Week 3 Changes

app/tools/web_search.py

@@ -4,11 +4,162 @@
 """
 
 
+from typing import List, Optional
+import random
+from datetime import datetime
+from app.config import settings
+
 def web_search(query: str, max_results: int = 5) -> str:
     """
-    Run a web search for the given query and return results as a string (or structured dict).
-    Expected: list of snippets/titles/URLs the agent can use for validation context.
-    TODO: Call search API or return mock list of results.
+    Run a web search for the given query and return results as a string.
+    Returns structured search results that the agent can use for validation context.
+
+    Week 3 Implementation: Mock data with keyword matching
+    Future Enhancement: Integrate with SerpAPI, Google Custom Search, or Serper API
+
+    Args:
+        query: Search query string
+        max_results: Maximum number of results to return (default: 5)
+
+    Returns:
+        Formatted string of search results for agent consumption
+    """
+
+    # Mock search results database
+    MOCK_SEARCH_RESULTS = [
+        {
+            "title": "AI Startup Market Analysis 2024",
+            "snippet": "The AI startup market is growing at 35% CAGR with over 10,000 new AI companies founded in 2023.",
+            "url": "https://techcrunch.com/ai-startup-market-2024"
+        },
+        {
+            "title": "Document Summarization Tools Comparison",
+            "snippet": "Top document summarization tools include Notion AI, ChatPDF, and Quillbot with varying pricing models.",
+            "url": "https://producthunt.com/document-summarization-tools"
+        },
+        {
+            "title": "EdTech Market Size and Growth",
+            "snippet": "EdTech market valued at $320B in 2024, expected to reach $404B by 2025 with 12% CAGR.",
+            "url": "https://statista.com/edtech-market-size"
+        },
+        {
+            "title": "Legal Tech Automation Trends",
+            "snippet": "Legal tech automation market growing 25% annually with document review and contract analysis leading.",
+            "url": "https://legaltech.com/automation-trends"
+        },
+        {
+            "title": "SaaS Startup Success Metrics",
+            "snippet": "Successful SaaS startups achieve $1M ARR in 18 months on average with strong product-market fit.",
+            "url": "https://a16z.com/saas-success-metrics"
+        },
+        {
+            "title": "Competitor Analysis Framework",
+            "snippet": "Framework for analyzing startup competitors: direct, indirect, and substitute products.",
+            "url": "https://ycombinator.com/competitor-analysis"
+        },
+        {
+            "title": "Market Validation Best Practices",
+            "snippet": "Key steps for validating startup ideas: customer interviews, landing page tests, and pre-orders.",
+            "url": "https://steveblank.com/market-validation"
+        },
+        {
+            "title": "AI Writing Tools Market Report",
+            "snippet": "AI writing tools market expected to reach $15B by 2027 with Grammarly and Jasper leading.",
+            "url": "https://gartner.com/ai-writing-tools"
+        }
+    ]
+
+    # Keyword matching to find relevant results
+    keywords = query.lower().split()
+    relevant_results = []
+
+    for result in MOCK_SEARCH_RESULTS:
+        # Check if result contains any idea keywords
+        content = f"{result['title']} {result['snippet']}".lower()
+        matches = sum(1 for keyword in keywords if keyword in content)
+
+        if matches >= 1:
+            relevant_results.append(result)
+
+    # If no matches found, return random subset
+    if not relevant_results:
+        relevant_results = random.sample(MOCK_SEARCH_RESULTS, min(max_results, len(MOCK_SEARCH_RESULTS)))
+
+    # Limit to max_results
+    relevant_results = relevant_results[:max_results]
+
+    # Format results as string for agent consumption
+    formatted_results = []
+    for i, result in enumerate(relevant_results, 1):
+        formatted_results.append(
+            f"[{i}] {result['title']}\n"
+            f"    {result['snippet']}\n"
+            f"    URL: {result['url']}\n"
+        )
+
+    return "\n\n".join(formatted_results)
+
+
+def web_search_structured(query: str, max_results: int = 5) -> List[dict]:
+    """
+    Alternative version that returns structured data (list of dicts).
+    Useful for programmatic access to search results.
+
+    Returns:
+        List of dicts with 'title', 'snippet', 'url' keys
+    """
+    # Reuse mock data logic
+    keywords = query.lower().split()
+    relevant_results = []
+
+    for result in MOCK_SEARCH_RESULTS:
+        content = f"{result['title']} {result['snippet']}".lower()
+        matches = sum(1 for keyword in keywords if keyword in content)
+
+        if matches >= 1:
+            relevant_results.append(result)
+
+    if not relevant_results:
+        relevant_results = random.sample(MOCK_SEARCH_RESULTS, min(max_results, len(MOCK_SEARCH_RESULTS)))
+
+    return relevant_results[:max_results]
+
+
+def web_search_real_api(query: str, max_results: int = 5, api_provider: str = "serper") -> str:
+    """
+    TODO: Week 3+ - Implement real API integration (SerpAPI, Google Custom Search, Serper)
+    This is a placeholder for future enhancement.
+
+    Args:
+        query: Search query string
+        max_results: Maximum number of results
+        api_provider: 'serper', 'serpapi', or 'google_custom'
+
+    Returns:
+        Formatted string of search results
     """
-    # TODO: Implement in Week 3
-    raise NotImplementedError("Implement web search (mock or API).")
+    # Example integration with Serper API (requires API key)
+    # import requests
+    # import os
+    #
+    # API_KEY = os.getenv("SERPER_API_KEY")
+    # if not API_KEY:
+    #     return web_search(query, max_results)  # Fallback to mock
+    #
+    # response = requests.get(
+    #     f"https://google.serper.dev/search?q={query}",
+    #     headers={"X-API-KEY": API_KEY}
+    # )
+    # results = response.json().get("organic", [])
+    #
+    # formatted_results = []
+    # for i, result in enumerate(results[:max_results], 1):
+    #     formatted_results.append(
+    #         f"[{i}] {result.get('title', '')}\n"
+    #         f"    {result.get('snippet', '')}\n"
+    #         f"    URL: {result.get('link', '')}\n"
+    #     )
+    #
+    # return "\n\n".join(formatted_results)
+
+    raise NotImplementedError("Real API integration not yet implemented.")