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/embeddings/vertexai.py

@@ -0,0 +1,56 @@
+from typing import Dict, List
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import root_validator
+
+from langchain_community.llms.vertexai import _VertexAICommon
+from langchain_community.utilities.vertexai import raise_vertex_import_error
+
+
+class VertexAIEmbeddings(_VertexAICommon, Embeddings):
+    """Google Cloud VertexAI embedding models."""
+
+    model_name: str = "textembedding-gecko"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validates that the python package exists in environment."""
+        cls._try_init_vertexai(values)
+        try:
+            from vertexai.language_models import TextEmbeddingModel
+        except ImportError:
+            raise_vertex_import_error()
+        values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"])
+        return values
+
+    def embed_documents(
+        self, texts: List[str], batch_size: int = 5
+    ) -> List[List[float]]:
+        """Embed a list of strings. Vertex AI currently
+        sets a max batch size of 5 strings.
+
+        Args:
+            texts: List[str] The list of strings to embed.
+            batch_size: [int] The batch size of embeddings to send to the model
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = []
+        for batch in range(0, len(texts), batch_size):
+            text_batch = texts[batch : batch + batch_size]
+            embeddings_batch = self.client.get_embeddings(text_batch)
+            embeddings.extend([el.values for el in embeddings_batch])
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embedding for the text.
+        """
+        embeddings = self.client.get_embeddings([text])
+        return embeddings[0].values