feat: Complete Phase 9 Source Cleanup (Remove WebTool) (#14)

* feat: complete Phase 9 source cleanup

- Removed WebTool (src/tools/websearch.py) and related tests
- Updated SearchHandler and models to support only PubMed
- Refactored all examples and tests to remove web search dependencies
- Added verification tests to ensure no WebTool references remain
- Fixed all unit tests to align with strict 'pubmed' source typing

* fix: address CodeRabbit review feedback

- Deduplicate tenacity dependency in pyproject.toml
- Remove 'and web' from search agent description
- Normalize Citation source to 'pubmed' in search agent
- Update app.py markdown to remove web search references
- Update embeddings demo with realistic PubMed URL/author
- Enhance SearchHandler tests to verify multi-tool aggregation

* refactor: replace mock data with real PubMed data in embeddings demo

* fix: address CodeRabbit review feedback

- Fix nested test_search_handler_pubmed_only (was inside another test, never ran)
- Improve test_no_webtool_references to catch `from src.tools import WebTool`
- Remove orphaned `# Search` comment in pyproject.toml

All 59 tests pass (was 58 - nested test now discovered and running)

examples/embeddings_demo/run_embeddings.py

@@ -2,77 +2,19 @@
 """
 Demo: Semantic Search & Deduplication (Phase 6).
 
-This script demonstrates embedding-based capabilities:
-- Text embedding with sentence-transformers
-- Semantic similarity search via ChromaDB
-- Duplicate detection by meaning (not just URL)
+This script demonstrates embedding-based capabilities using REAL data:
+- Fetches REAL abstracts from PubMed
+- Embeds text with sentence-transformers
+- Performs semantic deduplication on LIVE research data
 
 Usage:
     uv run python examples/embeddings_demo/run_embeddings.py
-
-No API keys required - uses local sentence-transformers model.
 """
 
 import asyncio
 
 from src.services.embeddings import EmbeddingService
-from src.utils.models import Citation, Evidence
-
-
-def create_sample_evidence() -> list[Evidence]:
-    """Create sample evidence with some semantic duplicates."""
-    return [
-        Evidence(
-            content="Metformin activates AMPK which inhibits mTOR signaling pathway.",
-            citation=Citation(
-                source="pubmed",
-                title="Metformin and AMPK activation",
-                url="https://pubmed.ncbi.nlm.nih.gov/11111/",
-                date="2023",
-                authors=["Smith J"],
-            ),
-        ),
-        Evidence(
-            content="The drug metformin works by turning on AMPK, blocking the mTOR pathway.",
-            citation=Citation(
-                source="pubmed",
-                title="AMPK-mTOR axis in diabetes treatment",
-                url="https://pubmed.ncbi.nlm.nih.gov/22222/",
-                date="2022",
-                authors=["Jones A"],
-            ),
-        ),
-        Evidence(
-            content="Sildenafil increases nitric oxide signaling for vasodilation.",
-            citation=Citation(
-                source="web",
-                title="How Viagra Works",
-                url="https://example.com/viagra-mechanism",
-                date="2023",
-                authors=["WebMD"],
-            ),
-        ),
-        Evidence(
-            content="Clinical trials show metformin reduces cancer incidence in diabetic patients.",
-            citation=Citation(
-                source="pubmed",
-                title="Metformin and cancer prevention",
-                url="https://pubmed.ncbi.nlm.nih.gov/33333/",
-                date="2024",
-                authors=["Lee K", "Park S"],
-            ),
-        ),
-        Evidence(
-            content="Metformin inhibits mTOR through AMPK activation mechanism.",
-            citation=Citation(
-                source="pubmed",
-                title="mTOR inhibition by Metformin",
-                url="https://pubmed.ncbi.nlm.nih.gov/44444/",
-                date="2023",
-                authors=["Brown M"],
-            ),
-        ),
-    ]
+from src.tools.pubmed import PubMedTool
 
 
 def create_fresh_service(name_suffix: str = "") -> EmbeddingService:
@@ -90,121 +32,73 @@ def create_fresh_service(name_suffix: str = "") -> EmbeddingService:
     return service
 
 
-async def demo_embedding() -> None:
-    """Demo single text embedding."""
+async def demo_real_pipeline() -> None:
+    """Run the demo using REAL PubMed data."""
     print("\n" + "=" * 60)
-    print("1. TEXT EMBEDDING DEMO")
+    print("DeepCritical Embeddings Demo (REAL DATA)")
     print("=" * 60)
 
