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Brendan Clement
758f738b8c
This PR is part of the documentation revamp. Once the full set of PRs is merged, we’ll publish the combined site to staging for review before updating docs.lancedb.com. Ingestion, search and integration examples are incomplete or inconsistent across languages, and some guidance misstates SDK behavior. This brings the guides and their example sources together. It includes complete Python, TypeScript and Rust workflows, isolated provider and reranker examples, the Superlinked corrections, and guidance on retries that can duplicate writes. Stacked on #4491. Each MDX file has one PR owner. Rebase onto main after #4491 is squash-merged. Local assembly, Mint validation and link checks pass. Example code is unchanged from the previously validated package. Publication stays held through integration.
66 lines
3.5 KiB
Plaintext
66 lines
3.5 KiB
Plaintext
---
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title: Watsonx Reranker
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sidebarTitle: "IBM watsonx.ai"
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description: Rerank LanceDB search results with the IBM watsonx.ai text rerank API. Supports vector, FTS, and hybrid search with configurable models, projects, and spaces.
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---
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import { PyRerankingWatsonxUsage } from '/snippets/provider_examples.mdx';
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# Watsonx Reranker
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This reranker uses the [IBM watsonx.ai](https://cloud.ibm.com/docs/apis/watsonx-ai#text-rerank) text rerank API to reorder search results. Pass `WatsonxReranker()` to the `rerank()` method on a query. Credentials come from the `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` (or `WATSONX_SPACE_ID`) environment variables, or can be passed explicitly as arguments.
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> **Note:** Supported query types – Hybrid, Vector, and FTS.
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```shell
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pip install ibm-watsonx-ai
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```
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Replace the credential placeholders with your provider credentials. In application code, load credentials 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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{PyRerankingWatsonxUsage}
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</CodeBlock>
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</CodeGroup>
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## Accepted Arguments {#accepted-arguments}
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| Argument | Type | Default | Description |
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| --- | --- | --- | --- |
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| `model_name` | `str` | `"cross-encoder/ms-marco-minilm-l-12-v2"` | The rerank model ID. See [supported rerank models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx#rerank). |
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| `column` | `str` | `"text"` | The name of the table column to use as document input. |
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| `top_n` | `int` | `None` | The number of results to return. If `None`, all results are returned. |
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| `return_score` | `str` | `"relevance"` | Options are `"relevance"` or `"all"`. Controls which score columns are kept on the result. |
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| `api_key` | `str` | `None` | IBM Cloud API key. Falls back to the `WATSONX_API_KEY` environment variable. |
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| `project_id` | `str` | `None` | watsonx.ai project ID. Falls back to `WATSONX_PROJECT_ID`. Mutually exclusive with `space_id`. |
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| `space_id` | `str` | `None` | watsonx.ai deployment space ID. Falls back to `WATSONX_SPACE_ID`. Mutually exclusive with `project_id`. |
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| `url` | `str` | `"https://us-south.ml.cloud.ibm.com"` | watsonx.ai service URL. |
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| `truncate_input_tokens` | `int` | `None` | Truncate each document to this many tokens before scoring. |
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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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## Supported scores for each query type {#supported-scores-for-each-query-type}
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You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
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### Hybrid Search {#hybrid-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
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| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
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### Vector Search {#vector-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
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### FTS Search {#fts-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
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