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/pipelineai.py

@@ -0,0 +1,115 @@
+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 BaseModel, 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 PipelineAI(LLM, BaseModel):
+    """PipelineAI large language models.
+
+    To use, you should have the ``pipeline-ai`` python package installed,
+    and the environment variable ``PIPELINE_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 PipelineAI
+            pipeline = PipelineAI(pipeline_key="")
+    """
+
+    pipeline_key: str = ""
+    """The id or tag of the target pipeline"""
+
+    pipeline_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any pipeline parameters valid for `create` call not
+    explicitly specified."""
+
+    pipeline_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("pipeline_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"""{field_name} was transferred to pipeline_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+        values["pipeline_kwargs"] = extra
+        return values
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        pipeline_api_key = get_from_dict_or_env(
+            values, "pipeline_api_key", "PIPELINE_API_KEY"
+        )
+        values["pipeline_api_key"] = pipeline_api_key
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            **{"pipeline_key": self.pipeline_key},
+            **{"pipeline_kwargs": self.pipeline_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "pipeline_ai"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call to Pipeline Cloud endpoint."""
+        try:
+            from pipeline import PipelineCloud
+        except ImportError:
+            raise ImportError(
+                "Could not import pipeline-ai python package. "
+                "Please install it with `pip install pipeline-ai`."
+            )
+        client = PipelineCloud(token=self.pipeline_api_key)
+        params = self.pipeline_kwargs or {}
+        params = {**params, **kwargs}
+
+        run = client.run_pipeline(self.pipeline_key, [prompt, params])
+        try:
+            text = run.result_preview[0][0]
+        except AttributeError:
+            raise AttributeError(
+                f"A pipeline run should have a `result_preview` attribute."
+                f"Run was: {run}"
+            )
+        if stop is not None:
+            # I believe this is required since the stop tokens
+            # are not enforced by the pipeline parameters
+            text = enforce_stop_tokens(text, stop)
+        return text