Update tools/tools_on_modal_labs.py

tools/tools_on_modal_labs.py

@@ -17,8 +17,7 @@ image = modal.Image.debian_slim().pip_install(
     "groundx",
     "llama-index",
     "llama-index-llms-nebius",
-    "duckduckgo-search",
-    "langchain-community")
+    "tavily-python")
 
 app = modal.App(name="hackathon-mcp-tools", image=image)
 
@@ -45,7 +44,7 @@ def search_rag_context(queryInput: QueryInput) -> str:
     print(f"RAG Search Result: {result}")
     print("=============================\n")
 
-    return
+    return result
 
 def search_groundx_for_rag_context(query) -> str:
     from groundx import GroundX
@@ -71,7 +70,7 @@ from llama_index.core.agent.workflow import (
     ToolCallResult,
 )
 
-from langchain.utilities import DuckDuckGoSearchAPIWrapper
+from tavily import AsyncTavilyClient
 
 @app.function(secrets=[
         modal.Secret.from_name("hackathon-secret", required_keys=["NEBIUS_API_KEY", "AGENT_MODEL"])
@@ -92,33 +91,13 @@ llm = NebiusLLM(
     is_function_calling_model=True
 )
 
-# Search tools using DuckDuckGo
-duckduckgo = DuckDuckGoSearchAPIWrapper()
-
-MAX_SEARCH_CALLS = 2 # Limit the number of searches to 2
-search_call_count = 0
-past_queries = set()
-
-async def duckduckgo_search(query: str) -> str:
-    """
-    A DuckDuckGo-based search limiting number of searches and avoiding duplicates.
-    """
-    global search_call_count, past_queries
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 
-    # Check for duplicate queries
-    if query in past_queries:
-        return f"Already searched for '{query}'."
-
-    # Check if we've reached the max search calls
-    if search_call_count >= MAX_SEARCH_CALLS:
-        return "Search limit reached."
-
-    # Otherwise, perform the search
-    search_call_count += 1
-    past_queries.add(query)
-
-    result = duckduckgo.run(query)
-    return str(result)
+# Search tools using Tavily
+async def search_web(query: str) -> str:
+    """Useful for using the web to answer questions."""
+    client = AsyncTavilyClient(api_key=TAVILY_API_KEY)
+    return str(await client.search(query))
 
 # Research tools
 async def save_research(ctx: Context, notes: str, notes_title: str) -> str:
@@ -166,8 +145,8 @@ async def review_report(ctx: Context, review: str) -> str:
 research_agent = FunctionAgent(
     name="ResearchAgent",
     description=(
-        "A research agent that searches the web using Google search through SerpAPI. "
-        "It must not exceed 2 searches total, and must avoid repeating the same query. "
+        "A research agent that searches the web using TavilyClient Search."
+        "It must not exceed 3 searches total, and must avoid repeating the same query. "
         "Once sufficient information is collected, it should hand off to the WriteAgent."
     ),
     system_prompt=(
@@ -178,10 +157,10 @@ research_agent = FunctionAgent(
     ),
     llm=llm,
     tools=[
-        duckduckgo_search,
+        search_web,
         save_research
     ],
-    max_iterations=2,  # Limit to 2 iterations to prevent infinite loops
+    max_iterations=3,  # Limit to 2 iterations to prevent infinite loops
     cooldown=5,  # Cooldown to prevent rapid re-querying
     can_handoff_to=["WriteAgent"]
 )
@@ -230,7 +209,7 @@ agent_workflow = AgentWorkflow(
     root_agent=research_agent.name,  # Start with the ResearchAgent
     initial_state={
         "research_notes": {},
-        "report_content": "Not written yet.",
+        "report_content": "No report has been generated after the search.",
         "review": "Review required.",
     },
 )