-    service = create_fresh_service("embed")
-
-    texts = [
-        "Metformin activates AMPK",
-        "Aspirin reduces inflammation",
-        "Metformin turns on the AMPK enzyme",
-    ]
-
-    print("\nEmbedding sample texts...")
-    embeddings = await service.embed_batch(texts)
-
-    for text, emb in zip(texts, embeddings, strict=False):
-        print(f"  '{text[:40]}...' -> [{emb[0]:.4f}, {emb[1]:.4f}, ... ] (dim={len(emb)})")
-
-    # Calculate similarity between text 0 and text 2 (semantically similar)
-    import numpy as np
-
-    sim_0_2 = np.dot(embeddings[0], embeddings[2]) / (
-        np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[2])
-    )
-    sim_0_1 = np.dot(embeddings[0], embeddings[1]) / (
-        np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])
-    )
-
-    print(f"\nSimilarity (Metformin AMPK) vs (Metformin turns on AMPK): {sim_0_2:.3f}")
-    print(f"Similarity (Metformin AMPK) vs (Aspirin inflammation):    {sim_0_1:.3f}")
-    print("  -> Semantically similar texts have higher cosine similarity!")
-
-
-async def demo_semantic_search() -> None:
-    """Demo semantic similarity search."""
-    print("\n" + "=" * 60)
-    print("2. SEMANTIC SEARCH DEMO")
-    print("=" * 60)
-
-    service = create_fresh_service("search")
-
-    # Add some documents to the vector store
-    docs = [
-        ("doc1", "Metformin activates AMPK enzyme in liver cells", {"source": "pubmed"}),
-        ("doc2", "Aspirin inhibits COX-2 to reduce inflammation", {"source": "pubmed"}),
-        ("doc3", "Statins lower cholesterol by inhibiting HMG-CoA reductase", {"source": "web"}),
-        ("doc4", "AMPK activation leads to improved glucose metabolism", {"source": "pubmed"}),
-        ("doc5", "Sildenafil works via nitric oxide pathway", {"source": "web"}),
-    ]
-
-    print("\nIndexing documents...")
-    for doc_id, content, meta in docs:
-        await service.add_evidence(doc_id, content, meta)
-        print(f"  Added: {doc_id}")
-
-    # Search for semantically related content
-    query = "drugs that activate AMPK"
-    print(f"\nSearching for: '{query}'")
-
-    results = await service.search_similar(query, n_results=3)
-
-    print("\nTop 3 results:")
+    # 1. Fetch Real Data
+    query = "metformin mechanism of action"
+    print(f"\n[1] Fetching real papers for: '{query}'...")
+    pubmed = PubMedTool()
+    # Fetch enough results to likely get some overlap/redundancy
+    evidence = await pubmed.search(query, max_results=10)
+
+    print(f"    Found {len(evidence)} papers.")
+    print("\n    Sample Titles:")
+    for i, e in enumerate(evidence[:3], 1):
+        print(f"    {i}. {e.citation.title[:80]}...")
+
+    # 2. Embed Data
+    print("\n[2] Embedding abstracts (sentence-transformers)...")
+    service = create_fresh_service("real_demo")
+
+    # 3. Semantic Search
+    print("\n[3] Semantic Search Demo")
+    print("    Indexing evidence...")
+    for e in evidence:
+        # Use URL as ID for uniqueness
+        await service.add_evidence(
+            evidence_id=e.citation.url,
+            content=e.content,
+            metadata={
+                "source": e.citation.source,
+                "title": e.citation.title,
+                "date": e.citation.date,
+            },
+        )
+
+    semantic_query = "activation of AMPK pathway"
+    print(f"    Searching for concept: '{semantic_query}'")
+    results = await service.search_similar(semantic_query, n_results=2)
+
+    print("    Top matches:")
     for i, r in enumerate(results, 1):
-        # Lower distance = more similar (cosine distance: 0=identical, 2=opposite)
         similarity = 1 - r["distance"]
-        print(f"  {i}. [{similarity:.2%} similar] {r['content'][:60]}...")
-
-
-async def demo_deduplication() -> None:
-    """Demo semantic deduplication."""
-    print("\n" + "=" * 60)
-    print("3. SEMANTIC DEDUPLICATION DEMO")
-    print("=" * 60)
-
-    # Create fresh service for clean demo
-    service = create_fresh_service("dedup")
-
-    evidence = create_sample_evidence()
-    print(f"\nOriginal evidence count: {len(evidence)}")
-    for i, e in enumerate(evidence, 1):
-        print(f"  {i}. {e.citation.title}")
-
-    print("\nRunning semantic deduplication (threshold=0.85)...")
-    unique = await service.deduplicate(evidence, threshold=0.85)
+        print(f"    {i}. [{similarity:.1%} match] {r['metadata']['title'][:70]}...")
 
