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/human.py

@@ -0,0 +1,85 @@
+from typing import Any, Callable, List, Mapping, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Field
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+
+def _display_prompt(prompt: str) -> None:
+    """Displays the given prompt to the user."""
+    print(f"\n{prompt}")
+
+
+def _collect_user_input(
+    separator: Optional[str] = None, stop: Optional[List[str]] = None
+) -> str:
+    """Collects and returns user input as a single string."""
+    separator = separator or "\n"
+    lines = []
+
+    while True:
+        line = input()
+        if not line:
+            break
+        lines.append(line)
+
+        if stop and any(seq in line for seq in stop):
+            break
+    # Combine all lines into a single string
+    multi_line_input = separator.join(lines)
+    return multi_line_input
+
+
+class HumanInputLLM(LLM):
+    """
+    It returns user input as the response.
+    """
+
+    input_func: Callable = Field(default_factory=lambda: _collect_user_input)
+    prompt_func: Callable[[str], None] = Field(default_factory=lambda: _display_prompt)
+    separator: str = "\n"
+    input_kwargs: Mapping[str, Any] = {}
+    prompt_kwargs: Mapping[str, Any] = {}
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """
+        Returns an empty dictionary as there are no identifying parameters.
+        """
+        return {}
+
+    @property
+    def _llm_type(self) -> str:
+        """Returns the type of LLM."""
+        return "human-input"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """
+        Displays the prompt to the user and returns their input as a response.
+
+        Args:
+            prompt (str): The prompt to be displayed to the user.
+            stop (Optional[List[str]]): A list of stop strings.
+            run_manager (Optional[CallbackManagerForLLMRun]): Currently not used.
+
+        Returns:
+            str: The user's input as a response.
+        """
+        self.prompt_func(prompt, **self.prompt_kwargs)
+        user_input = self.input_func(
+            separator=self.separator, stop=stop, **self.input_kwargs
+        )
+
+        if stop is not None:
+            # I believe this is required since the stop tokens
+            # are not enforced by the human themselves
+            user_input = enforce_stop_tokens(user_input, stop)
+        return user_input