fix(graphiti): address CodeRabbit review comments

- Apply ruff formatting to Python files
- Fix ENV_GET handler to populate graphitiProviderConfig from .env
- Add Google AI to get_available_providers() function
- Fix type assertions to include google/groq/huggingface providers
- Fix asyncio deprecation: use get_running_loop() instead of get_event_loop()

auto-claude-ui/src/main/ipc-handlers/env-handlers.ts

@@ -317,6 +317,45 @@ ${existingVars['GRAPHITI_DATABASE'] ? `GRAPHITI_DATABASE=${existingVars['GRAPHIT
         config.enableFancyUi = false;
       }
 
+      // Populate graphitiProviderConfig from .env file
+      const llmProvider = vars['GRAPHITI_LLM_PROVIDER'];
+      const embeddingProvider = vars['GRAPHITI_EMBEDDER_PROVIDER'];
+      if (llmProvider || embeddingProvider || vars['ANTHROPIC_API_KEY'] || vars['AZURE_OPENAI_API_KEY'] ||
+          vars['VOYAGE_API_KEY'] || vars['GOOGLE_API_KEY'] || vars['OLLAMA_BASE_URL']) {
+        config.graphitiProviderConfig = {
+          llmProvider: (llmProvider as 'openai' | 'anthropic' | 'azure_openai' | 'ollama' | 'google' | 'groq') || 'openai',
+          embeddingProvider: (embeddingProvider as 'openai' | 'voyage' | 'azure_openai' | 'ollama' | 'google' | 'huggingface') || 'openai',
+          // OpenAI
+          openaiApiKey: vars['OPENAI_API_KEY'],
+          openaiModel: vars['OPENAI_MODEL'],
+          openaiEmbeddingModel: vars['OPENAI_EMBEDDING_MODEL'],
+          // Anthropic
+          anthropicApiKey: vars['ANTHROPIC_API_KEY'],
+          anthropicModel: vars['GRAPHITI_ANTHROPIC_MODEL'],
+          // Azure OpenAI
+          azureOpenaiApiKey: vars['AZURE_OPENAI_API_KEY'],
+          azureOpenaiBaseUrl: vars['AZURE_OPENAI_BASE_URL'],
+          azureOpenaiLlmDeployment: vars['AZURE_OPENAI_LLM_DEPLOYMENT'],
+          azureOpenaiEmbeddingDeployment: vars['AZURE_OPENAI_EMBEDDING_DEPLOYMENT'],
+          // Voyage
+          voyageApiKey: vars['VOYAGE_API_KEY'],
+          voyageEmbeddingModel: vars['VOYAGE_EMBEDDING_MODEL'],
+          // Google
+          googleApiKey: vars['GOOGLE_API_KEY'],
+          googleLlmModel: vars['GOOGLE_LLM_MODEL'],
+          googleEmbeddingModel: vars['GOOGLE_EMBEDDING_MODEL'],
+          // Ollama
+          ollamaBaseUrl: vars['OLLAMA_BASE_URL'],
+          ollamaLlmModel: vars['OLLAMA_LLM_MODEL'],
+          ollamaEmbeddingModel: vars['OLLAMA_EMBEDDING_MODEL'],
+          ollamaEmbeddingDim: vars['OLLAMA_EMBEDDING_DIM'] ? parseInt(vars['OLLAMA_EMBEDDING_DIM'], 10) : undefined,
+          // FalkorDB
+          falkorDbHost: vars['GRAPHITI_FALKORDB_HOST'],
+          falkorDbPort: vars['GRAPHITI_FALKORDB_PORT'] ? parseInt(vars['GRAPHITI_FALKORDB_PORT'], 10) : undefined,
+          falkorDbPassword: vars['GRAPHITI_FALKORDB_PASSWORD'],
+        };
+      }
+
       return { success: true, data: config };
     }
   );

auto-claude-ui/src/renderer/components/project-settings/MemoryBackendSection.tsx

@@ -146,7 +146,7 @@ export function MemoryBackendSection({
               onValueChange={(value) => onUpdateConfig({
                 graphitiProviderConfig: {
                   ...envConfig.graphitiProviderConfig,
-                  llmProvider: value as 'openai' | 'anthropic' | 'azure_openai' | 'ollama',
+                  llmProvider: value as 'openai' | 'anthropic' | 'azure_openai' | 'ollama' | 'google' | 'groq',
                   embeddingProvider: envConfig.graphitiProviderConfig?.embeddingProvider || 'openai',
                 }
               })}
@@ -176,7 +176,7 @@ export function MemoryBackendSection({
                 graphitiProviderConfig: {
                   ...envConfig.graphitiProviderConfig,
                   llmProvider: envConfig.graphitiProviderConfig?.llmProvider || 'openai',
-                  embeddingProvider: value as 'openai' | 'voyage' | 'azure_openai' | 'ollama',
+                  embeddingProvider: value as 'openai' | 'voyage' | 'azure_openai' | 'ollama' | 'google' | 'huggingface',
                 }
               })}
             >

