The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)

Key Concepts

• Retrieval Augmented Generation (RAG)
• Contextual Retrieval/Embedding
• Vector Database (Neon Postgress, Superbase, Quadrant)
• Chunking
• Embedding Model (OpenAI)
• Prompt Caching
• AI Agents
• Hybrid RAG (BM25)
• Re-ranking
• Query Expansion
• Agentic RAG

1. Introduction to Contextual Retrieval for RAG Accuracy

• The video emphasizes the importance of strategies like contextual retrieval for improving the accuracy of Retrieval Augmented Generation (RAG) systems.
• Basic RAG involves extracting text from documents (e.g., Google Drive), chunking it, creating vector embeddings, and storing them in a vector database.
• The agent then uses these embeddings to retrieve relevant chunks based on user queries.
• The problem with basic RAG is its inaccuracy in retrieving the necessary information to answer questions.

2. Basic RAG Implementation

• Example: An N8N workflow is presented as an example of basic RAG.
• The workflow monitors a Google Drive folder for new or updated files.
• It extracts text from the documents and inserts it into a vector database (Neon Postgress).
• An AI agent uses a tool to search this knowledge base and answer questions.
• Limitation: Basic RAG often fails to retrieve the correct chunks, leading to inaccurate answers.

3. Contextual Retrieval Explained

• Contextual retrieval builds upon basic RAG by adding context to each chunk.
• Process: For each chunk, a prompt is used to provide additional information about its role and relevance within the larger document.
• This helps the LLM understand how the chunk relates to the overall knowledge base.
• The extra context is prepended to the chunk's content before embedding.
• Benefit: Even a small amount of extra context can significantly improve RAG accuracy.

4. Anthropic's Research and Evaluation

• The video references an article by Anthropic that introduces contextual retrieval and provides evaluation data.
• Data: Regular RAG has a failure rate of 9.9% (i.e., failing to retrieve the proper chunks).
• Combining contextual retrieval with other strategies reduces the failure rate to less than 3%.
• Contextual embedding is highlighted as the most important strategy.

5. Implementing Contextual Retrieval in N8N

• The video demonstrates how to implement contextual retrieval in an N8N workflow.
• Workflow:
  • Trigger: Watches a Google Drive folder for new/updated files.
  • Loop: Processes each file individually.
  • Download File: Downloads the file from Google Drive.
  • Chunking: Splits the document into chunks (400 characters, no overlap) using custom JavaScript code.
  • LLM Call: Uses a prompt (from Anthropic's article) to generate context for each chunk.
  • Postgress Node: Inserts the chunks with prepended context into the Neon Postgress database.
• Prompt: The prompt asks the LLM to provide a short, succinct context to situate the chunk within the document.
• Cost Optimization:
  • Prompt caching is used to reduce the cost of sending the entire document for each chunk.
  • A small, cheap LLM (GPT-4.0 Nano) is used for generating the context.
• Database: Neon is used as the Postgress database, offering autoscaling and database branching features.

6. AI Agent Configuration

• The AI agent uses a simple system prompt and the OpenAI LLM.
• It has a tool to search the knowledge base (Postgress retrieve documents tool).
• The tool is configured to retrieve the four most relevant chunks, including metadata (title, URL).
• The same embedding model is used for both the agent and the pipeline.

7. Testing and Results

• The video demonstrates a test where the agent is asked a question based on a document in the knowledge base.
• The agent successfully retrieves the relevant chunk and answers the question accurately.
• The prepended context is shown to be helpful in guiding the agent.

8. Contextual Retrieval in Python (Crawl for AI RAG MCP Server)

• The video also shows an implementation of contextual retrieval in Python within a self-hosted Superbase environment.
• The code uses the same prompt and process as the N8N implementation.
• The code is open-source and available for users to examine and adapt.

9. Additional Strategies and Future Improvements

• The video mentions other RAG strategies, such as BM25 for hybrid RAG and re-ranking.
• The speaker plans to continue improving the RAG system by incorporating these strategies.
• Chunking strategies will also be explored in future content.

10. Conclusion

• Contextual retrieval is a crucial strategy for improving the accuracy of RAG systems.
• It involves adding context to each chunk to help the LLM understand its relevance within the larger document.
• The video provides practical examples of how to implement contextual retrieval in both N8N and Python.
• The speaker encourages viewers to experiment with the provided resources and stay tuned for future content on RAG strategies.