Contextual Retrieval with Any LLM: A Step-by-Step Guide
Summary
The video discusses Anthropic's contextual retrieval strategy, detailing how it reduces failure rates by 49% when combined with pm25. It explains the process of converting documents into chunks, embedding them using a model, and storing them in a search index like bm25 for keyword-based searches. The use of prompts to enhance context, locating relevant chunks, updating them in a vector store for accuracy, and utilizing long context models like HiU3 are demonstrated. The creation of vector stores, indexing based on bm25, and customizing prompts for tailored financial analysis needs are also highlighted. The video showcases examples of retrieval using vector stores and bm25 index to provide comprehensive answers based on both original and contextualized chunks.
Contextual Retrieval Strategy
Discusses the contextual retrieval strategy introduced by Anthropic to improve failure rates in combination with pm25, reducing failure rates by 49%.
Chunking Strategy Overview
Explains the process of converting documents into independent chunks, embedding these chunks using a model, and storing them in a search index such as bm25 to create a keyword-based search index for user queries.
Enhancing Context with Prompts
Describes enhancing context by using prompts that consider the whole document, locating relevant chunks, updating them in a vector store for increased accuracy.
Contextualizing Chunks
Demonstrates the use of long context models like HiU3 for chunk context, using open models with large context windows for varying token sizes.
Generating Contextualized Chunks
Illustrates the process of generating contextualized chunks from a document, providing relevant context and maintaining consistency.
Creating Vector Stores
Explains the creation of vector stores for contextualized chunks, indexing based on bm25, and generating search indexes for queries.
Retrieval and Answers
Shows examples of retrieval using vector stores and bm25 index to provide answers based on original and contextualized chunks.
Customizing Prompts
Discusses customizing prompts to add specific context for financial analysis and generating detailed search indexes for tailored needs.
FAQ
Q: What is the purpose of the contextual retrieval strategy introduced by Anthropic?
A: The purpose of the contextual retrieval strategy introduced by Anthropic is to improve failure rates in combination with pm25 by reducing failure rates by 49%.
Q: Can you explain the process of converting documents into independent chunks in the context of the discussed strategy?
A: In the discussed strategy, the process involves converting documents into independent chunks, embedding these chunks using a model, and storing them in a search index like bm25 to create a keyword-based search index for user queries.
Q: How is context enhanced in the strategy through prompts?
A: Context is enhanced in the strategy through prompts that consider the whole document, locate relevant chunks, and update them in a vector store for increased accuracy.
Q: What are some examples of the models used for chunk context in the strategy?
A: Some examples of models used for chunk context in the strategy include long context models like HiU3 and open models with large context windows for varying token sizes.
Q: How are vector stores created for contextualized chunks in the discussed strategy?
A: Vector stores for contextualized chunks are created by indexing them based on bm25 and generating search indexes for queries.
Q: Can you describe how customization is done for specific needs in the strategy?
A: Customization in the strategy involves adding specific context through prompts for financial analysis and generating detailed search indexes to meet tailored needs.
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