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$
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I will build a rag chatbot trained on your pdfs and website
Asif Karim
Über diesen Service
Want a chatbot that answers from YOUR documents instead of guessing? I build RAG chatbots trained on your PDFs, website pages and business documents, with answers grounded in your content.
WHAT I BUILD
- RAG chatbot for PDFs, Word files, text and web pages
- Answers with source references, so you can verify them
- Vector database setup (Chroma, FAISS, Qdrant or Pinecone)
- Chat interface or embeddable widget for your website
- FastAPI backend so any app can connect
- Works with OpenAI and Gemini
- Customer support, FAQ, internal knowledge and study bots
WHAT YOU GET
- Working source code
- Setup guide and short walkthrough
- Basic testing on your documents
HOW IT WORKS
1. Send me your documents and goal
2. I confirm the scope
3. I build and test the chatbot
4. You review, request revisions and receive the files
Message me before ordering. API usage and hosting costs are not included.
Lerne Asif Karim kennen
Asif Karim
AI Engineer AI Automation RAG and LLM Integration FastAPI Developer
- AusPakistan
- Mitglied seitMärz 2025
Sprachen
Urdu, Englisch
AI Engineer specializing in AI Automation, RAG, and LLM Integration. I build production-ready Generative AI systems using FastAPI, LangChain, and vector databases (Pinecone, Qdrant, FAISS) — including custom RAG pipelines, AI chatbots, and Voice AI Agents for booking, support, and call automation. I connect LLMs to real business workflows using n8n, REST APIs, and CRM/calendar integrations. Strong CS foundation in machine learning and system design, with hands-on freelance experience delivering AI Automation and Voice AI solutions for clients worldwide. Let's automate your business with AI.
Mein Portfolio
FAQ
Q1: What is a RAG system?
A RAG (Retrieval-Augmented Generation) system retrieves relevant documents and combines them with generative AI (LLMs) to provide accurate, contextual answers.
Q2: What documents can this system handle?
It can handle PDFs, text files, and DOCX files. Additional formats can be added on request.
Q3: Which vector databases do you use?
I use FAISS, ChromaDB, Pinecone, or Weaviate based on your preference and use case.
Q4: Can this system work offline or with open-source models?
Yes, I can integrate Hugging Face models that run locally if you don’t want to use cloud APIs like OpenAI.
Q5: Can you deploy the app for me?
Yes! Deployment via Docker or a step-by-step guide can be included in the Premium package or as an extra.
Q6: Will the code be documented and reusable?
Absolutely. The code will be clean, modular, and include comments and setup instructions.
