chore: import upstream snapshot with attribution

examples/llm_integration/basic_custom_llm.py

@@ -0,0 +1,129 @@
+"""
+Example of using custom LangChain LLMs with Local Deep Research.
+
+This example shows how to integrate your own LLM implementations or wrappers
+with LDR's research functions.
+"""
+
+from typing import Any, List, Optional
+
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessage, BaseMessage
+from langchain_core.outputs import ChatGeneration, ChatResult
+
+from local_deep_research.api import (
+    create_settings_snapshot,
+    detailed_research,
+    quick_summary,
+)
+
+
+class CustomLLM(BaseChatModel):
+    """Example custom LLM implementation."""
+
+    model_name: str = "custom-model"
+    temperature: float = 0.7
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[Any] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Generate a response. This is where you'd call your custom model."""
+        # This is a mock implementation - replace with your actual model call
+        response_text = f"This is a response from {self.model_name} to: {messages[-1].content}"
+
+        # Create the response
+        message = AIMessage(content=response_text)
+        generation = ChatGeneration(message=message)
+
+        return ChatResult(generations=[generation])
+
+    @property
+    def _llm_type(self) -> str:
+        """Return identifier for this LLM."""
+        return "custom"
+
+
+def custom_llm_factory(
+    model_name: str = "factory-model", temperature: float = 0.5, **kwargs
+):
+    """Factory function that creates a custom LLM instance."""
+    return CustomLLM(model_name=model_name, temperature=temperature)
+
+
+def main():
+    # Example 1: Using a custom LLM instance
+    custom_llm = CustomLLM(model_name="my-custom-model", temperature=0.8)
+
+    snapshot = create_settings_snapshot(
+        provider="my_custom",
+        overrides={"search.tool": "wikipedia"},
+    )
+    result = quick_summary(
+        query="What are the latest advances in quantum computing?",
+        llms={"my_custom": custom_llm},
+        settings_snapshot=snapshot,
+    )
+
+    print("Summary with custom LLM instance:")
+    print(result["summary"])
+    print("-" * 80)
+
+    # Example 2: Using a factory function
+    snapshot = create_settings_snapshot(
+        provider="factory_llm",
+        temperature=0.3,
+        overrides={"search.tool": "wikipedia"},
+    )
+    result = quick_summary(
+        query="Explain the benefits of renewable energy",
+        llms={"factory_llm": custom_llm_factory},
+        model_name="renewable-expert",  # This gets passed to the factory
+        settings_snapshot=snapshot,
+    )
+
+    print("\nSummary with factory-created LLM:")
+    print(result["summary"])
+    print("-" * 80)
+
+    # Example 3: Multiple custom LLMs
+    llms = {
+        "technical": CustomLLM(model_name="technical-writer", temperature=0.2),
+        "creative": CustomLLM(model_name="creative-writer", temperature=0.9),
+    }
+
+    # Technical analysis
+    snapshot = create_settings_snapshot(
+        provider="technical",
+        overrides={"search.tool": "arxiv"},
+    )
+    technical_result = detailed_research(
+        query="How do neural networks work?",
+        llms=llms,
+        settings_snapshot=snapshot,
+    )
+
+    print("\nTechnical analysis:")
+    print(technical_result["summary"])
+    print("-" * 80)
+
+    # Creative exploration
+    snapshot = create_settings_snapshot(
+        provider="creative",
+        overrides={"search.tool": "wikipedia"},
+    )
+    creative_result = quick_summary(
+        query="What are the philosophical implications of AI?",
+        llms=llms,
+        settings_snapshot=snapshot,
+    )
+
+    print("\nCreative exploration:")
+    print(creative_result["summary"])
+
+
+if __name__ == "__main__":
+    main()