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
2025-09-24 03:52:10 +00:00 · 2023-12-11 13:53:30 -08:00
parent c0f4b95aa9
commit ed58eeb9c5
2446 changed files with 171805 additions and 137118 deletions
--- a/libs/community/langchain_community/embeddings/self_hosted_hugging_face.py
+++ b/libs/community/langchain_community/embeddings/self_hosted_hugging_face.py
@@ -0,0 +1,168 @@
+import importlib
+import logging
+from typing import Any, Callable, List, Optional
+
+from langchain_community.embeddings.self_hosted import SelfHostedEmbeddings
+
+DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
+DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
+DEFAULT_QUERY_INSTRUCTION = (
+    "Represent the question for retrieving supporting documents: "
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
+    """Inference function to send to the remote hardware.
+
+    Accepts a sentence_transformer model_id and
+    returns a list of embeddings for each document in the batch.
+    """
+    return client.encode(*args, **kwargs)
+
+
+def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
+    """Load the embedding model."""
+    if not instruct:
+        import sentence_transformers
+
+        client = sentence_transformers.SentenceTransformer(model_id)
+    else:
+        from InstructorEmbedding import INSTRUCTOR
+
+        client = INSTRUCTOR(model_id)
+
+    if importlib.util.find_spec("torch") is not None:
+        import torch
+
+        cuda_device_count = torch.cuda.device_count()
+        if device < -1 or (device >= cuda_device_count):
+            raise ValueError(
+                f"Got device=={device}, "
+                f"device is required to be within [-1, {cuda_device_count})"
+            )
+        if device < 0 and cuda_device_count > 0:
+            logger.warning(
+                "Device has %d GPUs available. "
+                "Provide device={deviceId} to `from_model_id` to use available"
+                "GPUs for execution. deviceId is -1 for CPU and "
+                "can be a positive integer associated with CUDA device id.",
+                cuda_device_count,
+            )
+
+        client = client.to(device)
+    return client
+
+
+class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
+    """HuggingFace embedding models on self-hosted remote hardware.
+
+    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
+    and Lambda, as well as servers specified
+    by IP address and SSH credentials (such as on-prem, or another cloud
+    like Paperspace, Coreweave, etc.).
+
+    To use, you should have the ``runhouse`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import SelfHostedHuggingFaceEmbeddings
+            import runhouse as rh
+            model_name = "sentence-transformers/all-mpnet-base-v2"
+            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
+            hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
+    """
+
+    client: Any  #: :meta private:
+    model_id: str = DEFAULT_MODEL_NAME
+    """Model name to use."""
+    model_reqs: List[str] = ["./", "sentence_transformers", "torch"]
+    """Requirements to install on hardware to inference the model."""
+    hardware: Any
+    """Remote hardware to send the inference function to."""
+    model_load_fn: Callable = load_embedding_model
+    """Function to load the model remotely on the server."""
+    load_fn_kwargs: Optional[dict] = None
+    """Keyword arguments to pass to the model load function."""
+    inference_fn: Callable = _embed_documents
+    """Inference function to extract the embeddings."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the remote inference function."""
+        load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
+        load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME)
+        load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
+        load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
+        super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
+
+
+class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
+    """HuggingFace InstructEmbedding models on self-hosted remote hardware.
+
+    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
+    and Lambda, as well as servers specified
+    by IP address and SSH credentials (such as on-prem, or another
+    cloud like Paperspace, Coreweave, etc.).
+
+    To use, you should have the ``runhouse`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import SelfHostedHuggingFaceInstructEmbeddings
+            import runhouse as rh
+            model_name = "hkunlp/instructor-large"
+            gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
+            hf = SelfHostedHuggingFaceInstructEmbeddings(
+                model_name=model_name, hardware=gpu)
+    """  # noqa: E501
+
+    model_id: str = DEFAULT_INSTRUCT_MODEL
+    """Model name to use."""
+    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
+    """Instruction to use for embedding documents."""
+    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
+    """Instruction to use for embedding query."""
+    model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
+    """Requirements to install on hardware to inference the model."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the remote inference function."""
+        load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
+        load_fn_kwargs["model_id"] = load_fn_kwargs.get(
+            "model_id", DEFAULT_INSTRUCT_MODEL
+        )
+        load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True)
+        load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
+        super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace instruct model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        instruction_pairs = []
+        for text in texts:
+            instruction_pairs.append([self.embed_instruction, text])
+        embeddings = self.client(self.pipeline_ref, instruction_pairs)
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a HuggingFace instruct model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        instruction_pair = [self.query_instruction, text]
+        embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
+        return embedding.tolist()