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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>
70 lines
3.5 KiB
Plaintext
70 lines
3.5 KiB
Plaintext
---
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title: IBM watsonx
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sidebarTitle: IBM WatsonX
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---
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import { PyEmbeddingIbmUsage } from '/snippets/provider_examples.mdx';
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Generate text embeddings using IBM's watsonx.ai platform.
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## Supported Models {#supported-models}
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You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are:
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| Model ID | Dimensions |
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|----------------------------------------------|------------|
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| `ibm/granite-embedding-278m-multilingual` | 768 |
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| `ibm/slate-125m-english-rtrvr-v2` | 768 |
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| `ibm/slate-30m-english-rtrvr-v2` | 384 |
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| `intfloat/multilingual-e5-large` | 1024 |
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<Info>
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For new tables, pass `name="ibm/granite-embedding-278m-multilingual"`. The default `name` is still the legacy `ibm/slate-125m-english-rtrvr`, so tables created with it reload with the same model. Legacy and deprecated IDs (such as `ibm/slate-125m-english-rtrvr`, `sentence-transformers/all-minilm-l6-v2` and `sentence-transformers/all-minilm-l12-v2`) still work for tables whose stored metadata references them, but they aren't listed above for new use.
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</Info>
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## Parameters {#parameters}
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The following parameters can be passed to the `create` method:
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| Parameter | Type | Default Value | Description |
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|------------|----------|----------------------------------|-----------------------------------------------------------|
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| name | str | `"ibm/slate-125m-english-rtrvr"` | The model ID of the watsonx.ai model to use. Pass one of the current supported IDs above (e.g. `"ibm/granite-embedding-278m-multilingual"`) when creating new tables. |
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| api_key | str | None | Optional IBM Cloud API key (or set `WATSONX_API_KEY`) |
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| project_id | str | None | Optional watsonx project ID (or set `WATSONX_PROJECT_ID`). Mutually exclusive with `space_id`. |
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| space_id | str | None | Optional watsonx deployment space ID (or set `WATSONX_SPACE_ID`). Mutually exclusive with `project_id`. |
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| url | str | None | Optional custom URL for the watsonx.ai instance |
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| params | dict | None | Optional additional parameters for the embedding model (e.g. `{"truncate_input_tokens": 512}`) |
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<Info>
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You must supply exactly one of `project_id` or `space_id` (either as an argument or via its environment variable). Setting both, or neither, raises a `ValueError`.
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</Info>
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## Usage Example {#usage-example}
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First, the watsonx.ai library is an optional dependency, so must be installed separately:
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```
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pip install ibm-watsonx-ai
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```
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Optionally set environment variables (if not passing credentials to `create` directly):
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<CodeGroup>
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```bash Bash icon="terminal"
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export WATSONX_API_KEY="YOUR_WATSONX_API_KEY"
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# Provide exactly one of the following:
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export WATSONX_PROJECT_ID="YOUR_WATSONX_PROJECT_ID"
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# export WATSONX_SPACE_ID="YOUR_WATSONX_SPACE_ID"
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```
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</CodeGroup>
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Replace the credential placeholders with your provider credentials. The registry
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variable keeps the API key out of stored embedding configuration. In application
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code, load its value from environment variables or a secrets manager.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingIbmUsage}
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</CodeBlock>
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</CodeGroup>
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