diff --git a/content/develop/ai/_index.md b/content/develop/ai/_index.md index 3b8ed3444d..2c3bb135a7 100644 --- a/content/develop/ai/_index.md +++ b/content/develop/ai/_index.md @@ -14,22 +14,20 @@ hideListLinks: true Redis stores and indexes vector embeddings that semantically represent unstructured data including text passages, images, videos, or audio. Store vectors and the associated metadata within [hashes]({{< relref "/develop/data-types/hashes" >}}) or [JSON]({{< relref "/develop/data-types/json" >}}) documents for [indexing]({{< relref "/develop/ai/search-and-query/indexing" >}}) and [querying]({{< relref "/develop/ai/search-and-query/query" >}}).
- {{< image-card image="images/ai-lib.svg" alt="AI Redis icon" title="Redis vector Python client library documentation" url="/develop/ai/redisvl/" >}} - {{< image-card image="images/ai-cube.svg" alt="AI Redis icon" title="Use Redis Search to search data" url="/develop/ai/search-and-query/" >}} - {{< image-card image="images/ai-brain.svg" alt="AI Redis icon" title="Give AI agents the context engine they need with Redis Iris." url="/develop/ai/context-engine/" >}} + {{< tile-card color="bg-redis-yellow-500" title="RedisVL" description="Redis vector Python client library documentation" url="/develop/ai/redisvl/" >}} + {{< tile-card color="bg-blue-300" title="Search & Query" description="Use Redis Search to search data" url="/develop/ai/search-and-query/" >}} + {{< tile-card color="bg-violet-300" title="Context Engine" description="Give AI agents the context engine they need with Redis Iris" url="/develop/ai/context-engine/" >}}
## What is Redis for AI and search? -Redis is an in-memory data platform purpose-built for the speed and structure that AI applications demand. It stores and indexes vector embeddings alongside structured metadata, enabling semantic search, real-time retrieval, and agent memory at millisecond latency — at any scale. +Redis is an in-memory data platform purpose-built for the speed and structure that AI applications demand. It stores and indexes vector embeddings alongside structured metadata, enabling semantic search, real-time retrieval, and agent memory at millisecond latency, at any scale. - +- **Vector search**: Store and query vector embeddings using KNN and range queries with metadata filters, across hashes and JSON documents +- **Semantic caching**: Reduce LLM API costs by reusing cached responses for semantically similar prompts with LangCache +- **Agent memory**: Give agents short-term session memory and long-term persistent memory that survives across interactions +- **Structured data access**: Turn your business data into governed tools agents can reliably query with Context Retriever +- **Live data sync**: Stream changes from relational databases into Redis in near real time so agents always work with current information ## Why use Redis for AI and search? @@ -47,7 +45,7 @@ Redis is an in-memory data platform purpose-built for the speed and structure th

For developers

@@ -62,18 +60,18 @@ Use [Redis Feature Form]({{< relref "/develop/ai/featureform/" >}}) to define, m AI agents are autonomous systems that combine LLMs with memory, tools, and planning to accomplish complex, multi-step tasks. Redis powers the core capabilities agents need: fast vector search, persistent memory, real-time data streaming, and structured access to business data. -- [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) — Use the interactive code generator to create a working agent in your preferred language with your choice of LLM. -- [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Learn the agent processing cycle, memory architecture, and why Redis is the foundation for production agents. -- [Context Engine]({{< relref "/develop/ai/context-engine" >}}) — The managed service suite that gives agents what they need: semantic caching, persistent memory, structured data access, and live data integration. +- [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}): Use the interactive code generator to create a working agent in your preferred language with your choice of LLM. +- [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}): Learn the agent processing cycle, memory architecture, and why Redis is the foundation for production agents. +- [Context Engine]({{< relref "/develop/ai/context-engine" >}}): The managed service suite that gives agents what they need: semantic caching, persistent memory, structured data access, and live data integration. ### Context Engine services -The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) includes four fully-managed services available on Redis Cloud: +The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) includes four services, available fully managed on Redis Cloud or self-managed on your own infrastructure: -- [LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Semantic caching that reduces LLM API costs and improves response times by reusing cached responses for similar queries. -- [Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Two-tier persistent memory (session and long-term) for agents, available through Python and TypeScript SDKs and a REST API. -- [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Turns your business data into structured, governed tools that agents can reliably use, defined once and reused across all agents. -- [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keeps your Redis Cloud database in sync with relational databases in near real time using Change Data Capture. +- [LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}): Semantic caching that reduces LLM API costs and improves response times by reusing cached responses for similar queries. +- [Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}): Two-tier persistent memory (session and long-term) for agents, available through Python and TypeScript SDKs and a REST API. +- [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}): Turns your business data into structured, governed tools that agents can reliably use, defined once and reused across all agents. +- [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}): Keeps your Redis Cloud database in sync with relational databases in near real time using Change Data Capture. ## How to's @@ -99,11 +97,11 @@ The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) i Learn to perform vector search, build AI agents, and use semantic caching and memory in your AI/ML projects.
