn (ASGI, local production-like)

hypercorn --bind 0.0.0.0:7860 src.app.asgi:app

Docker

docker build -t fedacq-rag-chatbot .
docker run -d -p 7860:7860 --name ragbot fedacq-rag-chatbot

The container uses Hypercorn to serve the ASGI‑wrapped Flask app.

Testing

pytest -q

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CI/CD Workflow (GitHub Actions)

The CI pipeline performs:

• LFS checkout (downloads prebuilt index)
• Python environment setup
• Dependency installation
• Test execution
• Docker image build
• Docker artifact upload

The CI pipeline does not rebuild the RAG index.
Index building is performed locally and versioned via Git LFS.

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Usage Example

Send a POST request:

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What does FAR 15.404 say about price analysis?"}'

Expected response:

• Summary of the regulation
• Citations
• Retrieved sections
• LLM‑generated explanation