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/bedrock.py

@@ -0,0 +1,131 @@
+from typing import Any, Dict, 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
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import Extra
+
+from langchain_community.chat_models.anthropic import (
+    convert_messages_to_prompt_anthropic,
+)
+from langchain_community.chat_models.meta import convert_messages_to_prompt_llama
+from langchain_community.llms.bedrock import BedrockBase
+from langchain_community.utilities.anthropic import (
+    get_num_tokens_anthropic,
+    get_token_ids_anthropic,
+)
+
+
+class ChatPromptAdapter:
+    """Adapter class to prepare the inputs from Langchain to prompt format
+    that Chat model expects.
+    """
+
+    @classmethod
+    def convert_messages_to_prompt(
+        cls, provider: str, messages: List[BaseMessage]
+    ) -> str:
+        if provider == "anthropic":
+            prompt = convert_messages_to_prompt_anthropic(messages=messages)
+        elif provider == "meta":
+            prompt = convert_messages_to_prompt_llama(messages=messages)
+        else:
+            raise NotImplementedError(
+                f"Provider {provider} model does not support chat."
+            )
+        return prompt
+
+
+class BedrockChat(BaseChatModel, BedrockBase):
+    """A chat model that uses the Bedrock API."""
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "amazon_bedrock_chat"
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        """Return whether this model can be serialized by Langchain."""
+        return True
+
+    @classmethod
+    def get_lc_namespace(cls) -> List[str]:
+        """Get the namespace of the langchain object."""
+        return ["langchain", "chat_models", "bedrock"]
+
+    @property
+    def lc_attributes(self) -> Dict[str, Any]:
+        attributes: Dict[str, Any] = {}
+
+        if self.region_name:
+            attributes["region_name"] = self.region_name
+
+        return attributes
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        provider = self._get_provider()
+        prompt = ChatPromptAdapter.convert_messages_to_prompt(
+            provider=provider, messages=messages
+        )
+
+        for chunk in self._prepare_input_and_invoke_stream(
+            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+        ):
+            delta = chunk.text
+            yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        completion = ""
+
+        if self.streaming:
+            for chunk in self._stream(messages, stop, run_manager, **kwargs):
+                completion += chunk.text
+        else:
+            provider = self._get_provider()
+            prompt = ChatPromptAdapter.convert_messages_to_prompt(
+                provider=provider, messages=messages
+            )
+
+            params: Dict[str, Any] = {**kwargs}
+            if stop:
+                params["stop_sequences"] = stop
+
+            completion = self._prepare_input_and_invoke(
+                prompt=prompt, stop=stop, run_manager=run_manager, **params
+            )
+
+        message = AIMessage(content=completion)
+        return ChatResult(generations=[ChatGeneration(message=message)])
+
+    def get_num_tokens(self, text: str) -> int:
+        if self._model_is_anthropic:
+            return get_num_tokens_anthropic(text)
+        else:
+            return super().get_num_tokens(text)
+
+    def get_token_ids(self, text: str) -> List[int]:
+        if self._model_is_anthropic:
+            return get_token_ids_anthropic(text)
+        else:
+            return super().get_token_ids(text)