# Datris

## Docs

- [AI Configuration](https://docs.datris.ai/ai-configuration.md): Configure AI features
- [AI Data Profiling](https://docs.datris.ai/ai-data-profiling.md): AI-powered data profiling
- [AI Error Explanation](https://docs.datris.ai/ai-error-explanation.md): AI-powered error explanations
- [AI data profiling](https://docs.datris.ai/api-reference/ai/ai-data-profiling.md): Upload a file and receive AI-generated summary statistics, quality issues, and suggested validation rules.
- [AI-powered question answering](https://docs.datris.ai/api-reference/ai/ai-powered-question-answering.md): Use AI to answer a question based on provided context. Ideal for RAG workflows — retrieve relevant chunks via vector search, then pass them as context along with the user's question.
- [AI schema generation](https://docs.datris.ai/api-reference/ai/ai-schema-generation.md): Upload a file and receive an AI-generated pipeline configuration with inferred field names and types.
- [Configuration API](https://docs.datris.ai/api-reference/config-api.md): Upload configuration files and generate schemas
- [AI-generate a validation schema](https://docs.datris.ai/api-reference/configuration/ai-generate-a-validation-schema.md): Uses AI to generate a JSON Schema (Draft 4) or W3C XSD from sample data. The schema is stored in MinIO.
- [Upload a configuration file](https://docs.datris.ai/api-reference/configuration/upload-a-configuration-file.md): Upload a JSON Schema for pipeline dataQuality schema validation to storage. The uploaded file can then be referenced by pipeline configurations.
- [Health Check API](https://docs.datris.ai/api-reference/health-api.md): Health check endpoints
- [Check backend service health](https://docs.datris.ai/api-reference/health/check-backend-service-health.md): Returns the health status of all configured backend services including PostgreSQL, MongoDB, MinIO, ActiveMQ, Kafka, and vector databases (Qdrant, Weaviate, Milvus, Chroma, pgvector). Each service reports "up", "down", or "not_configured".
- [List live, reachable vector stores](https://docs.datris.ai/api-reference/health/list-live-reachable-vector-stores.md): Returns the subset of `[qdrant, weaviate, pgvector, milvus, chroma]` whose Vault secret is present AND whose service responds to a probe. Drives the document-tap pipeline wizard's store picker. Secret presence alone isn't sufficient — the dev stack seeds placeholder secrets for every store, so this…
- [Data Ingestion API](https://docs.datris.ai/api-reference/ingestion-api.md): Data ingestion endpoints
- [Upload a file for processing](https://docs.datris.ai/api-reference/ingestion/upload-a-file-for-processing.md): Upload a data file to a registered pipeline. Returns a pipeline token for tracking job status.
- [Metadata API](https://docs.datris.ai/api-reference/metadata-api.md): Database and vector store metadata discovery
- [List Chroma collections](https://docs.datris.ai/api-reference/metadata/list-chroma-collections.md): List all collections in the Chroma vector database. Returns an empty array if Chroma is not configured.
- [List Milvus collections](https://docs.datris.ai/api-reference/metadata/list-milvus-collections.md): List all collections in the Milvus vector database. Returns an empty array if Milvus is not configured.
- [List MongoDB collections](https://docs.datris.ai/api-reference/metadata/list-mongodb-collections.md): List MongoDB collections. If database is specified, lists collections in that database. Otherwise lists all collections across all databases in "db.collection" format.
- [List MongoDB databases](https://docs.datris.ai/api-reference/metadata/list-mongodb-databases.md): List all MongoDB databases available in the platform. Excludes system databases (admin, config, local).
- [List PostgreSQL databases](https://docs.datris.ai/api-reference/metadata/list-postgresql-databases.md): List all PostgreSQL databases available in the platform.
- [List PostgreSQL schemas](https://docs.datris.ai/api-reference/metadata/list-postgresql-schemas.md): List all schemas in a PostgreSQL database.
- [List PostgreSQL tables](https://docs.datris.ai/api-reference/metadata/list-postgresql-tables.md): List all tables in a PostgreSQL schema. Set vectorOnly to true to filter to tables with an embedding column (pgvector tables).
