Preeti
Question answering using large language models (LLMs) involves combining LLMs with new data that they were not trained on, utilizing the knowledge from the trained data to retrieve queries from the new data. Tools like RetrivalQA, CSVLoader, and DocArrayInMemorySearch are used in this process. LLMs can only inspect a few thousand words at a time, so embeddings are used to represent text data as dense, high-dimensional vectors.
A vector database in LangChain is designed to handle high-dimensional vector representations of text, enabling efficient indexing, retrieval, and similarity search. The process involves creating document loaders, loading documents, creating chunks, creating embeddings, and creating a vector database for the embeddings. Different methods like Stuff, Map Reduce, Refine, and Map Rerank are used to process documents for question answering, each with its own pros and cons.
The steps to create a retrieval system involve creating a doc loader for CSV files, loading documents, creating chunks, creating embeddings, creating a vector database, querying the database, creating a retriever, importing LLM, and combining documents into a single piece of text. These steps can be encapsulated using chains. Overall, the process involves careful consideration of chunk size and method selection to ensure relevant and accurate results.
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Source link: https://medium.com/@xs1siddharth1014/streamline-your-llm-application-development-with-langchain-af66ba59a36a?source=rss——openai-5
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