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/spacy_embeddings.py

@@ -0,0 +1,113 @@
+import importlib.util
+from typing import Any, Dict, List
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+
+
+class SpacyEmbeddings(BaseModel, Embeddings):
+    """Embeddings by SpaCy models.
+
+    It only supports the 'en_core_web_sm' model.
+
+    Attributes:
+        nlp (Any): The Spacy model loaded into memory.
+
+    Methods:
+        embed_documents(texts: List[str]) -> List[List[float]]:
+            Generates embeddings for a list of documents.
+        embed_query(text: str) -> List[float]:
+            Generates an embedding for a single piece of text.
+    """
+
+    nlp: Any  # The Spacy model loaded into memory
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid  # Forbid extra attributes during model initialization
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """
+        Validates that the Spacy package and the 'en_core_web_sm' model are installed.
+
+        Args:
+            values (Dict): The values provided to the class constructor.
+
+        Returns:
+            The validated values.
+
+        Raises:
+            ValueError: If the Spacy package or the 'en_core_web_sm'
+            model are not installed.
+        """
+        # Check if the Spacy package is installed
+        if importlib.util.find_spec("spacy") is None:
+            raise ValueError(
+                "Spacy package not found. "
+                "Please install it with `pip install spacy`."
+            )
+        try:
+            # Try to load the 'en_core_web_sm' Spacy model
+            import spacy
+
+            values["nlp"] = spacy.load("en_core_web_sm")
+        except OSError:
+            # If the model is not found, raise a ValueError
+            raise ValueError(
+                "Spacy model 'en_core_web_sm' not found. "
+                "Please install it with"
+                " `python -m spacy download en_core_web_sm`."
+            )
+        return values  # Return the validated values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        Generates embeddings for a list of documents.
+
+        Args:
+            texts (List[str]): The documents to generate embeddings for.
+
+        Returns:
+            A list of embeddings, one for each document.
+        """
+        return [self.nlp(text).vector.tolist() for text in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        """
+        Generates an embedding for a single piece of text.
+
+        Args:
+            text (str): The text to generate an embedding for.
+
+        Returns:
+            The embedding for the text.
+        """
+        return self.nlp(text).vector.tolist()
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        Asynchronously generates embeddings for a list of documents.
+        This method is not implemented and raises a NotImplementedError.
+
+        Args:
+            texts (List[str]): The documents to generate embeddings for.
+
+        Raises:
+            NotImplementedError: This method is not implemented.
+        """
+        raise NotImplementedError("Asynchronous embedding generation is not supported.")
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """
+        Asynchronously generates an embedding for a single piece of text.
+        This method is not implemented and raises a NotImplementedError.
+
+        Args:
+            text (str): The text to generate an embedding for.
+
+        Raises:
+            NotImplementedError: This method is not implemented.
+        """
+        raise NotImplementedError("Asynchronous embedding generation is not supported.")