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Jack YeandClaude Sonnet 5
de95ed3f6d
A broad documentation pass across the areas LanceDB Enterprise and Embedded diverge on most: **Data Curation** -- GPU Function execution, Slurm scheduling (and what Slurm is, for readers new to it), blob late materialization as its own page, joins, aggregation/profiling, sampling, near-duplicate detection, chunking, distributed shuffle, and IVF-partition clustering. **Feature Engineering** -- computed columns and materialized views, restructured to lead with the Function-based path and show the SQL form side by side with Python throughout; a single Auto Refresh page instead of duplicating scheduling details on every refresh page; adaptive execution; checkpointing; GPU and Slurm placement for refresh workers; a Training Cache page for the PyTorch data loader's automatic cache benefit. **Security & Governance** -- an Audit Log reference (schema, access, grants), Secrets promoted to an ordinary catalog object with their own SQL DDL and a vending/rotation guide, and privilege-table fixes verified against the authorization source. **Deployment & Maintenance** -- Horizontal Scaling and OpenTelemetry moved into a real Deployment area; Automated Cleanup folded into Intelligent Auto-Maintenance (every other maintenance decision already lived there); RTE Compaction and Advanced Conflict Resolution documented in depth. **Landing page** -- the Enterprise feature comparison table reorganized (several redundant or stale rows removed, several new areas added), and the "why Enterprise" bullets replaced with ones tied to a concrete page (Performance, Scale, Fault tolerance, Multicloud deployment, Simplify operations) instead of vague promises. Plus a full pass addressing lancedb-gatekeeper's review: a real test-safety bug (a quickstart test could silently overwrite and delete a deployment's own `frames` table), a fabricated SQL statement with no backing implementation, an actual dedup-guarantee bug in `take_row_ids`, and dozens of documentation-accuracy fixes verified against source rather than taken on faith -- each either fixed, or left with an explicit note on why it needs a feature or deployment owner's sign-off instead of being guessed at. 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_018PXMCbRH6i5w47TsjeEUMT --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
57 lines
2.4 KiB
Plaintext
57 lines
2.4 KiB
Plaintext
---
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title: Cohere
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sidebarTitle: Cohere
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---
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import { PyEmbeddingCohereUsage } from '/snippets/integrations.mdx';
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Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
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You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
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<CodeGroup>
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```bash Bash icon="terminal"
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export COHERE_API_KEY="your-api-key"
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```
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</CodeGroup>
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Supported models are:
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- embed-english-v3.0
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- embed-multilingual-v3.0
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- embed-english-light-v3.0
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- embed-multilingual-light-v3.0
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The v2.0 models (`embed-english-v2.0`, `embed-english-light-v2.0` and `embed-multilingual-v2.0`) were retired by Cohere on April 4, 2026.
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The previous default, `embed-multilingual-v2.0`, produced 768-dimensional vectors.
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The new default, `embed-multilingual-v3.0`, produces 1024-dimensional vectors.
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Existing tables containing vectors from a v2.0 model must be re-embedded with an
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active model into a table whose vector column has the matching dimension.
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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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|---|---|--------|---------|
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| `name` | `str` | `"embed-multilingual-v3.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0 |
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| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
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| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
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Cohere supports following input types:
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| Input type | Description |
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| --- | --- |
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| `search_document` | Used for embeddings stored in a vector database for search use cases. |
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| `search_query` | Used for embeddings of search queries run against a vector database. |
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| `semantic_similarity` | Specifies that the text will be used for semantic textual similarity (STS). |
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| `classification` | Used for embeddings passed through a text classifier. |
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| `clustering` | Used for embeddings run through a clustering algorithm. |
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Usage Example:
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingCohereUsage}
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</CodeBlock>
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</CodeGroup>
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