Feat: add volcengine embedding support

pyproject.toml

@@ -27,6 +27,7 @@ sentence-transformers = "^4.1.0"
 sqlalchemy = "^2.0.41"
 redis = "^6.2.0"
 schedule = "^1.2.2"
+volcengine-python-sdk = "^4.0.4"
 
 [tool.poetry.group.dev]
 optional = false

src/memos/configs/embedder.py

@@ -18,6 +18,14 @@ class OllamaEmbedderConfig(BaseEmbedderConfig):
     api_base: str = Field(default="http://localhost:11434", description="Base URL for Ollama API")
 
 
+class ArkEmbedderConfig(BaseEmbedderConfig):
+    api_key: str = Field(..., description="Ark API key")
+    api_base: str = Field(
+        default="https://ark.cn-beijing.volces.com/api/v3/", description="Base URL for Ark API"
+    )
+    chunk_size: int = Field(default=1, description="Chunk size for Ark API")
+
+
 class SenTranEmbedderConfig(BaseEmbedderConfig):
     """Configuration class for Sentence Transformer embeddings."""
 
@@ -36,6 +44,7 @@ class EmbedderConfigFactory(BaseConfig):
     backend_to_class: ClassVar[dict[str, Any]] = {
         "ollama": OllamaEmbedderConfig,
         "sentence_transformer": SenTranEmbedderConfig,
+        "ark": ArkEmbedderConfig,
     }
 
     @field_validator("backend")

src/memos/embedders/ark.py

@@ -0,0 +1,67 @@
+from volcenginesdkarkruntime import Ark
+from volcenginesdkarkruntime.types.multimodal_embedding import (
+    EmbeddingInputParam,
+    MultimodalEmbeddingContentPartTextParam,
+    MultimodalEmbeddingResponse,
+)
+
+from memos.configs.embedder import ArkEmbedderConfig
+from memos.embedders.base import BaseEmbedder
+from memos.log import get_logger
+
+
+logger = get_logger(__name__)
+
+
+class ArkEmbedder(BaseEmbedder):
+    """Ark Embedder class."""
+
+    def __init__(self, config: ArkEmbedderConfig):
+        self.config = config
+
+        if self.config.embedding_dims is not None:
+            logger.warning(
+                "Ark does not support specifying embedding dimensions. "
+                "The embedding dimensions is determined by the model."
+                "`embedding_dims` will be set to None."
+            )
+            self.config.embedding_dims = None
+
+        # Default model if not specified
+        if not self.config.model_name_or_path:
+            self.config.model_name_or_path = "doubao-embedding-vision-250615"
+
+        # Initialize ark client
+        self.client = Ark(api_key=self.config.api_key, base_url=self.config.api_base)
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for the given texts.
+
+        Args:
+            texts: List of texts to embed.
+
+        Returns:
+            List of embeddings, each represented as a list of floats.
+        """
+        texts_input = [
+            MultimodalEmbeddingContentPartTextParam(text=text, type="text") for text in texts
+        ]
+        return self.multimodal_embeddings(texts_input, chunk_size=self.config.chunk_size)
+
+    def multimodal_embeddings(
+        self, inputs: list[EmbeddingInputParam], chunk_size: int | None = None
+    ) -> list[list[float]]:
+        chunk_size_ = chunk_size or self.config.chunk_size
+        embeddings: list[list[float]] = []
+
+        for i in range(0, len(inputs), chunk_size_):
+            response: MultimodalEmbeddingResponse = self.client.multimodal_embeddings.create(
+                model=self.config.model_name_or_path,
+                input=inputs[i : i + chunk_size_],
+            )
+
+            data = [response.data] if isinstance(response.data, dict) else response.data
+            embeddings.extend(r["embedding"] for r in data)
+
+        return embeddings

src/memos/embedders/factory.py

@@ -1,6 +1,7 @@
 from typing import Any, ClassVar
 
 from memos.configs.embedder import EmbedderConfigFactory
+from memos.embedders.ark import ArkEmbedder
 from memos.embedders.base import BaseEmbedder
 from memos.embedders.ollama import OllamaEmbedder
 from memos.embedders.sentence_transformer import SenTranEmbedder
@@ -12,6 +13,7 @@ class EmbedderFactory(BaseEmbedder):
     backend_to_class: ClassVar[dict[str, Any]] = {
         "ollama": OllamaEmbedder,
         "sentence_transformer": SenTranEmbedder,
+        "ark": ArkEmbedder,
     }
 
