---
title: ColPali
sidebarTitle: ColPali
---

import {
  PyEmbeddingColpaliSetup,
  PyEmbeddingColpaliTextSearch,
} from '/snippets/integrations.mdx';

We support [ColPali](https://github.com/illuin-tech/colpali) model embeddings for multimodal multi-vector retrieval. ColPali produces multiple embedding vectors per input (multi-vector), enabling more nuanced similarity matching between text queries and image documents.

Using ColPali requires the colpali-engine package, which can be installed using `pip install colpali-engine`.

<Info>
ColPali produces **multi-vector** embeddings, meaning each input generates multiple embedding vectors rather than a single vector. Use `MultiVector(func.ndims())` instead of `Vector(func.ndims())` when defining your schema.
</Info>

Supported models are:

- Metric-AI/ColQwen2.5-3b-multilingual-v1.0 (default)
- vidore/colpali-v1.3
- vidore/colqwen2-v1.0
- vidore/colSmol-256M

Supported parameters (to be passed in `create` method) are:

| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `model_name` | `str` | `"Metric-AI/ColQwen2.5-3b-multilingual-v1.0"` | The name of the model to use. |
| `device` | `str` | `"auto"` | The device for inference. Can be `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`. |
| `dtype` | `str` | `"bfloat16"` | Data type for model weights (bfloat16, float16, float32, float64). |
| `pooling_strategy` | `str` | `"hierarchical"` | Token pooling strategy: `"hierarchical"`, `"lambda"`, or `None`. |
| `pool_factor` | `int` | `2` | Factor to reduce sequence length when pooling is enabled. |
| `batch_size` | `int` | `2` | Batch size for processing inputs. |
| `quantization_config` | `Optional[BitsAndBytesConfig]` | `None` | Quantization configuration for the model (requires bitsandbytes). |

This embedding function supports ingesting images as both bytes and URLs. You can query them using text.

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingColpaliSetup}
  </CodeBlock>
</CodeGroup>

Now we can search using text queries:

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingColpaliTextSearch}
  </CodeBlock>
</CodeGroup>