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