update

app.py

@@ -20,50 +20,50 @@ def subtract(a: int, b: int) -> int:
     """Subtracts the second number from the first and returns the result."""
     return a - b
 
-llm = HuggingFaceInferenceAPI(
-    model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
-)
+async def main():
+    llm = HuggingFaceInferenceAPI(
+        model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
+    )
 
-calculator_agent = ReActAgent(
-    name="calculator_agent",
-    description="A calculator agent that can add and subtract numbers.",
-    system_prompt="You are a calculator assistant. Use your tools for any math operation.",
-    tools=[add, subtract],
-    llm=llm,
-)
+    calculator_agent = ReActAgent(
+        name="calculator_agent",
+        description="A calculator agent that can add and subtract numbers.",
+        system_prompt="You are a calculator assistant. Use your tools for any math operation.",
+        tools=[add, subtract],
+        llm=llm,
+    )
 
-# Create a vector store
-db = chromadb.PersistentClient(path="./alfred_chroma_db")
-chroma_collection = db.get_or_create_collection("alfred")
-vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+    # Create a vector store
+    db = chromadb.PersistentClient(path="./alfred_chroma_db")
+    chroma_collection = db.get_or_create_collection("alfred")
+    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
 
-# Create a query engine
-embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
-index = VectorStoreIndex.from_vector_store(
-    vector_store=vector_store, embed_model=embed_model
-)
-query_engine = index.as_query_engine(llm=llm)
-query_engine_tool = QueryEngineTool.from_defaults(
-    query_engine=query_engine,
-    name="personas",
-    description="descriptions for various types of personas",
-    return_direct=False,
-)
+    # Create a query engine
+    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+    index = VectorStoreIndex.from_vector_store(
+        vector_store=vector_store, embed_model=embed_model
+    )
+    query_engine = index.as_query_engine(llm=llm)
+    query_engine_tool = QueryEngineTool.from_defaults(
+        query_engine=query_engine,
+        name="personas",
+        description="descriptions for various types of personas",
+        return_direct=False,
+    )
 
-query_agent = ReActAgent(
-    name="query_agent",
-    description="A query agent that can query the internet.",
-    system_prompt="use your tool to query a RAG system to answer informaiton about XYZ.",
-    tools=[query_engine_tool],
-    llm=llm,
-)
+    query_agent = ReActAgent(
+        name="query_agent",
+        description="A query agent that can query the internet.",
+        system_prompt="use your tool to query a RAG system to answer informaiton about XYZ.",
+        tools=[query_engine_tool],
+        llm=llm,
+    )
 
-agent = AgentWorkflow(
-    agents=[calculator_agent, query_agent],
-    root_agent="calculator_agent",
-)
+    agent = AgentWorkflow(
+        agents=[calculator_agent, query_agent],
+        root_agent="calculator_agent",
+    )
 
-async def main():
     response = await agent.run(user_msg="What is 10 + 5?")
     print(response)