Update agents/agents_nodes.py

agents/agents_nodes.py

@@ -3,24 +3,54 @@ from langchain_core.messages import AIMessage, ToolMessage
 from langgraph.prebuilt import ToolNode
 from utils.state_utils import AgentState
 from tools.financial_tools import time_value_tool
-from langchain_ollama import ChatOllama
 
 
-# LLL instantation
 
-llm = ChatOllama(model="qwen3:4b", temperature=0)
-llm_instantiated = llm.bind_tools(    
+# # LLL instantation
+
+# llm = ChatOllama(model="qwen3:4b", temperature=0)
+# llm_instantiated = llm.bind_tools(    
+#     [time_value_tool],
+#     tool_choice={"type": "function", "function": {"name": "time_value_tool"}}
+# )
+
+# def agent_node(state: AgentState):
+#     response = llm_instantiated.invoke(state["messages"])
+#     if not (hasattr(response, 'tool_calls') and response.tool_calls):
+#         error_message = AIMessage(content="Error: Model failed to generate tool call.")
+#         return {"messages": [error_message]}
+#     return {"messages": [response]}
+
+import os
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.messages import AIMessage
+
+# Initialize Hugging Face endpoint (replace with your model)
+HF_MODEL = "google/gemma-2b-it"
+llm_endpoint = HuggingFaceEndpoint(
+    endpoint_url=f"https://api-inference.huggingface.co/models/{HF_MODEL}",
+    huggingfacehub_api_token=os.environ["HF_TOKEN"],
+    max_new_tokens=500,
+    temperature=0
+)
+
+# Wrap in chat model
+llm = ChatHuggingFace(llm=llm_endpoint)
+
+# Bind tools to model
+llm_instantiated = llm.bind_tools(
     [time_value_tool],
     tool_choice={"type": "function", "function": {"name": "time_value_tool"}}
 )
 
 def agent_node(state: AgentState):
     response = llm_instantiated.invoke(state["messages"])
-    if not (hasattr(response, 'tool_calls') and response.tool_calls):
-        error_message = AIMessage(content="Error: Model failed to generate tool call.")
-        return {"messages": [error_message]}
+    if not hasattr(response, "tool_calls") or not response.tool_calls:
+        return {"messages": [AIMessage(content="Error: No tool call generated")]}
     return {"messages": [response]}
 
+
 # Tool node executes the tool
 tool_node = ToolNode([time_value_tool])