- {{< image-card image="images/ai-search.svg" alt="AI Redis icon" title="Vector search guide" url="/develop/ai/search-and-query/query/vector-search" >}} - {{< image-card image="images/ai-brain.svg" alt="AI agent icon" title="How AI agents work with Redis" url="/develop/ai/agent-builder/agent-concepts" >}} - {{< image-card image="images/ai-LLM-memory.svg" alt="LLM memory icon" title="Store memory for LLMs" url="https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/" >}} - {{< image-card image="images/ai-brain-2.svg" alt="AI Redis icon" title="Semantic caching for faster, smarter LLM apps" url="https://redis.io/blog/what-is-semantic-caching" >}} - {{< image-card image="images/ai-model.svg" alt="AI Redis icon" title="Deploy an enhanced gateway with Redis" url="https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/" >}} + {{< tile-card color="bg-blue-300" title="Vector Search" description="Vector search guide" url="/develop/ai/search-and-query/query/vector-search" >}} + {{< tile-card color="bg-violet-300" title="AI Agents" description="How AI agents work with Redis" url="/develop/ai/agent-builder/agent-concepts" >}} + {{< tile-card color="bg-teal-300" title="LLM Memory" description="Store memory for LLMs" url="https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/" >}} + {{< tile-card color="bg-rose-300" title="Caching" description="Semantic caching for faster, smarter LLM apps" url="https://redis.io/blog/what-is-semantic-caching" >}} + {{< tile-card color="bg-redis-red-500" title="AI Gateways" description="Deploy an enhanced gateway with Redis" url="https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/" >}}
## Quickstarts @@ -132,17 +130,17 @@ Additional resources: #### Agents AI agents can act autonomously to plan and execute tasks for the user. -* [Build your first AI agent]({{< relref "/develop/ai/agent-builder" >}}) — Use the interactive agent builder to generate production-ready agent code. -* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Learn the agent processing cycle, memory architecture, and Redis data structures for agents. -* [Redis Notebooks for LangGraph](https://github.com/redis-developer/langgraph-redis/tree/main/examples) — End-to-end agent examples using LangGraph and Redis. +* [Build your first AI agent]({{< relref "/develop/ai/agent-builder" >}}): Use the interactive agent builder to generate production-ready agent code. +* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}): Learn the agent processing cycle, memory architecture, and Redis data structures for agents. +* [Redis Notebooks for LangGraph](https://github.com/redis-developer/langgraph-redis/tree/main/examples): End-to-end agent examples using LangGraph and Redis. #### Context Engine The Context Engine provides managed services for agent memory and data access. -* [Get started with LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Add semantic caching to reduce LLM costs in minutes. -* [Get started with Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Add persistent two-tier memory to any agent using the REST API. -* [Get started with Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Expose your business data as governed tools that agents can reliably query. -* [Get started with Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keep Redis in sync with your primary database so agents always have fresh data. +* [Get started with LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}): Add semantic caching to reduce LLM costs in minutes. +* [Get started with Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}): Add persistent two-tier memory to any agent using the REST API. +* [Get started with Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}): Expose your business data as governed tools that agents can reliably query. +* [Get started with Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}): Keep Redis in sync with your primary database so agents always have fresh data. ## Tutorials Need a deeper-dive through different use cases and topics? @@ -201,19 +199,19 @@ See how we stack up against the competition. See how leaders in the industry are building their AI apps. #### Agents and architecture -* [AI Agent vs Chatbot: Key Differences Explained](https://redis.io/en/blog/ai-agent-vs-chatbot/) — Understand the architectural differences between chatbots and agents and when to use each based on task complexity, cost, and