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/modelscope_hub.py

@@ -0,0 +1,73 @@
+from typing import Any, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra
+
+
+class ModelScopeEmbeddings(BaseModel, Embeddings):
+    """ModelScopeHub embedding models.
+
+    To use, you should have the ``modelscope`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import ModelScopeEmbeddings
+            model_id = "damo/nlp_corom_sentence-embedding_english-base"
+            embed = ModelScopeEmbeddings(model_id=model_id, model_revision="v1.0.0")
+    """
+
+    embed: Any
+    model_id: str = "damo/nlp_corom_sentence-embedding_english-base"
+    """Model name to use."""
+    model_revision: Optional[str] = None
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the modelscope"""
+        super().__init__(**kwargs)
+        try:
+            from modelscope.pipelines import pipeline
+            from modelscope.utils.constant import Tasks
+        except ImportError as e:
+            raise ImportError(
+                "Could not import some python packages."
+                "Please install it with `pip install modelscope`."
+            ) from e
+        self.embed = pipeline(
+            Tasks.sentence_embedding,
+            model=self.model_id,
+            model_revision=self.model_revision,
+        )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a modelscope embedding model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        texts = list(map(lambda x: x.replace("\n", " "), texts))
+        inputs = {"source_sentence": texts}
+        embeddings = self.embed(input=inputs)["text_embedding"]
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a modelscope embedding model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        text = text.replace("\n", " ")
+        inputs = {"source_sentence": [text]}
+        embedding = self.embed(input=inputs)["text_embedding"][0]
+        return embedding.tolist()