> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/poweroutlet2/openground/llms.txt
> Use this file to discover all available pages before exploring further.

# openground query

> Search documentation using hybrid semantic and keyword search

The `openground query` command performs hybrid search (semantic + BM25) against the local vector database.

## Usage

```bash theme={null}
openground query <query> [OPTIONS]
```

## Arguments

<ParamField path="query" type="string" required>
  Query string for hybrid search
</ParamField>

## Options

<ParamField path="--version" type="string" default="latest">
  Version to filter results by.

  **Aliases:** `-v`
</ParamField>

<ParamField path="--library" type="string" default="None">
  Optional library name filter. If provided, only searches within this library.

  **Aliases:** `-l`
</ParamField>

<ParamField path="--top-k" type="integer" default="5">
  Number of results to return. Default value comes from configuration (`query.top_k`).

  **Aliases:** `-k`

  **Constraints:** Minimum value is 1
</ParamField>

## Search Algorithm

OpenGround uses **hybrid search** combining:

1. **Semantic Search:** Vector similarity using embeddings
2. **BM25 (Keyword Search):** Traditional full-text search

Results are ranked by combining both scores, providing relevant results even when exact keywords don't match.

## Examples

### Basic Query

```bash theme={null}
openground query "How to create a vector database"
```

### Query Specific Library

```bash theme={null}
openground query "authentication" --library langchain
```

### Query Specific Version

```bash theme={null}
openground query "new features" --version v2.0.0
```

### Get More Results

```bash theme={null}
openground query "embeddings" --top-k 10
```

### Combined Options

```bash theme={null}
openground query "API reference" -l mylib -v v1.5.0 -k 3
```

## Output Format

Results are returned in markdown format with:

* **Title:** Page title
* **URL:** Source URL
* **Library & Version:** Which library and version
* **Content:** Relevant excerpt from the page

### Output Example

```markdown theme={null}
# Result 1

**Title:** Vector Database Guide
**URL:** https://docs.example.com/vector-db
**Library:** mylib (v1.0.0)

Vector databases store high-dimensional embeddings and support 
semantic search queries. To create a vector database, you first 
need to generate embeddings for your documents...

---

# Result 2

**Title:** Embeddings Overview
**URL:** https://docs.example.com/embeddings
**Library:** mylib (v1.0.0)

Embeddings are numerical representations of text that capture 
semantic meaning. OpenGround supports multiple embedding backends...
```

## Configuration

The default `top_k` value can be configured:

```bash theme={null}
openground config set query.top_k 10
```

## Performance Tips

1. **Be Specific:** More specific queries return better results
2. **Use Library Filter:** Filter by library when you know which docs to search
3. **Adjust top\_k:** Increase for broader searches, decrease for focused results

## Related Commands

* [openground list](/cli/list) - View available libraries
* [openground add](/cli/add) - Add documentation
* [openground config](/cli/config) - Configure query settings