latency. -* [Agentic AI Architecture: 5 Patterns Explained](https://redis.io/en/blog/agentic-ai-architecture-examples/) — Learn five production agentic patterns and the data layer requirements needed to support them. -* [AI Agents vs Workflows: When to Use Each](https://redis.io/en/blog/agents-vs-workflows/) — Understand the distinction between deterministic workflows and autonomous agents and how to combine them in production. -* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Agent memory patterns, data structure selection, and production deployment considerations. +* [AI Agent vs Chatbot: Key Differences Explained](https://redis.io/en/blog/ai-agent-vs-chatbot/): Understand the architectural differences between chatbots and agents and when to use each based on task complexity, cost, and latency. +* [Agentic AI Architecture: 5 Patterns Explained](https://redis.io/en/blog/agentic-ai-architecture-examples/): Learn five production agentic patterns and the data layer requirements needed to support them. +* [AI Agents vs Workflows: When to Use Each](https://redis.io/en/blog/agents-vs-workflows/): Understand the distinction between deterministic workflows and autonomous agents and how to combine them in production. +* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}): Agent memory patterns, data structure selection, and production deployment considerations. #### Memory and context -* [Context Engineering for AI: What It Is & How to Build It](https://redis.io/en/blog/context-engineering-ai/) — Learn the discipline of designing what an LLM receives at inference time, including the four core operations and how Redis provides the infrastructure. -* [Long-Term Memory Architectures for AI Agents](https://redis.io/en/blog/long-term-memory-architectures-ai-agents/) — Design persistent memory systems that retain information across sessions, with guidance on memory types and design tradeoffs. -* [Context Pruning: Cut LLM Tokens Without Losing Quality](https://redis.io/en/blog/context-pruning-llm-tokens/) — Selectively remove low-value tokens from LLM input to reduce costs and improve quality, with benchmarks and failure modes. +* [Context Engineering for AI: What It Is & How to Build It](https://redis.io/en/blog/context-engineering-ai/): Learn the discipline of designing what an LLM receives at inference time, including the four core operations and how Redis provides the infrastructure. +* [Long-Term Memory Architectures for AI Agents](https://redis.io/en/blog/long-term-memory-architectures-ai-agents/): Design persistent memory systems that retain information across sessions, with guidance on memory types and design tradeoffs. +* [Context Pruning: Cut LLM Tokens Without Losing Quality](https://redis.io/en/blog/context-pruning-llm-tokens/): Selectively remove low-value tokens from LLM input to reduce costs and improve quality, with benchmarks and failure modes. #### Performance -* [What is semantic caching](https://redis.io/blog/what-is-semantic-caching) — When and how to apply semantic caching in your AI applications. -* [Streaming LLM Responses: Make Your AI App Feel Fast](https://redis.io/en/blog/streaming-llm-responses/) — Deliver tokens incrementally via Server-Sent Events and combine streaming with caching and context optimization in production. +* [What is semantic caching](https://redis.io/blog/what-is-semantic-caching): When and how to apply semantic caching in your AI applications. +* [Streaming LLM Responses: Make Your AI App Feel Fast](https://redis.io/en/blog/streaming-llm-responses/): Deliver tokens incrementally via Server-Sent Events and combine streaming with caching and context optimization in production. #### RAG * [Get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/) diff --git a/content/develop/ai/agent-builder/agent-concepts.md b/content/develop/ai/agent-builder/agent-concepts.md index 3c03081c8b..d5a7947a10 100644 --- a/content/develop/ai/agent-builder/agent-concepts.md +++ b/content/develop/ai/agent-builder/agent-concepts.md @@ -112,6 +112,30 @@ Create intelligent recommendation systems that: [Build a recommendation agent →](../) +
+

Knowledge assistants (RAG)

+ +Build retrieval-augmented generation agents that: +- Ingest documents and answer questions with citations +- Combine vector search with semantic caching for fast, grounded responses +- Reduce hallucinations by retrieving relevant context before generating answers +- Scale to large document collections + +[Build a knowledge assistant →](../) +
