Phoenix - Your AI AssistantAI Chatbot Development — LLM-Powered Bot With Knowledge Base
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5 days
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What you get with this Offer
I will build a fully functional LLM-powered chatbot connected to your own knowledge base using Retrieval-Augmented Generation (RAG) — enabling your bot to answer questions accurately from your documents, FAQs, product manuals, or internal knowledge base rather than hallucinating generic responses from its training data alone. RAG-based chatbots combine the conversational fluency of large language models with the factual precision of your own content, making them genuinely useful in customer-facing and internal support contexts.
The build covers document ingestion and chunking, vector embedding generation (OpenAI Embeddings or open-source alternative), vector store setup (Pinecone, Chroma, or FAISS), LangChain or LlamaIndex orchestration, and a chat interface deployable as a web widget, API endpoint, or Slack/Teams bot. Conversation history, source citation in responses, and a confidence threshold for fallback handling are included.
This service suits businesses that want to deploy an AI assistant capable of answering questions from their own content — support documentation, product catalogues, HR policies, legal contracts, or training materials — without generic or hallucinated responses.
The build covers document ingestion and chunking, vector embedding generation (OpenAI Embeddings or open-source alternative), vector store setup (Pinecone, Chroma, or FAISS), LangChain or LlamaIndex orchestration, and a chat interface deployable as a web widget, API endpoint, or Slack/Teams bot. Conversation history, source citation in responses, and a confidence threshold for fallback handling are included.
This service suits businesses that want to deploy an AI assistant capable of answering questions from their own content — support documentation, product catalogues, HR policies, legal contracts, or training materials — without generic or hallucinated responses.
What the Freelancer needs to start the work
Please share your knowledge base content (PDFs, Word docs, URLs, or plain text), describe the chatbot's intended use case and audience, confirm your preferred deployment target (web widget, API, Slack, Teams), and provide your OpenAI API key or preferred LLM provider.