Feat add volcano embedding (#14693)

Description: Volcano Ark is an enterprise-grade large-model service
platform for developers, providing a full range of functions and
services such as model training, inference, evaluation, fine-tuning. You
can visit its homepage at https://www.volcengine.com/docs/82379/1099455
for details. This change could help developers use the platform for
embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns

---------

Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>

libs/community/langchain_community/embeddings/volcengine.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class VolcanoEmbeddings(BaseModel, Embeddings):
+    """`Volcengine Embeddings` embedding models."""
+
+    volcano_ak: Optional[str] = None
+    """volcano access key
+    learn more from: https://www.volcengine.com/docs/6459/76491#ak-sk"""
+
+    volcano_sk: Optional[str] = None
+    """volcano secret key
+    learn more from: https://www.volcengine.com/docs/6459/76491#ak-sk"""
+
+    host: str = "maas-api.ml-platform-cn-beijing.volces.com"
+    """host
+    learn more from https://www.volcengine.com/docs/82379/1174746"""
+    region: str = "cn-beijing"
+    """region
+    learn more from https://www.volcengine.com/docs/82379/1174746"""
+
+    model: str = "bge-large-zh"
+    """Model name
+    you could get from https://www.volcengine.com/docs/82379/1174746
+    for now, we support bge_large_zh
+    """
+
+    version: str = "1.0"
+    """ model version """
+
+    chunk_size: int = 100
+    """Chunk size when multiple texts are input"""
+
+    client: Any
+    """volcano client"""
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """
+        Validate whether volcano_ak and volcano_sk in the environment variables or
+        configuration file are available or not.
+
+        init volcano embedding client with `ak`, `sk`, `host`, `region`
+
+        Args:
+
+            values: a dictionary containing configuration information, must include the
+            fields of volcano_ak and volcano_sk
+        Returns:
+
+            a dictionary containing configuration information. If volcano_ak and
+            volcano_sk are not provided in the environment variables or configuration
+            file,the original values will be returned; otherwise, values containing
+            volcano_ak and volcano_sk will be returned.
+        Raises:
+
+            ValueError: volcengine package not found, please install it with
+            `pip install volcengine`
+        """
+        values["volcano_ak"] = get_from_dict_or_env(
+            values,
+            "volcano_ak",
+            "VOLC_ACCESSKEY",
+        )
+        values["volcano_sk"] = get_from_dict_or_env(
+            values,
+            "volcano_sk",
+            "VOLC_SECRETKEY",
+        )
+
+        try:
+            from volcengine.maas import MaasService
+
+            client = MaasService(values["host"], values["region"])
+            client.set_ak(values["volcano_ak"])
+            client.set_sk(values["volcano_sk"])
+            values["client"] = client
+        except ImportError:
+            raise ImportError(
+                "volcengine package not found, please install it with "
+                "`pip install volcengine`"
+            )
+        return values
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.embed_documents([text])[0]
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        Embeds a list of text documents using the AutoVOT algorithm.
+
+        Args:
+            texts (List[str]): A list of text documents to embed.
+
+        Returns:
+            List[List[float]]: A list of embeddings for each document in the input list.
+                            Each embedding is represented as a list of float values.
+        """
+        text_in_chunks = [
+            texts[i : i + self.chunk_size]
+            for i in range(0, len(texts), self.chunk_size)
+        ]
+        lst = []
+        for chunk in text_in_chunks:
+            req = {
+                "model": {
+                    "name": self.model,
+                    "version": self.version,
+                },
+                "input": chunk,
+            }
+            try:
+                from volcengine.maas import MaasException
+
+                resp = self.client.embeddings(req)
+                lst.extend([res["embedding"] for res in resp["data"]])
+            except MaasException as e:
+                raise ValueError(f"embed by volcengine Error: {e}")
+        return lst