- [List Qdrant collections](https://docs.datris.ai/api-reference/metadata/list-qdrant-collections.md): List all collections in the Qdrant vector database. Returns an empty array if Qdrant is not configured.
- [List table columns](https://docs.datris.ai/api-reference/metadata/list-table-columns.md): List all columns and their data types for a specific PostgreSQL table.
- [List Weaviate classes](https://docs.datris.ai/api-reference/metadata/list-weaviate-classes.md): List all classes in the Weaviate vector database. Returns an empty array if Weaviate is not configured.
- [Pipeline Configuration API](https://docs.datris.ai/api-reference/pipeline-api.md): Pipeline configuration endpoints
- [Create or update a pipeline configuration](https://docs.datris.ai/api-reference/pipelines/create-or-update-a-pipeline-configuration.md)
- [Delete a pipeline configuration](https://docs.datris.ai/api-reference/pipelines/delete-a-pipeline-configuration.md)
- [Get a pipeline configuration](https://docs.datris.ai/api-reference/pipelines/get-a-pipeline-configuration.md)
- [List all pipeline configurations](https://docs.datris.ai/api-reference/pipelines/list-all-pipeline-configurations.md)
- [Query API](https://docs.datris.ai/api-reference/query-api.md): Query data endpoints
- [Query MongoDB](https://docs.datris.ai/api-reference/query/query-mongodb.md): Query a MongoDB collection with optional filter and projection. Blocks $where and $function operators.
- [Query PostgreSQL](https://docs.datris.ai/api-reference/query/query-postgresql.md): Execute a read-only SQL SELECT query against PostgreSQL. Enforces SELECT-only, blocks dangerous keywords, and auto-appends LIMIT.
- [AI Schema Generation API](https://docs.datris.ai/api-reference/schema-generation-api.md): AI schema generation endpoints
- [Search API](https://docs.datris.ai/api-reference/search-api.md): Search endpoints
- [Semantic search — Chroma](https://docs.datris.ai/api-reference/search/semantic-search-—-chroma.md)
- [Semantic search — Milvus](https://docs.datris.ai/api-reference/search/semantic-search-—-milvus.md)
- [Semantic search — pgvector](https://docs.datris.ai/api-reference/search/semantic-search-—-pgvector.md)
- [Semantic search — Qdrant](https://docs.datris.ai/api-reference/search/semantic-search-—-qdrant.md): Search a Qdrant vector database collection using natural language. Generates an embedding from the query and returns the most similar document chunks.
- [Semantic search — Weaviate](https://docs.datris.ai/api-reference/search/semantic-search-—-weaviate.md)
- [Secrets API](https://docs.datris.ai/api-reference/secrets-api.md): Manage secrets in HashiCorp Vault
- [Create or update a secret](https://docs.datris.ai/api-reference/secrets/create-or-update-a-secret.md): Creates or updates a secret with the provided key-value fields.
- [Delete a secret](https://docs.datris.ai/api-reference/secrets/delete-a-secret.md)
- [Get secret details](https://docs.datris.ai/api-reference/secrets/get-secret-details.md): Returns the key-value fields of a secret. Sensitive fields (password, apiKey, secretKey, token) are masked unless reveal=true.
- [List all secrets](https://docs.datris.ai/api-reference/secrets/list-all-secrets.md): Lists all secret names under the current environment path in Vault.
- [Pipeline Status API](https://docs.datris.ai/api-reference/status-api.md): Pipeline status endpoints
- [Get job status](https://docs.datris.ai/api-reference/status/get-job-status.md): Query job status by pipeline token or pipeline name. Returns detailed status when queried by token, or paginated summary when queried by pipeline name.
- [Get server version](https://docs.datris.ai/api-reference/status/get-server-version.md)
- [Kill a running job](https://docs.datris.ai/api-reference/status/kill-a-running-job.md): Interrupt and cancel a running pipeline job by its pipeline token.
