语义情报@:部分 @ 9 @ @ sselfK2SelfK}通过线性治疗工具#-Aware Prutning
当工具自我破损时 @ ,% 您的系统应该记住为什么并且永远不再重犯错误
何时 *DiSE 日志 谋杀
> **注::** 这是DISE' 下一个进化飞跃的投机性设计 —a a self\ -Healing 工具生态系统,跟踪线条{,} 检测bugs,prunes 失败的分支MS K5 并永远从错误中吸取教训
打破自我的工具
在这里,'}一个让我在晚上睡不着的假想
Tool: data_validator_v2.3.0
Status: Working perfectly ✓
Evolution triggered: "Optimize for speed"
↓
Tool: data_validator_v2.4.0
Status: 40% faster! ✓
Side effect: Now accepts invalid emails ✗
Applications using v2.4.0: 47
Bugs introduced: 47
Developer frustration: ∞
目前的 DISE 系统可以发展工具来更好地改进@.}但是当进化形成它们时会发生什么呢? 更严重“?”如果优化引入一个关键错误“? ” 如果工具突变打破了生产系统 ‘?’?
现在 , 我们检测到失败了 也许升级了 ,也许手动修好它
但是我们不做 学习 以更深层的,结构方式
我们不做 'T:
- 记住 突变导致错误
- 预防 相关工具的类似突变
- 批量 进化树的分支失败
- 宣传 了解后继工具
- 自动-恢复 由已知的"-"好祖先再生
从本质上说,我们没有创造出一种疫苗 并配有相关的检测系统 和对一个固定的....的研究
今日的改变
这是如何设计它的设计 能够 工作=.
Big Idea @ :工具作为 Git DAG Q+进化内存
将DISE中的每一个工具都视为 Git- 的节点,
graph TD
A[validator_v1.0.0<br/>Initial implementation] --> B[validator_v1.1.0<br/>Added regex patterns]
A --> C[validator_v1.0.1<br/>Bug fix: null handling]
B --> D[validator_v2.0.0<br/>Rewrote for performance]
C --> D
D --> E[validator_v2.1.0<br/>Added email validation]
E --> F[validator_v2.2.0<br/>💥 BUG: Accepts invalid emails]
F -.-> |Detected failure| G[validator_v2.2.1<br/>Auto-regenerated from v2.1.0]
style F stroke:#c92a2a,stroke-width:3px
style G stroke:#2f9e44,stroke-width:3px
每个工具都知道:
- 他从何而来 父母节点 家长节点
- 变化什么 ( 变换元数据 @)
- 失败的 (bug历史)
- 如何避免 @(_ 继承警告@)}
当检测到一个关键错误时 @ , @ the system@ MS K1 {
- 指定故障点 - 哪个版本引入了bug?
- 事务组 - 标记失败版本, 后代被污染
- 预防警告 -Adds @"aalese@"标签以防止类似的突变
- 自动-再生器 \ - 创建来自上一个已知的{-}{好祖先的新版本
- 更新线条 记录进化树的失败
结果 #:\ 一个自我的 - healing system 让错误变成永久的机构记忆
数据结构:工具线条元数据
第一,我们需要比我们目前多跟踪更多 比我们现在做 . n这里>'}这是加强的元数据看起来像什么:
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Set
from datetime import datetime
from enum import Enum
class NodeHealth(Enum):
HEALTHY = "healthy"
DEGRADED = "degraded"
FAILED = "failed"
PRUNED = "pruned"
REGENERATED = "regenerated"
class MutationType(Enum):
OPTIMIZATION = "optimization"
BUG_FIX = "bug_fix"
FEATURE_ADD = "feature_add"
REFACTOR = "refactor"
SECURITY_PATCH = "security"
@dataclass
class MutationRecord:
"""Record of what changed in this evolution"""
mutation_type: MutationType
description: str
timestamp: datetime
fitness_before: float
fitness_after: float
code_diff_hash: str
prompt_used: str
@dataclass
class FailureRecord:
"""Record of a bug or failure"""
failure_type: str
description: str
stack_trace: Optional[str]
test_case_failed: Optional[str]
detection_method: str # "test", "runtime", "static_analysis"
timestamp: datetime
severity: str # "critical", "high", "medium", "low"
@dataclass
class AvoidanceRule:
"""Rules about what NOT to do (learned from failures)"""
rule_id: str
description: str
pattern_to_avoid: str # Regex or semantic description
reason: str # Why this is bad
source_failure: str # Which node failure created this rule
propagation_scope: str # "descendants", "all_similar", "global"
created_at: datetime
@dataclass
class ToolLineage:
"""Complete lineage and health tracking for a tool"""
# Identity
tool_id: str
version: str
full_name: str # e.g., "data_validator_v2.2.0"
# Lineage
parent_ids: List[str] = field(default_factory=list)
child_ids: List[str] = field(default_factory=list)
ancestor_path: List[str] = field(default_factory=list) # Path to root
# Health
health_status: NodeHealth = NodeHealth.HEALTHY
failure_count: int = 0
failures: List[FailureRecord] = field(default_factory=list)
# Evolution
mutations: List[MutationRecord] = field(default_factory=list)
generation: int = 0 # Distance from root