-    print(f"\nUnique evidence count: {len(unique)}")
-    print(f"Removed {len(evidence) - len(unique)} semantic duplicates\n")
+    # 4. Semantic Deduplication
+    print("\n[4] Semantic Deduplication Demo")
+    # Create a FRESH service for deduplication so we don't clash with Step 3's index
+    dedup_service = create_fresh_service("dedup_demo")
 
-    for i, e in enumerate(unique, 1):
-        print(f"  {i}. {e.citation.title}")
+    print("    Checking for redundant papers (threshold=0.85)...")
 
-    print("\n  -> Notice: Papers about 'Metformin AMPK mTOR' were deduplicated!")
-    print("     Different titles, same semantic meaning = duplicate removed.")
+    # To force a duplicate for demo purposes, let's double the evidence list
+    # simulating finding the same papers again or very similar ones
+    duplicated_evidence = evidence + evidence[:2]
+    print(f"    Input pool: {len(duplicated_evidence)} items (with artificial duplicates added)")
 
+    unique = await dedup_service.deduplicate(duplicated_evidence, threshold=0.85)
 
-async def main() -> None:
-    """Run all embedding demos."""
-    print("\n" + "=" * 60)
-    print("DeepCritical Embeddings Demo (Phase 6)")
-    print("Using: sentence-transformers + ChromaDB")
-    print("=" * 60)
-
-    await demo_embedding()
-    await demo_semantic_search()
-    await demo_deduplication()
+    print(f"    Output pool: {len(unique)} unique items")
+    print(f"    Removed {len(duplicated_evidence) - len(unique)} duplicates.")
 
     print("\n" + "=" * 60)
-    print("Demo complete! Embeddings enable:")
-    print("  - Finding papers by MEANING, not just keywords")
-    print("  - Removing duplicate findings automatically")
-    print("  - Building diverse evidence sets for research")
+    print("Demo complete! Verified with REAL PubMed data.")
     print("=" * 60 + "\n")
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(demo_real_pipeline())

examples/full_stack_demo/run_full.py

@@ -3,7 +3,7 @@
 Demo: Full Stack DeepCritical Agent (Phases 1-8).
 
 This script demonstrates the COMPLETE REAL drug repurposing research pipeline:
-- Phase 2: REAL Search (PubMed + Web API calls)
+- Phase 2: REAL Search (PubMed only)
 - Phase 6: REAL Embeddings (sentence-transformers + ChromaDB)
 - Phase 7: REAL Hypothesis (LLM mechanistic reasoning)
 - Phase 3: REAL Judge (LLM evidence assessment)
@@ -118,12 +118,11 @@ async def run_full_demo(query: str, max_iterations: int) -> None:
     from src.services.embeddings import EmbeddingService
     from src.tools.pubmed import PubMedTool
     from src.tools.search_handler import SearchHandler
-    from src.tools.websearch import WebTool
 
     # Initialize REAL services
     print("[Init] Loading embedding model...")
     embedding_service = EmbeddingService()
-    search_handler = SearchHandler(tools=[PubMedTool(), WebTool()], timeout=30.0)
+    search_handler = SearchHandler(tools=[PubMedTool()], timeout=30.0)
     judge_handler = JudgeHandler()
 
     # Shared evidence store
@@ -134,7 +133,7 @@ async def run_full_demo(query: str, max_iterations: int) -> None:
         print_step(iteration, f"ITERATION {iteration}/{max_iterations}")
 
         # Step 1: REAL Search
-        print("\n[Search] Querying PubMed and Web (REAL API calls)...")
+        print("\n[Search] Querying PubMed (REAL API calls)...")
         all_evidence = await _run_search_iteration(
             query, iteration, evidence_store, all_evidence, search_handler, embedding_service
         )
@@ -175,7 +174,7 @@ async def main() -> None:
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 This demo runs the COMPLETE pipeline with REAL API calls:
-  1. REAL search: Actual PubMed + DuckDuckGo queries
+  1. REAL search: Actual PubMed queries
   2. REAL embeddings: Actual sentence-transformers model
   3. REAL hypothesis: Actual LLM generating mechanistic chains
   4. REAL judge: Actual LLM assessing evidence quality
@@ -224,7 +223,7 @@ Examples:
     print("  DeepCritical Full Stack Demo Complete!")
     print("  ")
     print("  Everything you just saw was REAL:")
-    print("    - Real PubMed/Web searches")
+    print("    - Real PubMed searches")
     print("    - Real embedding computations")
     print("    - Real LLM reasoning")
     print("    - Real scientific report")

examples/hypothesis_demo/run_hypothesis.py

@@ -3,7 +3,7 @@
 Demo: Hypothesis Generation (Phase 7).
 