- [Taps API](https://docs.datris.ai/api-reference/taps-api.md): API endpoints for managing taps
- [Brainstorm chat (AI)](https://docs.datris.ai/api-reference/taps/brainstorm-chat-ai.md): Multi-turn conversational endpoint that helps refine a vague tap idea into a clear instruction. The AI asks one focused clarifying question at a time, suggests data sources, recognizes Datris platform tables, and returns an updated instruction draft on every turn.
- [Clear the ledger (force re-scan) or delete one entry (force re-process)](https://docs.datris.ai/api-reference/taps/clear-the-ledger-force-re-scan-or-delete-one-entry-force-re-process.md): With only `name`, clears the entire ledger for the tap and deletes every staged MinIO object — the next run re-ingests every document from source. With `name` and `uri`, deletes one entry and its staged object so that specific document is re-processed on the next run.
- [Create or update a tap](https://docs.datris.ai/api-reference/taps/create-or-update-a-tap.md): Creates a new tap or updates an existing tap with the same `name`.
- [Delete a tap](https://docs.datris.ai/api-reference/taps/delete-a-tap.md): Deletes the tap configuration and its script from MinIO.
- [Fix tap script (AI diagnosis)](https://docs.datris.ai/api-reference/taps/fix-tap-script-ai-diagnosis.md): Uses AI to fix a script based on a diagnosis of what went wrong.
- [Generate CRON expression (AI)](https://docs.datris.ai/api-reference/taps/generate-cron-expression-ai.md): Converts a plain-English schedule description to a Quartz CRON expression.
- [Generate tap script (AI)](https://docs.datris.ai/api-reference/taps/generate-tap-script-ai.md): Uses AI to generate a Python `fetch()` script from a plain-English description. The system prompt branches on `tapType` — document taps get a prompt that instructs the LLM to return `{uri, filename, content}` dicts, never chunk or embed, and never fall back to scanning the local filesystem.
- [Get a tap configuration](https://docs.datris.ai/api-reference/taps/get-a-tap-configuration.md): Returns a single tap configuration, including the script content from MinIO.
- [Get tap run history](https://docs.datris.ai/api-reference/taps/get-tap-run-history.md): Returns the last 50 run log entries for a tap, sorted by most recent first.
- [List all taps](https://docs.datris.ai/api-reference/taps/list-all-taps.md): Returns all registered tap configurations.
- [Read a document tap's ledger](https://docs.datris.ai/api-reference/taps/read-a-document-taps-ledger.md): Returns every ledger entry owned by the tap. The ledger records which documents the tap has already staged so re-runs skip unchanged files.
- [Run a tap](https://docs.datris.ai/api-reference/taps/run-a-tap.md): Executes a saved tap. Optionally sends data to the configured pipeline.
- [Test a tap](https://docs.datris.ai/api-reference/taps/test-a-tap.md): Executes the tap script without sending data to a pipeline. Returns results, logs, and an AI-generated diagnosis if errors or zero records are detected.
- [Version API](https://docs.datris.ai/api-reference/version-api.md): Version information endpoints
- [Changelog](https://docs.datris.ai/changelog.md): Release history for Datris
- [Datris CLI](https://docs.datris.ai/cli.md): Command-line interface reference
- [Configuration Reference](https://docs.datris.ai/configuration-reference.md): Complete configuration reference
- [Configuring Claude](https://docs.datris.ai/configuring-claude.md): Connect Claude Desktop and Claude Code to the Datris MCP server
- [Data Catalog](https://docs.datris.ai/data-catalog.md): A logical grouping for related taps and pipelines
- [Data Quality — AI Rule](https://docs.datris.ai/data-quality/ai-rules.md): AI-powered data quality rules
- [CSV Header Validation](https://docs.datris.ai/data-quality/header-validation.md): Validate CSV headers
- [JSON/XML Schema Validation](https://docs.datris.ai/data-quality/schema-validation.md): Validate data against JSON/XML schemas
- [ActiveMQ Destination](https://docs.datris.ai/destinations/activemq.md): Send data to ActiveMQ
