community: support Hunyuan Embedding (#23160)

## description

- I refactor `Chathunyuan` using tencentcloud sdk because I found the
original one can't work in my application
- I add `HunyuanEmbeddings` using tencentcloud sdk
- Both of them are extend the basic class of langchain. I have fully
tested them in my application

## Dependencies
- tencentcloud-sdk-python

---------

Co-authored-by: centonhuang <[email protected]>
Co-authored-by: Erick Friis <[email protected]>

libs/community/langchain_community/embeddings/__init__.py

@@ -101,6 +101,9 @@
     from langchain_community.embeddings.huggingface_hub import (
         HuggingFaceHubEmbeddings,
     )
+    from langchain_community.embeddings.hunyuan import (
+        HunyuanEmbeddings,
+    )
     from langchain_community.embeddings.infinity import (
         InfinityEmbeddings,
     )
@@ -327,6 +330,7 @@
     "XinferenceEmbeddings",
     "YandexGPTEmbeddings",
     "ZhipuAIEmbeddings",
+    "HunyuanEmbeddings",
 ]
 
 _module_lookup = {
@@ -412,6 +416,7 @@
     "YandexGPTEmbeddings": "langchain_community.embeddings.yandex",
     "AscendEmbeddings": "langchain_community.embeddings.ascend",
     "ZhipuAIEmbeddings": "langchain_community.embeddings.zhipuai",
+    "HunyuanEmbeddings": "langchain_community.embeddings.hunyuan",
 }
 
 

libs/community/langchain_community/embeddings/hunyuan.py

@@ -0,0 +1,124 @@
+import json
+from typing import Any, Dict, List, Literal, Optional, Type
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.runnables.config import run_in_executor
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import BaseModel, Field, SecretStr, model_validator
+
+
+class HunyuanEmbeddings(Embeddings, BaseModel):
+    """Tencent Hunyuan embedding models API by Tencent.
+
+    For more information, see https://cloud.tencent.com/document/product/1729
+    """
+
+    hunyuan_secret_id: Optional[SecretStr] = Field(alias="secret_id", default=None)
+    """Hunyuan Secret ID"""
+    hunyuan_secret_key: Optional[SecretStr] = Field(alias="secret_key", default=None)
+    """Hunyuan Secret Key"""
+    region: Literal["ap-guangzhou", "ap-beijing"] = "ap-guangzhou"
+    """The region of hunyuan service."""
+    embedding_ctx_length: int = 1024
+    """The max embedding context length of hunyuan embedding (defaults to 1024)."""
+    show_progress_bar: bool = False
+    """Show progress bar when embedding. Default is False."""
+
+    client: Any = Field(default=None, exclude=True)
+    """The tencentcloud client."""
+    request_cls: Optional[Type] = Field(default=None, exclude=True)
+    """The request class of tencentcloud sdk."""
+
+    @model_validator(mode="before")
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        values["hunyuan_secret_id"] = convert_to_secret_str(
+            get_from_dict_or_env(
+                values,
+                "hunyuan_secret_id",
+                "HUNYUAN_SECRET_ID",
+            )
+        )
+        values["hunyuan_secret_key"] = convert_to_secret_str(
+            get_from_dict_or_env(
+                values,
+                "hunyuan_secret_key",
+                "HUNYUAN_SECRET_KEY",
+            )
+        )
+
+        try:
+            from tencentcloud.common.credential import Credential
+            from tencentcloud.common.profile.client_profile import ClientProfile
+            from tencentcloud.hunyuan.v20230901.hunyuan_client import HunyuanClient
+            from tencentcloud.hunyuan.v20230901.models import GetEmbeddingRequest
+        except ImportError:
+            raise ImportError(
+                "Could not import tencentcloud sdk python package. Please install it "
+                'with `pip install "tencentcloud-sdk-python>=3.0.1139"`.'
+            )
+
+        client_profile = ClientProfile()
+        client_profile.httpProfile.pre_conn_pool_size = 3
+
+        credential = Credential(
+            values["hunyuan_secret_id"].get_secret_value(),
+            values["hunyuan_secret_key"].get_secret_value(),
+        )
+
+        values["request_cls"] = GetEmbeddingRequest
+
+        values["client"] = HunyuanClient(credential, values["region"], client_profile)
+        return values
+
+    def _embed_text(self, text: str) -> List[float]:
+        if self.request_cls is None:
+            raise AssertionError("Request class is not initialized.")
+        request = self.request_cls()
+        request.Input = text
+
+        response = self.client.GetEmbedding(request)
+
+        _response: Dict[str, Any] = json.loads(response.to_json_string())
+
+        data: Optional[List[Dict[str, Any]]] = _response.get("Data")
+        if not data:
+            raise RuntimeError("Occur hunyuan embedding error: Data is empty")
+
+        embedding = data[0].get("Embedding")
+        if not embedding:
+            raise RuntimeError("Occur hunyuan embedding error: Embedding is empty")
+
+        return embedding
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        embeddings = []
+        if self.show_progress_bar:
+            try:
+                from tqdm import tqdm
+            except ImportError as e:
+                raise ImportError(
+                    "Package tqdm must be installed if show_progress_bar=True. "
+                    "Please install with 'pip install tqdm' or set "
+                    "show_progress_bar=False."
+                ) from e
+            _iter = tqdm(iterable=texts, desc="Hunyuan Embedding")
+        else:
+            _iter = texts
+        for text in _iter:
+            embeddings.append(self.embed_query(text))
+
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return self._embed_text(text)
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Asynchronous Embed search docs."""
+        return await run_in_executor(None, self.embed_documents, texts)
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Asynchronous Embed query text."""
+        return await run_in_executor(None, self.embed_query, text)

libs/community/tests/integration_tests/embeddings/test_hunyuan.py

@@ -0,0 +1,25 @@
+from langchain_community.embeddings.hunyuan import HunyuanEmbeddings
+
+
+def test_embedding_query() -> None:
+    query = "foo"
+    embedding = HunyuanEmbeddings()
+    output = embedding.embed_query(query)
+    assert len(output) == 1024
+
+
+def test_embedding_document() -> None:
+    documents = ["foo bar"]
+    embedding = HunyuanEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 1024
+
+
+def test_embedding_documents() -> None:
+    documents = ["foo", "bar"]
+    embedding = HunyuanEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 1024
+    assert len(output[1]) == 1024

libs/community/tests/unit_tests/embeddings/test_imports.py

@@ -83,6 +83,7 @@
     "AscendEmbeddings",
     "ZhipuAIEmbeddings",
     "TextEmbedEmbeddings",
+    "HunyuanEmbeddings",
 ]