 This script demonstrates the REAL hypothesis generation pipeline:
-1. REAL search: PubMed + Web (actual API calls)
+1. REAL search: PubMed (actual API calls)
 2. REAL embeddings: Semantic deduplication
 3. REAL LLM: Mechanistic hypothesis generation
 
@@ -23,7 +23,6 @@ from src.agents.hypothesis_agent import HypothesisAgent
 from src.services.embeddings import EmbeddingService
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 
 
 async def run_hypothesis_demo(query: str) -> None:
@@ -36,8 +35,8 @@ async def run_hypothesis_demo(query: str) -> None:
         print(f"{'='*60}\n")
 
         # Step 1: REAL Search
-        print("[Step 1] Searching PubMed + Web...")
-        search_handler = SearchHandler(tools=[PubMedTool(), WebTool()], timeout=30.0)
+        print("[Step 1] Searching PubMed...")
+        search_handler = SearchHandler(tools=[PubMedTool()], timeout=30.0)
         result = await search_handler.execute(query, max_results_per_tool=5)
 
         print(f"  Found {result.total_found} results from {result.sources_searched}")
@@ -129,7 +128,7 @@ Examples:
 
     print("\n" + "=" * 60)
     print("Demo complete! This was a REAL pipeline:")
-    print("  1. REAL search: Actual PubMed + Web API calls")
+    print("  1. REAL search: Actual PubMed API calls")
     print("  2. REAL embeddings: Actual sentence-transformers")
     print("  3. REAL LLM: Actual hypothesis generation")
     print("=" * 60 + "\n")

examples/orchestrator_demo/run_agent.py

@@ -3,7 +3,7 @@
 Demo: DeepCritical Agent Loop (Search + Judge + Orchestrator).
 
 This script demonstrates the REAL Phase 4 orchestration:
-- REAL Iterative Search (PubMed + Web API calls)
+- REAL Iterative Search (PubMed only)
 - REAL Evidence Evaluation (LLM Judge)
 - REAL Orchestration Loop
 - REAL Final Synthesis
@@ -26,7 +26,6 @@ from src.agent_factory.judges import JudgeHandler
 from src.orchestrator import Orchestrator
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 from src.utils.models import OrchestratorConfig
 
 MAX_ITERATIONS = 10
@@ -39,7 +38,7 @@ async def main() -> None:
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 This demo runs the REAL search-judge-synthesize loop:
-  1. REAL search: Actual PubMed + DuckDuckGo queries
+  1. REAL search: Actual PubMed queries
   2. REAL judge: Actual LLM assessing evidence quality
   3. REAL loop: Actual iterative refinement based on LLM decisions
   4. REAL synthesis: Actual research summary generation
@@ -78,7 +77,7 @@ Examples:
     print(f"{'='*60}\n")
 
     # Setup REAL components
-    search_handler = SearchHandler(tools=[PubMedTool(), WebTool()], timeout=30.0)
+    search_handler = SearchHandler(tools=[PubMedTool()], timeout=30.0)
     judge_handler = JudgeHandler()  # REAL LLM judge
 
     config = OrchestratorConfig(max_iterations=args.iterations)
@@ -102,7 +101,7 @@ Examples:
 
     print("\n" + "=" * 60)
     print("Demo complete! Everything was REAL:")
-    print("  - Real PubMed/Web searches")
+    print("  - Real PubMed searches")
     print("  - Real LLM judge decisions")
     print("  - Real iterative refinement")
     print("=" * 60 + "\n")

examples/orchestrator_demo/run_magentic.py

@@ -20,7 +20,6 @@ from src.agent_factory.judges import JudgeHandler
 from src.orchestrator_factory import create_orchestrator
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 from src.utils.models import OrchestratorConfig
 
 
@@ -43,7 +42,7 @@ async def main() -> None:
     print(f"{ '='*60}\n")
 
     # 1. Setup Search Tools
-    search_handler = SearchHandler(tools=[PubMedTool(), WebTool()], timeout=30.0)
+    search_handler = SearchHandler(tools=[PubMedTool()], timeout=30.0)
 
     # 2. Setup Judge
     judge_handler = JudgeHandler()

examples/search_demo/run_search.py

@@ -4,7 +4,6 @@ Demo: Search for drug repurposing evidence.
 
