Remove unnecessary nesting in embed_text and add DummyEmbeddingEngine

cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py

@@ -28,24 +28,19 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
         self.dimensions = dimensions
 
     async def embed_text(self, text: List[str]) -> List[List[float]]:
-        async def get_embedding(text_):
+        try:
-            try:
+            response = await litellm.aembedding(
-                response = await litellm.aembedding(
+                self.model,
-                    self.model,
+                input = text,
-                    input = text_,
+                api_key = self.api_key,
-                    api_key = self.api_key,
+                api_base = self.endpoint,
-                    api_base = self.endpoint,
+                api_version = self.api_version
-                    api_version = self.api_version
+            )
-                )
+        except litellm.exceptions.BadRequestError as error:
-            except litellm.exceptions.BadRequestError as error:
+            logger.error("Error embedding text: %s", str(error))
-                logger.error("Error embedding text: %s", str(error))
+            raise error
-                raise error
 
-            return [data["embedding"] for data in response.data]
+        return [data["embedding"] for data in response.data]
-
-        # tasks = [get_embedding(text_) for text_ in text]
-        result = await get_embedding(text)
-        return result
 
     def get_vector_size(self) -> int:
         return self.dimensions

profiling/util/DummyEmbeddingEngine.py

@@ -0,0 +1,9 @@
+import numpy as np
+from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
+
+class DummyEmbeddingEngine(EmbeddingEngine):
+    async def embed_text(self, text: list[str]) -> list[list[float]]:
+        return(list(list(np.random.randn(3072))))
+
+    def get_vector_size(self) -> int:
+        return(3072)