Better suggested queries

agents/python/src/lib/mcp_integration.py

@@ -278,7 +278,8 @@ async def _call_mcp_tool(tool_name: str, arguments: Dict[str, Any]) -> Any:
 async def search_knowledge_base(
     query: str,
     count: int = 5,
-    search_effort: str = "fast"
+    search_effort: str = "fast",
+    source_indexes: Optional[List[str]] = None
 ) -> List[Dict[str, Any]]:
     """
     Search the knowledge base via MCP server.
@@ -287,19 +288,25 @@ async def search_knowledge_base(
         query: Search query
         count: Number of results to return
         search_effort: Search effort level ('fast', 'medium', or 'deep')
+        source_indexes: Optional list of source indexes to search in priority order
+                       (e.g., ['tako'], ['web'], ['tako', 'web'])
 
     Returns:
         List of search results with metadata
     """
 
-    result = await _call_mcp_tool("knowledge_search", {
+    args = {
         "query": query,
         "api_token": TAKO_API_TOKEN,
         "count": count,
         "search_effort": search_effort,
         "country_code": "US",
         "locale": "en-US"
-    })
+    }
+    if source_indexes:
+        args["source_indexes"] = source_indexes
+
+    result = await _call_mcp_tool("knowledge_search", args)
 
     if result and "results" in result:
         formatted_results = []

agents/python/src/lib/search.py

@@ -164,14 +164,16 @@ async def search_node(state: AgentState, config: RunnableConfig):
             fallback_tasks = []
             fallback_logs = []
 
-            # Fallback web searches for fast questions
+            # Fallback Tako web searches for fast questions
             if fast_questions:
-                logger.info("No Tako results found, falling back to web searches for Tako questions")
-                fallback_web_queries = [q["question"] for q in fast_questions[:2]]
-                for query in fallback_web_queries:
-                    fallback_logs.append(("web", query))
-                    state["logs"].append({"message": f"Fallback web search: {query}", "done": False})
-                fallback_tasks.extend([async_tavily_search(query) for query in fallback_web_queries])
+                logger.info("No Tako results found, falling back to Tako web index for questions")
+                for q_obj in fast_questions[:2]:
+                    fallback_logs.append(("tako_web", q_obj["question"]))
+                    state["logs"].append({"message": f"Tako web search: {q_obj['question']}", "done": False})
+                fallback_tasks.extend([
+                    search_knowledge_base(q["question"], search_effort="fast", source_indexes=["web"])
+                    for q in fast_questions[:2]
+                ])
 
             # Deep search for prediction market questions
             if prediction_market_questions:
@@ -192,13 +194,8 @@ async def search_node(state: AgentState, config: RunnableConfig):
                 for i, result in enumerate(fallback_results):
                     task_type, _ = fallback_logs[i]
                     if isinstance(result, Exception):
-                        if task_type == "web":
-                            search_results.append({"error": str(result)})
-                        else:
-                            tako_results.append({"error": str(result)})
-                    elif task_type == "web":
-                        search_results.append(result)
-                    elif result:  # deep Tako result
+                        tako_results.append({"error": str(result)})
+                    elif result:  # Tako result (web or deep)
                         # Add resources immediately for streaming
                         existing_urls = {r.get("url") for r in state["resources"]}
                         existing_titles = {r.get("title", "").lower() for r in state["resources"] if r.get("resource_type") == "tako_chart"}

src/app/Main.tsx

@@ -13,8 +13,8 @@ const CHAT_SUGGESTIONS = [
     message: "Tell me about Vietnam's economy",
   },
   {
-    title: "Nvidia vs Intel",
-    message: "Compare the performance of Nvidia and Intel over the last 10 years",
+    title: "Amazon vs Walmart",
+    message: "Compare the performance of Amazon and Walmart over the last 10 years",
   },
   {
     title: "Commodities Prices",
@@ -25,12 +25,16 @@ const CHAT_SUGGESTIONS = [
     message: "What is the trend of global military spending since the Ukraine war started?",
   },
   {
-    title: "Performance of AI companies",
-    message: "How has the performance of AI company's stock prices and funding evolved since 2020 (also include traffic trends)?",
+    title: "Semiconductor Companies",
+    message: "How has the performance of semiconductor companies (like NVIDIA, AMD, TSMC, Intel) evolved since 2020 in terms of stock prices, revenue, and market share?",
   },
   {
     title: "Rent & Inflation",
     message: "How has the rent, inflation, and average wages trended in top US cities?",
+  },
+  {
+    title: "Crime & Public Health",
+    message: "How do crime rates (homicides, violent crime) and public health issues (fentanyl overdoses, drug use) correlate with population changes in major US cities like San Francisco, Chicago, and New York?",
   }
 ];