Show HN: Offline RAG System Using Docker and Llama 3 (No Cloud APIs)
11 by PhilYeh | 1 comments on Hacker News.
I'm sharing a fully offline RAG (Retrieval-Augmented Generation) stack I built to solve a crucial problem in industrial environments: data privacy and recurring API costs. We deal with sensitive proprietary datasheets and schematics daily, making cloud-based LLMs like ChatGPT non-compliant. The Solution: A containerized architecture that ensures data never leaves the local network. The Stack: LLM: Llama 3 (via Ollama) Vector DB: ChromaDB Deployment: Docker Compose (One-click setup) Benefit: Zero API costs, no security risks, fast local performance. The code and architecture are available here: https://ift.tt/UpTuY1P... Happy to answer questions about the GPU passthrough setup or document ingestion pipeline.
I'm sharing a fully offline RAG (Retrieval-Augmented Generation) stack I built to solve a crucial problem in industrial environments: data privacy and recurring API costs. We deal with sensitive proprietary datasheets and schematics daily, making cloud-based LLMs like ChatGPT non-compliant. The Solution: A containerized architecture that ensures data never leaves the local network. The Stack: LLM: Llama 3 (via Ollama) Vector DB: ChromaDB Deployment: Docker Compose (One-click setup) Benefit: Zero API costs, no security risks, fast local performance. The code and architecture are available here: https://ift.tt/UpTuY1P... Happy to answer questions about the GPU passthrough setup or document ingestion pipeline. 1 https://ift.tt/iKEpFcJ 11 Show HN: Offline RAG System Using Docker and Llama 3 (No Cloud APIs)
11 by PhilYeh | 1 comments on Hacker News.
I'm sharing a fully offline RAG (Retrieval-Augmented Generation) stack I built to solve a crucial problem in industrial environments: data privacy and recurring API costs. We deal with sensitive proprietary datasheets and schematics daily, making cloud-based LLMs like ChatGPT non-compliant. The Solution: A containerized architecture that ensures data never leaves the local network. The Stack: LLM: Llama 3 (via Ollama) Vector DB: ChromaDB Deployment: Docker Compose (One-click setup) Benefit: Zero API costs, no security risks, fast local performance. The code and architecture are available here: https://ift.tt/UpTuY1P... Happy to answer questions about the GPU passthrough setup or document ingestion pipeline.
I'm sharing a fully offline RAG (Retrieval-Augmented Generation) stack I built to solve a crucial problem in industrial environments: data privacy and recurring API costs. We deal with sensitive proprietary datasheets and schematics daily, making cloud-based LLMs like ChatGPT non-compliant. The Solution: A containerized architecture that ensures data never leaves the local network. The Stack: LLM: Llama 3 (via Ollama) Vector DB: ChromaDB Deployment: Docker Compose (One-click setup) Benefit: Zero API costs, no security risks, fast local performance. The code and architecture are available here: https://ift.tt/UpTuY1P... Happy to answer questions about the GPU passthrough setup or document ingestion pipeline. 1 https://ift.tt/iKEpFcJ 11 Show HN: Offline RAG System Using Docker and Llama 3 (No Cloud APIs)
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