 This script demonstrates Phase 2 functionality:
 - PubMed search (biomedical literature)
-- Web search (DuckDuckGo)
 - SearchHandler (parallel scatter-gather orchestration)
 
 Usage:
@@ -23,7 +22,6 @@ import sys
 
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 
 
 async def main(query: str) -> None:
@@ -35,11 +33,10 @@ async def main(query: str) -> None:
 
     # Initialize tools
     pubmed = PubMedTool()
-    web = WebTool()
-    handler = SearchHandler(tools=[pubmed, web], timeout=30.0)
+    handler = SearchHandler(tools=[pubmed], timeout=30.0)
 
     # Execute search
-    print("Searching PubMed and Web in parallel...")
+    print("Searching PubMed in parallel...")
     result = await handler.execute(query, max_results_per_tool=5)
 
     # Display results

pyproject.toml

@@ -19,9 +19,6 @@ dependencies = [
     "beautifulsoup4>=4.12",          # HTML parsing
     "xmltodict>=0.13",               # PubMed XML -> dict
 
-    # Search
-    "duckduckgo-search>=6.0",        # Free web search
-
     # UI
     "gradio>=5.0",                   # Chat interface
 

src/agents/search_agent.py

@@ -28,7 +28,7 @@ class SearchAgent(BaseAgent):  # type: ignore[misc]
     ) -> None:
         super().__init__(
             name="SearchAgent",
-            description="Searches PubMed and web for drug repurposing evidence",
+            description="Searches PubMed for drug repurposing evidence",
         )
         self._handler = search_handler
         self._evidence_store = evidence_store
@@ -95,7 +95,7 @@ class SearchAgent(BaseAgent):  # type: ignore[misc]
                         citation=Citation(
                             title=meta.get("title", "Related Evidence"),
                             url=item["id"],
-                            source=meta.get("source", "vector_db"),
+                            source="pubmed",
                             date=meta.get("date", "n.d."),
                             authors=authors,
                         ),

src/app.py

@@ -10,7 +10,6 @@ from src.agent_factory.judges import JudgeHandler, MockJudgeHandler
 from src.orchestrator_factory import create_orchestrator
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 from src.utils.models import OrchestratorConfig
 
 
@@ -33,7 +32,7 @@ def configure_orchestrator(use_mock: bool = False, mode: str = "simple") -> Any:
 
     # Create search tools
     search_handler = SearchHandler(
-        tools=[PubMedTool(), WebTool()],
+        tools=[PubMedTool()],
         timeout=config.search_timeout,
     )
 
@@ -128,7 +127,7 @@ def create_demo() -> Any:
         ## AI-Powered Drug Repurposing Research Agent
 
         Ask questions about potential drug repurposing opportunities.
-        The agent will search PubMed and the web, evaluate evidence, and provide recommendations.
+        The agent will search PubMed, evaluate evidence, and provide recommendations.
 
         **Example questions:**
         - "What drugs could be repurposed for Alzheimer's disease?"
@@ -161,7 +160,7 @@ def create_demo() -> Any:
         **Note**: This is a research tool and should not be used for medical decisions.
         Always consult healthcare professionals for medical advice.
 
-        Built with 🤖 PydanticAI + 🔬 PubMed + 🦆 DuckDuckGo
+        Built with 🤖 PydanticAI + 🔬 PubMed
         """)
 
     return demo

src/services/embeddings.py

@@ -18,7 +18,7 @@ class EmbeddingService:
     """Handles text embedding and vector storage.
 
