N8N Workflows: Context-Based Chunking in RAG Geek

Introduction

• Topic: Enhancing retrieval accuracy in RAG systems using context-based chunking.
• Objective: Explore how context-based chunking improves retrieval accuracy.

Understanding Retrieval Augmented Generation (RAG)

• Definition: RAG is a technique that enhances the accuracy of generative AI by using relevant data sources.
• Process:
  • Provide an LLM with a specific data source.
  • Retrieve and use relevant information before generating a response.
  • Outcome: More precise and contextually accurate output.

Challenges with Traditional RAG

• Difficulty in retrieving highly relevant data based on context.
• Can lead to inaccurate or incomplete answers.

Improving Retrieval Accuracy

• Chunking Strategies:
  • Recursive text splitting with overlap to retain context.
• Focus of Video: Implementing context-based chunking.

Context-Based Chunking

• Described by: Anthropic Contextual Retrieval.
• Method: Attach each chunk to a broader context from the entire document.
• Benefits:
  • Maintain coherence.
  • Improve retrieval accuracy.
  • Help the model understand the bigger picture.

N8n Workflow Implementation

1. Retrieving the Source Document: From Google Drive.
2. Extracting Text Data: From the document.
3. Adding Boundary Lines:
   • Mark different sections in the document.
   • Split text into meaningful chunks.
4. Using Code Node:
   • Write script to divide document into structured chunks.
5. Loop Through Chunks: Using loop node in N8n.
6. Agent Node:
   • Generate contextual info for each chunk by referencing the entire document.
   • Use OpenAI's GPT 4.0 Mini from OpenRouter as LLM.
7. Prepending Context:
   • Enriched chunk sent to next node.
8. Creating Embeddings:
   • Use Google's Gemini Text-Embedding Oak 04 model.
   • Store in Pinecone vector database.
9. Recursive Text Splitter:
   • Effect minimal due to large chunk size.

Conclusion

• Outcome: Enhanced retrieval accuracy and efficiency.
• Setup Components:
  • Document retrieval and extraction.
  • Context generation and embedding creation.
  • Storage in vector database.
• Result: More accurate and context-aware RAG system.

Additional Information

• Workflow Link: Available in the video description.
• Call to Action: Like, share, and subscribe for more tutorials.

Closing

• Encouragement to try out the setup.
• Thanks for watching and see you in the next tutorial.