scriptoriumadmin/ReMe
1
0
Fork 0
mirror of https://github.com/agentscope-ai/ReMe.git synced 2026-09-06 08:16:00 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

add prompts for step extractor&generator

Browse source
This commit is contained in:
鸣山 2025-06-13 15:20:00 +08:00
parent 4dd7e3f856
commit 12a769ea09
2 changed files with 345 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

110
experiencemaker/module/context_generator/step_context_generator_prompt.yaml Normal file
View file

@ -0,0 +1,110 @@
context_rewrite_prompt: |
You are an expert AI assistant tasked with rewriting and reorganizing context content to make it more relevant and actionable for the current task.
Your task is to take the original context (containing multiple experiences) and rewrite it as a cohesive, task-specific guidance that directly addresses the current situation.
REWRITING GUIDELINES:
● TASK ALIGNMENT: Adapt the language and focus to match the current task requirements
● CONSOLIDATION: Merge similar insights and eliminate redundancy
● PRIORITIZATION: Emphasize the most relevant experiences for the current context
● ACTIONABILITY: Make the guidance immediately actionable for the current situation
● FLOW: Create a coherent narrative that builds from general principles to specific actions
# Current Task/Query
{current_query}
# Current Context/Situation
{current_context}
# Original Context Content (Multiple Experiences)
{original_context}
OUTPUT FORMAT:
Provide the rewritten context:
```json
{{
"rewritten_context": "A cohesive, task-specific context message that reorganizes and adapts the original experiences for the current task. This should be written as a unified guidance rather than separate experience items.",
"key_adaptations": [
"How you adapted experience 1 for current task",
"How you consolidated experiences 2 and 3",
"What you emphasized for this specific context"
],
"priority_focus": "The main focus area for this specific task based on the experiences"
}}
```
Guidelines:
- Rewrite as a unified, flowing guidance rather than bullet points
- Adapt terminology and examples to match the current task domain
- Consolidate overlapping insights into coherent recommendations
- Prioritize experiences most relevant to the current situation
- Make the guidance feel custom-written for this specific task
experience_rerank_prompt: |
You are an expert AI analyst tasked with reranking retrieved experiences based on their relevance to a specific query.
Your task is to analyze the candidates and rank them by relevance, considering:
● DIRECT RELEVANCE: How directly applicable the experience is to the current query
● SITUATION SIMILARITY: How similar the experience context is to the current situation
● ACTIONABILITY: How actionable and specific the experience is
● QUALITY: The overall quality and clarity of the experience
# Current Query
{query}
# Candidate Experiences (Total: {num_candidates})
{candidates}
OUTPUT FORMAT:
Provide a ranked list of candidate indices (0-based) from most relevant to least relevant:
```json
{{
"ranked_indices": [2, 0, 4, 1, 3],
"reasoning": "Brief explanation of ranking rationale"
}}
```
Note: Include ALL candidate indices in the ranking, even if some are less relevant.
context_generation_prompt: |
You are an expert AI assistant tasked with synthesizing retrieved experiences into actionable context for an AI agent.
Your task is to create a coherent, actionable context message that helps the agent leverage relevant past experiences.
SYNTHESIS GUIDELINES:
● RELEVANCE FOCUS: Emphasize the most relevant aspects of each experience
● ACTIONABLE INSIGHTS: Extract specific, actionable guidance
● COHERENT NARRATIVE: Create a flowing narrative rather than disconnected tips
● SITUATIONAL AWARENESS: Adapt the guidance to the current situation
# Current Query/Task
{query}
# Current Step Context
{current_step}
# Retrieved Experiences ({num_experiences} total)
{retrieved_experiences}
OUTPUT FORMAT:
Create a synthesized context message:
```json
{{
"context": "A coherent, actionable context message that synthesizes the relevant experiences and provides specific guidance for the current task",
"key_insights": [
"Key pattern from successful approaches",
"Common pitfall to avoid",
"Specific technique that worked well"
],
"recommended_actions": [
"Specific action recommendation based on experiences",
"Decision point with recommended choice"
]
}}
```
Guidelines:
- Make the context immediately actionable
- Prioritize the most relevant experiences
- Use clear, direct language
- Focus on practical guidance rather than abstract principles

