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/petals.py

@@ -0,0 +1,153 @@
+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 Extra, Field, 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 Petals(LLM):
+    """Petals Bloom models.
+
+    To use, you should have the ``petals`` python package installed, and the
+    environment variable ``HUGGINGFACE_API_KEY`` set with your API key.
+
+    Any parameters that are valid to be passed to the call can be passed
+    in, even if not explicitly saved on this class.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import petals
+            petals = Petals()
+
+    """
+
+    client: Any
+    """The client to use for the API calls."""
+
+    tokenizer: Any
+    """The tokenizer to use for the API calls."""
+
+    model_name: str = "bigscience/bloom-petals"
+    """The model to use."""
+
+    temperature: float = 0.7
+    """What sampling temperature to use"""
+
+    max_new_tokens: int = 256
+    """The maximum number of new tokens to generate in the completion."""
+
+    top_p: float = 0.9
+    """The cumulative probability for top-p sampling."""
+
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens
+    to keep for top-k-filtering."""
+
+    do_sample: bool = True
+    """Whether or not to use sampling; use greedy decoding otherwise."""
+
+    max_length: Optional[int] = None
+    """The maximum length of the sequence to be generated."""
+
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for `create` call
+    not explicitly specified."""
+
+    huggingface_api_key: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic config."""
+
+        extra = Extra.forbid
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name not in all_required_field_names:
+                if field_name in extra:
+                    raise ValueError(f"Found {field_name} supplied twice.")
+                logger.warning(
+                    f"""WARNING! {field_name} is not default parameter.
+                    {field_name} was transferred to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+        values["model_kwargs"] = extra
+        return values
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        huggingface_api_key = get_from_dict_or_env(
+            values, "huggingface_api_key", "HUGGINGFACE_API_KEY"
+        )
+        try:
+            from petals import AutoDistributedModelForCausalLM
+            from transformers import AutoTokenizer
+
+            model_name = values["model_name"]
+            values["tokenizer"] = AutoTokenizer.from_pretrained(model_name)
+            values["client"] = AutoDistributedModelForCausalLM.from_pretrained(
+                model_name
+            )
+            values["huggingface_api_key"] = huggingface_api_key
+
+        except ImportError:
+            raise ImportError(
+                "Could not import transformers or petals python package."
+                "Please install with `pip install -U transformers petals`."
+            )
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Petals API."""
+        normal_params = {
+            "temperature": self.temperature,
+            "max_new_tokens": self.max_new_tokens,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "do_sample": self.do_sample,
+            "max_length": self.max_length,
+        }
+        return {**normal_params, **self.model_kwargs}
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model_name": self.model_name}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "petals"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call the Petals API."""
+        params = self._default_params
+        params = {**params, **kwargs}
+        inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
+        outputs = self.client.generate(inputs, **params)
+        text = self.tokenizer.decode(outputs[0])
+        if stop is not None:
+            # I believe this is required since the stop tokens
+            # are not enforced by the model parameters
+            text = enforce_stop_tokens(text, stop)
+        return text