- [Chroma Vector Database Destination](https://docs.datris.ai/destinations/chroma.md): Send data to Chroma vector database
- [Kafka Destination](https://docs.datris.ai/destinations/kafka.md): Send data to Kafka
- [Milvus Vector Database Destination](https://docs.datris.ai/destinations/milvus.md): Send data to Milvus vector database
- [MongoDB Destination](https://docs.datris.ai/destinations/mongodb.md): Send data to MongoDB
- [Object Store Destination (MinIO)](https://docs.datris.ai/destinations/object-store.md): Send data to MinIO object store
- [pgvector (PostgreSQL) Vector Database Destination](https://docs.datris.ai/destinations/pgvector.md): Send data to pgvector
- [PostgreSQL Destination](https://docs.datris.ai/destinations/postgres.md): Send data to PostgreSQL
- [Qdrant Vector Database Destination](https://docs.datris.ai/destinations/qdrant.md): Send data to Qdrant vector database
- [REST Endpoint Destination](https://docs.datris.ai/destinations/rest-endpoint.md): Send data to REST endpoints
- [Weaviate Vector Database Destination](https://docs.datris.ai/destinations/weaviate.md): Send data to Weaviate vector database
- [Discovery](https://docs.datris.ai/discovery.md): AI-powered wizard that goes from "I want this data" to running taps + pipelines in six steps
- [Examples](https://docs.datris.ai/examples.md): Example configurations and use cases
- [Chat Vector Store](https://docs.datris.ai/examples/chat-vector-store.md): Chat with a vector store example
- [Kafka CSV Loader](https://docs.datris.ai/examples/kafka-csv-loader.md): Load CSVs into Kafka example
- [Market Macro Agent](https://docs.datris.ai/examples/market-macro-agent.md): Market macro agent example
- [Preprocessor](https://docs.datris.ai/examples/preprocessor.md): Preprocessor example
- [MCP Server Test](https://docs.datris.ai/examples/test-mcp-server.md): Test MCP server example
- [Topic Subscriber](https://docs.datris.ai/examples/topic-subscriber.md): Topic subscriber example
- [Datris](https://docs.datris.ai/index.md): The first AI Agent-native data platform
- [Supported Data Types](https://docs.datris.ai/ingestion/data-types.md): Supported data types for ingestion
- [Database Source Pulling](https://docs.datris.ai/ingestion/database-pull.md): Pull data from database sources
- [API File Upload](https://docs.datris.ai/ingestion/file-upload.md): Ingest data via API file upload
- [Kafka Topic Consumption](https://docs.datris.ai/ingestion/kafka.md): Consume data from Kafka topics
- [MinIO Bucket Ingestion](https://docs.datris.ai/ingestion/object-store.md): Ingest from MinIO object store
- [Installation](https://docs.datris.ai/installation.md): How to install and set up the Datris Data Platform
- [MCP Server (AI Agent Integration)](https://docs.datris.ai/mcp-server.md): MCP server for AI agent integration
- [Monitoring](https://docs.datris.ai/monitoring.md): Monitor your pipelines
- [Pipeline Notifications](https://docs.datris.ai/notifications.md): Set up pipeline notifications
- [Pipeline Configuration](https://docs.datris.ai/pipeline-configuration.md): Configure data pipelines
- [Preprocessor](https://docs.datris.ai/preprocessor.md): Data preprocessing configuration
- [Quick Start](https://docs.datris.ai/quick-start.md): Get up and running with Datris in minutes
- [Schema Definition & Auto-Generation](https://docs.datris.ai/schemas.md): Define and auto-generate schemas
- [Source Code](https://docs.datris.ai/source-code.md): Open-source repository and project structure
- [Tap Prompt Fragments](https://docs.datris.ai/tap-prompts.md): Inject source-specific context into the LLM when you create a tap
- [Taps](https://docs.datris.ai/taps.md): AI-generated Python scripts that fetch data from external sources
- [AI Transformation](https://docs.datris.ai/transformation/ai-transformation.md): AI-powered data transformation
- [Dropping Columns](https://docs.datris.ai/transformation/dropping-columns.md): Remove columns from data

## OpenAPI Specs

- [openapi](https://docs.datris.ai/openapi.yaml)