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Brandon Hancock
2025-03-26 14:51:53 -04:00
parent e6b90699a8
commit e11c7d1fd8
3 changed files with 47 additions and 52 deletions
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82
examples/lite_agent_example.py
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@@ -25,7 +25,7 @@ class WebSearchTool(BaseTool):
"""Search the web for information about a topic."""
# This is a mock implementation
if "tokyo" in query.lower():
return "Tokyo's population in 2023 was approximately 14 million people in the city proper, and 37 million in the greater metropolitan area."
return "Tokyo's population in 2023 was approximately 21 million people in the city proper, and 37 million in the greater metropolitan area."
elif "climate change" in query.lower() and "coral" in query.lower():
return "Climate change severely impacts coral reefs through: 1) Ocean warming causing coral bleaching, 2) Ocean acidification reducing calcification, 3) Sea level rise affecting light availability, 4) Increased storm frequency damaging reef structures. Sources: NOAA Coral Reef Conservation Program, Global Coral Reef Alliance."
else:
@@ -80,8 +80,8 @@ async def main():
# print(f"Raw response: {result.raw}")
# Example 2: Query with structured output
# print("\n=== Example 2: Structured Output ===")
# structured_query = """
print("\n=== Example 2: Structured Output ===")
structured_query = """
# Research the impact of climate change on coral reefs.
# YOU MUST format your response as a valid JSON object with the following structure:
@@ -94,48 +94,48 @@ async def main():
# Include at least 3 key points and 2 sources. Wrap your JSON in ```json and ``` tags.
# """
# result = await agent.kickoff_async(structured_query)
result = await agent.kickoff_async(structured_query)
# if result.pydantic:
# # Cast to the specific type for better IDE support
# research_result = cast(ResearchResult, result.pydantic)
# print(f"Main findings: {research_result.main_findings}")
# print("\nKey points:")
# for i, point in enumerate(research_result.key_points, 1):
# print(f"{i}. {point}")
# print("\nSources:")
# for i, source in enumerate(research_result.sources, 1):
# print(f"{i}. {source}")
# else:
# print(f"Raw response: {result.raw}")
# print(
# "\nNote: Structured output was not generated. The LLM may need more explicit instructions to format the response as JSON."
# )
# print("Usage metrics:")
# print(result.usage_metrics)
if result.pydantic:
# Cast to the specific type for better IDE support
research_result = cast(ResearchResult, result.pydantic)
print(f"Main findings: {research_result.main_findings}")
print("\nKey points:")
for i, point in enumerate(research_result.key_points, 1):
print(f"{i}. {point}")
print("\nSources:")
for i, source in enumerate(research_result.sources, 1):
print(f"{i}. {source}")
else:
print(f"Raw response: {result.raw}")
print(
"\nNote: Structured output was not generated. The LLM may need more explicit instructions to format the response as JSON."
)
print("Usage metrics:")
print(result.usage_metrics)
# Example 3: Multi-turn conversation
print("\n=== Example 3: Multi-turn Conversation ===")
messages = [
{"role": "user", "content": "I'm planning a trip to Japan."},
{
"role": "assistant",
"content": "That sounds exciting! Japan is a beautiful country with rich culture, delicious food, and stunning landscapes. What would you like to know about Japan to help with your trip planning?",
},
{
"role": "user",
"content": "What are the best times to visit Tokyo and Kyoto?",
},
]
# # Example 3: Multi-turn conversation
# print("\n=== Example 3: Multi-turn Conversation ===")
# messages = [
# {"role": "user", "content": "I'm planning a trip to Japan."},
# {
# "role": "assistant",
# "content": "That sounds exciting! Japan is a beautiful country with rich culture, delicious food, and stunning landscapes. What would you like to know about Japan to help with your trip planning?",
# },
# {
# "role": "user",
# "content": "What are the best times to visit Tokyo and Kyoto?",
# },
# ]
result = await agent.kickoff_async(messages)
print(f"Response: {result.raw}")
# result = await agent.kickoff_async(messages)
# print(f"Response: {result.raw}")
# Print usage metrics if available
if result.usage_metrics:
print("\nUsage metrics:")
for key, value in result.usage_metrics.items():
print(f"{key}: {value}")
# # Print usage metrics if available
# if result.usage_metrics:
# print("\nUsage metrics:")
# for key, value in result.usage_metrics.items():
# print(f"{key}: {value}")
if __name__ == "__main__":