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I will build a custom rag chatbot using langchain, gpt4, and vector databases
Asim S
About this gig
Still waiting for users to manually search through hundreds of pages?
Your customers and team deserve instant, accurate answers. I build production-ready RAG (Retrieval-Augmented Generation) chatbots that turn your documents, knowledge base, or website into natural, intelligent conversations.
What I Build:
- Custom RAG pipelines (LangChain / LlamaIndex)
- Vector DB integration (Pinecone, Weaviate, FAISS)
- Multi-document parsing (PDF, DOCX, CSV, Web pages)
- Memory-aware, context-rich conversations
- Clean, deployed UI (Streamlit, React, or FastAPI backend)
Tech stack
Python · LangChain · LlamaIndex · LLMs
Pinecone · Weaviate · FAISS · FastAPI · Streamlit · Docker
Use Cases Built
- SaaS Customer Support bots
- Legal & HR policy knowledge assistants
- E-commerce product recommenders
- Research paper summarization tools.
Why choose me:
- 3+ years building real-world AI systems (not just tutorials)
- Production-grade code with clean documentation
- I deliver deployed apps, not Colab notebooks
- Responsive communication avg reply under 1 hour
- Source code + architecture diagram included in all packages
Message me before ordering I offer a FREE 15-minute scoping call to make sure we build exactly what you need.
Get to know Asim S
Asim S
AI Engineer
- FromPakistan
- Member sinceJun 2026
- Avg. response time1 hour
Languages
Urdu, English
I am an AI Engineer with over 2 years of experience building agentic AI systems and multi-tenant LLM backends. I specialize in LangGraph orchestration, RAG pipeline design, and async FastAPI development. I have a proven track record of shipping end-to-end platforms and translating complex business requirements into observable AI systems.
FAQ
What exactly is a RAG chatbot, and how does it work?
A RAG chatbot connects AI models like GPT-4 to your private data. It securely searches your knowledge base, retrieves accurate context, and generates natural responses based strictly on your files, completely preventing AI hallucinations.
What data sources can your RAG pipeline handle?
Built with LangChain and LlamaIndex, my pipelines effortlessly ingest unstructured data from PDFs, Word docs (DOCX), Excel/CSV sheets, text files, and live website URLs.
Which vector database do you recommend?
For lightweight, local prototypes, I use a FAISS vector database. For enterprise, production-grade applications requiring massive scale and speed, I recommend cloud-native options like Pinecone or Weaviate.
Is my private business data safe and secure?
Yes. Your data is processed securely through API integrations and context retrieval. Your proprietary business documents are never used to train public LLM models, ensuring total privacy.
What will the user interface (UI) look like?
The basic tier includes a clean, responsive Streamlit UI. For standard and premium tiers, we can scale up to an advanced Streamlit dashboard or a fully custom, modern React frontend.
Can you integrate the bot into my existing website?
Yes. I use Python and FastAPI to build secure API endpoints. This allows you to seamlessly embed the RAG chatbot or connect the backend pipeline to any existing app or website.
What is the main difference between the packages?
Basic is a 1-doc Streamlit prototype (FAISS). Standard upgrades to a multi-doc cloud system using Pinecone and GPT-4. Premium delivers a full multi-user SaaS application with an admin dashboard.
Will I receive the source code and documentation?
Absolutely. Every package includes clean, commented Python source code. Higher tiers also include system architecture diagrams and step-by-step cloud deployment documentation.
Why should I message you before placing an order?
RAG systems depend heavily on your specific data structure. Messaging me first allows us to check your files, pick the right vector database, and set up our free 15-minute scoping call!
