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I will build a rag chatbot for your documents and knowledge base
furkan369
About this gig
Turn your documents into a reliable AI assistant. I will build a custom Retrieval-Augmented Generation (RAG) chatbot that answers questions using your PDFs, manuals, policies, knowledge base, or internal content.
The solution can include document ingestion, text chunking, embeddings, vector search, citations, conversation memory, metadata filtering, API endpoints, database integration, and a simple chat interface. I work with OpenAI, Claude, LangChain, LlamaIndex, Python, C#, ASP.NET Core, PostgreSQL, SQL Server, and vector databases.
You will receive:
- Clean and maintainable source code
- Secure configuration and error handling
- Retrieval tuned for your content
- Clear setup and usage instructions
- Honest scope evaluation before development
Please message me before ordering. Share the document types, approximate volume, expected users, preferred AI provider, and deployment environment. Provider usage and hosting fees are not included.
Get to know furkan369
furkan369
Senior Dot NET Developer and AI API Integration Expert
- FromTurkey
- Member sinceAug 2020
Languages
Turkish, English
I am a backend developer with 6 years of experience building enterprise applications using C#, ASP.NET Core, REST APIs, SQL Server, PostgreSQL, Oracle, Docker, and IIS. For the past year, I have also developed AI-powered applications with OpenAI API, Claude API, Semantic Kernel, LangChain, and LlamaIndex. I can help with API development, .NET bug fixing, AI integration, RAG chatbots, PDF and OCR extraction, web scraping, database integration, Docker deployment, and backend architecture. I provide clean code, clear communication, source delivery, and honest scope evaluation.
FAQ
What document formats can you process?
I can work with PDFs, text files, Word documents, web content, and structured knowledge-base exports. Please share samples before ordering.
Will the chatbot provide source citations?
Yes. I can return the relevant source, page, section, or document metadata when the source content supports it.
Which vector databases do you support?
I can work with PostgreSQL pgvector, Pinecone, Qdrant, Chroma, and other suitable vector stores based on your environment.
Can you integrate the RAG system into my existing app?
Yes. I can expose the system through an API or integrate it into an existing C#, ASP.NET Core, Python, or web application.
Are AI provider and hosting fees included?
No. OpenAI, Anthropic, vector database, cloud, and hosting usage fees are paid directly by you.
