Context-Based Chunking in Retrieval Augmented Generation (RAG)
Introduction
- Purpose of Video: Explore context-based chunking in RAG to enhance retrieval accuracy.
What is Retrieval Augmented Generation (RAG)?
- Definition: A technique to enhance the accuracy and reliability of generative AI models by using specific and relevant data sources.
- Process: Providing a Large Language Model (LLM) with a data source to retrieve and utilize relevant information before generating a response.
- Benefit: Makes output more precise and contextually accurate.
Challenges with Traditional RAG
- Difficulty in retrieving highly relevant data based on current context.
- May lead to inaccurate or incomplete answers.
Improving Retrieval Accuracy
Chunking Strategies
- Recursive Text Splitting with Overlap: Helps retain context across chunks.
Context-Based Chunking (Anthropic Contextual Retrieval)
- Concept: Each chunk is attached to a broader document context, maintaining coherence and improving retrieval accuracy.
Implementation in n8n Workflow
Steps
- Retrieve Source Document
- Extract Text Data
- Extracted from the document with clear boundary lines marking different sections for meaningful chunking.
- Divide Document into Sections
- Use a code node in n8n to create structured chunks ensuring context retention.
- Loop Through Each Chunk
- Generate Contextual Information
- Use an agent node.
- Reference entire document to maintain context.
- Model: OpenAI's GPT 4.0 mini via Open Router.
- Create Embeddings for Storage
- Vector Store: Pinecone.
- Text to Vector Conversion: Google's Gemini I Text Embedding model (oak4).
- Recursive Text Splitter: Set large chunk size, minimal effect.
Conclusion
- Outcome: With context-enriched chunks and optimized retrieval, the system is now more accurate and efficient.
- Call to Action: Try out the setup; workflow link provided in the description.
Additional Information
- Encouragement to Engage: Like, share, and subscribe for more tutorials.
Note: See video description for a workflow link.