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/chat_models/ollama.py

@@ -0,0 +1,123 @@
+import json
+from typing import Any, Iterator, List, Optional
+
+from langchain_core.callbacks import (
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+
+from langchain_community.llms.ollama import _OllamaCommon
+
+
+def _stream_response_to_chat_generation_chunk(
+    stream_response: str,
+) -> ChatGenerationChunk:
+    """Convert a stream response to a generation chunk."""
+    parsed_response = json.loads(stream_response)
+    generation_info = parsed_response if parsed_response.get("done") is True else None
+    return ChatGenerationChunk(
+        message=AIMessageChunk(content=parsed_response.get("response", "")),
+        generation_info=generation_info,
+    )
+
+
+class ChatOllama(BaseChatModel, _OllamaCommon):
+    """Ollama locally runs large language models.
+
+    To use, follow the instructions at https://ollama.ai/.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.chat_models import ChatOllama
+            ollama = ChatOllama(model="llama2")
+    """
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "ollama-chat"
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        """Return whether this model can be serialized by Langchain."""
+        return False
+
+    def _format_message_as_text(self, message: BaseMessage) -> str:
+        if isinstance(message, ChatMessage):
+            message_text = f"\n\n{message.role.capitalize()}: {message.content}"
+        elif isinstance(message, HumanMessage):
+            message_text = f"[INST] {message.content} [/INST]"
+        elif isinstance(message, AIMessage):
+            message_text = f"{message.content}"
+        elif isinstance(message, SystemMessage):
+            message_text = f"<<SYS>> {message.content} <</SYS>>"
+        else:
+            raise ValueError(f"Got unknown type {message}")
+        return message_text
+
+    def _format_messages_as_text(self, messages: List[BaseMessage]) -> str:
+        return "\n".join(
+            [self._format_message_as_text(message) for message in messages]
+        )
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Call out to Ollama's generate endpoint.
+
+        Args:
+            messages: The list of base messages to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            Chat generations from the model
+
+        Example:
+            .. code-block:: python
+
+                response = ollama([
+                    HumanMessage(content="Tell me about the history of AI")
+                ])
+        """
+
+        prompt = self._format_messages_as_text(messages)
+        final_chunk = super()._stream_with_aggregation(
+            prompt, stop=stop, run_manager=run_manager, verbose=self.verbose, **kwargs
+        )
+        chat_generation = ChatGeneration(
+            message=AIMessage(content=final_chunk.text),
+            generation_info=final_chunk.generation_info,
+        )
+        return ChatResult(generations=[chat_generation])
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        prompt = self._format_messages_as_text(messages)
+        for stream_resp in self._create_stream(prompt, stop, **kwargs):
+            if stream_resp:
+                chunk = _stream_response_to_chat_generation_chunk(stream_resp)
+                yield chunk
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )