Retrieve information from multiple files using LangChain and ChromaDB.

🔍 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.

📁 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.

🔍 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.

🔍 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.

💡 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.

🔑 CMA stands for the competition and markets authority.

🔑 The chain retriever type is similarity.

🔑 The chromaDB is used as the vector store.

📚 Using GPT 3.5 turbo API to retrieve answers

💡 Considering system and human prompts for accurate results

💻 Exploring vector database and future possibilities