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/baseten.py

@@ -0,0 +1,73 @@
+import logging
+from typing import Any, Dict, List, Mapping, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Field
+
+logger = logging.getLogger(__name__)
+
+
+class Baseten(LLM):
+    """Baseten models.
+
+    To use, you should have the ``baseten`` python package installed,
+    and run ``baseten.login()`` with your Baseten API key.
+
+    The required ``model`` param can be either a model id or model
+    version id. Using a model version ID will result in
+    slightly faster invocation.
+    Any other model parameters can also
+    be passed in with the format input={model_param: value, ...}
+
+    The Baseten model must accept a dictionary of input with the key
+    "prompt" and return a dictionary with a key "data" which maps
+    to a list of response strings.
+
+    Example:
+        .. code-block:: python
+            from langchain_community.llms import Baseten
+            my_model = Baseten(model="MODEL_ID")
+            output = my_model("prompt")
+    """
+
+    model: str
+    input: Dict[str, Any] = Field(default_factory=dict)
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            **{"model_kwargs": self.model_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of model."""
+        return "baseten"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call to Baseten deployed model endpoint."""
+        try:
+            import baseten
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import Baseten Python package. "
+                "Please install it with `pip install baseten`."
+            ) from exc
+
+        # get the model and version
+        try:
+            model = baseten.deployed_model_version_id(self.model)
+            response = model.predict({"prompt": prompt, **kwargs})
+        except baseten.common.core.ApiError:
+            model = baseten.deployed_model_id(self.model)
+            response = model.predict({"prompt": prompt, **kwargs})
+        return "".join(response)