community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes

libs/community/langchain_community/llms/mlflow.py

@@ -0,0 +1,122 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Mapping, Optional
+from urllib.parse import urlparse
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import LLM
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field, PrivateAttr
+
+
+# Ignoring type because below is valid pydantic code
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class Params(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
+    """Parameters for MLflow"""
+
+    temperature: float = 0.0
+    n: int = 1
+    stop: Optional[List[str]] = None
+    max_tokens: Optional[int] = None
+
+
+class Mlflow(LLM):
+    """Wrapper around completions LLMs in MLflow.
+
+    To use, you should have the `mlflow[genai]` python package installed.
+    For more information, see https://mlflow.org/docs/latest/llms/deployments/server.html.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import Mlflow
+
+            completions = Mlflow(
+                target_uri="http://localhost:5000",
+                endpoint="test",
+                params={"temperature": 0.1}
+            )
+    """
+
+    endpoint: str
+    """The endpoint to use."""
+    target_uri: str
+    """The target URI to use."""
+    temperature: float = 0.0
+    """The sampling temperature."""
+    n: int = 1
+    """The number of completion choices to generate."""
+    stop: Optional[List[str]] = None
+    """The stop sequence."""
+    max_tokens: Optional[int] = None
+    """The maximum number of tokens to generate."""
+    extra_params: Dict[str, Any] = Field(default_factory=dict)
+    """Any extra parameters to pass to the endpoint."""
+
+    """Extra parameters such as `temperature`."""
+    _client: Any = PrivateAttr()
+
+    def __init__(self, **kwargs: Any):
+        super().__init__(**kwargs)
+        self._validate_uri()
+        try:
+            from mlflow.deployments import get_deploy_client
+
+            self._client = get_deploy_client(self.target_uri)
+        except ImportError as e:
+            raise ImportError(
+                "Failed to create the client. "
+                "Please run `pip install mlflow[genai]` to install "
+                "required dependencies."
+            ) from e
+
+    def _validate_uri(self) -> None:
+        if self.target_uri == "databricks":
+            return
+        allowed = ["http", "https", "databricks"]
+        if urlparse(self.target_uri).scheme not in allowed:
+            raise ValueError(
+                f"Invalid target URI: {self.target_uri}. "
+                f"The scheme must be one of {allowed}."
+            )
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        return {
+            "target_uri": self.target_uri,
+            "endpoint": self.endpoint,
+            "temperature": self.temperature,
+            "n": self.n,
+            "stop": self.stop,
+            "max_tokens": self.max_tokens,
+            "extra_params": self.extra_params,
+        }
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return self._default_params
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        data: Dict[str, Any] = {
+            "prompt": prompt,
+            "temperature": self.temperature,
+            "n": self.n,
+            **self.extra_params,
+            **kwargs,
+        }
+        if stop := self.stop or stop:
+            data["stop"] = stop
+        if self.max_tokens is not None:
+            data["max_tokens"] = self.max_tokens
+
+        resp = self._client.predict(endpoint=self.endpoint, inputs=data)
+        return resp["choices"][0]["text"]
+
+    @property
+    def _llm_type(self) -> str:
+        return "mlflow"