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/amazon_api_gateway.py

@@ -0,0 +1,104 @@
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Extra
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+
+class ContentHandlerAmazonAPIGateway:
+    """Adapter to prepare the inputs from Langchain to a format
+    that LLM model expects.
+
+    It also provides helper function to extract
+    the generated text from the model response."""
+
+    @classmethod
+    def transform_input(
+        cls, prompt: str, model_kwargs: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        return {"inputs": prompt, "parameters": model_kwargs}
+
+    @classmethod
+    def transform_output(cls, response: Any) -> str:
+        return response.json()[0]["generated_text"]
+
+
+class AmazonAPIGateway(LLM):
+    """Amazon API Gateway to access LLM models hosted on AWS."""
+
+    api_url: str
+    """API Gateway URL"""
+
+    headers: Optional[Dict] = None
+    """API Gateway HTTP Headers to send, e.g. for authentication"""
+
+    model_kwargs: Optional[Dict] = None
+    """Keyword arguments to pass to the model."""
+
+    content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
+    """The content handler class that provides an input and
+    output transform functions to handle formats between LLM
+    and the endpoint.
+    """
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        _model_kwargs = self.model_kwargs or {}
+        return {
+            **{"api_url": self.api_url, "headers": self.headers},
+            **{"model_kwargs": _model_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "amazon_api_gateway"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to Amazon API Gateway model.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = se("Tell me a joke.")
+        """
+        _model_kwargs = self.model_kwargs or {}
+        payload = self.content_handler.transform_input(prompt, _model_kwargs)
+
+        try:
+            response = requests.post(
+                self.api_url,
+                headers=self.headers,
+                json=payload,
+            )
+            text = self.content_handler.transform_output(response)
+
+        except Exception as error:
+            raise ValueError(f"Error raised by the service: {error}")
+
+        if stop is not None:
+            text = enforce_stop_tokens(text, stop)
+
+        return text