> ## Documentation Index
> Fetch the complete documentation index at: https://morphik.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Retrieve Chunks

> Search and retrieve relevant chunks from your documents

Search your documents and retrieve the most relevant chunks based on a natural language query.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from morphik import Morphik

    db = Morphik("your-uri")

    chunks = db.retrieve_chunks(
        query="What are the quarterly results?",
        filters={"department": {"$eq": "sales"}},
        k=5,
        min_score=0.0,
        use_colpali=True,
        folder_name="/reports",
        padding=1,
        output_format="url"
    )

    for chunk in chunks:
        print(f"Score: {chunk.score:.2f}")
        print(f"Content: {chunk.content[:200]}...")
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    import Morphik from 'morphik';

    // For Teams/Enterprise, use your dedicated host: https://companyname-api.morphik.ai
    const client = new Morphik({
      apiKey: process.env.MORPHIK_API_KEY,
      baseURL: 'https://api.morphik.ai'
    });

    const chunks = await client.retrieve.chunks.create({
      query: 'What are the quarterly results?',
      filters: { department: { $eq: 'sales' } },
      k: 5,
      min_score: 0.0,
      use_colpali: true,
      folder_name: '/reports',
      padding: 1,
      output_format: 'url'
    });

    chunks.forEach(chunk => {
      console.log(`Score: ${chunk.score.toFixed(2)}`);
      console.log(`Content: ${chunk.content.slice(0, 200)}...`);
    });
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "https://api.morphik.ai/retrieve/chunks" \
      -H "Authorization: Bearer $MORPHIK_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "query": "What are the quarterly results?",
        "filters": {"department": {"$eq": "sales"}},
        "k": 5,
        "min_score": 0.0,
        "use_colpali": true,
        "folder_name": "/reports",
        "padding": 1,
        "output_format": "url"
      }'
    ```
  </Tab>
</Tabs>

For advanced filtering with operators like `$and`, `$or`, `$gte`, `$in`, see [Metadata Filtering](/concepts/metadata-filtering).

## Parameters

| Parameter       | Type    | Default    | Description                                                               |
| --------------- | ------- | ---------- | ------------------------------------------------------------------------- |
| `query`         | string  | required   | Natural language search query                                             |
| `k`             | int     | `4`        | Maximum number of chunks to return                                        |
| `min_score`     | float   | `0.0`      | Minimum similarity score threshold                                        |
| `use_colpali`   | boolean | `true`     | Use Morphik multimodal embeddings (better for images/tables)              |
| `filters`       | object  | `null`     | Metadata filters (see [Metadata Filtering](/concepts/metadata-filtering)) |
| `padding`       | int     | `0`        | Extra pages to include before/after matches (multimodal only)             |
| `output_format` | string  | `"base64"` | Image format: `base64`, `url`, or `text`                                  |

## Response

```json theme={null}
[
  {
    "document_id": "doc_abc123",
    "chunk_number": 3,
    "content": "Q4 revenue increased by 15% year-over-year...",
    "score": 0.89,
    "metadata": { "department": "sales" },
    "filename": "quarterly_report.pdf"
  }
]
```

<Note>
  Use `use_colpali=true` for documents with images, charts, or tables. Morphik multimodal embeddings provide significantly better retrieval accuracy for visual content.
</Note>