Datris FAQs

Name: Datris
Author: datris

Question 1

Can Datris be used for Retrieval-Augmented Generation (RAG) pipelines?

Accepted Answer

Yes, Datris features a full RAG pipeline built in. It supports document extraction, various chunking strategies, embedding providers, and integration with 5 major vector databases (Qdrant, Weaviate, Milvus, Chroma, pgvector) for building complete RAG workflows.

Question 2

What is Datris and who is it for?

Accepted Answer

Datris is the first AI agent-native data platform designed to orchestrate comprehensive data pipelines for ingestion, validation, transformation, storage, and retrieval. It serves both AI agents (via MCP) and human developers using configuration-driven flows.

Question 3

How does Datris enable AI agent integration?

Accepted Answer

Datris includes a native MCP (Model Context Protocol) server, making it natively accessible to AI agents. Agents like Claude or Cursor can register pipelines, upload files, trigger processing, monitor jobs, profile data, and query databases through natural language conversations.

Question 4

What AI-powered features are built into Datris?

Accepted Answer

Datris offers extensive AI features including AI-powered data quality validation (natural language rules), transformations, schema generation from raw files, data profiling, and intelligent error explanations. It supports various AI providers like Anthropic and OpenAI.

Question 5

Is Datris an open-source and flexible platform?

Accepted Answer

Datris is built entirely on proven open-source infrastructure (MinIO, MongoDB, ActiveMQ, Kafka, Spark) and designed for self-hosting. It offers configuration-driven pipelines, no vendor lock-in, and can be deployed on any cloud, on-premise, or locally.

Datris

Datris

主要功能

使用案例

主要功能

使用案例