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Deploy a private on-prem conversational RAG system with configurable containers.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "minima" yet — see the docs or source repo.
Use minima to deploy an on-prem conversational RAG system in containers, connect our product docs and FAQs, and provide the recommended service components, deployment steps, and baseline configuration suggestions.
An internal knowledge assistant deployment plan with container components, document ingestion, and configuration guidance.
I want to use minima to build a private Q&A system for the engineering team. Plan how to import API docs, design the retrieval flow, configure container services, and support multiple users.
A private RAG plan for engineering teams covering document ingestion, retrieval architecture, and container deployment design.
Use minima to design a conversational retrieval solution that stays inside the internal network, uses local deployment and configurable containers, and explain key architecture and operations considerations for sensitive materials.
A security-focused knowledge retrieval architecture recommendation emphasizing on-prem deployment and container configuration.
Set up a local RAG server for private knowledge search and QA.
Search local knowledge packs and retrieve chunks for stronger AI answers.
Build modular RAG workflows for document Q&A, semantic search, and knowledge bases.
Search and question PDF documents with Pinecone and local AI models.
Search local Markdown files and return full document contents for use.
Retrieve relevant document chunks and generate suggested LLM prompts via REST and MCP.