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/fastembed.py

@@ -0,0 +1,107 @@
+from typing import Any, Dict, List, Literal, Optional
+
+import numpy as np
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+
+
+class FastEmbedEmbeddings(BaseModel, Embeddings):
+    """Qdrant FastEmbedding models.
+    FastEmbed is a lightweight, fast, Python library built for embedding generation.
+    See more documentation at:
+    * https://github.com/qdrant/fastembed/
+    * https://qdrant.github.io/fastembed/
+
+    To use this class, you must install the `fastembed` Python package.
+
+    `pip install fastembed`
+    Example:
+        from langchain_community.embeddings import FastEmbedEmbeddings
+        fastembed = FastEmbedEmbeddings()
+    """
+
+    model_name: str = "BAAI/bge-small-en-v1.5"
+    """Name of the FastEmbedding model to use
+    Defaults to "BAAI/bge-small-en-v1.5"
+    Find the list of supported models at
+    https://qdrant.github.io/fastembed/examples/Supported_Models/
+    """
+
+    max_length: int = 512
+    """The maximum number of tokens. Defaults to 512.
+    Unknown behavior for values > 512.
+    """
+
+    cache_dir: Optional[str]
+    """The path to the cache directory.
+    Defaults to `local_cache` in the parent directory
+    """
+
+    threads: Optional[int]
+    """The number of threads single onnxruntime session can use.
+    Defaults to None
+    """
+
+    doc_embed_type: Literal["default", "passage"] = "default"
+    """Type of embedding to use for documents
+    "default": Uses FastEmbed's default embedding method
+    "passage": Prefixes the text with "passage" before embedding.
+    """
+
+    _model: Any  # : :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that FastEmbed has been installed."""
+        try:
+            from fastembed.embedding import FlagEmbedding
+
+            model_name = values.get("model_name")
+            max_length = values.get("max_length")
+            cache_dir = values.get("cache_dir")
+            threads = values.get("threads")
+            values["_model"] = FlagEmbedding(
+                model_name=model_name,
+                max_length=max_length,
+                cache_dir=cache_dir,
+                threads=threads,
+            )
+        except ImportError as ie:
+            raise ImportError(
+                "Could not import 'fastembed' Python package. "
+                "Please install it with `pip install fastembed`."
+            ) from ie
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for documents using FastEmbed.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings: List[np.ndarray]
+        if self.doc_embed_type == "passage":
+            embeddings = self._model.passage_embed(texts)
+        else:
+            embeddings = self._model.embed(texts)
+        return [e.tolist() for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate query embeddings using FastEmbed.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        query_embeddings: np.ndarray = next(self._model.query_embed(text))
+        return query_embeddings.tolist()