The One RAG Method for Incredibly Accurate Responses (n8n) #n8n #aiagents #rag

Key Concepts:

• RAG (Retrieval-Augmented Generation) agents
• Metadata filters
• Supabase
• Pinecone
• Vector database
• Embedding model
• Data ingestion
• Chunking
• AI-powered metadata extraction
• Dynamic filtering
• n8n

Metadata Filters for Enhanced RAG Agents

The video addresses the problem of RAG agents providing irrelevant or incorrect information. The solution presented involves equipping agents with advanced metadata filters that work with both Supabase and Pinecone vector databases.

Workflow and Implementation

1. Query Reception and Filter Creation: When the agent receives a message, a sub-workflow is triggered. This sub-workflow dynamically creates a sophisticated filter based on the query's content. The video emphasizes the specificity that these filters can achieve.

2. Data Ingestion and Chunking: During the data ingestion phase of the RAG pipeline, data from various sources (documents, web pages, etc.) is loaded and broken down into smaller chunks.

3. Vector Embedding and Storage: An embedding model is used to generate vectors from these chunks. These vectors are then stored in a vector database (Supabase or Pinecone).

4. Metadata Enrichment: The key innovation is the enrichment of these chunks with relevant metadata. An AI model is used to extract metadata from the documents. This metadata is then stored alongside the chunks in the vector database.

5. Metadata Storage in Databases:

   • Supabase: The video shows how metadata fields are populated within Supabase. A separate table is created to dynamically add metadata fields and their allowed values. These values are then automatically injected into the prompts in the RAG template.
   • Pinecone: Similar metadata storage is implemented in Pinecone.

6. Custom n8n Implementation: The video highlights that these features are not built-in to n8n. For Supabase, a custom database function was created to handle the filtering logic. While Pinecone allows for complex filter creation, the integration with n8n was achieved using an HTTP request node instead of the standard n8n Pinecone node.

Technical Details and Customization

• The video emphasizes the use of AI to extract relevant metadata, enabling more precise filtering.
• The dynamic nature of the filters is a key advantage, allowing the agent to adapt to different queries.
• The use of custom functions and HTTP requests in n8n demonstrates the flexibility required to implement these advanced features.

Conclusion

The video demonstrates a method for improving the accuracy and relevance of RAG agent responses by using AI-powered metadata extraction and dynamic filtering within vector databases like Supabase and Pinecone. The implementation requires custom solutions within n8n, showcasing the need for flexibility when building advanced RAG pipelines. The main takeaway is that enriching data with metadata and using it for filtering significantly enhances the performance of RAG agents.