RAGLeap — What this workspace gives you
Feature overview and quick guide — use the AI Chat on the right to ask about your documents, datasets, or project knowledge.
Vector + Graph Search
Hybrid search that combines semantic vectors with a knowledge graph for precise, contextual results across documents and entities.
Smart Retrieval
Intelligent ranking merges semantic similarity, citation relevance, and graph proximity so you find exactly what you need.
RAG Chat (AI Chat)
Ask questions conversationally — the AI Chat synthesizes answers from retrieved passages and cites sources. Great for summaries, code help, and research.
Lightning Fast
Optimized for local speed: vector search + graph traversal deliver low-latency retrieval for interactive chat.
Quick workflow
- Ingest: Upload docs, sync DBs, or connect cloud storages.
- Preprocess: OCR, clean, dedupe, and extract metadata.
- Index: Create vector embeddings and populate the knowledge graph.
- Query: Use AI Chat or search UI to retrieve ranked passages and graph results.
- Refine: Provide feedback or extra prompts to improve answers and add citations.
Welcome Back
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