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Brendan ClementandClaude Opus 5.5
ca343a5c06
## Summary Fixes the problems a page-by-page review of the docs found before launch, after checking each one against the current source and, where noted, the code. The matching fixes for the dataset and Geneva pages, which live in `lancedb/docs`, are in lancedb/docs#402. **Tables** - `integrations/embedding/cohere`: each input-type description was split across two table rows and wrapped in literal quotes (`"`search_document`"`). Now one row per type. - `integrations/embedding/gemini`: removes the same stray quotes from the task-type table. - `integrations/ai/langchain`: the MMR search table's `kwargs` row was missing a cell, so its description sat under Type. - Six reranker pages (answerdotai, cohere, colbert, jina, openai, voyageai): closes the backtick before `_relevance_score`. - `indexing/vector-index`: finishes a cell cut off at "with scalar". - `sql/dql/dedupe`: labels the empty header cell in the resolution table. **Corrections** - `curation/sampling`: sampling works in LanceDB Embedded through the Python SDK, so the page is available in both, with the SQL sections marked Enterprise. - `feature-engineering/index`, `add-computed-column`: automatic refresh is on by default for each computed column and takes effect once a deployment enables its scheduler. `refresh-column` already said so; these two implied it was off. - `indexing/scalar-index`, `indexing/index`: `LABEL_LIST` accelerates `array_has_all`/`array_has_any`; `array_contains_*` doesn't exist. Also merges scalar-index's two "nested fields" sections into one. - `integrations/embedding/ibm`: lists the four current models from `CURRENT_MODELS` (it listed a deprecated one) and explains that the default stays a legacy ID so existing tables reload with their model. - `integrations/reranking/openai`: the class is `OpenaiReranker`, not `OpenAI()`. - `integrations/ai/synthetic-data-kit`: the CLI is `synthetic-data-kit` throughout, each step runs one command, each step passes the file it writes to the next, and `save-as` uses `-f`/`-o`. - `integrations/data/dlt`: `secrets.toml` defines `api_token`, which the pipeline reads, and the run command runs the file the page creates. - `search/vector-search`: the multivector walkthrough connected to Enterprise in step 1 and a local path in step 2. It now runs locally throughout, with a note on connecting to Enterprise, and is renamed so it doesn't read as a duplicate of the short section above. **Structure and wording** - `integrations/embedding/nvidia`: adds headings, and drops test-suite details aimed at contributors. - `integrations/ai/genkit`: makes the retrieval test a real heading and describes the query step. - `integrations/data/voxel51`: replaces a raw MkDocs `!!! note` with a `<Note>`. - `sql/ddl/table`, `sql/ddl/job`: "Errors and limits" is a real heading, not bold text. - `curation/windowing`: sidebar title matches the curation overview ("Rank and Deduplicate"). - `deployment/storage`, `basics/agentic-skills`: fixes a garbled sentence and a stale reference to the quickstart. **Checked:** no new Vale alerts on the changed pages. Assembled together with lancedb/docs#402, the site passes `mint validate` and `mint broken-links --check-anchors --check-redirects`. Every changed page was reviewed in a local preview. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
55 lines
2.3 KiB
Plaintext
55 lines
2.3 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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```shell
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export COHERE_API_KEY="your-api-key"
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```
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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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