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add quickstart files
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cookbook/step_agent/examples.py
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262
cookbook/step_agent/examples.py
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"""
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Step Experience Service Usage Examples
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This file demonstrates how to use the step-level experience extraction and context generation service.
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"""
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import json
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import requests
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from typing import List, Dict, Any
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from experiencemaker.enumeration.role import Role
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from experiencemaker.schema.trajectory import Trajectory, Message, ToolCall
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# Service configuration
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SERVICE_URL = "http://localhost:8001"
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WORKSPACE_ID = "test_workspace"
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def create_sample_trajectory(query: str, steps: List[Message], done: bool = True) -> Trajectory:
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"""Create a sample trajectory for testing"""
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trajectory = Trajectory(
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query=query,
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steps=steps,
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done=done,
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current_step=len(steps) - 1,
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metadata={
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"domain": "coding",
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"task_type": "problem_solving"
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}
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)
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return trajectory
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def example_1_extract_step_experiences():
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"""Example 1: Extract step-level experiences from trajectories"""
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print("=== Example 1: Extract Step-Level Experiences ===")
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# Create sample successful trajectory
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successful_trajectory = create_sample_trajectory(
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query="How to implement a binary search algorithm?",
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steps=[
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Message(
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role=Role.USER,
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content="How to implement a binary search algorithm?"
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),
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Message(
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role=Role.ASSISTANT,
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content="I'll help you implement a binary search algorithm. Let me start by explaining the concept.",
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reasoning_content="Need to first explain the concept before implementation"
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),
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Message(
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role=Role.ASSISTANT,
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content="Here's the implementation:\n\ndef binary_search(arr, target):\n left, right = 0, len(arr) - 1\n while left <= right:\n mid = (left + right) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n return -1",
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tool_calls=[
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ToolCall(
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index=0,
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id="call_1",
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name="code_execution",
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arguments='{"code": "def binary_search(arr, target): ..."}',
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result="Code executed successfully"
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)
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]
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),
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Message(
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role=Role.TOOL,
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content="Code executed successfully. Binary search implementation is correct."
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)
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],
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done=True
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)
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# Create sample failed trajectory
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failed_trajectory = create_sample_trajectory(
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query="How to implement a binary search algorithm?",
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steps=[
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Message(
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role=Role.USER,
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content="How to implement a binary search algorithm?"
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),
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Message(
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role=Role.ASSISTANT,
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content="Here's a binary search:\n\ndef binary_search(arr, target):\n for i in range(len(arr)):\n if arr[i] == target:\n return i\n return -1",
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reasoning_content="Implementing binary search as linear search by mistake"
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),
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Message(
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role=Role.TOOL,
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content="Error: This is actually a linear search, not binary search. Binary search requires sorted array and divide-and-conquer approach."
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)
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],
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done=False
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)
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# Extract experiences using summarizer
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summarizer_request = {
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"trajectories": [successful_trajectory.model_dump(), failed_trajectory.model_dump()],
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"workspace_id": WORKSPACE_ID
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}
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try:
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response = requests.post(f"{SERVICE_URL}/summarizer", json=summarizer_request)
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response.raise_for_status()
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result = response.json()
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print(f"✅ Extracted {len(result['experiences'])} step-level experiences")
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for i, experience in enumerate(result['experiences']):
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print(f"\nExperience {i + 1}:")
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print(f" Condition: {experience['experience_desc']}")
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print(f" Content: {experience['experience_content'][:100]}...")
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print(f" Role: {experience['experience_role']}")
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except requests.exceptions.RequestException as e:
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print(f"❌ Error extracting experiences: {e}")
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def example_2_generate_step_context():
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"""Example 2: Generate context from step-level experiences"""
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print("\n=== Example 2: Generate Step-Level Context ===")
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# Create a new trajectory that needs context
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current_trajectory = create_sample_trajectory(
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query="How to implement a quick sort algorithm?",
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steps=[
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Message(
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role=Role.USER,
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content="How to implement a quick sort algorithm?"
