initial commit

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

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+from typing import Any, Dict, List
+
+from langchain_core.embeddings import Embeddings
+from pydantic import BaseModel, model_validator
+
+
+class AwaEmbeddings(BaseModel, Embeddings):
+    """Embedding documents and queries with Awa DB.
+
+    Attributes:
+        client: The AwaEmbedding client.
+        model: The name of the model used for embedding.
+         Default is "all-mpnet-base-v2".
+    """
+
+    client: Any  #: :meta private:
+    model: str = "all-mpnet-base-v2"
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
+        """Validate that awadb library is installed."""
+
+        try:
+            from awadb import AwaEmbedding
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import awadb library. "
+                "Please install it with `pip install awadb`"
+            ) from exc
+        values["client"] = AwaEmbedding()
+        return values
+
+    def set_model(self, model_name: str) -> None:
+        """Set the model used for embedding.
+        The default model used is all-mpnet-base-v2
+
+        Args:
+            model_name: A string which represents the name of model.
+        """
+        self.model = model_name
+        self.client.model_name = model_name
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using AwaEmbedding.
+
+        Args:
+            texts: The list of texts need to be embedded
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        return self.client.EmbeddingBatch(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using AwaEmbedding.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.client.Embedding(text)