initial commit

venv/Lib/site-packages/langchain_community/embeddings/edenai.py

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+from typing import Any, Dict, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+)
+
+from langchain_community.utilities.requests import Requests
+
+
+class EdenAiEmbeddings(BaseModel, Embeddings):
+    """EdenAI embedding.
+    environment variable ``EDENAI_API_KEY`` set with your API key, or pass
+    it as a named parameter.
+    """
+
+    edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
+
+    provider: str = "openai"
+    """embedding provider to use (eg: openai,google etc.)"""
+
+    model: Optional[str] = None
+    """
+    model name for above provider (eg: 'gpt-3.5-turbo-instruct' for openai)
+    available models are shown on https://docs.edenai.co/ under 'available providers'
+    """
+
+    model_config = ConfigDict(
+        extra="forbid",
+    )
+
+    @pre_init
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key exists in environment."""
+        values["edenai_api_key"] = convert_to_secret_str(
+            get_from_dict_or_env(values, "edenai_api_key", "EDENAI_API_KEY")
+        )
+        return values
+
+    @staticmethod
+    def get_user_agent() -> str:
+        from langchain_community import __version__
+
+        return f"langchain/{__version__}"
+
+    def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
+        """Compute embeddings using EdenAi api."""
+        url = "https://api.edenai.run/v2/text/embeddings"
+
+        headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+            "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
+            "User-Agent": self.get_user_agent(),
+        }
+
+        payload: Dict[str, Any] = {"texts": texts, "providers": self.provider}
+
+        if self.model is not None:
+            payload["settings"] = {self.provider: self.model}
+
+        request = Requests(headers=headers)
+        response = request.post(url=url, data=payload)
+        if response.status_code >= 500:
+            raise Exception(f"EdenAI Server: Error {response.status_code}")
+        elif response.status_code >= 400:
+            raise ValueError(f"EdenAI received an invalid payload: {response.text}")
+        elif response.status_code != 200:
+            raise Exception(
+                f"EdenAI returned an unexpected response with status "
+                f"{response.status_code}: {response.text}"
+            )
+
+        temp = response.json()
+
+        provider_response = temp[self.provider]
+        if provider_response.get("status") == "fail":
+            err_msg = provider_response.get("error", {}).get("message")
+            raise Exception(err_msg)
+
+        embeddings = []
+        for embed_item in temp[self.provider]["items"]:
+            embedding = embed_item["embedding"]
+
+            embeddings.append(embedding)
+
+        return embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using EdenAI.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        return self._generate_embeddings(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using EdenAI.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self._generate_embeddings([text])[0]