Fix memory embeddings for google with aysncio event loop

tradingagents/agents/utils/memory.py

@@ -2,6 +2,9 @@ import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
 import os
+import asyncio
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from sentence_transformers import SentenceTransformer
 
 
 class FinancialSituationMemory:
@@ -12,9 +15,20 @@ class FinancialSituationMemory:
             self.embedding = "text-embedding-3-small"
             self.client = OpenAI(base_url=config["backend_url"])
             self.embedding_model = None
+        elif self.provider == "google":
+            import asyncio
+            try:
+                asyncio.get_running_loop()
+            except RuntimeError:
+                asyncio.set_event_loop(asyncio.new_event_loop())
+            google_api_key = os.getenv("GOOGLE_API_KEY")
+            self.embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=google_api_key)
+            self.client = None
+        elif self.provider == "anthropic":
+            self.embedding_model = None
+            self.client = None
         else:
-            # Use a local embedding model for non-OpenAI providers
+            # Use a local embedding model for other non-OpenAI providers
-            from sentence_transformers import SentenceTransformer
             self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
             self.client = None
         self.chroma_client = chromadb.Client(Settings(allow_reset=True))
@@ -26,6 +40,10 @@ class FinancialSituationMemory:
                 model=self.embedding, input=text
             )
             return response.data[0].embedding
+        elif self.provider == "google":
+            return self.embedding_model.embed_query(text)
+        elif self.provider == "anthropic":
+            raise NotImplementedError("Memory features are currently not supported for Anthropic provider. Please use OpenAI or Google for memory-enabled workflows.")
         else:
             # Use local embedding model
             return self.embedding_model.encode(text).tolist()
@@ -54,7 +72,7 @@ class FinancialSituationMemory:
         )
 
     def get_memories(self, current_situation, n_matches=1):
-        """Find matching recommendations using OpenAI embeddings"""
+        """Find matching recommendations using provider-appropriate embeddings"""
         query_embedding = self.get_embedding(current_situation)
 
         results = self.situation_collection.query(