Upload agent.py

agent.py

@@ -279,7 +279,7 @@ tools = [
 ]
 
 # Build graph function
-def build_graph(provider: str = "anthropic"):
+def build_graph(provider: str = "groq"):
     """Build the graph"""
     # Load environment variables from .env file
     if provider == "openai":
@@ -287,15 +287,12 @@ def build_graph(provider: str = "anthropic"):
     elif provider == "anthropic":
         llm = ChatAnthropic(model="claude-v1", temperature=0)
     elif provider == "google":
-        # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-            llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
     elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
         llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
+            llm = HuggingFaceEndpoint( 
                 endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                 temperature=0,
             ),
@@ -307,8 +304,9 @@ def build_graph(provider: str = "anthropic"):
 
     # Node
     def assistant(state: MessagesState):
-        """Assistant node"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+        messages_with_sys = [sys_msg] + state["messages"]
+        return {"messages": [llm_with_tools.invoke(messages_with_sys)]}
+
     
     def retriever(state: MessagesState):
         """Retriever node"""