rag_pipeline_PanScience_Innovations

Built a document parsing and LLM query application that extracts and structures information from PDFs, enabling natural language queries on the content using a vector database and local LLMs.

Note: To function properly, this pipeline requires a running Mistral model on Ollama, which serves as the local LLM backend for generating responses.

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Features

• Parse PDF documents and extract structured information.
• Store and query data using a vector database.
• Query PDFs using natural language via a local LLM (Mistral on Ollama).
• Fully containerized using Docker for easy setup.
• Works on Windows and Mac. For Linux, future modifications may be required.

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Prerequisites

• Docker installed on your system (Docker Desktop).
• Docker Compose installed (usually included with Docker Desktop).
• Ollama installed and the Mistral model running locally.
• (Optional) Git, if you want to clone the repository.

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Setup Instructions

1. Pull the Docker Images

The Docker Compose file will pull both the application and MongoDB images automatically. If you want to pull manually:

docker pull ghcr.io/harshindcoder/rag-pipeline-panscience-innovations:latest
docker pull mongo:latest

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2. Run the Services via Docker Compose

Create a file named docker-compose.yml (or use the one provided) with the following content:

version: "3.9"

services:
  app:
    image: ghcr.io/harshindcoder/rag-pipeline-panscience-innovations:latest
    container_name: rrag_app
    ports:
      - "8000:8000"
    depends_on:
      - mongo

  mongo:
    image: mongo:latest
    container_name: rrag_mongo
    ports:
      - "27017:27017"

Then, in the terminal, run:

docker-compose up

• To run in the background (detached mode):

docker-compose up -d

• To view logs:

docker-compose logs -f

• To stop all services:

docker-compose down

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3. Start Ollama with Mistral

Open another terminal and run:

ollama run mistral

• The container expects to connect to Ollama at http://localhost:11434.
• Mistral must be running while using the API.

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4. Upload a PDF

Open a new terminal and run:

curl -X POST -F "file=@/path/to/your/document.pdf" http://localhost:8000/upload

• Replace /path/to/your/document.pdf with the actual path of your PDF.
• This uploads the PDF to the pipeline for parsing and vectorization.

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5. Ask Questions

Once the file is uploaded, in the same terminal (or a new one), run:

curl -X POST -F "question=What is this file about?" http://localhost:8000/ask

• The pipeline will query the parsed document via Mistral on Ollama and return the answer.

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Here’s the 6th point refined for your README:

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6. Quick Manual Checks

For now, you can verify that everything is running with simple commands:

• Check Docker containers:

docker ps

• Check FastAPI is responding:

curl -X GET http://localhost:8000/docs

• Parser and VectorDB are verified via pytest:

pytest -v tests/

Tests are available in the repo but are not included in the production Docker image. Run them locally using pytest.

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Notes

• Terminals: You need at least two terminals: one for Docker Compose (FastAPI + MongoDB), one for Ollama. The curl commands can run in the same terminal or separate ones.
• File paths: Replace /path/to/your/document.pdf with your PDF location.
• Ports: FastAPI runs on 8000 and MongoDB on 27017. Adjust the ports in docker-compose.yml if needed.

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Future Improvements

• Add Linux compatibility.
• Multi-user support for larger document processing.
• Better error handling for missing Mistral/Ollama connections.

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