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AI & MLMajor project
AI Agents with Retrieval
Upload PDFs and web pages, then ask questions or give tasks in a chat interface. The agent retrieves the most relevant passages, decides which tools to call, generates an answer with an LLM, and shows which documents it used.
- Level
- Major project
- Delivery
- 5–7 working days
- Stack
- Python, FastAPI
- Best for
- Final-year B.Tech / B.E. (CSE, AI & DS)
Features
- Document upload with automatic chunking and embedding
- Vector search with pgvector
- Tool calling for multi-step tasks such as compare and summarise
- Answers with inline source citations
- Chat history per user
- Simple evaluation page to check answer quality
How it's built
The modules you'll walk through with our engineer.
- 01
Ingestion pipeline
- PDF and web parsing
- Chunking strategies
- Embeddings and storage
- 02
Retrieval and agent loop
- Similarity search
- Tool calling
- Citations
- 03
Application
- FastAPI backend
- React chat interface
- Evaluation page
Tech stack
PythonFastAPILangChainPostgreSQLpgvectorReact
What you get
- Complete source code with a README
- Setup video including API key configuration
- Project report template with architecture and diagrams
- Presentation slides for your review
- Two 45-minute 1:1 walkthroughs of the code
Best for
- Final-year B.Tech / B.E. (CSE, AI & DS)
- M.Tech and MCA students
Learn it, don't just submit it
Every project comes with a 1:1 walkthrough
An engineer walks you through the architecture and code so you can extend it and answer viva questions with confidence. Check your institution's rules on using reference implementations before you submit.
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Get this project
Request AI Agents with Retrieval
- Delivered in 5–7 working days
- Customisations quoted before you pay
- Walkthrough booked at a time that suits you