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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>
53 lines
2.6 KiB
Plaintext
53 lines
2.6 KiB
Plaintext
---
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title: Answer.AI Rerankers
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sidebarTitle: "Answer.AI"
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description: Use AnswerDotAI's lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options.
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---
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import { PyRerankingAnswerdotaiUsage } from '/snippets/integrations.mdx';
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# Answer.AI Rerankers
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This integration uses [AnswersDotAI's rerankers](https://github.com/AnswerDotAI/rerankers) to rerank the search results, providing a lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
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> **Note:** Supported query types: Hybrid, Vector, and FTS.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingAnswerdotaiUsage}
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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_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. |
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| `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | The name of the reranker model to use. |
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| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
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| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score` column. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
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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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