Andrew Chen
5b347afd99
## What `JinaEmbeddings._generate_image_input_dict()` crashes with `AttributeError: 'function' object has no attribute 'urlparse'` on any image given as a URL string, local path string, or `pathlib.Path` — i.e. every documented `jina-clip-v1` image-embedding use case except raw `bytes`. ## Why ```python from urllib.parse import urlparse ... parsed = urlparse.urlparse(image) ``` `urlparse` is imported as a function, then called as if it were the `urllib.parse` module (`urlparse.urlparse(...)`). The module-level `is_valid_url()` a few lines above does it correctly (`urlparse(text)`), which is why this reads as a typo rather than intentional. Fixed to `urlparse(str(image))` — `str()` is needed because `urlparse()` only accepts `str`/`bytes` and raises a different `AttributeError` on a raw `Path`. ## Testing Added `test_jina_generate_image_input_dict_local_path`, which fails with the original `AttributeError` before the fix and passes after, covering both a `str` path and a `pathlib.Path`. Verified locally (built the Rust extension, ran red→green, then the full `test_embeddings.py` file: 15 passed / 8 skipped, no regressions) and with `ruff check`/`ruff format`. --- Disclosure: this PR was drafted with AI assistance (Claude); I reviewed, tested, and take responsibility for the change. --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
The Multimodal AI Lakehouse
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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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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