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Complete example of how to build an Agentic RAG architecture with Redis, Amazon Bedrock, and LlamaIndex.

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- architecture.png
- bedrock.png
- llamaindex.png
- llamaindex_agentic_rag_architecture.png
- redis.png
.gitignore
Agentic_RAG_Redis_LlamaIndex.ipynb
env.json
LICENSE
README.md

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<h1 align="center">🦾 Agentic RAG with Redis, AWS Bedrock, and LlamaIndex</h1>

## Overview
This demo demonstrates the integration of [Redis](https://redis.io), [Amazon Bedrock](https://aws.amazon.com/bedrock/), and [LlamaIndex](https://docs.llamaindex.ai/en/stable/) for creating a **customer support chatbot** specifically tailored for Chevy vehicles. The system is powered by an "agentic RAG" architecture.

## Key Components
<p align="left">
  <img src="images/redis.png" alt="Redis Logo" width="145px"/>
  <img src="images/bedrock.png" alt="Bedrock Logo" width=50px/>
  <img src="images/llamaindex.png" alt="LlamaIndex Logo" width="45px"/>
</p>

- **[Redis](https://redis.io)**: A versatile db within the architecture, Redis functions as the document store, ingestion cache, vector store, chat history store, and semantic cache.
- **[Amazon Bedrock](https://aws.amazon.com/bedrock/)**: Provides foundation models and embeddings models through the Bedrock API.
- **[LlamaIndex](https://docs.llamaindex.ai/en/stable/)**: Acts as the central framework that ties together the entire system, enabling seamless integration with various services and tools to enhance functionality.

## Getting Started

Launch this notebook in a Google Colab environment for a hands-on experience:

<p align="left">
  <a href="https://colab.research.google.com/github/redis-developer/agentic-rag/blob/main/Agentic_RAG_Redis_LlamaIndex.ipynb" target="_blank"><button style="padding: 10px 20px; font-size: 16px; color: white; background-color: #007BFF; border: none; border-radius: 5px; cursor: pointer;">Open in Colab</button></a>
</p>

## Architecture Diagram
 This architecture highlights document ingestion and inference with the AI agent.

## Additional Resources
For further reading and resources related to the technologies and approaches used in this project, consider the following links:
- [Redis Documentation](https://redis.io/docs/)
- [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/)
- [LlamaIndex <> Redis Integration](https://docs.llamaindex.ai/en/latest/examples/vector_stores/RedisIndexDemo/)
- [Amazon Bedrock Console](https://aws.amazon.com/bedrock/)

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