fix: vision embedding

Author: zakahan

veadk/configs/model_configs.py

@@ -23,6 +23,7 @@
     DEFAULT_MODEL_AGENT_API_BASE,
     DEFAULT_MODEL_AGENT_NAME,
     DEFAULT_MODEL_AGENT_PROVIDER,
+    DEFAULT_MODEL_EMBEDDING_NAME,
 )
 
 
@@ -46,7 +47,7 @@ def api_key(self) -> str:
 class EmbeddingModelConfig(BaseSettings):
     model_config = SettingsConfigDict(env_prefix="MODEL_EMBEDDING_")
 
-    name: str = "doubao-embedding-text-240715"
+    name: str = DEFAULT_MODEL_EMBEDDING_NAME
     """Model name for embedding."""
 
     dim: int = 2560

veadk/consts.py

@@ -75,3 +75,5 @@
 
 DEFAULT_NACOS_GROUP = "VEADK_GROUP"
 DEFAULT_NACOS_INSTANCE_NAME = "veadk"
+
+DEFAULT_MODEL_EMBEDDING_NAME = "doubao-embedding-vision-250615"

veadk/models/ark_embedding.py

@@ -0,0 +1,115 @@
+from typing import Any, Dict, Optional, List
+
+import httpx
+from llama_index.core.base.embeddings.base import BaseEmbedding, Embedding
+from llama_index.core.callbacks.base import CallbackManager
+# from llama_index.embeddings.openai import OpenAIEmbedding
+
+
+class ArkEmbedding(BaseEmbedding):
+    """
+    OpenAI-Like class for embeddings.
+
+    Args:
+        model_name (str):
+            Model for embedding.
+        api_key (str):
+            The API key (if any) to use for the embedding API.
+        api_base (str):
+            The base URL for the embedding API.
+        api_version (str):
+            The version for the embedding API.
+        max_retries (int):
+            The maximum number of retries for the embedding API.
+        timeout (float):
+            The timeout for the embedding API.
+        reuse_client (bool):
+            Whether to reuse the client for the embedding API.
+        callback_manager (CallbackManager):
+            The callback manager for the embedding API.
+        default_headers (Dict[str, str]):
+            The default headers for the embedding API.
+        additional_kwargs (Dict[str, Any]):
+            Additional kwargs for the embedding API.
+        dimensions (int):
+            The number of dimensions for the embedding API.
+
+    Example:
+        ```bash
+        pip install llama-index-embeddings-openai-like
+        ```
+
+        ```python
+        from llama_index.embeddings.openai_like import OpenAILikeEmbedding
+
+        embedding = ArkEmbedding(
+            model_name="my-model-name",
+            api_base="http://localhost:1234/v1",
+            api_key="fake",
+            embed_batch_size=10,
+        )
+        ```
+
+    """
+
+    def _get_query_embedding(self, query: str) -> Embedding:
+        # client = self._get_client()
+        # retry_decorator = self._create_retry_decorator()
+
+        pass
+
+    async def _aget_query_embedding(self, query: str) -> Embedding:
+        pass
+
+    def _get_text_embedding(self, text: str) -> Embedding:
+        pass
+
+    def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]: ...
+
+    def __init__(
+        self,
+        model_name: str,
+        embed_batch_size: int = 10,
+        dimensions: Optional[int] = None,
+        additional_kwargs: Optional[Dict[str, Any]] = None,
+        api_key: str = "fake",
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        max_retries: int = 10,
+        timeout: float = 60.0,
+        reuse_client: bool = True,
+        callback_manager: Optional[CallbackManager] = None,
+        default_headers: Optional[Dict[str, str]] = None,
+        http_client: Optional[httpx.Client] = None,
+        async_http_client: Optional[httpx.AsyncClient] = None,
+        num_workers: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        # ensure model is not passed in kwargs, will cause error in parent class
+        if "model" in kwargs:
+            raise ValueError(
+                "Use `model_name` instead of `model` to initialize OpenAILikeEmbedding"
+            )
+
+        super().__init__(
+            model_name=model_name,
+            embed_batch_size=embed_batch_size,
+            dimensions=dimensions,
+            callback_manager=callback_manager,
+            additional_kwargs=additional_kwargs,
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            max_retries=max_retries,
+            reuse_client=reuse_client,
+            timeout=timeout,
+            default_headers=default_headers,
+            http_client=http_client,
+            async_http_client=async_http_client,
+            num_workers=num_workers,
+            **kwargs,
+        )
+
+    @classmethod
+    def class_name(cls) -> str:
+        return "ArkEmbedding"