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`OpenClipEmbeddings` loads its model with
`open_clip.create_model_and_transforms()`, which hands the model back in
training mode (the open_clip README calls `model.eval()` right after it
for that reason). `OpenClipEmbeddings` never switches it out of training
mode, so layers that behave differently during training stay active
while we embed: stochastic depth in the timm-based image towers
(ViTamin, ConvNeXt, MobileCLIP2-L-14), dropout in the Hugging Face text
towers (`xlm-roberta-*`, `roberta-ViT-B-32`, `nllb-clip-*`), and batch
norm in the ResNet towers loaded with non-OpenAI weights. The
`torch.no_grad()` blocks turn off autograd but don't change how these
layers behave.
The default `ViT-B-32` has none of those layers, which is probably why
nobody has run into this. With dropout or stochastic depth active, the
same input gives a different vector on every call, so the vector stored
at ingestion and the one computed for an identical query don't match.
With batch norm in training mode, an image's vector depends on the other
images in its batch:
```python
import numpy as np
from PIL import Image
from lancedb.embeddings import get_registry
func = get_registry().get("open-clip").create(name="ViTamin-S", pretrained="datacomp1b")
image = Image.new("RGB", (256, 256), "red")
a = func.generate_image_embedding(image)
b = func.generate_image_embedding(image)
print(func._model.training, np.array_equal(a, b))
# before: True False
# after: False True
```
The fix calls `model.eval()` once after loading, same as the SigLIP,
ColPali and ImageBind embedding functions already do.
The new test swaps in a stand-in for the `open_clip` module, so it runs
without torch or a model download, and checks that the model handed back
by `create_model_and_transforms` ends up in eval mode.
BREAKING CHANGE: Embeddings change for OpenCLIP models with
training-sensitive layers, including ViTamin, ConvNeXt, MobileCLIP2,
Hugging Face RoBERTa/XLM-RoBERTa/NLLB text towers, and ResNet models
loaded with non-OpenAI weights. For tables embedded with affected
models, regenerate the stored embeddings in eval mode and rebuild their
vector indexes before querying with the new embeddings. Rebuilding an
index alone does not regenerate embeddings. The default ViT-B-32 model
is unaffected.
Co-authored-by: devjoinedthechat <235875317+devjoinedthechat@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
The Multimodal Lakehouse for AI
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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⭐ Click here ⭐ to see how fast we're growing!
Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
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We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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