Vedana is an open-source multi-agent RAG system built around a typed knowledge graph. It is designed for questions that require structure, completeness, and traceability instead of simple text similarity. The system lets agents navigate data step by step through Cypher queries, vector search, document lookup, and source verification. Its architecture combines a knowledge graph, pgvector-based embeddings, incremental ETL, and a backoffice interface for chat, metrics, prompt tuning, and data loading. It also includes JIMS, a framework for persistent conversational agents with typed events and pluggable pipelines. Overall, Vedana is useful for teams that need reliable answers from real data, especially when relationships, counts, rules, and source-backed reasoning matter.

Features

  • Multi-agent RAG over a typed knowledge graph
  • Cypher query generation and vector search tools
  • Traceable answers built from nodes, edges, and document chunks
  • Incremental ETL from Grist into Memgraph and pgvector
  • Backoffice UI for chat, ETL, metrics, and prompt tuning
  • API, Telegram, terminal UI, and embeddable widget interfaces

Project Samples

Project Activity

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Categories

Semantic Search

License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python Semantic Search Tool

Registered

9 hours ago