RAG-Based AI Placement Assistant

Turn your college's placement chaos into a chatbot that actually knows the answers. The RAG-Based AI Placement Assistant (built as the PlaceMate app) is a retrieval-augmented generation chatbot for college placement cells: instead of a generic LLM that hallucinates policy details, PlaceMate uploads your real documents — placement policies, past company visit archives, eligibility criteria, FAQs — chunks and embeds them into a vector database, and answers every student query with cited sources pulled from your own data. This is the standout 2026 final-year project: RAG is the hottest LLM architecture in the industry right now, and no Indian final-year project seller has productized a placement-cell RAG chatbot yet. You get a working LangChain pipeline, a clean Streamlit interface, and citations on every answer — exactly the kind of demonstrable, viva-proof AI system that wins top grades.

1. The placement cell uploads documents (policies, company archives, FAQs) through the PlaceMate admin panel
2. The ingestion pipeline cleans the text, splits it into chunks, generates embeddings and stores them in the vector database (FAISS/Chroma)
3. A student asks a question in the PlaceMate chat interface — e.g. "What is the eligibility criteria for the TCS drive?"
4. LangChain retrieves the most relevant chunks from the vector store and passes them to the LLM as grounded context
5. The LLM generates an answer strictly from the retrieved documents, with citations pointing to the exact source
6. Chat history is maintained so follow-up questions like "and what's the package for it?" resolve to the right company

• Document ingestion pipeline — upload PDFs, DOCX files and webpages (placement policies, company archives, FAQ sheets) into the PlaceMate knowledge base
• RAG chatbot with cited answers — every response includes the source documents and page references it was retrieved from
• LangChain orchestration — modular chains for ingestion, retrieval and generation, easy to explain in your viva
• Vector database retrieval (FAISS/Chroma) — semantic search over embeddings, not keyword matching
• Pluggable LLM backends — works with OpenAI-compatible and Gemini-compatible LLM APIs, swappable in one config line
• Placement-cell tuned UI — PlaceMate Streamlit app with chat history, source viewer and admin upload panel
• Hallucination control — answers constrained to retrieved context; the bot says "not found in documents" instead of inventing policy
• Conversation memory — follow-up questions resolve correctly using chat history context

• Complete PlaceMate source code (ingestion pipeline + RAG chain + Streamlit app)
• Ready-to-run setup with requirements file and configuration guide
• Sample placement documents dataset for demo and testing
• Detailed project report PDF (synopsis, architecture, implementation, testing, results)
• Presentation PPT for your final demo
• Viva Q&A set covering RAG, LangChain, embeddings, vector databases and LLM concepts
• Setup and demo support until your submission

• Answer quality depends on the quality and completeness of the uploaded documents — garbage in, garbage out
• LLM API calls require an API key and internet access (OpenAI-compatible or Gemini-compatible); a small per-query cost may apply on paid tiers
• Very large document collections (tens of thousands of pages) may need FAISS tuning for retrieval speed
• The bot cannot answer questions outside the uploaded documents — it is intentionally constrained to retrieved context
• Streamlit app is designed for demo and departmental use, not high-concurrency production traffic