Project: We seek an experienced AI/ML developer to build a self-hosted AI chatbot system utilizing Retrieval-Augmented Generation (RAG) for our health related course Website. We have a wiki, over 1,200 articles, a 70k Youtube channel, podcast, lots of content. End goal is to have everything relevant RAG-ed for LLM use.
We plan for users to have access to two tiers of LLM to answer their question:
Free Tier: Intelligent FAQ using public content, guiding users to resources. Paid Tier: Expert-level assistant with secure access to proprietary support databases and premium materials.
**** This project is structured in paid, progressive milestones, beginning with a full prototype. We're looking for a long-term development partner. ******
Objective: Deliver a complete, functional RAG chatbot prototype. This milestone will serve as an immediate proof-of-concept, demonstrating core RAG pipeline functionality, response quality, and technical execution in a self-hosted environment.
Scope of Work: Ingest and process a small sample of public website content provided. Implement text chunking and generate embeddings using a local embedding model. Store data in a local vector database (e.g., ChromaDB, FAISS). Set up local LLM serving (e.g., Ollama) with open-source models (e.g., Mistral, Gemma). Develop Python script: Query -> Retrieve Context -> Prompt LLM -> Generate Response. Implement a simple Command-Line Interface (CLI) for interaction.
Deliverables for Milestone 1: A fully functional Python script demonstrating the RAG process. The populated local vector database files. Clear setup instructions for local LLM serving and running the script. A brief README explaining models and prototype functionality. A demonstration of the chatbot responding to example queries. Milestone 1 Budget: We have allocated $800 - $1800 USD for this pilot milestone. Please quote your price for this specific scope.
Evaluation Criteria: Functionality, AI response quality/relevance, code clarity & documentation, technical competence in local LLM/DB setup, communication.
Full Project Vision & Long-Term Income Potential: Successful Milestone 1 completion leads to an invitation for subsequent phases, building the complete system. The estimated total development budget for the entire project is $7,000 - $20,000+ USD. Future milestones will involve: scaling data ingestion (public & proprietary), building a web UI, implementing secure paid-tier logic, and production deployment. This offers a significant opportunity for a stable, long-term engagement.
Preferred Technology Approach: Python, LangChain/LlamaIndex, Ollama for local LLM serving, and local vector databases like ChromaDB/FAISS. Experience with FastAPI/Flask for backend APIs and Streamlit for UIs is a plus. We value documented expertise in open-source LLMs and RAG principles. We are open to well-reasoned alternative technologies that align with our self-hosted, cost-effective, performant goals.
How to Apply: Please submit your proposal including:
Your quote for Milestone 1. A brief outline (max 500 words) of your strategy for the full project's future milestones. Your relevant experience with RAG systems, self-hosted LLMs, Python, and related frameworks. Links to your portfolio or examples of similar AI development projects.
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