141 lines
4.0 KiB
Python
141 lines
4.0 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Async example demonstrating PocketFlow tracing with Langfuse.
|
|
|
|
This example shows how to use the @trace_flow decorator with AsyncFlow
|
|
and AsyncNode to trace asynchronous workflows.
|
|
"""
|
|
|
|
import asyncio
|
|
import sys
|
|
import os
|
|
from dotenv import load_dotenv
|
|
|
|
# Load environment variables
|
|
load_dotenv()
|
|
|
|
# Add parent directory to path to import pocketflow and tracing
|
|
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
|
|
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
|
|
|
|
from pocketflow import AsyncNode, AsyncFlow
|
|
from tracing import trace_flow, TracingConfig
|
|
|
|
|
|
class AsyncDataFetchNode(AsyncNode):
|
|
"""An async node that simulates fetching data."""
|
|
|
|
async def prep_async(self, shared):
|
|
"""Extract the query from shared data."""
|
|
query = shared.get("query", "default")
|
|
return query
|
|
|
|
async def exec_async(self, query):
|
|
"""Simulate async data fetching."""
|
|
print(f"🔍 Fetching data for query: {query}")
|
|
|
|
# Simulate async operation
|
|
await asyncio.sleep(1)
|
|
|
|
# Return mock data
|
|
data = {
|
|
"query": query,
|
|
"results": [f"Result {i} for {query}" for i in range(3)],
|
|
"timestamp": "2024-01-01T00:00:00Z",
|
|
}
|
|
return data
|
|
|
|
async def post_async(self, shared, prep_res, exec_res):
|
|
"""Store the fetched data."""
|
|
shared["fetched_data"] = exec_res
|
|
return "process"
|
|
|
|
|
|
class AsyncDataProcessNode(AsyncNode):
|
|
"""An async node that processes the fetched data."""
|
|
|
|
async def prep_async(self, shared):
|
|
"""Get the fetched data."""
|
|
return shared.get("fetched_data", {})
|
|
|
|
async def exec_async(self, data):
|
|
"""Process the data asynchronously."""
|
|
print("⚙️ Processing fetched data...")
|
|
|
|
# Simulate async processing
|
|
await asyncio.sleep(0.5)
|
|
|
|
# Process the results
|
|
processed_results = []
|
|
for result in data.get("results", []):
|
|
processed_results.append(f"PROCESSED: {result}")
|
|
|
|
return {
|
|
"original_query": data.get("query"),
|
|
"processed_results": processed_results,
|
|
"result_count": len(processed_results),
|
|
}
|
|
|
|
async def post_async(self, shared, prep_res, exec_res):
|
|
"""Store the processed data."""
|
|
shared["processed_data"] = exec_res
|
|
return "default"
|
|
|
|
|
|
@trace_flow(flow_name="AsyncDataProcessingFlow")
|
|
class AsyncDataProcessingFlow(AsyncFlow):
|
|
"""An async flow that fetches and processes data."""
|
|
|
|
def __init__(self):
|
|
# Create async nodes
|
|
fetch_node = AsyncDataFetchNode()
|
|
process_node = AsyncDataProcessNode()
|
|
|
|
# Connect nodes
|
|
fetch_node - "process" >> process_node
|
|
|
|
# Initialize async flow
|
|
super().__init__(start=fetch_node)
|
|
|
|
|
|
async def main():
|
|
"""Run the async tracing example."""
|
|
print("🚀 Starting PocketFlow Async Tracing Example")
|
|
print("=" * 50)
|
|
|
|
# Create the async flow
|
|
flow = AsyncDataProcessingFlow()
|
|
|
|
# Prepare shared data
|
|
shared = {"query": "machine learning tutorials"}
|
|
|
|
print(f"📥 Input: {shared}")
|
|
|
|
# Run the async flow (this will be automatically traced)
|
|
try:
|
|
result = await flow.run_async(shared)
|
|
print(f"📤 Output: {shared}")
|
|
print(f"🎯 Result: {result}")
|
|
print("✅ Async flow completed successfully!")
|
|
|
|
# Print the processed data
|
|
if "processed_data" in shared:
|
|
processed = shared["processed_data"]
|
|
print(
|
|
f"🎉 Processed {processed['result_count']} results for query: {processed['original_query']}"
|
|
)
|
|
for result in processed["processed_results"]:
|
|
print(f" - {result}")
|
|
|
|
except Exception as e:
|
|
print(f"❌ Async flow failed with error: {e}")
|
|
raise
|
|
|
|
print("\n📊 Check your Langfuse dashboard to see the async trace!")
|
|
langfuse_host = os.getenv("LANGFUSE_HOST", "your-langfuse-host")
|
|
print(f" Dashboard URL: {langfuse_host}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|