feat: add Jina integration in Python for Embedding and Reranker (#1424)

Integration of Jina Embeddings and Rerankers through its API

docs/src/embeddings/index.md

@@ -68,6 +68,39 @@ table.add(
     ]
 )
 
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
+
+### Jina Embeddings
+LanceDB registers the JinaAI embeddings function in the registry as `jina`. You can pass any supported model name to the `create`. By default it uses `"jina-clip-v1"`.
+`jina-clip-v1` can handle both text and images and other models only support `text`.
+
+You need to pass `JINA_API_KEY` in the environment variable or pass it as `api_key` to `create` method.
+
+```python
+import os
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+os.environ['JINA_API_KEY'] = "jina_*"
+
+db = lancedb.connect("/tmp/db")
+func = get_registry().get("jina").create(name="jina-clip-v1")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
+
+table = db.create_table("words", schema=Words, mode="overwrite")
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+    )
+
 query = "greetings"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]
 print(actual.text)

docs/src/reranking/jina.md

@@ -0,0 +1,78 @@
+# Jina Reranker
+
+This re-ranker uses the [Jina](https://jina.ai/reranker/) API to rerank the search results. You can use this re-ranker by passing `JinaReranker()` to the `rerank()` method. Note that you'll either need to set the `JINA_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
+
+
+!!! note
+    Supported Query Types: Hybrid, Vector, FTS
+
+
+```python
+import os
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import JinaReranker
+
+os.environ['JINA_API_KEY'] = "jina_*"
+
+
+embedder = get_registry().get("jina").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = JinaReranker(api_key="key")
+
+# Run vector search with a reranker
+result = tbl.search("hello").rerank(reranker=reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"jina-reranker-v2-base-multilingual"` | The name of the reranker model to use. You can find the list of available models in https://jina.ai/reranker/|
+| `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 |
+
+
+
+## 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 | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |