Can build a docker image?

[karthik18495]

Issue: Package RAG-Based Document Retriever in Docker

Description:
We need to package the entire Retrieval Augmented Generation (RAG)-based document retriever for the Electron-Ion Collider (EIC) in a Docker container to simplify setup, ensure a consistent environment, and make it easier for new contributors to start working on the project. This will come after #2

This issue outlines the steps needed to:

1. Download all necessary resources (documents, embeddings, etc.).
2. Set up a vector database (e.g., FAISS or Pinecone).
3. Build the LangChain app within the container.
4. Provide instructions for building and running the Docker image.

Objective:
To create a Docker image that packages the RAG-based document retriever system with all dependencies and resources, making it easy for collaborators to set up, run, and contribute to the project.

Tasks:

1. Download Resources:

   • Write a script (e.g., download_resources.py) that:
     • Downloads any required documents (e.g., PDFs, datasets).
     • Preprocesses the documents for input into the RAG system.
     • Downloads pre-trained models and embeddings (if applicable).
   • Ensure that this script can be run as part of the Docker build process, downloading everything needed for the app to function.

2. Set Up Vector Database:

   • Choose the vector database technology (e.g., FAISS, Pinecone).
   • Write setup instructions to integrate the vector database within the Docker container.
   • Ensure that the database is pre-configured with the downloaded resources so that new users can immediately run the system after building the Docker image.
   • If using FAISS:
     • Ensure it installs properly in the Docker container.
     • Create a script (setup_faiss.py) that adds the downloaded documents to the FAISS index.
   • If using Pinecone or another external service:
     • Include clear instructions for configuring the API keys and setting up the environment.

3. Build LangChain App:

   • Write a langchain_app.py file that defines the LangChain-based RAG system.
     • Ensure the app connects to the vector database.
     • Create an endpoint (e.g., using FastAPI or Flask) that users can query to retrieve documents.
   • Ensure all dependencies are properly installed and configured.

4. Dockerize the Application:

   • Create a Dockerfile that:
     • Sets up a Python environment.
     • Installs poetry and uses it to install dependencies from pyproject.toml.
     • Runs the download_resources.py and vector database setup scripts as part of the build process.
     • Exposes necessary ports for the LangChain app.
   • Example Dockerfile structure:

     FROM python:3.9-slim
     
     # Install dependencies
     RUN pip install poetry
     COPY pyproject.toml poetry.lock ./
     RUN poetry install
     
     # Add application code
     COPY . /app
     WORKDIR /app
     
     # Download resources and set up vector DB
     RUN poetry run python download_resources.py
     RUN poetry run python setup_faiss.py  # or setup_pinecone.py
     
     # Expose the necessary port for the LangChain app
     EXPOSE 8000
     
     # Run the LangChain app
     CMD ["poetry", "run", "python", "langchain_app.py"]

5. Write Instructions for Building and Running the Docker Image:

   • In the README.md file, add detailed instructions for:
     • Building the Docker image.
     • Running the container and starting the LangChain app.
     • Making contributions (e.g., adding new documents, extending the vector database, etc.).

   Example:

   ## Running the RAG-Based Document Retriever
   
   1. **Build the Docker Image**:
      ```bash
      docker build -t rag-document-retriever .

   2. Run the Docker Container:

      docker run -p 8000:8000 rag-document-retriever

   3. Access the LangChain App:
      Once the container is running, you can access the document retriever by visiting http://localhost:8000.

   4. Contributing:

      • To add new documents, place them in the resources/ directory and re-run the Docker build process.
      • To modify the RAG system, edit the langchain_app.py and make a pull request.

Acceptance Criteria:

• A functional Docker image that contains the entire RAG-based document retriever system.
• The Docker image successfully sets up the vector database and downloads all necessary resources.
• Clear and accurate documentation in the README.md for building and running the Docker image.
• The LangChain app is accessible via a REST API after running the Docker container.

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