Advanced Techniques for Semantic Search in LangChain

Using semantic search only where it makes sense

Introduction to self querying retrieval

Applying specific queries for metadata-based searches

🔍 The video introduces the concept of self-querying retrieval and its application in semantic search.

💡 OpenAI embeddings are used in this demonstration, but other models can also be utilized.

💻 The example focuses on searching for wines based on their descriptions, utilizing metadata such as name, year, rating, grape type, color, and country of origin.

🍇🍷 Using metadata for self-querying retrieval in LangChain

📊 Converting CSV data into metadata for semantic search

🔍 Creating a self-querying retriever with specific metadata info

🍷 The video discusses the use of ratings and semantic descriptions in a retrieval system for wine data.

⚖️ Different data types, such as integers and floats, are used for ratings and country variables, respectively.

📝 The model generates queries based on user input to filter and compare wine data.

🔍 The video demonstrates self-querying retrieval using attributes and values.

🍷 Semantic search allows users to find wines based on specific characteristics and descriptions.

🌎 The retrieval system can also filter results based on country of origin and other criteria.

🔍 The video demonstrates how to perform a variety of searches using both metadata filtering and semantic lookup in a vector store.

🔢 A limit can be applied to the search results to avoid returning a large number of items.

💡 The language model used can handle incorrect capitalization and spelling to accurately interpret and fix queries.

🔍 The video demonstrates how to use metadata and semantic search to build a more advanced retrieval augmented generation system.

🌍 The concept can be applied to different language models, not just OpenAI.

💡 This approach allows users to go beyond basic semantic search and perform more complex tasks.