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LIGHTNINGRAG
Full-stack RAG platform with Go backend and visual agent orchestration
Apache-2.0
ABOUT
Building production RAG applications requires integrating document ingestion, parsing, chunking, embedding, vector storage, and LLM retrieval — typically across multiple Python services that are slow to start and resource-intensive. LightningRAG solves this by providing a full-stack RAG platform in Go and Vue that offers higher performance, compiled deployment, smaller artifacts, and stronger concurrency than equivalent Python stacks — with built-in knowledge base management, pluggable LLM providers, and a visual agent canvas for orchestrating retrieval workflows.
INTEGRATION GUIDE
1. Create knowledge bases by ingesting documents with automatic parsing, chunking, and vector embedding pipelines
2. Build RAG-powered chat applications with pluggable LLM providers, embedding models, and vector stores
3. Orchestrate multi-step retrieval workflows using a visual agent canvas with drag-and-drop node composition
4. Deploy RAG applications as compiled Go binaries with minimal dependencies and fast cold-start times
5. Connect webhook channels (Feishu, Slack) to RAG knowledge bases for automated question-answering workflows
TAGS
ragknowledge-basevector-searchfull-stackgovuedocument-processing