+ +
+

Redis Iris conversational assistants

+ +Build conversational agents backed by managed Redis Iris Agent Memory that: +- Get session and long-term memory without building a vector index +- Persist user preferences and context across conversations +- Extract durable memories automatically in the background +- Run on Redis Cloud as a fully managed service + +[Build a Redis Iris agent →](../) +
+

Task automation agents

diff --git a/content/develop/ai/context-engine/_index.md b/content/develop/ai/context-engine/_index.md index d43396f728..f00075d8a4 100644 --- a/content/develop/ai/context-engine/_index.md +++ b/content/develop/ai/context-engine/_index.md @@ -4,35 +4,34 @@ categories: - docs - develop - ai -description: Redis Iris is a suite of fully-managed services. +description: Redis Iris is a suite of managed and self-managed services for agent memory, semantic caching, and governed data access. hideListLinks: true linktitle: Redis Iris context engine title: Redis Iris context engine weight: 30 +bannerText: LangCache, Agent Memory, and Context Retriever are currently available in preview. Features and behavior are subject to change. --- Give your AI agents the context layer they need to reliably act on business data. -Redis Iris eliminates the infrastructure burden of building context-aware AI agents — persistent memory, semantic caching, governed data access, and live data sync, all on Redis Cloud. +Redis Iris eliminates the infrastructure burden of building context-aware AI agents: persistent memory, semantic caching, governed data access, and live data sync, fully managed on Redis Cloud or self-managed on your own infrastructure.
- {{< image-card image="images/ai-model.svg" alt="Concepts icon" title="Concepts — What happens when an agent asks Redis Iris for context" url="/develop/ai/context-engine/concepts" >}} - {{< image-card image="images/ai-brain.svg" alt="Agent Memory icon" title="Agent Memory — Persistent short-term and long-term memory across agent interactions" url="/develop/ai/context-engine/agent-memory" >}} - {{< image-card image="images/ai-LLM-memory.svg" alt="LangCache icon" title="LangCache — Semantic caching to reduce LLM costs and improve response times" url="/develop/ai/context-engine/langcache" >}} - {{< image-card image="images/ai-cube.svg" alt="Context Retriever icon" title="Context Retriever — Governed, schema-first data access tools for agents" url="/develop/ai/context-engine/context-retriever" >}} + {{< tile-card color="bg-blue-300" title="Concepts" description="What happens when an agent asks Redis Iris for context" url="/develop/ai/context-engine/concepts" >}} + {{< tile-card color="bg-violet-300" title="Agent Memory" description="Persistent short-term and long-term memory across agent interactions" url="/develop/ai/context-engine/agent-memory" >}} + {{< tile-card color="bg-teal-300" title="LangCache" description="Semantic caching to reduce LLM costs and improve response times" url="/develop/ai/context-engine/langcache" >}} + {{< tile-card color="bg-rose-300" title="Context Retriever" description="Governed, schema-first data access tools for agents" url="/develop/ai/context-engine/context-retriever" >}}
## What is Redis Iris? Redis Iris is a production-ready context engine for AI agents that: - +- **Reduces LLM costs**: Semantic caching returns cached responses for similar queries in milliseconds +- **Adds persistent memory**: Agents remember past interactions and user preferences across sessions +- **Structures business data access**: Context Retriever generates governed tools agents can safely call at runtime +- **Keeps data fresh**: Data Integration streams live changes from relational databases into Redis within seconds +- **Deploys your way**: All four services are available fully managed on Redis Cloud or self-managed on your own infrastructure, via REST API See [how Redis Iris works](/content/develop/ai/context-engine/concepts/_index.md) for the mental model before you start building. @@ -45,23 +44,23 @@ See [how Redis Iris works](/content/develop/ai/context-engine/concepts/_index.md
  • Agents that remember context across sessions and users
  • Faster responses and lower costs through semantic caching
  • Reliable, structured access to live business data
    • -
  • No stale data — near real-time sync from your source databases
    • +
