mirror of
https://github.com/lancedb/lancedb.git
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Jack YeandBrendan Clement
47b7babc08
Move the public website pages into `docs/web` beside the SDK, with
shared navigation and executable example sources in `docs/web-tests`.
The combined website is assembled by
[lancedb/docs#355](https://github.com/lancedb/docs/pull/355), with six
Enterprise replacements from
[lancedb/sophon#7681](https://github.com/lancedb/sophon/pull/7681).
This includes Function guidance, runnable Quickstarts, regenerated
snippets, production contact/demo fixes, the legacy SQL route, and the
LeRobot redirect. LeRobot remains attributed to Geneva; all 34 Geneva
pages remain in the docs repository until Function officially launches.
Materialized-view guidance follows the full rebuild refresh introduced
by #4373: local refresh writes the complete result, has no `full`
argument, supports `source_version`, and no longer requires stable row
IDs. Deployment SQL refresh options and stable-ID setup are qualified by
version/configuration. Function input guidance distinguishes ordinary
stored columns from computed inputs supported by newer deployments.
**Validation:** independently accepted local checks include 95 Python
tests (real Enterprise excluded), eight TypeScript tests, Rust
Quickstart, snippet parity, typos, deterministic assembly, Mint
validation and links/anchors/redirects. The combined baseline retains
all 181 production routes and 17 legacy redirects. Examples ran locally
against source-built Python SDK 0.40.0b13; existing GitHub workflows do
not run `docs/web-tests`. New main at `069b2f7d` is 0.41.0b0 and adds
computed Function inputs; that SDK was not exercised by the accepted
tests. Latest prose corrections receive focused site validation.
**Merge/rollout:** merge this and #7681 before #355's combined CI, which
reads producer `main`. This is migration infrastructure: section
overlays, source-owned skills and automatic versioned paired releases
remain follow-ups. The current manual publisher is transitional. Hosted
validation, a supported SDK/Enterprise release pair and a real
Enterprise walkthrough remain pending.
Production Mintlify watches `lancedb/docs:deploy-freeze`, directory
`docs`; preserve that source through review. The separate SDK GitHub
Pages reference workflow rebuilds on main merges.
**Updated-head CI (9a50f8db):**
[Typos](https://github.com/lancedb/lancedb/actions/runs/37654185443/job/112904990558),
[PR
title](https://github.com/lancedb/lancedb/actions/runs/37654179507/job/112904974595)
and
[Aikido](https://app.aikido.dev/featurebranch/scan/215386145?groupId=74229)
pass. Critical findings in the example lockfiles were patched: all Sharp
copies use 0.35.5, protobufjs 7.6.6, tar 7.5.22, quinn-proto 0.11.19,
zerovec 0.11.8 and zerovec-derive 0.11.6. The SDK's product dependencies
are unchanged. The revised lockfiles pass eight TypeScript examples,
HuggingFace decode/resize/crop/rescale/normalize plus AVIF roundtrip,
and Rust Quickstart. Aikido passing does not mean every advisory is
cleared; npm still reports 29 high and five moderate advisories in the
example dependency graph. No scanner bypass or suppression was added.
The Function prose correction passes combined/standalone Mint,
deterministic assembly, routes and assets. The revised 325-file site
checksum is
`175bec06864abb400dd292e667ba93448c0479b8359fd7bf6146afabf9040647`. This
PR remains open for independent review; further merges are on hold.
---------
Co-authored-by: Brendan Clement <brendan.clement@lancedb.com>
54 lines
2.1 KiB
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54 lines
2.1 KiB
Plaintext
---
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title: ColPali
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sidebarTitle: ColPali
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---
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import {
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PyEmbeddingColpaliSetup,
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PyEmbeddingColpaliTextSearch,
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} from '/snippets/integrations.mdx';
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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.
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Using ColPali requires the colpali-engine package, which can be installed using `pip install colpali-engine`.
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<Info>
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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.
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</Info>
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Supported models are:
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- Metric-AI/ColQwen2.5-3b-multilingual-v1.0 (default)
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- vidore/colpali-v1.3
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- vidore/colqwen2-v1.0
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- vidore/colSmol-256M
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Supported parameters (to be passed in `create` method) are:
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| Parameter | Type | Default Value | Description |
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| `model_name` | `str` | `"Metric-AI/ColQwen2.5-3b-multilingual-v1.0"` | The name of the model to use. |
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| `device` | `str` | `"auto"` | The device for inference. Can be `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`. |
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| `dtype` | `str` | `"bfloat16"` | Data type for model weights (bfloat16, float16, float32, float64). |
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| `pooling_strategy` | `str` | `"hierarchical"` | Token pooling strategy: `"hierarchical"`, `"lambda"`, or `None`. |
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| `pool_factor` | `int` | `2` | Factor to reduce sequence length when pooling is enabled. |
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| `batch_size` | `int` | `2` | Batch size for processing inputs. |
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| `quantization_config` | `Optional[BitsAndBytesConfig]` | `None` | Quantization configuration for the model (requires bitsandbytes). |
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This embedding function supports ingesting images as both bytes and URLs. You can query them using text.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingColpaliSetup}
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</CodeBlock>
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</CodeGroup>
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Now we can search using text queries:
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingColpaliTextSearch}
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</CodeBlock>
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</CodeGroup>
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