Update agent.py

agent.py

@@ -26,6 +26,7 @@ import mimetypes
 from PIL import Image
 from langchain_core.tools import tool
 from tavily import TavilyClient
+from langchain_groq import ChatGroq
 from dataclasses import dataclass, field
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, START, END, MessagesState
@@ -38,10 +39,11 @@ from langchain_community.document_loaders import ArxivLoader
 from langgraph.prebuilt import ToolNode, tools_condition
 from langchain_core.messages import SystemMessage, HumanMessage
 
-GPT_4o_API_KEY  = os.environ["META_LLAMA_4_MAVERICK"]
 # GPT_4o_API_KEY  = os.environ["GPT_4o_API_KEY"]
-TAVILY_API_KEY  = os.environ["TAVILY_API_KEY"]
-Pixtral12B_API  = os.environ["Pixtral12B_API"]
+GPT_4o_API_KEY    = os.environ["META_LLAMA_4_MAVERICK"]
+GROQ_API_KEY      = os.environ["GROQ_API_KEY"]
+TAVILY_API_KEY    = os.environ["TAVILY_API_KEY"]
+Pixtral12B_API    = os.environ["Pixtral12B_API"]
 
 API_URL   = "https://openrouter.ai/api/v1/chat/completions"
 tavily = TavilyClient(api_key=TAVILY_API_KEY)
@@ -345,24 +347,39 @@ def file_reader(file_path: str) -> str:
         return f"Error analyzing text file: {str(e)}"
 
 
+
+
+
+
+
+
+
 tools = [python_repl_tool,
-          web_search_with_LLM,
+          # web_search_with_LLM,
           web_search,
           vision_caption,
-          vision_caption_with_LLM,
+          # vision_caption_with_LLM,
           arvix_search,
           wiki_search,
           download_file_from_url,
-          file_reader_with_LLM,
+          # file_reader_with_LLM,
           file_reader]
+
+
+
 class EsiAgent:
-    def __init__(self):
+    def __init__(self, level=1):
         # 1) Instantiate the LLM
         llm = ChatOpenAI(
-            openai_api_key=GPT_4o_API_KEY,
-            base_url="https://openrouter.ai/api/v1",
+            base_url="https://api.groq.com/openai/v1",  # Groq's endpoint
+            openai_api_key=GROQ_API_KEY,
+            # model_name='qwen-qwq-32b',
+            # model_name="mixtral-8x7b-32768", 
+            model_name="meta-llama/llama-4-scout-17b-16e-instruct", #, llama3-70b-8192, etc.
+            # or gemma-7b-it, llama3-70b-8192, etc.
             temperature=0,
-            max_tokens=256
+            # max_tokens=128
+            max_tokens=512
         )
         # 2) Bind your tools
         llm_with_tools = llm.bind_tools(tools)
@@ -371,7 +388,11 @@ class EsiAgent:
         self.sys_msg = SystemMessage(content=SYSTEM_PROMPT)
 
         # 4) Assistant node
+        self.tool_numbers = level * 3
         def assistant(state):
+            if self.tool_numbers == 0:
+              return state["messages"]
+            self.tool_numbers -= 1
             # Prepend sys_msg only if it's not already there
             return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
@@ -396,15 +417,64 @@ class EsiAgent:
             "messages": [HumanMessage(content=question)] + [self.sys_msg]
 
         }
-
         final_state = self.graph.invoke(initial_state)
         # Extract final assistant message
         last_msg = final_state["messages"][-1].content
-
         # If you need to strip off "FINAL ANSWER: " prefix, do it here:
         if last_msg.startswith("FINAL ANSWER:"):
             return last_msg[len("FINAL ANSWER:"):].strip()
         return last_msg.strip()
+        
+# class EsiAgent:
+#     def __init__(self):
+#         # 1) Instantiate the LLM
+#         llm = ChatOpenAI(
+#             openai_api_key=GPT_4o_API_KEY,
+#             base_url="https://openrouter.ai/api/v1",
+#             temperature=0,
+#             max_tokens=256
+#         )
+#         # 2) Bind your tools
+#         llm_with_tools = llm.bind_tools(tools)
+
+#         # 3) Single system message
+#         self.sys_msg = SystemMessage(content=SYSTEM_PROMPT)
+
+#         # 4) Assistant node
+#         def assistant(state):
+#             # Prepend sys_msg only if it's not already there
+#             return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+#         # 5) Build the graph
+#         builder = StateGraph(MessagesState)
+#         builder.add_node("assistant", assistant)
+#         builder.add_node("tools", ToolNode(tools))
+#         builder.add_edge(START, "assistant")
+#         builder.add_conditional_edges("assistant", tools_condition)
+#         builder.add_edge("tools", "assistant")
+
+#         # 6) Compile once
+#         self.graph = builder.compile()
+
+
+#     def run(self, question: str, files=None, level: int = 1) -> str:
+#         """
+#         Wraps graph.invoke to run end-to-end on a plain question.
+#         """
+#         # Build a minimal GAIA payload (if your MessagesState expects more, adjust here)
+#         initial_state = {
+#             "messages": [HumanMessage(content=question)] + [self.sys_msg]
+
+#         }
+
+#         final_state = self.graph.invoke(initial_state)
+#         # Extract final assistant message
+#         last_msg = final_state["messages"][-1].content
+
+#         # If you need to strip off "FINAL ANSWER: " prefix, do it here:
+#         if last_msg.startswith("FINAL ANSWER:"):
+#             return last_msg[len("FINAL ANSWER:"):].strip()
+#         return last_msg.strip()