chore: import upstream snapshot with attribution

examples/api_usage/programmatic/minimal_working_example.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+Minimal working example for programmatic access to Local Deep Research.
+
+This shows how to use the core functionality without database dependencies.
+"""
+
+from langchain_ollama import ChatOllama
+from local_deep_research.search_system import AdvancedSearchSystem
+
+# Re-enable logging after import (it gets disabled in __init__.py)
+from loguru import logger
+import sys
+
+logger.remove()
+# diagnose=False: loguru defaults to True, which renders repr() of every
+# local in every traceback frame on exception. Users copy this snippet
+# into their own scripts, so leaving the default on would propagate the
+# credential-in-traceback leak (#4185) wherever the snippet lands.
+logger.add(
+    sys.stderr,
+    level="WARNING",
+    format="{time} {level} {message}",
+    diagnose=False,
+)
+logger.enable("local_deep_research")
+
+
+class MinimalSearchEngine:
+    """Minimal search engine that returns hardcoded results."""
+
+    def __init__(self, settings_snapshot=None):
+        self.settings_snapshot = settings_snapshot or {}
+
+    def run(self, query, research_context=None):
+        """Return some fake search results."""
+        return [
+            {
+                "title": "Introduction to AI",
+                "link": "https://example.com/ai-intro",
+                "snippet": "Artificial Intelligence (AI) is the simulation of human intelligence...",
+                "full_content": "Full article about AI basics...",
+                "rank": 1,
+            },
+            {
+                "title": "Machine Learning Explained",
+                "link": "https://example.com/ml-explained",
+                "snippet": "Machine learning is a subset of AI that enables systems to learn...",
+                "full_content": "Detailed explanation of machine learning...",
+                "rank": 2,
+            },
+        ]
+
+
+def main():
+    """Minimal example of programmatic access."""
+    print("=== Minimal Local Deep Research Example ===\n")
+
+    # 1. Create LLM
+    print("1. Creating Ollama LLM...")
+    llm = ChatOllama(model="gemma3:12b")
+
+    # 2. Create minimal search engine
+    print("2. Creating minimal search engine...")
+
+    # Settings for search system (without programmatic_mode)
+    settings = {
+        "search.iterations": 1,
+        "search.strategy": "direct",
+    }
+
+    search = MinimalSearchEngine(settings)
+
+    # 3. Create search system
+    print("3. Creating AdvancedSearchSystem...")
+    # IMPORTANT: Pass programmatic_mode=True to avoid database dependencies
+    system = AdvancedSearchSystem(
+        llm=llm,
+        search=search,
+        settings_snapshot=settings,
+        programmatic_mode=True,
+    )
+
+    # 4. Run a search
+    print("\n4. Running search...")
+    result = system.analyze_topic("What is artificial intelligence?")
+
+    # 5. Show results
+    print("\n=== RESULTS ===")
+    print(f"Found {len(result['findings'])} findings")
+    print(f"\nSummary:\n{result['current_knowledge']}")
+
+    print("\n✓ Success! Programmatic access works without database.")
+
+
+if __name__ == "__main__":
+    main()