Contextual RAG is stupidly brilliant!

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AI Summary

Summary of Video Transcript on Retrieval Augmented Generation (RAG)

• Importance of RAG:
  • RAG is crucial for enterprise companies as it translates to direct business value.
  • Improvements in RAG can lead to significant financial benefits.
• Anthropic’s Contextual Retrieval Technique:
  • Anthropic introduced a new retrieval technique for efficient RAG called contextual retrieval.
  • The technique simplifies the retrieval process and may also serve as an upsell for Anthropic.
• Typical RAG System:
  • Involves a text corpus within a company that could include various document types.
  • The system chunks the corpus, creates embeddings and TF-IDF, and stores them in databases.
  • User queries are processed by retrieving data from these databases, fusing the results, and then generating a response using a language model.
• Anthropic’s Suggestion:
  • Before embedding and TF-IDF, send each chunk through a large language model (LLM) to create a contextual sentence.
  • This contextual sentence situates the chunk within the larger document, improving retrieval accuracy.
• Example of Contextual Retrieval:
  • A chunk from an SEC filing is given a context that situates it within the document, improving the search retrieval process.
  • The prompt template provided by Anthropic helps create this contextualized chunk.
• Impact of Contextual Retrieval:
  • Contextual retrieval has been shown to reduce the number of failed retrievals by 35%.
  • The technique involves creating contextualized embeddings and a BM25 index.
• Considerations for Implementing Contextual Retrieval:
  • The technique adds uncertainty, overhead, and maintenance to the model.
  • It is important to assess whether the improvements are business-critical before implementation.
• Additional Improvements with Re-ranking:
  • Adding re-ranking to the retrieval process can further improve the system.
  • Re-ranking involves scoring chunks for relevance and importance before generating the final result.
• Key Takeaways:
  • Embeddings combined with BM25 outperform embeddings alone.
  • Voyage and Gemini embeddings are particularly effective.
  • Retrieving the top 20 chunks is more effective than top 10 or top 5.
  • Adding context to chunks improves retrieval accuracy.
  • Re-ranking is beneficial but introduces latency and additional costs.
• Final Thoughts:
  • The approach is practical and could be useful for enterprise companies implementing RAG.
  • The video presenter considers the possibility of creating an open-source solution based on this approach.

Detailed Instructions and Tips (if any provided in the transcript):

• No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.