π This video demonstrates the use of LangChain and ChromaDB for retrieving QA over multiple documents.
πΎ A database is created using ChromaDB to store multiple text files, and a citation information is included for query results.
π The video also introduces the usage of the new GPT-3.5-turbo API for language model and embeddings.
π The first step is to set the directory and gather the files, with different loaders for different file types.
π¨ The data is then split into chunks and a vector store is created to store the embeddings.
πΎ The embeddings are generated from the documents and saved to a database, which can be loaded later.
π By saving a vector database, we can reuse it instead of embedding all documents every time.
π Using a retriever, relevant documents can be retrieved based on queries and the number of documents can be adjusted.
π’ Different search types and multiple indexes can be utilized for more advanced retrieval.
π The video discusses the setup of a language model chain for retrieval QA over multiple files.
π‘ The process involves passing the retriever and conducting a query to obtain relevant documents.
π° The example query asks about the amount of money raised by a company, and the retrieved documents provide the desired information.
π‘ LangChain retrieval QA allows for easy access to original source HTML pages.
π The retrieval QA function provides detailed information about news articles and their sources.
π Generative AI and the acquisition of Okera are also discussed in the video.
π CMA stands for the competition and markets authority.
π The chain retriever type is similarity.
π The chromaDB is used as the vector store.
π Using GPT 3.5 turbo API to retrieve answers
π‘ Considering system and human prompts for accurate results
π» Exploring vector database and future possibilities