Advanced Techniques for Semantic Search in LangChain
Learn how to perform semantic search using self querying retrieval with OpenAI embeddings in LangChain.
00:00:00 The video discusses the importance of using semantic search only where it makes sense, and introduces the concept of self-querying retrieval for extracting semantic meaning.
Using semantic search only where it makes sense
Introduction to self querying retrieval
Applying specific queries for metadata-based searches
00:01:51 Learn how to use self querying retrieval with OpenAI embeddings to perform semantic search on wine descriptions in LangChain using chroma as the vector store.
🔍 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.
00:03:38 This video discusses self querying retrieval in LangChain, including metadata information and embedding documents for vector storage.
🍇🍷 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
00:05:18 This video explains how to use a language model to retrieve information based on specific queries and metadata fields.
🍷 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.
00:07:03 This video explores advanced self-querying retrieval techniques in RAG model, showcasing semantic searches, filtering by attributes, and composite queries.
🔍 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.
00:08:52 This video demonstrates how to use advanced querying techniques, including filtering metadata and using semantic lookups, to retrieve specific data. It also shows how to limit search results and highlights the capabilities of a large language model in fixing misspellings and capitalization errors.
🔍 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.
00:10:40 Learn how to use metadata and semantic search together to create a more advanced retrieval augmented generation system.
🔍 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.
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