Introduction

Vedana is an open-source framework for building AI assistants whose answers can be verified and reproduced. Vedana stores domain knowledge as a knowledge graph, and the assistant explores this graph step by step through explicitly described tools — vector search and Cypher queries against the graph database.

Unlike classic RAG, Vedana:

  • doesn’t guess the answer based on top-K similar text fragments — it builds a structured query;
  • returns complete sets (not samples), exact values (not approximations), and explicit sources (not “somewhere in the documents”);
  • lets you describe your domain as a data model (anchors, attributes, links, queries) and get deterministic behaviour.

Who needs this

Vedana is designed for domains where mistakes are not allowed or are expensive:

  • e-commerce — product catalogs, compatibility, in-store availability, delivery policies;
  • legal and compliance — which requirements apply to a product, which documents regulate a category;
  • internal knowledge bases — answers based on documentation, regulations, organisational structure;
  • customer support — stable answers to FAQs, intent-based routing, exact values instead of “roughly that”.

If your assistant’s answers must be verifiable and aligned with real business logic — Vedana is your case. If “summarize this PDF” is enough, classic RAG will do.

What’s in the box

  • JIMS — a framework for managing threads, events, and pipelines. Supports several interfaces: Telegram, Terminal UI, HTTP API, web widget, Reflex backoffice.
  • Vedana Core — a RAG pipeline with data model filtering, an agent, and the cypher and vector_text_search tools.
  • Vedana ETL — an incremental pipeline on top of Datapipe for loading the data model and data from Grist into Memgraph and pgvector.
  • Vedana Backoffice — a Reflex admin UI with a chat, an ETL pipeline inspector, an evaluation harness, and prompt settings.
  • LiteLLM support: you can use OpenAI, OpenRouter, Google/VertexAI, and any compatible providers.
  • Observability: OpenTelemetry traces, Prometheus metrics, Sentry integration.

What’s next

  • Quick Start — bring Vedana up locally and ask your first question.
  • Concepts — the theory: what Semantic RAG is, why classic RAG breaks, how the data model is organised.
  • Architecture Overview — how the code is structured and how the components fit together.