LLM / RAG Chatbot for Your DocumentsTaron Babayan
I build an AI chatbot that answers questions using your PDFs / documents and returns grounded responses with citations (so it doesn’t just “guess”). This is ideal for internal knowledge bases, legal/HR docs, product manuals, policies, or any document-heavy workflow.
What you’ll get
Document ingestion (PDFs, text files; more formats if needed)
Chunking + embeddings + vector search (FAISS)
Retrieval-augmented answering (RAG) with sources/citations
An API (FastAPI) for integration with your product or internal tools
Optional simple web UI (chat interface)
Deliverables
Working RAG chatbot (codebase + runnable demo)
Configurable settings (top-K retrieval, chunk size, etc.)
Clear setup + deployment instructions
Basic evaluation: sample queries + checks for grounding/citation quality
Typical use-cases
“Ask our policies/manuals and get referenced answers”
“Search and summarize contracts / regulations with sources”
“Customer support assistant trained on internal docs”
“Team knowledge bot for onboarding and FAQs”
What I need from you
Your documents (PDFs or links/files)
A short list of “must-answer” questions (10–30 is enough)
Any tone/style requirements (formal, short answers, etc.)
Tech stack
Python • FastAPI • FAISS • Embeddings (SentenceTransformers) • LLM integration • PDF processing
Example work
Starting at$250 /wk
Tags
Python
AI Chatbot Developer
AI Developer
LLM Integration
ML Engineer
RAG
Service provided by
Taron Babayan Yerevan, Armenia
LLM / RAG Chatbot for Your DocumentsTaron Babayan
Starting at$250 /wk
Tags
Python
AI Chatbot Developer
AI Developer
LLM Integration
ML Engineer
RAG
I build an AI chatbot that answers questions using your PDFs / documents and returns grounded responses with citations (so it doesn’t just “guess”). This is ideal for internal knowledge bases, legal/HR docs, product manuals, policies, or any document-heavy workflow.
What you’ll get
Document ingestion (PDFs, text files; more formats if needed)
Chunking + embeddings + vector search (FAISS)
Retrieval-augmented answering (RAG) with sources/citations
An API (FastAPI) for integration with your product or internal tools
Optional simple web UI (chat interface)
Deliverables
Working RAG chatbot (codebase + runnable demo)
Configurable settings (top-K retrieval, chunk size, etc.)
Clear setup + deployment instructions
Basic evaluation: sample queries + checks for grounding/citation quality
Typical use-cases
“Ask our policies/manuals and get referenced answers”
“Search and summarize contracts / regulations with sources”
“Customer support assistant trained on internal docs”
“Team knowledge bot for onboarding and FAQs”
What I need from you
Your documents (PDFs or links/files)
A short list of “must-answer” questions (10–30 is enough)
Any tone/style requirements (formal, short answers, etc.)
Tech stack
Python • FastAPI • FAISS • Embeddings (SentenceTransformers) • LLM integration • PDF processing
Example work
$250 /wk