Update eb_agent_module.py

eb_agent_module.py

@@ -174,38 +174,41 @@ class AdvancedRAGSystem:
             return None
         
         try:
-            # Standard google-generativeai call:
-            # embedding_response = genai.embed_content(
-            # model=self.embedding_model_name, # e.g., "models/text-embedding-004"
-            # content=text,
-            # task_type="RETRIEVAL_DOCUMENT" # or "SEMANTIC_SIMILARITY"
-            # )
-            # return np.array(embedding_response['embedding'])
-
-            # Using the provided client.models.embed_content structure:
-            # This might require specific types for config.
             embed_config_payload = None
-            if GENAI_AVAILABLE and hasattr(types, 'EmbedContentConfig'): # Assuming types.EmbedContentConfig is relevant
-                 # The task_type for EmbedContentConfig might differ, e.g., "SEMANTIC_SIMILARITY" or "RETRIEVAL_DOCUMENT"
+            if GENAI_AVAILABLE and hasattr(types, 'EmbedContentConfig'):
                 embed_config_payload = types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
-
-
-            response = client.models.embed_content( # This is the user's original call structure
+    
+            response = client.models.embed_content(
                 model=f"models/{self.embedding_model_name}" if not self.embedding_model_name.startswith("models/") else self.embedding_model_name,
-                contents=text, # Original used 'contents', genai.embed_content uses 'content'
-                config=embed_config_payload # Original passed 'config'
+                contents=text,
+                config=embed_config_payload
             )
             
-            # Adapt response parsing based on actual client.models.embed_content behavior
+            # Debug logging to understand the response structure
+            logging.info(f"Embedding response type: {type(response)}")
+            logging.info(f"Response attributes: {dir(response)}")
+            
             if hasattr(response, 'embeddings') and isinstance(response.embeddings, list) and len(response.embeddings) > 0:
-                 # This structure `response.embeddings[0]` seems specific.
-                 # Standard genai.embed_content returns a dict `{'embedding': [values]}`
-                return np.array(response.embeddings[0]) 
-            elif hasattr(response, 'embedding'): # Common for genai.embed_content
-                return np.array(response.embedding)
+                embedding_obj = response.embeddings[0]
+                logging.info(f"Embedding object type: {type(embedding_obj)}")
+                logging.info(f"Embedding object attributes: {dir(embedding_obj)}")
+                
+                # Try to extract values
+                if hasattr(embedding_obj, 'values'):
+                    logging.info(f"Found 'values' attribute with type: {type(embedding_obj.values)}")
+                    return np.array(embedding_obj.values)
+                elif hasattr(embedding_obj, 'embedding'):
+                    logging.info(f"Found 'embedding' attribute with type: {type(embedding_obj.embedding)}")
+                    return np.array(embedding_obj.embedding)
+                else:
+                    logging.error(f"ContentEmbedding object has no 'values' or 'embedding' attribute")
+                    logging.error(f"Available attributes: {[attr for attr in dir(embedding_obj) if not attr.startswith('_')]}")
+                    return None
+                    
             else:
-                logging.error(f"Unexpected embedding response format: {response}")
+                logging.error(f"Unexpected response structure")
                 return None
+                
         except Exception as e:
             logging.error(f"Error in _embed_single_document_sync for text '{text[:50]}...': {e}", exc_info=True)
             raise