  • No stale data: near real-time sync from your source databases

    • For developers

    ## Quick example -Search LangCache before calling your LLM — return a cached response in milliseconds if a semantically similar prompt has been seen before: +Search LangCache before calling your LLM; return a cached response in milliseconds if a semantically similar prompt has been seen before: ```json POST /v1/caches/{cacheId}/entries/search @@ -89,7 +88,7 @@ Redis Iris context engine includes four services: - **[Context Retriever](/content/develop/ai/context-engine/context-retriever/_index.md)**: Turns your business data into structured tools that AI agents can safely and reliably use, defined once and reused across all agents. - **[Data integration](/content/develop/ai/context-engine/data-integration/_index.md)**: Syncs live data from your existing relational databases into Redis Cloud so agents always have access to fresh, accurate business data. -All four services are available on [Redis Cloud](/content/operate/iris/_index.md) using the REST API, with no database setup or management required. +All four services are available fully managed on [Redis Cloud](/content/operate/iris/_index.md) using the REST API, with no database setup or management required, or self-managed on your own infrastructure. ## LangCache diff --git a/content/develop/ai/context-engine/agent-memory/_index.md b/content/develop/ai/context-engine/agent-memory/_index.md index e9e8f3b7a8..c1eb7eb183 100644 --- a/content/develop/ai/context-engine/agent-memory/_index.md +++ b/content/develop/ai/context-engine/agent-memory/_index.md @@ -24,10 +24,10 @@ When enabled, automatic summarization compacts session memory by summarizing old Access Redis Agent Memory through the Python and TypeScript SDKs or its REST API. It works with any agent framework or LLM provider.
    - {{< image-card image="images/ai-model.svg" alt="Overview icon" title="Overview — What's the same and what's different if you already know Redis" url="/develop/ai/context-engine/agent-memory/overview" >}} - {{< image-card image="images/python-sdk-mark.svg" alt="Python SDK mark" title="Python SDK quickstart — Explore Redis Agent Memory with Python" url="/develop/ai/context-engine/agent-memory/python-sdk-quickstart" >}} - {{< image-card image="images/typescript-sdk-mark.svg" alt="TypeScript SDK mark" title="TypeScript SDK quickstart — Explore Redis Agent Memory with TypeScript" url="/develop/ai/context-engine/agent-memory/typescript-sdk-quickstart" >}} - {{< image-card image="images/rest-api-mark.svg" alt="REST API mark" title="REST API quickstart — Explore Redis Agent Memory with curl" url="/develop/ai/context-engine/agent-memory/rest-api-quickstart" >}} + {{< tile-card color="bg-blue-300" title="Overview" description="What's the same and what's different if you already know Redis" url="/develop/ai/context-engine/agent-memory/overview" >}} + {{< tile-card color="bg-redis-yellow-500" title="Python SDK" description="Explore Redis Agent Memory with Python" url="/develop/ai/context-engine/agent-memory/python-sdk-quickstart" >}} + {{< tile-card color="bg-redis-yellow-500" title="TypeScript SDK" description="Explore Redis Agent Memory with TypeScript" url="/develop/ai/context-engine/agent-memory/typescript-sdk-quickstart" >}} + {{< tile-card color="bg-teal-300" title="REST API" description="Explore Redis Agent Memory with curl" url="/develop/ai/context-engine/agent-memory/rest-api-quickstart" >}}
    ## Why use Redis Agent Memory? diff --git a/content/develop/ai/context-engine/context-retriever/_index.md b/content/develop/ai/context-engine/context-retriever/_index.md index 095fcb1085..e25ea4bee9 100644 --- a/content/develop/ai/context-engine/context-retriever/_index.md +++ b/content/develop/ai/context-engine/context-retriever/_index.md @@ -13,28 +13,26 @@ bannerText: Redis Context Retriever is currently available in preview. Features bannerChildren: true --- -Give your agents structured, governed access to business data — without building custom tools for every project. +Give your agents structured, governed access to business data, without building custom tools for every project. Context Retriever lets you define your data model once. It automatically generates the retrieval tools agents call at runtime, so agents always work with accurate, live data through a controlled interface rather than guessing at SQL or calling databases directly.
