Retrieve information from multiple files using LangChain and ChromaDB.
Learn how to retrieve QA over multiple files using LangChain Retrieval with ChromaDB.
00:00:00 This video demonstrates using Lang chain with multiple documents and ChromaDB. It shows how to set up the database and load multiple text files for retrieval queries.
🔍 This video demonstrates the use of LangChain and ChromaDB for retrieving QA over multiple documents.
💾 A database is created using ChromaDB to store multiple text files, and a citation information is included for query results.
🆕 The video also introduces the usage of the new GPT-3.5-turbo API for language model and embeddings.
00:01:46 Learn how to retrieve QA over multiple files using LangChain Retrieval with ChromaDB. Set the directory, glob the files, split the data into chunks, create a vector store in a folder called DB, and save the documents in the index.
📁 The first step is to set the directory and gather the files, with different loaders for different file types.
🔨 The data is then split into chunks and a vector store is created to store the embeddings.
💾 The embeddings are generated from the documents and saved to a database, which can be loaded later.
00:03:22 This video demonstrates how to retrieve relevant documents using a vector database and a retriever. It also explains how to set parameters for the search type and number of documents to return.
🔍 By saving a vector database, we can reuse it instead of embedding all documents every time.
📚 Using a retriever, relevant documents can be retrieved based on queries and the number of documents can be adjusted.
🔢 Different search types and multiple indexes can be utilized for more advanced retrieval.
00:05:04 Learn how to retrieve information from multiple files using ChromaDB for language model chain in this video.
🔍 The video discusses the setup of a language model chain for retrieval QA over multiple files.
💡 The process involves passing the retriever and conducting a query to obtain relevant documents.
💰 The example query asks about the amount of money raised by a company, and the retrieved documents provide the desired information.
00:06:49 The video demonstrates how to retrieve information from multiple files using LangChain and ChromaDB, providing a concise summary and alternative title.
💡 LangChain retrieval QA allows for easy access to original source HTML pages.
🔍 The retrieval QA function provides detailed information about news articles and their sources.
📚 Generative AI and the acquisition of Okera are also discussed in the video.
00:08:32 This video explains the process of retrieving QA results from multiple files using LangChain with ChromaDB.
🔑 CMA stands for the competition and markets authority.
🔑 The chain retriever type is similarity.
🔑 The chromaDB is used as the vector store.
00:10:19 LangChain Retrieval QA using GPT 3.5 turbo API, discussing prompts and prompts for the version, and future plans to use pine cones and custom embeddings.
📚 Using GPT 3.5 turbo API to retrieve answers
💡 Considering system and human prompts for accurate results
💻 Exploring vector database and future possibilities
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