RAG-Based Q&A System

Overview

This project is a RAG (Retrieval-Augmented Generation) based Q&A system that allows users to upload documents, select relevant ones, and ask questions. The system utilizes FAISS for vector search, FastAPI for API development, and PostgreSQL for structured data storage.

Features

• Document Ingestion: Upload text documents and generate vector embeddings.
• Document Selection: Select relevant documents from the stored database.
• Question Answering: Retrieve relevant document passages based on the question.
• FastAPI Integration: Provides interactive API documentation with Swagger.
• Test Coverage: Achieved 83%+ test coverage with pytest.

Tech Stack

• Backend: FastAPI, SQLAlchemy, Pydantic
• Vector Search: FAISS
• Database: PostgreSQL
• Embedding Models: SentenceTransformers
• Testing: Pytest
• Logging: Python Logging
• Deployment: Docker

Folder Structure

Below is the structured layout of the project:

rag_qna_project/
├── alembic/
│   ├── env.py
│   ├── script.py.mako
│   └── versions/
│       └── e117fe54c208_initial_migration.py
├── alembic.ini
├── app/
│   ├── api/
│   │   ├── __init__.py
│   │   ├── document_ingestion.py
│   │   ├── document_selection.py
│   │   ├── question_answering.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── config.py
│   │   ├── logging_config.py
│   ├── main.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── db_models.py
│   │   ├── embedding_store.py
│   ├── services/
│   │   ├── __init__.py
│   │   ├── embedding_service.py
│   │   ├── retrieval_service.py
│   ├── tests/
│       ├── __init__.py
│       ├── test_document_ingestion.py
│       ├── test_document_selection.py
│       ├── test_question_answering.py
├── configs/
├── deployment/
│   ├── Dockerfile
│   ├── docker-compose.yml
├── docs/
│   ├── API_DOCS.md
│   ├── README.md
├── requirements.txt
├── sample_data/
│   └── sample.txt

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Installation Steps

Follow these steps to set up and run the project.

1️⃣ Clone the Repository

git clone https://github.com/your-username/rag_qna_project.git
cd rag_qna_project

2️⃣ Create a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate  # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Environment Variables

Create a .env file in the configs/ directory:

touch configs/.env

Add the following configurations:

DATABASE_URL=postgresql://user:password@localhost:5432/rag_db

5️⃣ Start PostgreSQL (If not running)

Make sure PostgreSQL is installed and running.

For Mac:

brew services restart postgresql

6️⃣ Run Migrations

alembic upgrade head

7️⃣ Start the API Server

uvicorn app.main:app --reload

API Access

Once the server is running, you can access:

• API Base URL: http://127.0.0.1:8000
• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc UI: http://127.0.0.1:8000/redoc

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Running the Dockerized RAG Q&A Project

1️⃣ Pull the Latest Docker Image

Run the following command to pull the latest image from Docker Hub:

docker pull ashishkumar16/rag_qna_project:latest

2️⃣ Verify the Downloaded Image

To check if the image has been successfully pulled:

docker images

3️⃣ Download the docker-compose.yml file

Download the docker-compose.yml from the /deployment folder.

4️⃣ Start the Containers Using Docker Compose

Run the following command to start the FastAPI app and PostgreSQL database in detached mode:

docker-compose up -d

5️⃣ Check Running Containers

To verify if the containers are running:

docker ps

6️⃣ View Logs of the FastAPI App

To monitor the logs of the FastAPI application:

docker logs -f fastapi_app

7️⃣ Apply Database Migrations

Run the Alembic migrations inside the FastAPI container:

docker exec -it fastapi_app alembic upgrade head

8️⃣ Verify Database Tables

To check if the necessary tables exist inside PostgreSQL:

docker exec -it postgres_db psql -U postgres -d rag_db -c "\dt"

✅ Your RAG Q&A System is Now Running!

You can now access the FastAPI application at:

http://localhost:8000/docs or http://0.0.0.0:8000/docs

This will open the Swagger API documentation, where you can test the endpoints.

Testing

Run the test suite with coverage:

PYTHONPATH=$(pwd) pytest app/tests --cov=app --disable-warnings

✅ Current Coverage:

================================================ test session starts ================================================
platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0
rootdir: /Users/ashish/Desktop/JK_Tech/rag_qna_project
plugins: cov-6.0.0, anyio-4.8.0
collected 3 items                                                                                                    

app/tests/test_document_ingestion.py .                                                                        [ 33%]
app/tests/test_document_selection.py .                                                                        [ 66%]
app/tests/test_question_answering.py .                                                                        [100%]

--------- coverage: platform darwin, python 3.10.16-final-0 ----------
Name                                     Stmts   Miss  Cover
------------------------------------------------------------
app/api/__init__.py                          0      0   100%
app/api/document_ingestion.py               30      4    87%
app/api/document_selection.py               25      4    84%
app/api/question_answering.py               31      5    84%
app/core/__init__.py                         0      0   100%
app/core/config.py                           4      0   100%
app/core/logging_config.py                   3      0   100%
app/main.py                                 12      2    83%
app/models/__init__.py                       0      0   100%
app/models/db_models.py                     18      2    89%
app/models/embedding_store.py               42     12    71%
app/models/embedding_store_instance.py       2      0   100%
app/services/__init__.py                     0      0   100%
app/services/embedding_service.py           11      3    73%
app/services/retrieval_service.py           22      5    77%
app/tests/__init__.py                        0      0   100%
app/tests/test_document_ingestion.py         7      0   100%
app/tests/test_document_selection.py         6      0   100%
app/tests/test_question_answering.py         7      0   100%
------------------------------------------------------------
TOTAL                                      220     37    83%
=========================================== 3 passed, 5 warnings in 8.36s ===========================================

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