# Search by meaning — Vector database

Botize method — `btzvectors.search` (action).

Searches a database and returns the documents closest in meaning to your query, even if they do not share a single word.

This is an action: it runs as a step of a task. Several can be chained.

## Fields you set

The field's internal name, its type and the values it accepts. These fields accept `{{...}}` tags from earlier steps.

- `base` *(string)* — Database name
- `query` *(string)* — What are you looking for
- `max_results` *(string, one of: "1" = 1; "3" = 3; "5" = 5; "10" = 10; "20" = 20; "50" = 50, default: "5")* — Maximum number of results
- `min_score` *(string, one of: "0" = Discard nothing; "40" = Discard what is clearly unrelated (recommended); "55" = Be demanding; "70" = Near-identical results only, default: "40")* — Discard results that are unrelated
- `filter` *(string)* — Filter by metadata (optional)

## Output variables

Data this step makes available. Reference them in later steps with the double-brace tag.

- `{{results}}` *(array)* — Results. List of matching documents, best first. Each one has position, doc_key, text, score (0-100) and meta.
- `{{results_count}}` *(int)* — Number of results. How many documents were returned
- `{{base}}` *(text)* — Database. Name of the database searched
- `{{documents}}` *(int)* — Documents in the database. How many documents the database holds

## This app

Every trigger and action for Vector database: https://botize.com/en/app/btzvectors.md

## How to build this

An AI assistant (Claude, ChatGPT, Cursor…) can create and edit these tasks in the user's account through the Botize MCP connector, included in every plan:

    https://webhook.botize.es/mcp

- Connection guide: https://botize.com/en/mcp
- Agent map: https://botize.com/llms.txt
- New account: https://botize.com/en/join (500 free operations, no card)

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Machine-readable summary for AI assistants. Human version: https://botize.com/en/method/btzvectors/search