A Retrieval-Augmented Generation (RAG) application for car information. Users can ask questions about cars, and the app retrieves relevant information from a database and generates accurate responses using a language model.

Using data that isnt very informative, plus dimensions are also 384 instead of the ideal 1024.

• Search and Q&A: Ask questions about car specs, pricing, features, etc.
• RAG Pipeline: Combines a vector database and a language model for context-aware responses.
• Embeddings-based Retrieval: Uses sentence-transformers/all-MiniLM-L6-v2 for semantic search.
• Fast and Responsive: Handles multiple queries efficiently.

• Python
• FastAPI
• LangChain for RAG pipeline
• Vector Database (e.g., Chroma, FAISS, Pinecone)
• Sentence Transformers (all-MiniLM-L6-v2) for embeddings

1. Clone the repository:

2. Create a virtual environment: python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate

3. Install dependencies: pip install -r requirements.txt

4. Set environment variables (if required, e.g., API keys):

5. Run the app

1. User query → Sent to FastAPI endpoint.
2. Embedding generation → Converts the query into a vector.
3. Vector search → Retrieves relevant documents from the car dataset.
4. Language model → Generates a response based on retrieved information.
5. Response → Sent back to the user.

• Car specifications dataset (CSV/JSON)
• Includes fields like make, model, year, price, fuel_type, mileage, etc.
• Vectorized and stored in a vector database for semantic search.

1. Feel free to submit PR's.

• Author: Rishabh Rawat
• Email: coolrishabhrawat@gmail.com
• GitHub: showtim3