# Learning
avoidance_rules: List[AvoidanceRule] = field(default_factory=list)
inherited_rules: Set[str] = field(default_factory=set) # Rule IDs from ancestors
# Performance
fitness_history: List[float] = field(default_factory=list)
execution_count: int = 0
success_rate: float = 1.0
# Metadata
created_at: datetime = field(default_factory=datetime.now)
last_executed: Optional[datetime] = None
pruned_at: Optional[datetime] = None
regenerated_from: Optional[str] = None
这是 批数 数据比我们目前追踪的数据多 但对于真实的自我来说,它就是必需的
我们怎么知道什么破碎的? ?吗?
可以通过多个渠道检测到关键虫子@: @%
@1. 测试失败 (#Immediate controduction@)#
class TestBasedDetection:
"""Detect bugs through test execution"""
async def validate_tool_health(
self,
tool_id: str,
lineage: ToolLineage
) -> Optional[FailureRecord]:
"""Run all tests and detect failures"""
# Load tool and its test suite
tool = await self.tools_manager.load_tool(tool_id)
test_suite = await self.test_discovery.find_tests(tool)
results = await self.test_runner.run_tests(test_suite)
# Check for test failures
if results.failed_count > 0:
critical_failures = [
test for test in results.failures
if test.is_critical # BDD scenarios, core functionality
]
if critical_failures:
return FailureRecord(
failure_type="test_failure",
description=f"{len(critical_failures)} critical tests failed",
test_case_failed=critical_failures[0].name,
stack_trace=critical_failures[0].stack_trace,
detection_method="test",
timestamp=datetime.now(),
severity="critical"
)
return None
async def regression_detection(
self,
new_version: str,
old_version: str
) -> Optional[FailureRecord]:
"""Detect if new version broke what old version did correctly"""
# Get test results for both versions
old_results = await self.get_cached_test_results(old_version)
new_results = await self.test_runner.run_tests(new_version)
# Find tests that USED to pass but now fail
regressions = [
test for test in old_results.passed
if test.name in [f.name for f in new_results.failures]
]
if regressions:
return FailureRecord(
failure_type="regression",
description=f"Broke {len(regressions)} previously working tests",
test_case_failed=regressions[0].name,
detection_method="regression_test",
timestamp=datetime.now(),
severity="critical"
)
return None
-=YTET -伊甸园字幕组=- 翻译:
class RuntimeMonitoring:
"""Detect bugs through execution monitoring"""
def __init__(self):
self.error_threshold = 0.05 # 5% error rate triggers investigation
self.execution_window = 100 # Last 100 executions
async def monitor_tool_health(
self,
tool_id: str,
lineage: ToolLineage
) -> Optional[FailureRecord]:
"""Monitor runtime behavior for anomalies"""
# Get recent execution history
recent_runs = await self.bugcatcher.get_recent_executions(
tool_id,
limit=self.execution_window
)
if len(recent_runs) < 10:
return None # Not enough data
# Calculate error rate
error_count = sum(1 for run in recent_runs if run.had_error)
error_rate = error_count / len(recent_runs)
if error_rate > self.error_threshold:
# Analyze error patterns
error_types = {}
for run in recent_runs:
if run.had_error:
error_types[run.error_type] = error_types.get(run.error_type, 0) + 1
most_common_error = max(error_types.items(), key=lambda x: x[1])
return FailureRecord(
failure_type="runtime_errors",
description=f"Error rate {error_rate:.1%} exceeds threshold",
stack_trace=recent_runs[-1].stack_trace if recent_runs[-1].had_error else None,
detection_method="runtime",
timestamp=datetime.now(),
severity="high" if error_rate > 0.20 else "medium"
)
# Check for performance degradation
if len(lineage.fitness_history) >= 5:
