This is an interactive resume app built with OpenAI (GPT3.5 Large Language Model), LangChain (LLM plumbing code), Chroma (vector store), and Chainlit (UI). The app is deployed with Docker on Google Cloud Platform.

https://tjaensch-resume-qa-bajcruqfrq-uc.a.run.app/

This repository contains a Chainlit application that provides a question-answering service using documents stored in a Chroma vector store. It allows users to upload PDF documents, which are then chunked, embedded, and indexed for efficient retrieval. When a user asks a question, the application retrieves relevant document chunks and uses OpenAI's language model to generate an answer, citing the sources it used.

The app.py script performs the following functions:

1. PDF Processing (process_pdfs): Chunks PDF files into smaller text segments, creates embeddings for each chunk, and stores them in Chroma.
2. Document Indexing (index): Uses SQLRecordManager to track document writes into the vector store.
3. Question Answering (on_message): When a user asks a question, the application retrieves relevant document chunks and generates an answer using OpenAI's language model, providing the sources for transparency.

• process_pdfs: Function that processes PDF files and indexes them into Chroma.
• on_chat_start: Event handler that sets up the Chainlit session with the necessary components for question answering.
• on_message: Event handler that processes user messages, retrieves relevant information, and sends back an answer.
• PostMessageHandler: Callback handler that posts the sources of the retrieved documents as a Chainlit element.

* Template code from the Chainlit cookbook repo https://github.com/Chainlit/cookbook/tree/main/chroma-qa-chat