Agentic RAG Platform & Databases
Publicada el 2026-07-22
Descripción de la oferta
I need an end-to-end Retrieval-Augmented Generation platform, powered by Ollama-hosted language models, that lets a user drop in a document—TXT, PDF, or DOCX—then automatically searches my custom knowledge bases for exact matches and contextually related material, finally returning a concise, well-structured summary. Scope • Data ingestion: raw folders of text must be cleaned, chunked, embedded and stored in a vector store you recommend (Faiss, Milvus, or similar). • User workflow: drag-and-drop upload, progress feedback, and a chat-style window that displays the sourced passages beside the generated synopsis. • Retrieval logic: combine exact-match look-ups with semantic search so the AI can cite both literal hits and topic-level associations. • Agentic orchestration: the system should chain tasks—retrieval, ranking, summarisation, and reference insertion—without manual prompts. • Deployment: containerised (Docker) so I can spin it up on my own GPU server alongside Ollama. Acceptance criteria 1. A running local instance that processes at least 1 GB of mixed documents in under five minutes. 2. Summaries must display clickable citations that trace back to the source paragraph. 3. A README explaining setup, environment variables, and how to add new data sets. If you have shipped similar RAG stacks before and can show a quick demo link or repo, that will move things along quickly.
Skills
Fuente original: freelancer