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/opaqueprompts.py

@@ -0,0 +1,116 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Extra, root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class OpaquePrompts(LLM):
+    """An LLM wrapper that uses OpaquePrompts to sanitize prompts.
+
+    Wraps another LLM and sanitizes prompts before passing it to the LLM, then
+        de-sanitizes the response.
+
+    To use, you should have the ``opaqueprompts`` python package installed,
+    and the environment variable ``OPAQUEPROMPTS_API_KEY`` set with
+    your API key, or pass it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import OpaquePrompts
+            from langchain_community.chat_models import ChatOpenAI
+
+            op_llm = OpaquePrompts(base_llm=ChatOpenAI())
+    """
+
+    base_llm: BaseLanguageModel
+    """The base LLM to use."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validates that the OpaquePrompts API key and the Python package exist."""
+        try:
+            import opaqueprompts as op
+        except ImportError:
+            raise ImportError(
+                "Could not import the `opaqueprompts` Python package, "
+                "please install it with `pip install opaqueprompts`."
+            )
+        if op.__package__ is None:
+            raise ValueError(
+                "Could not properly import `opaqueprompts`, "
+                "opaqueprompts.__package__ is None."
+            )
+
+        api_key = get_from_dict_or_env(
+            values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default=""
+        )
+        if not api_key:
+            raise ValueError(
+                "Could not find OPAQUEPROMPTS_API_KEY in the environment. "
+                "Please set it to your OpaquePrompts API key."
+                "You can get it by creating an account on the OpaquePrompts website: "
+                "https://opaqueprompts.opaque.co/ ."
+            )
+        return values
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call base LLM with sanitization before and de-sanitization after.
+
+        Args:
+            prompt: The prompt to pass into the model.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = op_llm("Tell me a joke.")
+        """
+        import opaqueprompts as op
+
+        _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
+
+        # sanitize the prompt by replacing the sensitive information with a placeholder
+        sanitize_response: op.SanitizeResponse = op.sanitize([prompt])
+        sanitized_prompt_value_str = sanitize_response.sanitized_texts[0]
+
+        # TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
+        # call the LLM with the sanitized prompt and get the response
+        llm_response = self.base_llm.predict(
+            sanitized_prompt_value_str,
+            stop=stop,
+        )
+
+        # desanitize the response by restoring the original sensitive information
+        desanitize_response: op.DesanitizeResponse = op.desanitize(
+            llm_response,
+            secure_context=sanitize_response.secure_context,
+        )
+        return desanitize_response.desanitized_text
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM.
+
+        This is an override of the base class method.
+        """
+        return "opaqueprompts"