Fast-ingesting tabular data RAG with cell-level citations.
Upload CSV/TSV, get vector-powered search and structured queries via REST or MCP.
Everything you need to make tabular data searchable and queryable.
Upload CSV/TSV files. Ingests via PostgreSQL COPY for high throughput, with automatic type detection.
Every row is embedded and indexed in Qdrant. Semantic search finds relevant data across your tables.
Query results point to exact cells, not just rows. Know precisely where each answer comes from.
Built-in aggregate and filter endpoints. SUM, AVG, COUNT with group-by, plus advanced row filtering.
Streamable HTTP MCP server built-in. Connect any AI assistant or tool runner directly to your data.
Upload, browse, search, and manage tables from a clean React frontend. Dark mode included.
From CSV to semantic search in three steps.
Drop a CSV or TSV file. Rows are parsed, typed, and bulk-inserted into PostgreSQL via COPY.
A background worker embeds every row using sentence-transformers and upserts vectors into Qdrant.
Search semantically, filter rows, or aggregate columns. Results include cell-level citations.
REST endpoints and MCP for AI tool integration.
| Method | Endpoint | Description |
|---|---|---|
| GET | /tables |
List all datasets with metadata |
| GET | /tables/{id}/columns |
Get column names for a dataset |
| GET | /tables/{id}/slice |
Browse raw rows with pagination |
| POST | /tables/{id}/rows_by_indices |
Fetch specific rows by index list |
| POST | /query |
Unified query — semantic search, aggregate, or filter |
| MCP | /mcp |
Streamable HTTP MCP server endpoint |
Up and running with Docker in under a minute.