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),
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Message(
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role=Role.ASSISTANT,
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content="I need to implement a quick sort algorithm. Let me think about the approach.",
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reasoning_content="Quick sort is a divide-and-conquer algorithm"
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)
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],
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done=False
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)
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# Generate context using context generator
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context_request = {
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"trajectory": current_trajectory.model_dump(),
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"workspace_id": WORKSPACE_ID
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}
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try:
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response = requests.post(f"{SERVICE_URL}/context_generator", json=context_request)
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response.raise_for_status()
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result = response.json()
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context_message = result['context_msg']
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print("✅ Generated step-level context:")
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print(f"Content: {context_message['content']}")
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if 'metadata' in context_message:
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print(f"Metadata: {context_message['metadata']}")
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except requests.exceptions.RequestException as e:
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print(f"❌ Error generating context: {e}")
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def example_3_full_agent_execution():
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"""Example 3: Full agent execution with step-level experience"""
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print("\n=== Example 3: Full Agent Execution with Step Experience ===")
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# Execute agent with step-level context
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agent_request = {
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"query": "Implement a merge sort algorithm with proper error handling",
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"workspace_id": WORKSPACE_ID
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}
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try:
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response = requests.post(f"{SERVICE_URL}/agent_wrapper", json=agent_request)
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response.raise_for_status()
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result = response.json()
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trajectory = result['trajectory']
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print("✅ Agent execution completed:")
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print(f"Query: {trajectory['query']}")
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print(f"Steps: {len(trajectory['steps'])}")
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print(f"Done: {trajectory['done']}")
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print(f"Answer: {trajectory.get('answer', 'No answer')[:200]}...")
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except requests.exceptions.RequestException as e:
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print(f"❌ Error in agent execution: {e}")
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def example_4_custom_configuration():
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"""Example 4: Custom configuration for different use cases"""
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print("\n=== Example 4: Custom Configuration Examples ===")
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# Configuration for research-intensive tasks
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research_config = {
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"context_generator": {
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"backend": "step",
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"enable_llm_rerank": True,
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"enable_context_rewrite": True,
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"enable_score_filter": True,
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"vector_retrieve_top_k": 20,
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"final_top_k": 8,
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"min_score_threshold": 0.2
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},
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"summarizer": {
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"backend": "step",
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"enable_step_segmentation": True,
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"enable_similarity_search": True,
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"enable_experience_validation": True,
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"max_retries": 5
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}
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}
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# Configuration for quick tasks
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quick_config = {
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"context_generator": {
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"backend": "step",
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"enable_llm_rerank": False,
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"enable_context_rewrite": False,
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"enable_score_filter": False,
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"vector_retrieve_top_k": 5,
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"final_top_k": 2,
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"min_score_threshold": 0.5
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},
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"summarizer": {
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"backend": "step",
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"enable_step_segmentation": False,
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"enable_similarity_search": False,
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"enable_experience_validation": False,
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"max_retries": 1
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}
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}
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print("📋 Research-intensive configuration:")
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print(json.dumps(research_config, indent=2))
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print("\n📋 Quick task configuration:")
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print(json.dumps(quick_config, indent=2))
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def main():
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"""Run all examples"""
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print("🚀 Step Experience Service Usage Examples")
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print("=" * 50)
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# Wait for service to be ready
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try:
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response = requests.get(f"{SERVICE_URL}/docs")
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print("✅ Service is running and ready")
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except requests.exceptions.RequestException:
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print("❌ Service is not running. Please start the service first:")
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print(" run.sh")
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return
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# Run examples
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example_1_extract_step_experiences()
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example_2_generate_step_context()
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example_3_full_agent_execution()
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example_4_custom_configuration()
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print("\n🎉 All examples completed!")
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if __name__ == "__main__":
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main()
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27
cookbook/step_agent/run.sh
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cookbook/step_agent/run.sh
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#!/bin/bash
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# Step Experience Service Startup Script
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# This script launches the experiencemaker service with step-level experience extraction and context generation
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# StepSummarizer Parameters
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# enable_step_segmentation: Segment trajectories into meaningful step sequences
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# enable_similarity_search: Compare similar sequences between success/failure
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# enable_experience_validation: Validate experience quality before storage
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# StepContextGenerator Parameters
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# enable_llm_rerank: Rerank retrieved experiences by relevance using LLM
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# enable_context_rewrite: Rewrite context to be more task-specific
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# enable_score_filter: Filter experiences by quality scores
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echo "Starting Step Experience Service..."
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python -m experiencemaker.em_service \
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--port=8001 \
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--llm='{"backend": "openai_compatible", "model_name": "qwen-max-2025-01-25", "temperature": 0.6}' \
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--embedding_model='{"backend": "openai_compatible", "model_name": "text-embedding-v4", "dimensions": 1024}' \
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--vector_store='{"backend": "local_file", "store_dir": "./step_experiences/"}' \
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--agent_wrapper='{"backend": "simple"}' \
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--context_generator='{"backend": "step", "enable_llm_rerank": true, "enable_context_rewrite": true, "enable_score_filter": false, "vector_retrieve_top_k": 15, "final_top_k": 5, "min_score_threshold": 0.3}' \
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--summarizer='{"backend": "step", "enable_step_segmentation": false, "enable_similarity_search": false, "enable_experience_validation": true, "max_retries": 3}'
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echo "Step Experience Service started on port 8001"
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