I will build a rag pipeline chatbot using langchain


Level 2
About this gig
Your business has valuable knowledge locked inside PDFs, contracts, manuals, wikis, or databases. I build RAG (Retrieval-Augmented Generation) pipelines that let you or your customers ask questions in plain language and get accurate, sourced answers from that private data.
No hallucinations. No generic AI answers. Only answers grounded in your actual documents.
What I build:
Document ingestion pipeline (PDF, DOCX, TXT, HTML, Notion, Confluence, URLs)
Intelligent chunking strategy and embedding (OpenAI, Cohere, or local models)
Vector database setup (Pinecone, ChromaDB, Weaviate, or FAISS)
LangChain or LlamaIndex orchestration layer
Conversational memory with source citation so users see where answers come from
Clean chat UI or integration into your existing website or app
Accuracy evaluation and testing before delivery
Use cases I've built:
Legal document Q&A, internal company knowledge base, product manual assistant, contract review tool, HR policy chatbot, customer support bot trained on your docs, medical protocol assistant.
Tell me your document type, volume, and use case I'll recommend the right stack and give you realistic accuracy expectations before you order.
Get to know habhibo
developer
Level 2
- FromTunisia
- Member sinceAug 2015
- Avg. response time1 hour
- Last delivery3 weeks
Languages
English, Arabic, French
My Portfolio
FAQ
What document formats do you support
PDF, DOCX, TXT, HTML, Markdown, Notion exports, Confluence exports, and any URL-based content. If you have a specific format, ask me before ordering.
How accurate will the answers be
Accuracy depends on document quality and question type. I include evaluation testing before delivery and will tell you honestly what to expect for your specific use case.