recent_fitness = lineage.fitness_history[-5:]
avg_recent = sum(recent_fitness) / len(recent_fitness)
historical_fitness = lineage.fitness_history[:-5]
avg_historical = sum(historical_fitness) / len(historical_fitness)
degradation = (avg_historical - avg_recent) / avg_historical
if degradation > 0.30: # 30% performance drop
return FailureRecord(
failure_type="performance_degradation",
description=f"Performance dropped {degradation:.1%}",
detection_method="runtime",
timestamp=datetime.now(),
severity="medium"
)
return None
-=YTET -伊甸园字幕组=- 翻译:
class StaticAnalysisDetection:
"""Detect potential bugs through static analysis"""
async def analyze_tool_safety(
self,
tool_id: str,
code: str
) -> Optional[FailureRecord]:
"""Run static analysis to find potential bugs"""
# Run pylint, mypy, bandit
static_runner = StaticAnalysisRunner()
results = await static_runner.analyze_code(code)
# Check for critical issues
critical_issues = [
issue for issue in results.issues
if issue.severity in ["error", "critical"]
]
if critical_issues:
return FailureRecord(
failure_type="static_analysis",
description=f"Found {len(critical_issues)} critical static issues",
detection_method="static_analysis",
timestamp=datetime.now(),
severity="high"
)
# Check for security vulnerabilities
security_issues = [
issue for issue in results.issues
if issue.category == "security"
]
if security_issues:
return FailureRecord(
failure_type="security_vulnerability",
description=f"Found {len(security_issues)} security issues",
detection_method="static_analysis",
timestamp=datetime.now(),
severity="critical"
)
return None
自 己 - @ healing loop:}探测 MS K2_ Prinning *→}恢复
当一个关键错误被检测到时, 魔法就会发生@ . @ @ label
sequenceDiagram
participant Tool as Tool Execution
participant Monitor as Health Monitor
participant Lineage as Lineage Tracker
participant Pruner as Branch Pruner
participant Generator as Auto-Regenerator
participant RAG as RAG Memory
Tool->>Monitor: Execute tool_v2.2.0
Monitor->>Monitor: Detect critical failure
Monitor->>Lineage: Report failure for tool_v2.2.0
Lineage->>Lineage: Identify failure point in DAG
Lineage->>Pruner: Trigger pruning for failed branch
Pruner->>Pruner: Mark v2.2.0 as PRUNED
Pruner->>Pruner: Mark descendants as TAINTED
Pruner->>Pruner: Extract mutation that caused bug
Pruner->>Lineage: Create avoidance rule
Lineage->>Lineage: Propagate rule to all descendants
Pruner->>Generator: Request regeneration from v2.1.0
Generator->>RAG: Load v2.1.0 as base
Generator->>Generator: Generate v2.2.1 avoiding known bug
Generator->>Monitor: Test v2.2.1
Monitor->>Monitor: All tests pass ✓
Generator->>Lineage: Register v2.2.1 as recovery
Lineage->>RAG: Update canonical version
RAG->>Tool: Route requests to v2.2.1
执行@: @%
class SelfHealingOrchestrator:
"""Orchestrates the complete self-healing loop"""
def __init__(
self,
tools_manager: ToolsManager,
lineage_tracker: LineageTracker,
health_monitor: HealthMonitor,
rag_memory: QdrantRAGMemory
):
self.tools_manager = tools_manager
self.lineage_tracker = lineage_tracker
self.health_monitor = health_monitor
self.rag_memory = rag_memory
self.pruner = BranchPruner(lineage_tracker)
self.regenerator = AutoRegenerator(tools_manager, rag_memory)
async def handle_failure(
self,
tool_id: str,
failure: FailureRecord
) -> Optional[str]:
"""
Complete self-healing cycle:
1. Detect failure (already done, passed in)
2. Prune failed branch
3. Create avoidance rules
4. Regenerate from last known-good
5. Validate recovery
6. Update routing
"""
