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I will build rag ai chatbot chromadb dify botpress rasa knowledge base integration
Emmanuel.C
About this gig
Need an AI chatbot that can answer questions using your own documents and business knowledge?
I will build a knowledge-based AI chatbot using RAG architecture and suitable technologies such as ChromaDB, Dify, Botpress or Rasa. Your chatbot can be designed to retrieve relevant information from documents, knowledge bases and approved business content before generating responses.
What I can help with:
- RAG chatbot development
- ChromaDB integration
- AI knowledge base
- Document chatbot
- Dify knowledge workflows
- Botpress AI chatbot
- Rasa conversational AI
- Embedding and retrieval workflows
- Business knowledge assistants
- AI document search
You can provide PDFs, FAQs, website content, product information or internal documentation depending on the project.
Send your documents, preferred platform and chatbot objective before ordering so I can determine the appropriate architecture.
Get to know Emmanuel.C
Emmanuel.C
AI Chatbot Developer, Botpress, Dify, Rasa, Twilio and AI Automation
- FromUnited Kingdom
- Member sinceSep 2026
- Avg. response time1 hour
Languages
English, German, French
Hi, I'm Emmanuel,
I build AI chatbots and conversational automation for businesses using Botpress, Dify, Rasa, Twilio and other modern chatbot platforms. I can create customer-support bots, AI assistants, knowledge-base chatbots, lead-generation flows and business automation connected to websites, CRMs and external services.
I focus on practical chatbot solutions that are easy to manage, scalable and designed around your actual business workflow.
FAQ
Can you build a RAG chatbot that answers questions directly from my business documents?
Yes. I can build a retrieval-based AI workflow that uses your approved documents or knowledge sources to retrieve relevant information before generating chatbot responses.
Can you use ChromaDB as the vector database for my RAG chatbot?
Yes. ChromaDB can be incorporated where it fits the project architecture, including workflows involving document embeddings, retrieval, knowledge search, and AI-generated responses.
Can the RAG chatbot work with PDFs, company documentation, FAQs, or large knowledge collections?
Yes. The implementation can be designed around supported document and knowledge sources, with the retrieval workflow structured according to the type, size, and organization of your content.
Can you integrate a RAG system with Dify, Botpress, or Rasa?
Yes. Depending on the technical requirements and capabilities of the selected platform, I can design the RAG workflow so the knowledge retrieval system works with the chatbot environment and required integrations.
How do you make sure the chatbot retrieves relevant information instead of giving generic AI answers?
The RAG architecture can be configured around your knowledge sources, document processing, embeddings, retrieval logic, and response workflow so the AI has relevant business information available when generating answers.
