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참고/neo4j-graphrag-python-main/examples/customize/llms/ollama_tool_calls.py

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+"""
+Example showing how to use Ollama tool calls with parameter extraction.
+Both synchronous and asynchronous examples are provided.
+
+To run this example:
+1. Make sure you have `ollama serve` running
+2. Run: python examples/tool_calls/ollama_tool_calls.py
+"""
+
+import asyncio
+import json
+from typing import Dict, Any
+
+from neo4j_graphrag.llm import OllamaLLM
+from neo4j_graphrag.llm.types import ToolCallResponse
+from neo4j_graphrag.tool import (
+    Tool,
+    ObjectParameter,
+    StringParameter,
+    IntegerParameter,
+)
+
+
+# Create a custom Tool implementation for person info extraction
+parameters = ObjectParameter(
+    description="Parameters for extracting person information",
+    properties={
+        "name": StringParameter(description="The person's full name"),
+        "age": IntegerParameter(description="The person's age"),
+        "occupation": StringParameter(description="The person's occupation"),
+    },
+    required_properties=["name"],
+    additional_properties=False,
+)
+person_info_tool = Tool(
+    name="extract_person_info",
+    description="Extract information about a person from text",
+    parameters=parameters,
+    execute_func=lambda **kwargs: kwargs,
+)
+
+# Create the tool instance
+TOOLS = [person_info_tool]
+
+
+def process_tool_calls(response: ToolCallResponse) -> Dict[str, Any]:
+    """Process all tool calls in the response and return the extracted parameters."""
+    if not response.tool_calls:
+        raise ValueError("No tool calls found in response")
+
+    print(f"\nNumber of tool calls: {len(response.tool_calls)}")
+    print(f"Additional content: {response.content or 'None'}")
+
+    results = []
+    for i, tool_call in enumerate(response.tool_calls):
+        print(f"\nTool call #{i + 1}: {tool_call.name}")
+        print(f"Arguments: {tool_call.arguments}")
+        results.append(tool_call.arguments)
+
+    # For backward compatibility, return the first tool call's arguments
+    return results[0] if results else {}
+
+
+async def main() -> None:
+    async with OllamaLLM(
+        model_name="mistral:latest", model_params={"options": {"temperature": 0}}
+    ) as llm:
+        # Example text containing information about a person
+        text = "Stella Hane is a 35-year-old software engineer who loves coding."
+
+        print("\n=== Synchronous Tool Call ===")
+        # Make a synchronous tool call
+        sync_response = llm.invoke_with_tools(
+            input=f"Extract information about the person from this text: {text}",
+            tools=TOOLS,
+        )
+        sync_result = process_tool_calls(sync_response)
+        print("\n=== Synchronous Tool Call Result ===")
+        print(json.dumps(sync_result, indent=2))
+
+        print("\n=== Asynchronous Tool Call ===")
+        # Make an asynchronous tool call with a different text
+        text2 = "Molly Hane, 32, works as a data scientist and enjoys machine learning."
+        async_response = await llm.ainvoke_with_tools(
+            input=f"Extract information about the person from this text: {text2}",
+            tools=TOOLS,
+        )
+        async_result = process_tool_calls(async_response)
+        print("\n=== Asynchronous Tool Call Result ===")
+        print(json.dumps(async_result, indent=2))
+
+
+if __name__ == "__main__":
+    # Run the async main function
+    asyncio.run(main())