logger.critical(f"Self-healing triggered for {tool_id}: {failure.description}")
# Step 1: Get lineage information
lineage = await self.lineage_tracker.get_lineage(tool_id)
# Step 2: Mark failure in lineage
lineage.health_status = NodeHealth.FAILED
lineage.failures.append(failure)
lineage.failure_count += 1
await self.lineage_tracker.update(lineage)
# Step 3: Identify what went wrong
failure_analysis = await self.analyze_failure(tool_id, failure, lineage)
if not failure_analysis.is_recoverable:
logger.error(f"Failure is not auto-recoverable: {failure_analysis.reason}")
return None
# Step 4: Prune the failed branch
pruning_result = await self.pruner.prune_branch(
failed_node=tool_id,
failure=failure,
lineage=lineage
)
# Step 5: Create avoidance rules
avoidance_rule = await self.create_avoidance_rule(
failure=failure,
analysis=failure_analysis,
pruning_result=pruning_result
)
# Step 6: Propagate avoidance rule to descendants
await self.lineage_tracker.propagate_rule(
rule=avoidance_rule,
scope=avoidance_rule.propagation_scope
)
# Step 7: Find last known-good ancestor
last_good_ancestor = await self.find_last_healthy_ancestor(lineage)
if not last_good_ancestor:
logger.error(f"No healthy ancestor found for {tool_id}")
return None
logger.info(f"Regenerating from {last_good_ancestor}")
# Step 8: Regenerate from healthy ancestor
new_version = await self.regenerator.regenerate_from_ancestor(
ancestor_id=last_good_ancestor,
original_goal=lineage.mutations[-1].description,
avoid_rules=[avoidance_rule]
)
if not new_version:
logger.error("Regeneration failed")
return None
# Step 9: Validate the regenerated version
validation_result = await self.health_monitor.validate_tool(new_version)
if not validation_result.is_healthy:
logger.error(f"Regenerated tool still unhealthy: {validation_result.issues}")
return None
# Step 10: Update lineage to mark recovery
new_lineage = await self.lineage_tracker.get_lineage(new_version)
new_lineage.health_status = NodeHealth.REGENERATED
new_lineage.regenerated_from = last_good_ancestor
new_lineage.inherited_rules.add(avoidance_rule.rule_id)
await self.lineage_tracker.update(new_lineage)
# Step 11: Update RAG routing to prefer new version
await self.rag_memory.mark_as_preferred(new_version)
await self.rag_memory.deprecate_version(tool_id)
logger.success(f"Self-healing complete: {tool_id} → {new_version}")
return new_version
async def analyze_failure(
self,
tool_id: str,
failure: FailureRecord,
lineage: ToolLineage
) -> FailureAnalysis:
"""Use LLM to analyze what went wrong"""
# Get the code for failed and parent versions
failed_code = await self.tools_manager.get_tool_code(tool_id)
if not lineage.parent_ids:
return FailureAnalysis(
is_recoverable=False,
reason="No parent to recover from"
)
parent_id = lineage.parent_ids[0]
parent_code = await self.tools_manager.get_tool_code(parent_id)
# Get the mutation that was applied
last_mutation = lineage.mutations[-1] if lineage.mutations else None
# Ask overseer LLM to analyze
analysis_prompt = f"""
Analyze this tool failure:
FAILED TOOL: {tool_id}
FAILURE: {failure.description}
FAILURE TYPE: {failure.failure_type}
PARENT TOOL: {parent_id}
MUTATION APPLIED: {last_mutation.description if last_mutation else "Unknown"}
CODE DIFF:
{self.generate_diff(parent_code, failed_code)}
STACK TRACE:
{failure.stack_trace or "None"}
Questions:
1. What specific change caused the failure?
2. Was it the mutation itself, or a side effect?
3. Can we regenerate from the parent with a better approach?
4. What should we avoid in future mutations?
Provide a structured analysis.
