Update agent.py

agent.py

@@ -1,33 +1,29 @@
 # agent.py
 
-from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.llms.openai import OpenAI
 from llama_index.core.agent.workflow import AgentWorkflow
 from llama_index.core.tools import FunctionTool
+
+# LangChain-based tools
 from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
 from langchain_experimental.tools.python.tool import PythonREPLTool
 from langchain_community.document_loaders import YoutubeLoader
 
-# Define all tool functions with type annotations
-
+# Tool wrappers
 def search_duckduckgo(query: str) -> str:
-    """Use DuckDuckGo to search the internet."""
     return DuckDuckGoSearchRun().run(query)
 
 def search_wikipedia(query: str) -> str:
-    """Use Wikipedia to look up facts."""
     return WikipediaQueryRun(api_wrapper=None).run(query)
 
 def run_python(code: str) -> str:
-    """Execute Python code and return output."""
     return PythonREPLTool().run(code)
 
 def get_youtube_transcript(url: str) -> str:
-    """Extract transcript from YouTube video."""
     loader = YoutubeLoader.from_youtube_url(url, add_video_info=False)
     docs = loader.load()
-    return " ".join(doc.page_content for doc in docs)
+    return " ".join(d.page_content for d in docs)
 
-# Build tool wrappers
 TOOLS = [
     FunctionTool.from_defaults(search_duckduckgo),
     FunctionTool.from_defaults(search_wikipedia),
@@ -35,20 +31,13 @@ TOOLS = [
     FunctionTool.from_defaults(get_youtube_transcript),
 ]
 
-# Load LLM
-llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
-
-# Create LlamaIndex agent
+llm = OpenAI(model="gpt-4")
 agent = AgentWorkflow.from_tools_or_functions(
     TOOLS,
     llm=llm,
-    system_prompt="You are a helpful and smart AI agent that solves tasks using reasoning and external tools."
+    system_prompt="You are a helpful AI agent using external tools."
 )
 
-# Optional: support context (stateful runs)
-from llama_index.core.workflow import Context
-ctx = Context(agent)
-
+import asyncio
 async def answer_question(question: str) -> str:
-    """Run the agent on a single question."""
     return await agent.arun(question)