     All embedding operations run in a thread pool to avoid blocking
-    the async event loop. See src/tools/websearch.py for the pattern.
+    the async event loop.
     """
 
     def __init__(self, model_name: str = "all-MiniLM-L6-v2"):

src/tools/__init__.py

@@ -3,7 +3,6 @@
 from src.tools.base import SearchTool
 from src.tools.pubmed import PubMedTool
 from src.tools.search_handler import SearchHandler
-from src.tools.websearch import WebTool
 
 # Re-export
-__all__ = ["PubMedTool", "SearchHandler", "SearchTool", "WebTool"]
+__all__ = ["PubMedTool", "SearchHandler", "SearchTool"]

src/tools/search_handler.py

@@ -49,7 +49,7 @@ class SearchHandler:
 
         # Process results
         all_evidence: list[Evidence] = []
-        sources_searched: list[Literal["pubmed", "web"]] = []
+        sources_searched: list[Literal["pubmed"]] = []
         errors: list[str] = []
 
         for tool, result in zip(self.tools, results, strict=True):
@@ -62,7 +62,7 @@ class SearchHandler:
                 all_evidence.extend(success_result)
 
                 # Cast tool.name to the expected Literal
-                tool_name = cast(Literal["pubmed", "web"], tool.name)
+                tool_name = cast(Literal["pubmed"], tool.name)
                 sources_searched.append(tool_name)
                 logger.info("Search tool succeeded", tool=tool.name, count=len(success_result))
 

src/tools/websearch.py

@@ -1,62 +0,0 @@
-"""Web search tool using DuckDuckGo."""
-
-import asyncio
-from typing import Any
-
-from duckduckgo_search import DDGS
-
-from src.utils.exceptions import SearchError
-from src.utils.models import Citation, Evidence
-
-
-class WebTool:
-    """Search tool for general web search via DuckDuckGo."""
-
-    def __init__(self) -> None:
-        pass
-
-    @property
-    def name(self) -> str:
-        return "web"
-
-    async def search(self, query: str, max_results: int = 10) -> list[Evidence]:
-        """
-        Search DuckDuckGo and return evidence.
-
-        Note: duckduckgo-search is synchronous, so we run it in executor.
-        """
-        loop = asyncio.get_running_loop()
-        try:
-            results = await loop.run_in_executor(
-                None,
-                lambda: self._sync_search(query, max_results),
-            )
-            return results
-        except Exception as e:
-            raise SearchError(f"Web search failed: {e}") from e
-
-    def _sync_search(self, query: str, max_results: int) -> list[Evidence]:
-        """Synchronous search implementation."""
-        evidence_list = []
-
-        with DDGS() as ddgs:
-            results: list[dict[str, Any]] = list(ddgs.text(query, max_results=max_results))
-
-        # Truncation rationale: LLM context limits + cost optimization
-        # - Content: 1000 chars (~250 tokens) - web snippets are shorter than abstracts
-        # - Title: 500 chars covers most web page titles
-        for result in results:
-            evidence_list.append(
-                Evidence(
-                    content=result.get("body", "")[:1000],
-                    citation=Citation(
-                        source="web",
-                        title=result.get("title", "Unknown")[:500],
-                        url=result.get("href", ""),
-                        date="Unknown",
-                        authors=[],
-                    ),
-                )
-            )
-
-        return evidence_list

src/utils/models.py

@@ -9,7 +9,7 @@ from pydantic import BaseModel, Field
 class Citation(BaseModel):
     """A citation to a source document."""
 
-    source: Literal["pubmed", "web"] = Field(description="Where this came from")
+    source: Literal["pubmed"] = Field(description="Where this came from")
     title: str = Field(min_length=1, max_length=500)
     url: str = Field(description="URL to the source")
     date: str = Field(description="Publication date (YYYY-MM-DD or 'Unknown')")
@@ -41,7 +41,7 @@ class SearchResult(BaseModel):
 
     query: str
     evidence: list[Evidence]
-    sources_searched: list[Literal["pubmed", "web"]]
+    sources_searched: list[Literal["pubmed"]]
     total_found: int
     errors: list[str] = Field(default_factory=list)
 

tests/conftest.py

@@ -44,7 +44,7 @@ def sample_evidence():
         Evidence(
             content="Drug repurposing offers faster path to treatment...",
             citation=Citation(
-                source="web",
+                source="pubmed",
                 title="Drug Repurposing Strategies",
                 url="https://example.com/drug-repurposing",
                 date="Unknown",

tests/unit/agent_factory/test_judges.py

@@ -158,7 +158,7 @@ class TestMockJudgeHandler:
             ),
             Evidence(
                 content="Content 2",
-                citation=Citation(source="web", title="T2", url="u2", date="2024"),
+                citation=Citation(source="pubmed", title="T2", url="u2", date="2024"),
             ),
         ]
 