auto-claude-ui/src/renderer/components/project-settings/SecuritySettings.tsx

@@ -164,7 +164,7 @@ export function SecuritySettings({
                     updateEnvConfig({
                       graphitiProviderConfig: {
                         ...currentConfig,
-                        llmProvider: value as 'openai' | 'anthropic' | 'azure_openai' | 'ollama',
+                        llmProvider: value as 'openai' | 'anthropic' | 'azure_openai' | 'ollama' | 'google' | 'groq',
                       }
                     });
                   }}
@@ -198,7 +198,7 @@ export function SecuritySettings({
                     updateEnvConfig({
                       graphitiProviderConfig: {
                         ...currentConfig,
-                        embeddingProvider: value as 'openai' | 'voyage' | 'azure_openai' | 'ollama',
+                        embeddingProvider: value as 'openai' | 'voyage' | 'azure_openai' | 'ollama' | 'google' | 'huggingface',
                       }
                     });
                   }}

auto-claude/integrations/graphiti/config.py

@@ -199,7 +199,9 @@ class GraphitiConfig:
         # Google AI settings
         google_api_key = os.environ.get("GOOGLE_API_KEY", "")
         google_llm_model = os.environ.get("GOOGLE_LLM_MODEL", "gemini-2.0-flash")
-        google_embedding_model = os.environ.get("GOOGLE_EMBEDDING_MODEL", "text-embedding-004")
+        google_embedding_model = os.environ.get(
+            "GOOGLE_EMBEDDING_MODEL", "text-embedding-004"
+        )
 
         # Ollama settings
         ollama_base_url = os.environ.get("OLLAMA_BASE_URL", DEFAULT_OLLAMA_BASE_URL)
@@ -542,6 +544,11 @@ def get_available_providers() -> dict:
     if config.voyage_api_key:
         available_embedder.append("voyage")
 
+    # Check Google AI
+    if config.google_api_key:
+        available_llm.append("google")
+        available_embedder.append("google")
+
     # Check Ollama
     if config.ollama_llm_model:
         available_llm.append("ollama")

auto-claude/integrations/graphiti/providers_pkg/embedder_providers/google_embedder.py

@@ -75,17 +75,17 @@ class GoogleEmbedder:
             text = str(input_data)
 
         # Run the synchronous API call in a thread pool
-        loop = asyncio.get_event_loop()
+        loop = asyncio.get_running_loop()
         result = await loop.run_in_executor(
             None,
             lambda: self._genai.embed_content(
                 model=f"models/{self.model}",
                 content=text,
-                task_type="retrieval_document"
+                task_type="retrieval_document",
-            )
+            ),
         )
 
-        return result['embedding']
+        return result["embedding"]
 
     async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
         """
@@ -100,29 +100,29 @@ class GoogleEmbedder:
         import asyncio
 
         # Google's API supports batch embedding
-        loop = asyncio.get_event_loop()
+        loop = asyncio.get_running_loop()
 
         # Process in batches to avoid rate limits
         batch_size = 100
         all_embeddings = []
 
         for i in range(0, len(input_data_list), batch_size):
-            batch = input_data_list[i:i + batch_size]
+            batch = input_data_list[i : i + batch_size]
 
             result = await loop.run_in_executor(
                 None,
                 lambda b=batch: self._genai.embed_content(
                     model=f"models/{self.model}",
                     content=b,
-                    task_type="retrieval_document"
+                    task_type="retrieval_document",
-                )
+                ),
             )
 
             # Handle single vs batch response
-            if isinstance(result['embedding'][0], list):
+            if isinstance(result["embedding"][0], list):
-                all_embeddings.extend(result['embedding'])
+                all_embeddings.extend(result["embedding"])
             else:
-                all_embeddings.append(result['embedding'])
+                all_embeddings.append(result["embedding"])
 
         return all_embeddings
 
@@ -146,7 +146,4 @@ def create_google_embedder(config: "GraphitiConfig") -> Any:
 
     model = config.google_embedding_model or DEFAULT_GOOGLE_EMBEDDING_MODEL
 
-    return GoogleEmbedder(
+    return GoogleEmbedder(api_key=config.google_api_key, model=model)
-        api_key=config.google_api_key,
-        model=model
-    )

auto-claude/integrations/graphiti/providers_pkg/llm_providers/google_llm.py

@@ -89,14 +89,13 @@ class GoogleLLMClient:
         # Create model with system instruction if provided
         if system_instruction:
             model = self._genai.GenerativeModel(
-                self.model,
+                self.model, system_instruction=system_instruction
-                system_instruction=system_instruction
             )
         else:
             model = self._model
 
         # Generate response
-        loop = asyncio.get_event_loop()
+        loop = asyncio.get_running_loop()
 
         if response_model:
             # For structured output, use JSON mode
@@ -107,13 +106,13 @@ class GoogleLLMClient:
             response = await loop.run_in_executor(
                 None,
                 lambda: model.generate_content(
-                    google_messages,
+                    google_messages, generation_config=generation_config
-                    generation_config=generation_config
+                ),
-                )
             )
 
             # Parse JSON response into the model
             import json
+
             try:
                 data = json.loads(response.text)
                 return response_model(**data)
@@ -122,8 +121,7 @@ class GoogleLLMClient:
                 return response.text
         else:
             response = await loop.run_in_executor(
-                None,
+                None, lambda: model.generate_content(google_messages)
-                lambda: model.generate_content(google_messages)
             )
 
             return response.text
@@ -169,7 +167,4 @@ def create_google_llm_client(config: "GraphitiConfig") -> Any:
 
     model = config.google_llm_model or DEFAULT_GOOGLE_LLM_MODEL
 
-    return GoogleLLMClient(
+    return GoogleLLMClient(api_key=config.google_api_key, model=model)
-        api_key=config.google_api_key,
-        model=model
-    )