Unlocking the Power of Retrieval Augmented Generation (RAG) with LlamaIndex and Amazon Bedrock
This post was co-written with Jerry Liu from LlamaIndex.
Retrieval Augmented Generation (RAG) has revolutionized the capabilities of large language models (LLMs) by combining external data sources with generative AI. This powerful technique enhances the ability of LLMs to tackle complex tasks that require both knowledge and creativity. From document-based question answering to advanced analysis, RAG techniques are now indispensable for enterprises of all sizes utilizing generative AI.
While building a basic RAG system is straightforward, creating production-ready RAG pipelines with advanced patterns poses a challenge. Developers often face issues of low response quality, with the pipeline failing to adequately answer a large number of questions. Common reasons include bad retrievals, incomplete responses, and hallucinations, necessitating the use of advanced techniques in query understanding, retrieval, and generation components.
Introducing LlamaIndex, an open-source library offering simple and advanced techniques for building robust RAG pipelines. LlamaIndex provides a flexible framework for integrating document indexes, LLMs, and implementing advanced RAG patterns with ease.
Enhancing RAG Pipelines with Amazon Bedrock
Amazon Bedrock, a managed service, provides access to high-performing foundation models (FMs) through a unified API, enabling customization and fine-tuning for generative AI applications. With features like model customization, continued pre-training, and RAG capabilities, Amazon Bedrock empowers developers to create intelligent agents that orchestrate FMs with enterprise systems and data.
In partnership with LlamaIndex, Amazon Bedrock offers the perfect ecosystem for building advanced RAG pipelines. Let’s explore how to set up:
- Simple RAG pipeline – Implement a basic RAG pipeline using LlamaIndex with Amazon Bedrock models and vector search.
- Router query – Automate query routing based on query nature (e.g., summarization or factual questions).
- Sub-question query – Break down complex queries into simpler sub-questions for comprehensive responses.
- Agentic RAG – Build a stateful agent for dynamic and adaptive RAG pipelines.
Simple RAG Pipeline
At its core, RAG involves retrieving data from external sources and using it to augment LLM prompts. By adding context from external knowledge bases, LLMs can generate responses tailored to specific queries. In Amazon Bedrock, documents are preprocessed and indexed, allowing efficient retrieval of relevant information at runtime for prompt augmentation.
The pipeline includes steps like loading documents, creating a vector store index, querying the index, and augmenting prompts for the LLM to generate responses. LlamaIndex extends these capabilities to implement sophisticated RAG patterns.
Router Query
The RouterQueryEngine enables intelligent routing of queries to different engines or indexes based on query content. For instance, summarization queries can be routed to a summary index, while factual queries can be directed to a vector store index.
Sub-Question Query
With the SubQuestionQueryEngine, complex queries are broken down into simpler sub-questions and their answers are combined to form a cohesive response. This feature is ideal for handling queries spanning multiple documents or requiring detailed analysis.
Agentic RAG
Agentic RAG introduces an adaptive approach by allowing LLMs to dynamically reason and select tools or indexes based on query content. This architectural model combines agent capabilities with knowledge bases for enhanced RAG workflows.
LlamaCloud and LlamaParse
LlamaCloud offers advanced managed services tailored for enterprise-grade context augmentation within LLM and RAG applications. Key components like LlamaParse, a proprietary parsing engine, and the Managed Ingestion and Retrieval API streamline data wrangling processes for enhanced response quality and context-aware question answering.
Integrate Amazon Bedrock and LlamaIndex for Advanced RAG Pipelines
Combining LlamaParse and LlamaIndex with Amazon Bedrock services enables the creation of robust RAG pipelines. By following high-level steps like downloading source documents, parsing with LlamaParse, and integrating with Amazon Bedrock, developers can build sophisticated RAG stacks for knowledge-intensive tasks.
Conclusion
By leveraging the capabilities of LlamaIndex and Amazon Bedrock, developers can build cutting-edge RAG pipelines that harness the full potential of large language models for complex tasks. Explore the advanced RAG patterns discussed here and unlock new possibilities in generative AI applications.
About the Authors
Shreyas Subramanian is a Principal Data Scientist specializing in Machine Learning on the AWS platform, with expertise in optimization and AI. Jerry Liu is the co-founder/CEO of LlamaIndex, bringing a wealth of experience in ML research and startup environments.