235
experiencemaker/module/summarizer/step_summarizer_prompt.yaml Normal file
View file

@ -0,0 +1,235 @@
success_step_experience_prompt: |
You are an expert AI analyst reviewing successful step sequences from an AI agent execution.
Your task is to extract reusable, actionable step-level experiences that can guide future agent executions.
Focus on identifying specific patterns, techniques, and decision points that contributed to success.
ANALYSIS FRAMEWORK:
● STEP PATTERN ANALYSIS: Identify the specific sequence of actions that led to success
● DECISION POINTS: Highlight critical decisions made during these steps
● TECHNIQUE EFFECTIVENESS: Analyze why specific approaches worked well
● REUSABILITY: Extract patterns that can be applied to similar scenarios
EXTRACTION PRINCIPLES:
● Focus on TRANSFERABLE TECHNIQUES and decision frameworks
● Frame insights as actionable guidelines and best practices
# Original Query
{query}
# Step Sequence Analysis
{step_sequence}
# Context Information
{context}
# Outcome
This step sequence was part of a {outcome} trajectory.
OUTPUT FORMAT:
Generate 1-3 step-level success insights as JSON objects:
```json
[
{{
"when_to_use": "Specific conditions when this step pattern should be applied",
"experience": "Detailed description of the successful step pattern and why it works",
"tags": ["relevant", "keywords", "for", "categorization"],
"confidence": 0.8,
"step_type": "reasoning|action|observation|decision",
"tools_used": ["list", "of", "tools"]
}}
]
```
failure_step_experience_prompt: |
You are an expert AI analyst reviewing failed step sequences from an AI agent execution.
Your task is to extract learning experiences from failures to prevent similar mistakes in future executions.
Focus on identifying error patterns, missed opportunities, and alternative approaches.
ANALYSIS FRAMEWORK:
● FAILURE POINT IDENTIFICATION: Pinpoint where and why the steps went wrong
● ERROR PATTERN ANALYSIS: Identify recurring mistakes or problematic approaches
● ALTERNATIVE APPROACHES: Suggest what could have been done differently
● PREVENTION STRATEGIES: Extract actionable insights to avoid similar failures
EXTRACTION PRINCIPLES:
● Extract GENERAL PRINCIPLES as well as SPECIFIC INSTRUCTIONS
● Focus on PATTERNS and RULES as well as particular instances
# Original Query
{query}
# Step Sequence Analysis
{step_sequence}
# Context Information
{context}
# Outcome
This step sequence was part of a {outcome} trajectory.
OUTPUT FORMAT:
Generate 1-3 step-level failure prevention insights as JSON objects:
```json
[
{{
"when_to_use": "Specific situations where this lesson should be remembered",
"experience": "Universal principle or rule extracted from the failure pattern ",
"tags": ["error_prevention", "failure_analysis", "relevant_keywords"],
"confidence": 0.7,
"step_type": "reasoning|action|observation|decision",
"tools_used": ["list", "of", "tools"]
}}
]
```
comparative_step_experience_prompt: |
You are an expert AI analyst comparing successful and failed step sequences to extract differential insights.
Your task is to identify the key differences between success and failure patterns at the step level.
Focus on critical decision points, technique variations, and approach differences.
COMPARATIVE ANALYSIS FRAMEWORK:
● DECISION CONTRAST: Compare critical decisions made in success vs failure cases
● TECHNIQUE VARIATIONS: Identify different approaches and their outcomes
● TIMING DIFFERENCES: Analyze when certain actions were taken and their impact
● SUCCESS FACTORS: Extract what specifically made the difference
EXTRACTION PRINCIPLES:
● Frame comparisons as PRINCIPLES as well as case-specific SOLUTIONS
● Identify PATTERNS that differentiate effective vs ineffective approaches
● Extract RULES that can guide future similar situations
● Focus on UNDERLYING MECHANISMS rather than surface-level differences
# Successful Step Sequence
{success_steps}
# Failed Step Sequence
{failure_steps}
# Similarity Score: {similarity_score}
OUTPUT FORMAT:
Generate 1-2 comparative insights as JSON objects:
```json
[
{{
"when_to_use": "Specific scenarios where this comparative insight applies",
