Add docs for MRRReranker and ColPali embeddings

docs/docs.json

@@ -209,6 +209,7 @@
                   "integrations/embedding/huggingface",
                   "integrations/embedding/aws",
                   "integrations/embedding/cohere",
+                  "integrations/embedding/colpali",
                   "integrations/embedding/gemini",
                   "integrations/embedding/ibm",
                   "integrations/embedding/imagebind",
@@ -230,6 +231,7 @@
                   "integrations/reranking/cross_encoder",
                   "integrations/reranking/jina",
                   "integrations/reranking/linear_combination",
+                  "integrations/reranking/mrr",
                   "integrations/reranking/openai",
                   "integrations/reranking/rrf",
                   "integrations/reranking/voyageai"

docs/integrations/embedding/colpali.mdx

@@ -0,0 +1,53 @@
+---
+title: ColPali
+sidebarTitle: ColPali
+---
+
+import {
+  PyEmbeddingColpaliSetup,
+  PyEmbeddingColpaliTextSearch,
+} from '/snippets/integrations.mdx';
+
+We support [ColPali](https://github.com/illuin-tech/colpali) model embeddings for multimodal multi-vector retrieval. ColPali produces multiple embedding vectors per input (multi-vector), enabling more nuanced similarity matching between text queries and image documents.
+
+Using ColPali requires the colpali-engine package, which can be installed using `pip install colpali-engine`.
+
+<Info>
+ColPali produces **multi-vector** embeddings, meaning each input generates multiple embedding vectors rather than a single vector. Use `MultiVector(func.ndims())` instead of `Vector(func.ndims())` when defining your schema.
+</Info>
+
+Supported models are:
+
+- Metric-AI/ColQwen2.5-3b-multilingual-v1.0 (default)
+- vidore/colpali-v1.3
+- vidore/colqwen2-v1.0
+- vidore/colSmol-256M
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `model_name` | `str` | `"Metric-AI/ColQwen2.5-3b-multilingual-v1.0"` | The name of the model to use. |
+| `device` | `str` | `"auto"` | The device for inference. Can be `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`. |
+| `dtype` | `str` | `"bfloat16"` | Data type for model weights (bfloat16, float16, float32, float64). |
+| `pooling_strategy` | `str` | `"hierarchical"` | Token pooling strategy: `"hierarchical"`, `"lambda"`, or `None`. |
+| `pool_factor` | `int` | `2` | Factor to reduce sequence length when pooling is enabled. |
+| `batch_size` | `int` | `2` | Batch size for processing inputs. |
+| `quantization_config` | `Optional[BitsAndBytesConfig]` | `None` | Quantization configuration for the model (requires bitsandbytes). |
+
+This embedding function supports ingesting images as both bytes and URLs. You can query them using text.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingColpaliSetup}
+  </CodeBlock>
+</CodeGroup>
+
+Now we can search using text queries:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingColpaliTextSearch}
+  </CodeBlock>
+</CodeGroup>
+

docs/integrations/reranking/answerdotai.mdx

@@ -27,7 +27,7 @@ Accepted Arguments
 | `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. |
 | `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | The name of the reranker model to use. |
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
 
 
 

docs/integrations/reranking/cohere.mdx

@@ -32,7 +32,7 @@ Accepted Arguments
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
 | `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
 | `api_key` | `str` | `None` | The API key for the Cohere API. If not provided, the `COHERE_API_KEY` environment variable is used. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
 
 
 

docs/integrations/reranking/colbert.mdx

@@ -26,7 +26,7 @@ Accepted Arguments
 | `model_name` | `str` | `"colbert-ir/colbertv2.0"` | The name of the reranker model to use.|
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
 | `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
 
 
 ## Supported Scores for each query type

docs/integrations/reranking/cross_encoder.mdx

@@ -26,7 +26,7 @@ Accepted Arguments
 | `model_name` | `str` | `""cross-encoder/ms-marco-TinyBERT-L-6"` | The name of the reranker model to use.|
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
 | `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
 
 ## Supported Scores for each query type
 You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:

docs/integrations/reranking/jina.mdx

@@ -29,7 +29,7 @@ Accepted Arguments
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
 | `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
 | `api_key` | `str` | `None` | The API key for the Jina API. If not provided, the `JINA_API_KEY` environment variable is used. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
 
 
 

docs/integrations/reranking/linear_combination.mdx

@@ -27,7 +27,7 @@ Accepted Arguments
 | Argument | Type | Default | Description |
 | --- | --- | --- | --- |
 | `weight` | `float` | `0.7` | The weight to use for the semantic search score. The weight for the full-text search score is `1 - weights`. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score. |
 
 
 ## Supported Scores for each query type

docs/integrations/reranking/mrr.mdx

@@ -0,0 +1,46 @@
+---
+title: MRR Reranker
+sidebarTitle: "MRR Algorithm"
+description: Combine and rerank search results using Mean Reciprocal Rank (MRR) algorithm in LanceDB. Supports weighted scoring for hybrid and multivector search.
+
+---
+
+import { PyRerankingMrrUsage } from '/snippets/integrations.mdx';
+
+# MRR Reranker
+
+This reranker uses the Mean Reciprocal Rank (MRR) algorithm to combine and rerank search results from vector and full-text search. You can use this reranker by passing `MRRReranker()` to the `rerank()` method. The MRR algorithm calculates the average of reciprocal ranks across different search results, providing a balanced way to merge results from multiple ranking systems.
+
+> **Note:** Supported query types – Hybrid and Multivector search.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingMrrUsage}
+  </CodeBlock>
+</CodeGroup>
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `weight_vector` | `float` | `0.5` | Weight for vector search results (0.0 to 1.0). |
+| `weight_fts` | `float` | `0.5` | Weight for FTS search results (0.0 to 1.0). |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+
+**Note:** `weight_vector` + `weight_fts` must equal 1.0.
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Multivector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector distances from all searches along with `_relevance_score`. |

docs/integrations/reranking/openai.mdx

@@ -26,8 +26,8 @@ Accepted Arguments
 | --- | --- | --- | --- |
 | `model_name` | `str` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.|
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
-| `api_key` | str | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `api_key` | `str` | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
 
 
 ## Supported Scores for each query type

docs/integrations/reranking/rrf.mdx

@@ -26,7 +26,7 @@ Accepted Arguments
 | Argument | Type | Default | Description |
 | --- | --- | --- | --- |
 | `K` | `int` | `60` | A constant used in the RRF formula (default is 60). Experiments indicate that k = 60 was near-optimal, but that the choice is not critical. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
 
 
 ## Supported Scores for each query type

docs/integrations/reranking/voyageai.mdx

@@ -31,7 +31,7 @@ Accepted Arguments
 | `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
 | `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
 | `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
 | `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. |