How to integrate OpenAI GPT and your knowledge base into a Voiceflow Assistant

OpenAI embeddings and vector databases are used to create a more efficient way of processing and understanding text data in machine learning models. This project utilizes OpenAI GPT, Langchain, HNSWLib, and Cheerio to generate answers to users' questions by creating vector representations of text data fetched from webpages and storing them in a local database linked to the Assistant API Key. The vector database is created using HNSWLib, allowing for efficient search for similar documents to a given query. Langchain plays a crucial role in this project by providing tools and utilities for natural language data processing, including creating embeddings, saving them in a local database, and using them with GPT to answer questions. Two endpoints are available: the parser endpoint fetches webpages, extracts text, and creates documents to add to the vector database, and the question endpoint generates answers using GPT and the context from the vector database.

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