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Avanish GargandXuanwo
2d54630a3c
Permanent embedding errors (HTTP 400/404/422 and other 4xx) were retried the full `max_retries` times with an uncapped, multiplicatively jittered backoff, so a typo'd model name looked like a hang of about an hour (median 50 min of sleeping with a 404; see the issue for the repro and numbers). Changes (`python/python/lancedb/embeddings/utils.py`, `embeddings/base.py`): - An HTTP status in 4xx other than 408/409/429 is raised immediately. The status is read from `status_code`, `http_status`, `response.status_code`, an integer `code` (google-api-core, urllib) or a botocore `response["ResponseMetadata"]["HTTPStatusCode"]`. AWS throttling reported as 400 stays retryable (matched by error code). `AuthenticationError` by name is unchanged. - Still retried: 408, 409, 429, >=500, and anything without a recognisable status (connection errors, timeouts, custom exceptions), so existing custom embedding functions behave as before. - New keyword `max_delay=60` (last in the signature, default keeps call sites working). `delay = min(delay * base, max_delay)` with equal jitter `delay/2 + random() * delay/2`. With defaults the waits are [1,2], [2,4], [4,8], [8,16], [16,32], [30,60], [30,60] s, worst case about 182 s instead of up to 2.5 h. - The "Maximum number of retries" exception now has `from e`. The `max_retries` comment in `base.py` describes the rules. Not changed: `embeddings/openai.py` (it swallows `BadRequestError`; open #4154/#4005/#3152 cover it), Retry-After handling (the code never used it), and the "C Data interface error" wrapping from the native layer (the original message is now included in it). Behaviour changes to be aware of: every 4xx except 408/409/429 fails fast, including 401/403 coming through `response.status_code` (requests/httpx), which used to be retried; a provider that signals transient failures with a 4xx and no AWS-style code is no longer retried; the first retry waits 1-2 s instead of 2-4 s. Tests (`python/python/tests/test_embeddings.py`): 48 new cases (23 test functions): permanent errors are not retried, transient errors are, botocore-style errors, the delay is capped, exact exponential growth without jitter, jitter bounds, no float overflow for a large `max_retries`, the original error is attached, and end to end through `table.add` and `table.search` with a stub function (one call and no sleep). On `main` 23 of them fail with the change stashed; the rest are guards that must not change. `test_embeddings.py`: 61 passed, 4 skipped. `ruff format`/`ruff check` are clean on the touched files. Verification caveat: `main`'s Python layer needs a newer native module than the 0.40.0 wheel, so the tests were run with the wheel's native module preloaded through a small shim (the same shim ran the baseline on `main`); a wider run of embeddings/pydantic/query/table tests has the same 36 failures before and after (that mismatch). Not run: real provider APIs (a fake OpenAI-style server was used), Node or Rust code. Fixes #4541 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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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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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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