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/predictionguard.py

@@ -0,0 +1,130 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+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
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+
+
+class PredictionGuard(LLM):
+    """Prediction Guard large language models.
+
+    To use, you should have the ``predictionguard`` python package installed, and the
+    environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass
+    it as a named parameter to the constructor. To use Prediction Guard's API along
+    with OpenAI models, set the environment variable ``OPENAI_API_KEY`` with your
+    OpenAI API key as well.
+
+    Example:
+        .. code-block:: python
+
+            pgllm = PredictionGuard(model="MPT-7B-Instruct",
+                                    token="my-access-token",
+                                    output={
+                                        "type": "boolean"
+                                    })
+    """
+
+    client: Any  #: :meta private:
+    model: Optional[str] = "MPT-7B-Instruct"
+    """Model name to use."""
+
+    output: Optional[Dict[str, Any]] = None
+    """The output type or structure for controlling the LLM output."""
+
+    max_tokens: int = 256
+    """Denotes the number of tokens to predict per generation."""
+
+    temperature: float = 0.75
+    """A non-negative float that tunes the degree of randomness in generation."""
+
+    token: Optional[str] = None
+    """Your Prediction Guard access token."""
+
+    stop: Optional[List[str]] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the access token and python package exists in environment."""
+        token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN")
+        try:
+            import predictionguard as pg
+
+            values["client"] = pg.Client(token=token)
+        except ImportError:
+            raise ImportError(
+                "Could not import predictionguard python package. "
+                "Please install it with `pip install predictionguard`."
+            )
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling the Prediction Guard API."""
+        return {
+            "max_tokens": self.max_tokens,
+            "temperature": self.temperature,
+        }
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model": self.model}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "predictionguard"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to Prediction Guard's model API.
+        Args:
+            prompt: The prompt to pass into the model.
+        Returns:
+            The string generated by the model.
+        Example:
+            .. code-block:: python
+                response = pgllm("Tell me a joke.")
+        """
+        import predictionguard as pg
+
+        params = self._default_params
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            params["stop_sequences"] = self.stop
+        else:
+            params["stop_sequences"] = stop
+
+        response = pg.Completion.create(
+            model=self.model,
+            prompt=prompt,
+            output=self.output,
+            temperature=params["temperature"],
+            max_tokens=params["max_tokens"],
+            **kwargs,
+        )
+        text = response["choices"][0]["text"]
+
+        # If stop tokens are provided, Prediction Guard's endpoint returns them.
+        # In order to make this consistent with other endpoints, we strip them.
+        if stop is not None or self.stop is not None:
+            text = enforce_stop_tokens(text, params["stop_sequences"])
+
+        return text