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Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
51 lines
2.3 KiB
Plaintext
51 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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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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- embed-english-v2.0
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- embed-english-light-v2.0
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- embed-multilingual-v2.0
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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-english-v2.0"` | 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, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
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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|
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| | database for search use-cases. |
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| "`search_query`" | Used for embeddings of search queries |
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| | run against a vector DB |
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| "`semantic_similarity`" | Specifies the given text will be used |
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| | for Semantic Textual Similarity (STS) |
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| "`classification`" | Used for embeddings passed through a |
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| | text classifier. |
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| "`clustering`" | Used for the embeddings run through a |
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| | 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> |