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---
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title: 'Chroma Q&A with Sources Element'
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tags: ['chroma', 'chainlit', 'qa']
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---
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# Chroma Q&A with Sources Element
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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.
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## High-Level Description
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The `app.py` script performs the following functions:
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1. **PDF Processing (`process_pdfs`)**: Chunks PDF files into smaller text segments, creates embeddings for each chunk, and stores them in Chroma.
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2. **Document Indexing (`index`)**: Uses `SQLRecordManager` to track document writes into the vector store.
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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.
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## Quickstart
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### Prerequisites
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- Python 3.11 or higher
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- Chainlit installed
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- PDF documents to be indexed
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### Setup and Run
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1. **Install Dependencies:**
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Install the required Python packages specified in `requirements.txt`.
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```shell
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pip install -r requirements.txt
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```
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2. **Process PDFs:**
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Place your PDF documents in the `./pdfs` directory.
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3. **Run the Application:**
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Use the provided `Dockerfile` to build and run the application.
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```shell
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docker build -t chroma-qa-chat .
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docker run -p 7860:7860 chroma-qa-chat
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```
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Access the application at `http://localhost:7860`.
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## Code Definitions
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- `process_pdfs`: Function that processes PDF files and indexes them into Chroma.
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- `on_chat_start`: Event handler that sets up the Chainlit session with the necessary components for question answering.
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- `on_message`: Event handler that processes user messages, retrieves relevant information, and sends back an answer.
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- `PostMessageHandler`: Callback handler that posts the sources of the retrieved documents as a Chainlit element.
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## See Also
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For a visual guide on how to use this application, watch the video by [Chris Alexiuk](https://www.youtube.com/watch?v=9SBUStfCtmk&ab_channel=ChrisAlexiuk).
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