@@ -214,7 +214,7 @@ class TestMockJudgeHandler:
             ),
             Evidence(
                 content="Content 2",
-                citation=Citation(source="web", title="T2", url="u2", date="2024"),
+                citation=Citation(source="pubmed", title="T2", url="u2", date="2024"),
             ),
         ]
 

tests/unit/agents/test_search_agent.py

@@ -103,7 +103,7 @@ async def test_run_uses_embeddings(mock_handler: AsyncMock) -> None:
         {
             "id": "u2",
             "content": "related content",
-            "metadata": {"source": "web", "title": "related", "date": "2024"},
+            "metadata": {"source": "pubmed", "title": "related", "date": "2024"},
             "distance": 0.1,
         }
     ]

tests/unit/services/test_embeddings.py

@@ -131,11 +131,11 @@ class TestEmbeddingService:
         evidence = [
             Evidence(
                 content="Duplicate content",
-                citation=Citation(source="web", url="u1", title="t1", date="2024"),
+                citation=Citation(source="pubmed", url="u1", title="t1", date="2024"),
             ),
             Evidence(
                 content="Unique content",
-                citation=Citation(source="web", url="u2", title="t2", date="2024"),
+                citation=Citation(source="pubmed", url="u2", title="t2", date="2024"),
             ),
         ]
 

tests/unit/test_no_webtool_references.py

@@ -0,0 +1,27 @@
+import ast
+import pathlib
+
+import pytest
+
+
+def test_examples_no_webtool_imports():
+    """No example files should import WebTool or the websearch module."""
+    examples_dir = pathlib.Path("examples")
+
+    for py_file in examples_dir.rglob("*.py"):
+        content = py_file.read_text()
+        tree = ast.parse(content)
+
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ImportFrom):
+                module = node.module or ""
+                if "websearch" in module:
+                    pytest.fail(f"{py_file} imports websearch (should be removed)")
+                # Also check for `from src.tools import WebTool`
+                for alias in node.names:
+                    if alias.name == "WebTool":
+                        pytest.fail(f"{py_file} imports WebTool (should be removed)")
+            elif isinstance(node, ast.Import):
+                for alias in node.names:
+                    if "websearch" in alias.name:
+                        pytest.fail(f"{py_file} imports websearch (should be removed)")

tests/unit/tools/test_search_handler.py

@@ -28,23 +28,14 @@ class TestSearchHandler:
         )
 
         mock_tool_2 = AsyncMock()
-        mock_tool_2.name = "web"
-        mock_tool_2.search = AsyncMock(
-            return_value=[
-                Evidence(
-                    content="Result 2",
-                    citation=Citation(source="web", title="T2", url="u2", date="2024"),
-                )
-            ]
-        )
+        mock_tool_2.name = "pubmed"  # Type system currently restricts to pubmed
+        mock_tool_2.search = AsyncMock(return_value=[])
 
         handler = SearchHandler(tools=[mock_tool_1, mock_tool_2])
         result = await handler.execute("test query")
 
-        expected_total = 2
-        assert result.total_found == expected_total
+        assert result.total_found == 1
         assert "pubmed" in result.sources_searched
-        assert "web" in result.sources_searched
         assert len(result.errors) == 0
 
     @pytest.mark.asyncio
@@ -62,7 +53,7 @@ class TestSearchHandler:
         )
 
         mock_tool_fail = AsyncMock()
-        mock_tool_fail.name = "web"
+        mock_tool_fail.name = "pubmed"  # Mocking a second pubmed instance failing
         mock_tool_fail.search = AsyncMock(side_effect=SearchError("API down"))
 
         handler = SearchHandler(tools=[mock_tool_ok, mock_tool_fail])
@@ -71,4 +62,22 @@ class TestSearchHandler:
         assert result.total_found == 1
         assert "pubmed" in result.sources_searched
         assert len(result.errors) == 1
-        assert "web" in result.errors[0]
+        # The error message format is "{tool.name}: {error!s}"
+        assert "pubmed: API down" in result.errors[0]
+
+    @pytest.mark.asyncio
+    async def test_search_handler_pubmed_only(self):
+        """SearchHandler should work with only PubMed tool."""
+        # This is the specific test requested in Phase 9 spec
+        from src.tools.pubmed import PubMedTool
+
+        mock_pubmed = AsyncMock(spec=PubMedTool)
+        mock_pubmed.name = "pubmed"
+        mock_pubmed.search.return_value = []
+
+        handler = SearchHandler(tools=[mock_pubmed], timeout=30.0)
+        result = await handler.execute("metformin diabetes", max_results_per_tool=3)
+
+        assert result.sources_searched == ["pubmed"]
+        assert "web" not in result.sources_searched
+        assert len(result.errors) == 0

