community[patch]: allow additional kwargs in MlflowEmbeddings for compatibility with Cohere API (#15242)

- **Description:** add support for kwargs in`MlflowEmbeddings`
`embed_document()` and `embed_query()` so that all the arguments
required by Cohere API (and others?) can be passed down to the server.
  - **Issue:** #15234 
- **Dependencies:** MLflow with MLflow Deployments (`pip install
mlflow[genai]`)

**Tests**
Now this code [adapted from the
docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example)
for the Cohere API works locally.

```python
"""
Setup
-----
export COHERE_API_KEY=...
mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml

Run
---
python /path/to/this/file.py
"""
embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
```

Output
```
[0.060455322, 0.028793335, -0.025848389]
[0.031707764, 0.021057129, -0.009361267]
```

libs/community/langchain_community/embeddings/mlflow.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Iterator, List
+from typing import Any, Dict, Iterator, List
 from urllib.parse import urlparse
 
 from langchain_core.embeddings import Embeddings
@@ -34,6 +34,10 @@ class MlflowEmbeddings(Embeddings, BaseModel):
     target_uri: str
     """The target URI to use."""
     _client: Any = PrivateAttr()
+    """The parameters to use for queries."""
+    query_params: Dict[str, str] = {}
+    """The parameters to use for documents."""
+    documents_params: Dict[str, str] = {}
 
     def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
@@ -63,12 +67,22 @@ class MlflowEmbeddings(Embeddings, BaseModel):
                 f"The scheme must be one of {allowed}."
             )
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed(self, texts: List[str], params: Dict[str, str]) -> List[List[float]]:
         embeddings: List[List[float]] = []
         for txt in _chunk(texts, 20):
-            resp = self._client.predict(endpoint=self.endpoint, inputs={"input": txt})
+            resp = self._client.predict(
+                endpoint=self.endpoint, inputs={"input": txt, **params}
+            )
             embeddings.extend(r["embedding"] for r in resp["data"])
         return embeddings
 
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self.embed(texts, params=self.documents_params)
+
     def embed_query(self, text: str) -> List[float]:
-        return self.embed_documents([text])[0]
+        return self.embed([text], params=self.query_params)[0]
+
+
+class MlflowCohereEmbeddings(MlflowEmbeddings):
+    query_params: Dict[str, str] = {"input_type": "search_query"}
+    documents_params: Dict[str, str] = {"input_type": "search_document"}