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/ctransformers.py

@@ -0,0 +1,140 @@
+from functools import partial
+from typing import Any, Dict, List, Optional, Sequence
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import root_validator
+
+
+class CTransformers(LLM):
+    """C Transformers LLM models.
+
+    To use, you should have the ``ctransformers`` python package installed.
+    See https://github.com/marella/ctransformers
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import CTransformers
+
+            llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2")
+    """
+
+    client: Any  #: :meta private:
+
+    model: str
+    """The path to a model file or directory or the name of a Hugging Face Hub
+    model repo."""
+
+    model_type: Optional[str] = None
+    """The model type."""
+
+    model_file: Optional[str] = None
+    """The name of the model file in repo or directory."""
+
+    config: Optional[Dict[str, Any]] = None
+    """The config parameters.
+    See https://github.com/marella/ctransformers#config"""
+
+    lib: Optional[str] = None
+    """The path to a shared library or one of `avx2`, `avx`, `basic`."""
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model": self.model,
+            "model_type": self.model_type,
+            "model_file": self.model_file,
+            "config": self.config,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "ctransformers"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that ``ctransformers`` package is installed."""
+        try:
+            from ctransformers import AutoModelForCausalLM
+        except ImportError:
+            raise ImportError(
+                "Could not import `ctransformers` package. "
+                "Please install it with `pip install ctransformers`"
+            )
+
+        config = values["config"] or {}
+        values["client"] = AutoModelForCausalLM.from_pretrained(
+            values["model"],
+            model_type=values["model_type"],
+            model_file=values["model_file"],
+            lib=values["lib"],
+            **config,
+        )
+        return values
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[Sequence[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Generate text from a prompt.
+
+        Args:
+            prompt: The prompt to generate text from.
+            stop: A list of sequences to stop generation when encountered.
+
+        Returns:
+            The generated text.
+
+        Example:
+            .. code-block:: python
+
+                response = llm("Tell me a joke.")
+        """
+        text = []
+        _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
+        for chunk in self.client(prompt, stop=stop, stream=True):
+            text.append(chunk)
+            _run_manager.on_llm_new_token(chunk, verbose=self.verbose)
+        return "".join(text)
+
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Asynchronous Call out to CTransformers generate method.
+        Very helpful when streaming (like with websockets!)
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: A list of strings to stop generation when encountered.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+                response = llm("Once upon a time, ")
+        """
+        text_callback = None
+        if run_manager:
+            text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose)
+
+        text = ""
+        for token in self.client(prompt, stop=stop, stream=True):
+            if text_callback:
+                await text_callback(token)
+            text += token
+
+        return text