tests/unit/tools/test_websearch.py

@@ -1,36 +0,0 @@
-"""Unit tests for WebTool."""
-
-from unittest.mock import MagicMock
-
-import pytest
-
-from src.tools.websearch import WebTool
-
-
-class TestWebTool:
-    """Tests for WebTool."""
-
-    @pytest.mark.asyncio
-    async def test_search_returns_evidence(self, mocker):
-        """WebTool should return Evidence objects from search."""
-        mock_results = [
-            {
-                "title": "Drug Repurposing Article",
-                "href": "https://example.com/article",
-                "body": "Some content about drug repurposing...",
-            }
-        ]
-
-        mock_ddgs = MagicMock()
-        mock_ddgs.__enter__ = MagicMock(return_value=mock_ddgs)
-        mock_ddgs.__exit__ = MagicMock(return_value=None)
-        mock_ddgs.text = MagicMock(return_value=mock_results)
-
-        mocker.patch("src.tools.websearch.DDGS", return_value=mock_ddgs)
-
-        tool = WebTool()
-        results = await tool.search("drug repurposing")
-
-        assert len(results) == 1
-        assert results[0].citation.source == "web"
-        assert "Drug Repurposing" in results[0].citation.title

tests/unit/tools/test_websearch_removed.py

@@ -0,0 +1,6 @@
+def test_websearch_module_deleted():
+    """WebTool should no longer exist."""
+    import pytest
+
+    with pytest.raises(ImportError):
+        from src.tools.websearch import WebTool  # noqa: F401

tests/unit/utils/test_text_utils.py

@@ -46,17 +46,17 @@ class TestTextUtils:
             Evidence(
                 content="A",
                 relevance=0.9,
-                citation=Citation(source="web", title="A", url="a", date="2023"),
+                citation=Citation(source="pubmed", title="A", url="a", date="2023"),
             ),
             Evidence(
                 content="B",
                 relevance=0.1,
-                citation=Citation(source="web", title="B", url="b", date="2023"),
+                citation=Citation(source="pubmed", title="B", url="b", date="2023"),
             ),
             Evidence(
                 content="C",
                 relevance=0.8,
-                citation=Citation(source="web", title="C", url="c", date="2023"),
+                citation=Citation(source="pubmed", title="C", url="c", date="2023"),
             ),
         ]
 
@@ -108,17 +108,17 @@ class TestTextUtils:
             Evidence(
                 content="A",
                 relevance=0.9,
-                citation=Citation(source="web", title="A", url="a", date="2023"),
+                citation=Citation(source="pubmed", title="A", url="a", date="2023"),
             ),
             Evidence(
                 content="B",
                 relevance=0.9,
-                citation=Citation(source="web", title="B", url="b", date="2023"),
+                citation=Citation(source="pubmed", title="B", url="b", date="2023"),
             ),
             Evidence(
                 content="C",
                 relevance=0.9,
-                citation=Citation(source="web", title="C", url="c", date="2023"),
+                citation=Citation(source="pubmed", title="C", url="c", date="2023"),
             ),
         ]
 

uv.lock

@@ -981,7 +981,6 @@ source = { editable = "." }
 dependencies = [
     { name = "anthropic" },
     { name = "beautifulsoup4" },
-    { name = "duckduckgo-search" },
     { name = "gradio" },
     { name = "httpx" },
     { name = "openai" },
@@ -1020,7 +1019,6 @@ requires-dist = [
     { name = "anthropic", specifier = ">=0.18.0" },
     { name = "beautifulsoup4", specifier = ">=4.12" },
     { name = "chromadb", marker = "extra == 'embeddings'", specifier = ">=0.4.0" },
-    { name = "duckduckgo-search", specifier = ">=6.0" },
     { name = "gradio", specifier = ">=5.0" },
     { name = "httpx", specifier = ">=0.27" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.10" },
@@ -1098,20 +1096,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "duckduckgo-search"
-version = "8.1.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "click" },
-    { name = "lxml" },
-    { name = "primp" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/10/ef/07791a05751e6cc9de1dd49fb12730259ee109b18e6d097e25e6c32d5617/duckduckgo_search-8.1.1.tar.gz", hash = "sha256:9da91c9eb26a17e016ea1da26235d40404b46b0565ea86d75a9f78cc9441f935", size = 22868 }
-wheels = [
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-]
-
 [[package]]
 name = "durationpy"
 version = "0.10"
@@ -2066,108 +2050,6 @@ wheels = [
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 ]
 
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-version = "6.0.2"
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