Developer Needed for Private AI Chat System (API-based)

Developer Needed for Private AI Chat System (API-based) Project Overview I need a private, self-hosted AI assistant that connects in real-time to third-party AI APIs (Anthropic Claude and/or Google Gemini) — NOT a local/offline LLM (no Ollama, no local model weights). The system must be accessible via a web interface from multiple devices (laptop, phone, tablet) and multiple locations, with my data staying under my control. What I Need Built 1. Backend server that securely calls the Anthropic API and/or Google Gemini API - API keys stored server-side only (never exposed to the browser/client) - Support for switching between or combining providers 2. Simple web-based chat interface - Responsive design (usable on desktop and mobile browsers) - User login/authentication (so only I — and anyone I authorize — can access it) 3. Document storage & retrieval (RAG - Retrieval-Augmented Generation) - Ability to upload my own documents/files (PDF, Word, text, etc.) through the web interface - Documents automatically split into chunks and converted into embeddings (numerical representations for semantic search) - Embeddings stored in a self-hosted vector database on my own server (e.g., Qdrant, Weaviate, or pgvector — developer to recommend based on scale) - At query time, the system retrieves only the most relevant chunks (semantic similarity search) and sends those — not the full documents — to the AI API along with my question - Full documents and the vector database must remain on my own infrastructure at all times; only the retrieved text snippets are sent externally to Claude/Gemini per query - Ability to update/delete documents from the knowledge base (re-indexing when content changes) - Source attribution: responses should indicate which document(s) the answer was drawn from 4. Content ingestion pipeline (for large personal knowledge sources) - Ability to bulk-ingest a large personal library of trading education content (e.g., YouTube video transcripts) into the RAG knowledge base - Pipeline should: fetch/extract transcripts, clean up the text (remove filler, fix formatting), chunk by topic/concept rather than fixed length, generate embeddings, and index into the vector database - Should also support ingesting reference/technical documentation (e.g., the official Pine Script language reference) as a separate, always-available knowledge source used specifically to ground any code generation and reduce errors/hallucinations - Reusable pipeline: I should be able to add new sources (new videos, new documents) later without needing the developer each time 5. Live web search capability - The assistant should be able to search the web in real time for current information (e.g., market news, recent Pine Script/TradingView updates) when a question needs up-to-date data - Implement via a web search tool/plugin connected to the AI API (e.g., Claude's built-in web search tool, or a search API such as Google/Bing/Perplexity Sonar integrated into the backend) - Should be clearly distinguished from the RAG knowledge base: RAG = my own static documents/strategies; web search = live, current information from the internet - Results should include source links so I can verify information 6. Hosting & deployment - Deployed on a VPS (I will provide access — see below) or recommend one with justification - Dockerized setup preferred, for portability and easy maintenance - Must remain accessible 24/7 via a secure URL (HTTPS) 7. Logging & basic security - Request logs (who asked what, when) - Basic protection against unauthorized access (rate limiting, authentication) 8. Documentation - Clear instructions on how to maintain, update, and restart the system - How to add/rotate API keys - How to add new users if needed Requirements for the Developer - Proven experience with backend development (Python/FastAPI or Node.js/Express) - Experience integrating LLM APIs (Anthropic, OpenAI, or Google Gemini API) - Experience with vector databases / RAG pipelines (e.g., Pinecone, Qdrant, Weaviate, or pgvector) - Experience deploying and managing applications on a VPS using Docker - Understanding of basic security practices (secrets management, authentication, HTTPS/SSL setup) - Able to communicate clearly in English and explain technical choices in plain language - Portfolio or examples of similar past projects (chatbots, AI integrations, RAG systems) - Experience with text extraction/ingestion pipelines (e.g., transcript extraction, document parsing, bulk chunking strategies) is a plus - Experience integrating web search tools/APIs (e.g., Claude's web search tool, Google Search API, Bing API, or Perplexity Sonar) is a plus This will be a project based contract (Upwork fixed contract with milestones), where gradual payments will be released upon milestones achievements. It will be considered finalized when everything will be done/completed. This is not a "pay-by-the-hour" contract. The total budget & exact milestones for this project will be discussed and agreed upon.