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/llamacpp.py

@@ -0,0 +1,126 @@
+from typing import Any, Dict, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
+
+
+class LlamaCppEmbeddings(BaseModel, Embeddings):
+    """llama.cpp embedding models.
+
+    To use, you should have the llama-cpp-python library installed, and provide the
+    path to the Llama model as a named parameter to the constructor.
+    Check out: https://github.com/abetlen/llama-cpp-python
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import LlamaCppEmbeddings
+            llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
+    """
+
+    client: Any  #: :meta private:
+    model_path: str
+
+    n_ctx: int = Field(512, alias="n_ctx")
+    """Token context window."""
+
+    n_parts: int = Field(-1, alias="n_parts")
+    """Number of parts to split the model into. 
+    If -1, the number of parts is automatically determined."""
+
+    seed: int = Field(-1, alias="seed")
+    """Seed. If -1, a random seed is used."""
+
+    f16_kv: bool = Field(False, alias="f16_kv")
+    """Use half-precision for key/value cache."""
+
+    logits_all: bool = Field(False, alias="logits_all")
+    """Return logits for all tokens, not just the last token."""
+
+    vocab_only: bool = Field(False, alias="vocab_only")
+    """Only load the vocabulary, no weights."""
+
+    use_mlock: bool = Field(False, alias="use_mlock")
+    """Force system to keep model in RAM."""
+
+    n_threads: Optional[int] = Field(None, alias="n_threads")
+    """Number of threads to use. If None, the number 
+    of threads is automatically determined."""
+
+    n_batch: Optional[int] = Field(8, alias="n_batch")
+    """Number of tokens to process in parallel.
+    Should be a number between 1 and n_ctx."""
+
+    n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers")
+    """Number of layers to be loaded into gpu memory. Default None."""
+
+    verbose: bool = Field(True, alias="verbose")
+    """Print verbose output to stderr."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that llama-cpp-python library is installed."""
+        model_path = values["model_path"]
+        model_param_names = [
+            "n_ctx",
+            "n_parts",
+            "seed",
+            "f16_kv",
+            "logits_all",
+            "vocab_only",
+            "use_mlock",
+            "n_threads",
+            "n_batch",
+            "verbose",
+        ]
+        model_params = {k: values[k] for k in model_param_names}
+        # For backwards compatibility, only include if non-null.
+        if values["n_gpu_layers"] is not None:
+            model_params["n_gpu_layers"] = values["n_gpu_layers"]
+
+        try:
+            from llama_cpp import Llama
+
+            values["client"] = Llama(model_path, embedding=True, **model_params)
+        except ImportError:
+            raise ModuleNotFoundError(
+                "Could not import llama-cpp-python library. "
+                "Please install the llama-cpp-python library to "
+                "use this embedding model: pip install llama-cpp-python"
+            )
+        except Exception as e:
+            raise ValueError(
+                f"Could not load Llama model from path: {model_path}. "
+                f"Received error {e}"
+            )
+
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using the Llama model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = [self.client.embed(text) for text in texts]
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using the Llama model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        embedding = self.client.embed(text)
+        return list(map(float, embedding))