feat:Embedding models Support for the Aliyun dashscope text-embedding-v1 and text-embedding-v2 (#2874)

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml

@@ -0,0 +1,4 @@
+model: text-embedding-v1
+model_type: text-embedding
+model_properties:
+  context_size: 2048

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml

@@ -0,0 +1,4 @@
+model: text-embedding-v2
+model_type: text-embedding
+model_properties:
+  context_size: 2048

api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py

@@ -0,0 +1,132 @@
+import time
+from typing import Optional
+
+import dashscope
+
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import (
+    EmbeddingUsage,
+    TextEmbeddingResult,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import (
+    TextEmbeddingModel,
+)
+from core.model_runtime.model_providers.tongyi._common import _CommonTongyi
+
+
+class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
+    """
+    Model class for Tongyi text embedding model.
+    """
+
+    def _invoke(
+            self,
+            model: str,
+            credentials: dict,
+            texts: list[str],
+            user: Optional[str] = None,
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+        dashscope.api_key = credentials_kwargs["dashscope_api_key"]
+        embeddings, embedding_used_tokens = self.embed_documents(model, texts)
+
+        return TextEmbeddingResult(
+            embeddings=embeddings,
+            usage=self._calc_response_usage(model, credentials_kwargs, embedding_used_tokens),
+            model=model
+        )
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        if len(texts) == 0:
+            return 0
+        total_num_tokens = 0
+        for text in texts:
+            total_num_tokens += self._get_num_tokens_by_gpt2(text)
+
+        return total_num_tokens
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # transform credentials to kwargs for model instance
+            credentials_kwargs = self._to_credential_kwargs(credentials)
+            dashscope.api_key = credentials_kwargs["dashscope_api_key"]
+            # call embedding model
+            self.embed_documents(model=model, texts=["ping"])
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @staticmethod
+    def embed_documents(model: str, texts: list[str]) -> tuple[list[list[float]], int]:
+        """Call out to Tongyi's embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text, and tokens usage.
+        """
+        embeddings = []
+        embedding_used_tokens = 0
+        for text in texts:
+            response = dashscope.TextEmbedding.call(model=model, input=text, text_type="document")
+            data = response.output["embeddings"][0]
+            embeddings.append(data["embedding"])
+            embedding_used_tokens += response.usage["total_tokens"]
+
+        return [list(map(float, e)) for e in embeddings], embedding_used_tokens
+
+    def _calc_response_usage(
+            self, model: str, credentials: dict, tokens: int
+    ) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.INPUT,
+            tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at
+        )
+
+        return usage

api/core/model_runtime/model_providers/tongyi/tongyi.yaml

@@ -17,6 +17,7 @@ help:
 supported_model_types:
   - llm
   - tts
+  - text-embedding
 configurate_methods:
   - predefined-model
 provider_credential_schema: