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I will build a rag ai chatbot with openai and vector database
Akin Stack
About this gig
Scattered information across documents, websites, and knowledge bases can make it difficult to find accurate answers quickly. Turn your business knowledge into an AI chatbot that retrieves relevant information and responds automatically.
Manual searching can waste time, slow customer support, and make valuable information difficult to access. Generic chatbots can also provide irrelevant answers when they cannot access your actual business data.
This RAG AI chatbot connects your documents, website, or knowledge base to an LLM and vector database, creating a searchable AI assistant grounded in your information.
How it works:
- Analyze your use case and data
- Process and index your information
- Configure embeddings and vector search
- Build the RAG pipeline
- Connect the LLM and chatbot
- Test and deploy the system
What you can get:
- RAG AI chatbot
- Knowledge base
- Vector database
- OpenAI/LLM integration
- Source citations
- Custom chatbot UI
- API integrations
What isn't included: third-party API or hosting costs, purchased datasets, or complex integrations outside the selected package.
Please message before ordering with your data source, use case, and required features.
Get to know Akin Stack
Akin Stack
AI Automation Engineer n8n AI Agents Full Stack Developer
- FromNigeria
- Member sinceOct 2025
- Avg. response time1 hour
Languages
English, French, German
I help businesses automate repetitive work, connect their tools, and build AI-powered systems that save time and reduce manual effort. I design and build n8n workflows, AI agents, API integrations, RAG systems, and custom web applications tailored to how your business actually operates. My approach is simple: understand the process first, map the right solution, then build, test, and document it properly. You can expect clear communication, practical solutions, reliable workflows, and clean, maintainable code.
FAQ
Can you build the RAG chatbot using my existing documents or website?
Yes. The system can use PDFs, Word documents, text files, website content, FAQs, product information, or other approved data sources as the knowledge base.
Can the chatbot provide sources for its answers?
Yes. Source citations can be included so users can see which document or knowledge source was used to generate an answer.
What happens when the chatbot cannot find an answer in my data?
The RAG system can be configured to recognize insufficient information and respond appropriately instead of simply generating an unsupported answer.
Can the RAG chatbot connect to my existing application or CRM?
Yes. API integrations can be added when your application, CRM, or other platform provides the required API access or webhooks.
Can you use my private business data without training the AI model on it?
Yes. RAG retrieves relevant information from your connected knowledge base at query time, allowing the system to use your business data without requiring that data to become part of the model's general training.