    - {{< image-card image="images/ai-model.svg" alt="Concepts icon" title="Concepts — Governed tool-calling instead of direct database access, and why it matters" url="/develop/ai/context-engine/context-retriever/concepts" >}} - {{< image-card image="images/ai-cube.svg" alt="Quick start icon" title="Quick Start — Create a Context Retriever service on Redis Cloud" url="/operate/iris/context-retriever/create-service" >}} - {{< image-card image="images/ai-lib.svg" alt="Python SDK icon" title="Python SDK and CLI — Model entities and deploy tools with the redis-context-retriever package" url="https://pypi.org/project/redis-context-retriever/" >}} - {{< image-card image="images/ai-brain.svg" alt="Admin keys icon" title="Manage Access — Create and manage agent keys to control what each agent can access" url="/operate/iris/context-retriever/view-admin-keys" >}} + {{< tile-card color="bg-blue-300" title="Concepts" description="Governed tool-calling instead of direct database access, and why it matters" url="/develop/ai/context-engine/context-retriever/concepts" >}} + {{< tile-card color="bg-redis-red-500" title="Quick Start" description="Create a Context Retriever service on Redis Cloud" url="/operate/iris/context-retriever/create-service" >}} + {{< tile-card color="bg-redis-yellow-500" title="Python SDK & CLI" description="Model entities and deploy tools with the redis-context-retriever package" url="https://pypi.org/project/redis-context-retriever/" >}} + {{< tile-card color="bg-teal-300" title="Manage Access" description="Create and manage agent keys to control what each agent can access" url="/operate/iris/context-retriever/view-admin-keys" >}}
    ## What is Context Retriever? Redis Context Retriever is a schema-first context layer for AI agents that: - +- **Defines business context once**: Model your entities, fields, and relationships in one place, reused across all agents +- **Auto-generates retrieval tools**: Tools are created from your data model, not hand-coded per agent +- **Keeps agents out of your database**: Agents call generated tools; the system handles data access safely +- **Governs access by design**: Each agent key has access tags that automatically filter what data it can see +- **Exposes tools via MCP**: Agents call tools through a standard Model Context Protocol (MCP) interface at runtime ## Why use Context Retriever? @@ -44,8 +42,8 @@ Redis Context Retriever is a schema-first context layer for AI agents that:
    @@ -53,8 +51,8 @@ Redis Context Retriever is a schema-first context layer for AI agents that:
    @@ -67,7 +65,7 @@ Install the Python client, which also includes the `ctxctl` CLI: pip install redis-context-retriever ``` -Use the `ctxctl` CLI, the Python client, or the Redis Cloud UI to model your entities and relationships. Context Retriever uses that model to automatically generate retrieval tools that agents call at runtime through its MCP interface — agents never access your database directly. +Use the `ctxctl` CLI, the Python client, or the Redis Cloud UI to model your entities and relationships. Context Retriever uses that model to automatically generate retrieval tools that agents call at runtime through its MCP interface. Agents never access your database directly. See the [Redis Cloud setup guide](/content/operate/iris/context-retriever/create-service.md) to create your first Context Retriever service. @@ -75,7 +73,7 @@ Redis Context Retriever helps teams expose operational context to AI agents thro ## Overview -Production agents fail not because the model is wrong, but because the context layer breaks. Enterprise data can be fragmented across multiple different databases, and can be disorganized. Teams try to patch this with text-to-SQL, OpenAPI-to-MCP wrappers, or hand-built tools — which works for demos but creates tool zoo sprawl, SQL risk, and agents that can't reliably choose the right path in production. Redis Context Retriever gives teams a governed, schema-first surface agents can traverse safely. +Production agents fail not because the model is wrong, but because the context layer breaks. Enterprise data can be fragmented