     @classmethod

src/memos/memories/textual/general.py

@@ -7,7 +7,7 @@
 from tenacity import retry, retry_if_exception_type, stop_after_attempt
 
 from memos.configs.memory import GeneralTextMemoryConfig
-from memos.embedders.factory import EmbedderFactory, OllamaEmbedder
+from memos.embedders.factory import ArkEmbedder, EmbedderFactory, OllamaEmbedder
 from memos.llms.factory import LLMFactory, OllamaLLM, OpenAILLM
 from memos.log import get_logger
 from memos.memories.textual.base import BaseTextMemory
@@ -28,7 +28,7 @@ def __init__(self, config: GeneralTextMemoryConfig):
         self.config: GeneralTextMemoryConfig = config
         self.extractor_llm: OpenAILLM | OllamaLLM = LLMFactory.from_config(config.extractor_llm)
         self.vector_db: QdrantVecDB = VecDBFactory.from_config(config.vector_db)
-        self.embedder: OllamaEmbedder = EmbedderFactory.from_config(config.embedder)
+        self.embedder: OllamaEmbedder | ArkEmbedder = EmbedderFactory.from_config(config.embedder)
 
     @retry(
         stop=stop_after_attempt(3),

tests/embedders/test_ark.py

@@ -0,0 +1,64 @@
+import unittest
+
+from unittest.mock import patch
+
+from memos.configs.embedder import EmbedderConfigFactory
+from memos.embedders.factory import ArkEmbedder, EmbedderFactory
+
+
+class TestEmbedderFactory(unittest.TestCase):
+    @patch.object(ArkEmbedder, "embed")
+    def test_embed_single_text(self, mock_embed):
+        """Test embedding a single text."""
+        mock_embed.return_value = [[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]]
+
+        config = EmbedderConfigFactory.model_validate(
+            {
+                "backend": "ark",
+                "config": {
+                    "model_name_or_path": "doubao-embedding-vision-250615",
+                    "embedding_dims": 2048,
+                    "api_key": "your-api-key",
+                    "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+                },
+            }
+        )
+        embedder = EmbedderFactory.from_config(config)
+        text = "This is a sample text for embedding generation."
+        result = embedder.embed([text])
+
+        mock_embed.assert_called_once_with([text])
+        self.assertEqual(len(result[0]), 6)
+
+    @patch.object(ArkEmbedder, "embed")
+    def test_embed_batch_text(self, mock_embed):
+        """Test embedding multiple texts at once."""
+        mock_embed.return_value = [
+            [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            [0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
+            [0.3, 0.4, 0.5, 0.6, 0.1, 0.2],
+        ]
+
+        config = EmbedderConfigFactory.model_validate(
+            {
+                "backend": "ark",
+                "config": {
+                    "model_name_or_path": "doubao-embedding-vision-250615",
+                    "embedding_dims": 2048,
+                    "api_key": "your-api-key",
+                    "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+                },
+            }
+        )
+        embedder = EmbedderFactory.from_config(config)
+        texts = [
+            "First sample text for batch embedding.",
+            "Second sample text for batch embedding.",
+            "Third sample text for batch embedding.",
+        ]
+
+        result = embedder.embed(texts)
+
+        mock_embed.assert_called_once_with(texts)
+        self.assertEqual(len(result), 3)
+        self.assertEqual(len(result[0]), 6)