Chat with PDF Documents using Embeddings

Goal is to convert lengthy documents into numerical representations (vector embeddings) and store them in a vector search engine.

When users engage in conversation with the documents, the system employs Approximate Nearest Neighbor search to find and return relevant text responses. This is achieved using:

– OpenAI’s cost-effective ChatGPT API (gpt-3.5-turbo)
– The vector database, Chroma, is suitable when used alongside LangChain for building applications with Large Language Models (LLMs).
– For production, Elasticsearch is recommended due to its widespread adoption, although its superiority to competitors may vary depending on specific needs.
– LangChain is a library designed to aid developers in integrating LLMs with other computational or knowledge sources.

To be continued
https://github.com/Appointat/Chat-with-Document-s-using-ChatGPT-API-and-Text-Embedding