across multiple different databases, and can be disorganized. Teams try to patch this with text-to-SQL, OpenAPI-to-MCP wrappers, or hand-built tools, which works for demos but creates tool zoo sprawl, SQL risk, and agents that can't reliably choose the right path in production. Redis Context Retriever gives teams a governed, schema-first surface agents can traverse safely. When you set up Redis Context Retriever, you model the objects that matter to your agent workflow and connect the relationships between them. You can do this either through the UI, using the [Context Surfaces Python Client](https://pypi.org/project/redis-context-retriever/), or the `ctxctl` CLI (available when you install the python client). Context Retriever will use those relationships to automatically create and deploy retrieval tools from your entity model. @@ -95,7 +93,7 @@ Get started with Redis Context Retriever on Redis Cloud or join the private prev Redis Context Retriever is available for self-managed deployment on Kubernetes as a private preview. See [Install Context Retriever](/content/develop/ai/context-engine/context-retriever/install/_index.md). -You need a license key to deploy — [contact Redis](https://redis.io/contact/) to request access. +You need a license key to deploy: [contact Redis](https://redis.io/contact/) to request access. {{< /multitabs >}} diff --git a/content/develop/ai/context-engine/data-integration/_index.md b/content/develop/ai/context-engine/data-integration/_index.md index 3e850c7d99..3db3679831 100644 --- a/content/develop/ai/context-engine/data-integration/_index.md +++ b/content/develop/ai/context-engine/data-integration/_index.md @@ -16,22 +16,20 @@ Stream live business data into Redis so agents always work with accurate, up-to- Redis Data Integration (RDI) keeps your Redis Cloud database in sync with your existing relational databases using [Change data capture](https://en.wikipedia.org/wiki/Change_data_capture) (CDC). Agents query Redis at full speed without ever querying your production databases directly.
    - {{< image-card image="images/ai-model.svg" alt="Quick start icon" title="Quick Start — Get a PostgreSQL pipeline running on Redis Cloud in minutes" url="/operate/rc/rdi/quick-start" >}} - {{< image-card image="images/ai-cube.svg" alt="Pipeline setup icon" title="Define Your Pipeline — Configure which tables to sync and how to map them to Redis" url="/operate/rc/rdi/define" >}} - {{< image-card image="images/ai-brain-2.svg" alt="RDI docs icon" title="Full RDI Documentation — Installation, configuration, and advanced pipeline options" url="/integrate/redis-data-integration" >}} + {{< tile-card color="bg-redis-red-500" title="Quick Start" description="Get a PostgreSQL pipeline running on Redis Cloud in minutes" url="/operate/rc/rdi/quick-start" >}} + {{< tile-card color="bg-violet-300" title="Define Pipeline" description="Configure which tables to sync and how to map them to Redis" url="/operate/rc/rdi/define" >}} + {{< tile-card color="bg-teal-300" title="RDI Documentation" description="Installation, configuration, and advanced pipeline options" url="/integrate/redis-data-integration" >}}
    ## What is Redis Data Integration? -Redis Data Integration (RDI) is a fully-managed pipeline service that: +Redis Data Integration (RDI) is a pipeline service, available fully managed on Redis Cloud or self-managed on your own infrastructure, that: - +- **Syncs data in near real time**: Changes in your source database propagate to Redis within seconds using CDC +- **Keeps agents away from source databases**: Agents query Redis at full speed, preserving performance and security +- **Requires no coding**: Define pipelines through configuration; transformations are handled automatically +- **Handles initial and streaming sync**: Full snapshot on first run, then continuous change capture from that point on +- **Supports major relational databases**: Oracle, MySQL, PostgreSQL, MariaDB, SQL Server, and AWS Aurora ## Why use Redis Data Integration? @@ -39,7 +37,7 @@ Redis Data Integration (RDI) is a fully-managed pipeline service that:

    For AI applications