"experience": "Detailed comparison highlighting why success approach works better",
"tags": ["comparative_analysis", "success_factors", "relevant_keywords"],
"confidence": 0.8,
"step_type": "reasoning|action|observation|decision"
}}
]
```
general_step_experience_prompt: |
You are an expert AI analyst reviewing step sequences to extract general patterns and insights.
Your task is to identify valuable step-level patterns without explicit success/failure labels.
Focus on effective techniques, common patterns, and general best practices.
ANALYSIS FRAMEWORK:
● PATTERN RECOGNITION: Identify recurring effective patterns in the steps
● TECHNIQUE ANALYSIS: Analyze the effectiveness of different approaches
● BEST PRACTICES: Extract general principles that appear beneficial
● APPLICABILITY: Determine when these patterns would be most useful
GENERALIZATION PRINCIPLES:
● Extract UNIVERSAL PATTERNS that transcend specific contexts
● Identify TRANSFERABLE METHODOLOGIES and approaches
● Focus on PRINCIPLE-LEVEL insights as well as tactical details
● Formulate insights as REUSABLE FRAMEWORKS and guidelines
GENERALIZATION PRINCIPLES:
● Extract UNIVERSAL PATTERNS that transcend specific contexts
● Identify TRANSFERABLE METHODOLOGIES and approaches
● Focus on PRINCIPLE-LEVEL insights
● Formulate insights as REUSABLE FRAMEWORKS and guidelines
# Original Query
{query}
# Step Sequence Analysis
{step_sequence}
# Context Information
{context}
OUTPUT FORMAT:
Generate 1-2 general step insights as JSON objects:
```json
[
{{
"when_to_use": "General conditions where this pattern is applicable",
"experience": "Detailed description of the effective step pattern",
"tags": ["general_pattern", "best_practice", "relevant_keywords"],
"confidence": 0.6,
"step_type": "reasoning|action|observation|decision",
"tools_used": ["list", "of", "tools"]
}}
]
```
step_segmentation_prompt: |
You are an expert AI analyst tasked with segmenting a trajectory into meaningful step sequences.
Your task is to identify natural breakpoints in the execution where one logical unit of work ends and another begins.
Consider factors like: task completion, context switches, tool changes, reasoning phases, and logical groupings.
SEGMENTATION CRITERIA:
● LOGICAL COMPLETION: Steps that complete a specific sub-task or reasoning phase
● CONTEXT SWITCHES: Points where the agent shifts focus or approach
● TOOL BOUNDARIES: Natural breaks around tool usage patterns
● REASONING PHASES: Distinct phases of analysis, planning, or execution
# Original Query
{query}
# Full Trajectory (Total steps: {total_steps})
{trajectory_content}
OUTPUT FORMAT:
Provide segmentation points as a JSON array of step indices where splits should occur:
```json
{{
"segment_points": [3, 7, 12, 18],
"reasoning": "Brief explanation of segmentation logic"
}}
```
Note: Segment points indicate the END of each segment. For example, [3, 7] means:
- Segment 1: steps 0-3
- Segment 2: steps 4-7
- Segment 3: steps 8-end
experience_validation_prompt: |
You are an expert AI analyst tasked with validating the quality and usefulness of extracted step-level experiences.
Your task is to assess whether the extracted experience is actionable, accurate, and valuable for future agent executions.
VALIDATION CRITERIA:
● ACTIONABILITY: Is the experience specific enough to guide future actions?
● ACCURACY: Does the experience correctly reflect the patterns observed?
● RELEVANCE: Is the experience applicable to similar future scenarios?
● CLARITY: Is the experience clearly articulated and understandable?
● UNIQUENESS: Does the experience provide novel insights or common knowledge?
# Experience to Validate
Condition: {condition}
Experience Content: {experience_content}
OUTPUT FORMAT:
Provide validation assessment:
```json
{{
"is_valid": true/false,
"score": 0.8,
"feedback": "Detailed explanation of validation decision",
"recommendations": "Suggestions for improvement if applicable"
}}
```
Score should be between 0.0 (poor quality) and 1.0 (excellent quality).
Mark as invalid if score is below 0.3 or if there are fundamental issues with the experience.