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
2025-09-22 11:00:37 +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/xinference.py
+++ b/libs/community/langchain_community/embeddings/xinference.py
@@ -0,0 +1,124 @@
+"""Wrapper around Xinference embedding models."""
+from typing import Any, List, Optional
+
+from langchain_core.embeddings import Embeddings
+
+
+class XinferenceEmbeddings(Embeddings):
+
+    """Xinference embedding models.
+
+    To use, you should have the xinference library installed:
+
+    .. code-block:: bash
+
+        pip install xinference
+
+    Check out: https://github.com/xorbitsai/inference
+    To run, you need to start a Xinference supervisor on one server and Xinference workers on the other servers.
+
+    Example:
+        To start a local instance of Xinference, run
+
+        .. code-block:: bash
+
+           $ xinference
+
+        You can also deploy Xinference in a distributed cluster. Here are the steps:
+
+        Starting the supervisor:
+
+        .. code-block:: bash
+
+           $ xinference-supervisor
+
+        Starting the worker:
+
+        .. code-block:: bash
+
+           $ xinference-worker
+
+    Then, launch a model using command line interface (CLI).
+
+    Example:
+
+    .. code-block:: bash
+
+       $ xinference launch -n orca -s 3 -q q4_0
+
+    It will return a model UID. Then you can use Xinference Embedding with LangChain.
+
+    Example:
+
+    .. code-block:: python
+
+        from langchain_community.embeddings import XinferenceEmbeddings
+
+        xinference = XinferenceEmbeddings(
+            server_url="http://0.0.0.0:9997",
+            model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
+        )
+
+    """  # noqa: E501
+
+    client: Any
+    server_url: Optional[str]
+    """URL of the xinference server"""
+    model_uid: Optional[str]
+    """UID of the launched model"""
+
+    def __init__(
+        self, server_url: Optional[str] = None, model_uid: Optional[str] = None
+    ):
+        try:
+            from xinference.client import RESTfulClient
+        except ImportError as e:
+            raise ImportError(
+                "Could not import RESTfulClient from xinference. Please install it"
+                " with `pip install xinference`."
+            ) from e
+
+        super().__init__()
+
+        if server_url is None:
+            raise ValueError("Please provide server URL")
+
+        if model_uid is None:
+            raise ValueError("Please provide the model UID")
+
+        self.server_url = server_url
+
+        self.model_uid = model_uid
+
+        self.client = RESTfulClient(server_url)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using Xinference.
+        Args:
+            texts: The list of texts to embed.
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        model = self.client.get_model(self.model_uid)
+
+        embeddings = [
+            model.create_embedding(text)["data"][0]["embedding"] for text in texts
+        ]
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query of documents using Xinference.
+        Args:
+            text: The text to embed.
+        Returns:
+            Embeddings for the text.
+        """
+
+        model = self.client.get_model(self.model_uid)
+
+        embedding_res = model.create_embedding(text)
+
+        embedding = embedding_res["data"][0]["embedding"]
+
+        return list(map(float, embedding))