That makes this a strong first project.
Here’s a README version that reflects that this is your first RAG project and is good for GitHub or interviews.
This is my first Retrieval-Augmented Generation (RAG) project built using Python and AI tools.
The application allows users to upload a PDF document and ask questions based on its content. The system retrieves the most relevant information from the uploaded document and generates intelligent answers using a large language model.
This project helped me understand how modern AI systems combine document retrieval + embeddings + vector search + LLMs to build real-world applications.
Users can:
✅ Upload a PDF document ✅ Ask questions related to the uploaded PDF ✅ Get AI-generated answers based only on document content
Example:
Upload: Computer Organization notes PDF Question: What is instruction cycle? Output: AI-generated explanation from the uploaded notes
- Flask
- Python
- Sentence Transformers
- all-MiniLM-L6-v2 → for embeddings
- FAISS → for semantic search
- Groq + Llama 3.3 → for answer generation
- PyPDF
- HTML
- CSS
- JavaScript
User uploads a PDF file.
The system extracts text from the PDF using PyPDF.
Large text is split into smaller chunks.
Each chunk is converted into vectors using all-MiniLM-L6-v2.
Vectors are stored in FAISS.
When the user asks a question, the system finds the most relevant chunks.
The retrieved chunks are sent to Groq, which generates the final answer.
Through this project, I learned:
- What RAG architecture is
- How embeddings work
- How vector databases work
- Semantic search
- Prompt engineering
- Building AI apps with Flask
- Integrating LLM APIs into real projects
I plan to add:
- Multiple PDF upload support
- Chat history
- Better document chunking
- Support for DOCX/TXT files
- User authentication
- Cloud deployment
As this is my first RAG project, I wanted to understand how AI assistants work behind the scenes and gain hands-on experience building an end-to-end AI application.