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I will build a custom rag chatbot using langchain
Md Sajedul
Über diesen Service
Need a reliable AI chatbot that can understand and answer questions from your documents or business data?
I will build a custom Retrieval-Augmented Generation (RAG) chatbot tailored to your data using Python, LangChain, vector search, and LLMs.
What I can build
PDF, TXT, JSON, and structured document processing
Context-aware document Q&A
Semantic vector search
Hybrid retrieval using Vector Search + BM25
Reranking for improved retrieval quality
LLM-powered answer generation
Source-grounded responses from your data
FastAPI backend and API integration
Customizable RAG pipeline based on your requirements
RAG Pipeline
Your data Document Processing Chunking Embeddings Retrieval Reranking LLM Grounded Answer
I focus on building practical RAG systems that retrieve relevant information from your data before generating responses, helping reduce irrelevant or unsupported answers.
Whether you need a document Q&A system, internal knowledge chatbot, or custom AI assistant, I can build a solution around your requirements.
Please contact me before ordering so I can review your data, requirements, and project scope.
Lerne Md Sajedul kennen
Md Sajedul
AI Developer for RAG OCR and AI Chatbots
- AusBangladesch
- Mitglied seitNov. 2022
- ⌀ Antwortzeit1 Stunde
- Letzte Lieferung3 Jahre
Sprachen
Englisch
Hi, I'm Sajedul, an AI Developer specializing in Generative AI, OCR, Retrieval-Augmented Generation (RAG), AI Chatbots, Python Automation, and API Integration. I build intelligent AI solutions that automate workflows, process documents, and create AI-powered applications. My expertise includes LangChain, LangGraph, FastAPI, OpenAI, Claude, Gemini, Llama, Mistral, Hugging Face, FAISS, Vector Databases, and REST APIs. I deliver clean, scalable, production-ready solutions with clear communication and on-time delivery
FAQ
What types of data can you use for the RAG chatbot?
I can work with PDF, TXT, JSON, and other text-based or structured data, depending on the project requirements.
Can the chatbot answer questions from my own documents?
Yes. The RAG chatbot retrieves relevant information from your provided documents or knowledge base before generating an answer.
Can you build a custom RAG chatbot for my business?
Yes. I can customize the RAG pipeline, retrieval method, LLM integration, prompts, and API based on your requirements.
Do you support hybrid search?
Yes. Advanced RAG solutions can use hybrid retrieval combining semantic vector search and BM25, with reranking to improve relevant results.
Can you integrate the chatbot with an API?
Yes. I can build a FastAPI backend and API endpoints for your RAG application.
Will I receive the source code?
Yes. Source code is included according to the selected package.
Can you work with large document collections?
Yes. The RAG pipeline can be designed to handle larger document collections with appropriate chunking, embeddings, retrieval, and indexing strategies.
What do you need from me before starting?
Please provide your documents or data, desired chatbot functionality, example questions, preferred LLM/API if any, and any specific technical requirements.
Should I contact you before placing an order?
Yes. Please contact me before ordering so I can review your requirements and confirm the appropriate package and scope.