"""
analysis_result = await self.overseer_llm.analyze(
analysis_prompt,
response_model=FailureAnalysis
)
return analysis_result
async def create_avoidance_rule(
self,
failure: FailureRecord,
analysis: FailureAnalysis,
pruning_result: PruningResult
) -> AvoidanceRule:
"""Create a rule to prevent similar failures"""
# Extract pattern from analysis
pattern = analysis.problematic_pattern
return AvoidanceRule(
rule_id=f"avoid_{uuid.uuid4().hex[:8]}",
description=analysis.rule_description,
pattern_to_avoid=pattern,
reason=failure.description,
source_failure=pruning_result.failed_node_id,
propagation_scope="descendants", # Or "all_similar" for broader impact
created_at=datetime.now()
)
async def find_last_healthy_ancestor(
self,
lineage: ToolLineage
) -> Optional[str]:
"""Walk up the lineage tree to find last healthy node"""
# Start with immediate parents
for parent_id in lineage.parent_ids:
parent_lineage = await self.lineage_tracker.get_lineage(parent_id)
if parent_lineage.health_status == NodeHealth.HEALTHY:
# Verify it still works
validation = await self.health_monitor.validate_tool(parent_id)
if validation.is_healthy:
return parent_id
# If parents are unhealthy, recurse up the tree
for parent_id in lineage.parent_ids:
parent_lineage = await self.lineage_tracker.get_lineage(parent_id)
ancestor = await self.find_last_healthy_ancestor(parent_lineage)
if ancestor:
return ancestor
return None
防止永远变异
pruner 标记失败的分支, 无法使用@ : @
class BranchPruner:
"""Prunes failed branches from the evolutionary tree"""
def __init__(self, lineage_tracker: LineageTracker):
self.lineage_tracker = lineage_tracker
async def prune_branch(
self,
failed_node: str,
failure: FailureRecord,
lineage: ToolLineage
) -> PruningResult:
"""
Prune a failed branch:
1. Mark the failed node as PRUNED
2. Mark all descendants as TAINTED
3. Remove from active routing
4. Preserve for learning (don't delete!)
"""
logger.warning(f"Pruning branch starting at {failed_node}")
# Mark the failed node
lineage.health_status = NodeHealth.PRUNED
lineage.pruned_at = datetime.now()
await self.lineage_tracker.update(lineage)
# Find all descendants
descendants = await self.lineage_tracker.get_all_descendants(failed_node)
pruned_count = 1
tainted_count = 0
# Mark descendants as tainted (they inherit the bug)
for descendant_id in descendants:
descendant = await self.lineage_tracker.get_lineage(descendant_id)
if descendant.health_status == NodeHealth.HEALTHY:
descendant.health_status = NodeHealth.DEGRADED
descendant.inherited_rules.add(f"tainted_by_{failed_node}")
await self.lineage_tracker.update(descendant)
tainted_count += 1
# Remove from RAG active routing
await self.rag_memory.mark_as_inactive(failed_node)
for descendant_id in descendants:
await self.rag_memory.mark_as_inactive(descendant_id)
logger.info(f"Pruned 1 node, tainted {tainted_count} descendants")
return PruningResult(
failed_node_id=failed_node,
pruned_count=pruned_count,
tainted_count=tainted_count,
descendants=descendants,
failure=failure
)
async def can_reuse_tool(
self,
tool_id: str,
context: Dict
) -> Tuple[bool, Optional[str]]:
"""Check if a tool is safe to reuse (not pruned or tainted)"""
lineage = await self.lineage_tracker.get_lineage(tool_id)
if lineage.health_status == NodeHealth.PRUNED:
return False, f"Tool {tool_id} has been pruned due to critical bug"
if lineage.health_status == NodeHealth.FAILED:
return False, f"Tool {tool_id} has known failures"
if lineage.health_status == NodeHealth.DEGRADED:
# Check if degradation is relevant to current context
for rule_id in lineage.inherited_rules:
rule = await self.lineage_tracker.get_rule(rule_id)
if self.rule_applies_to_context(rule, context):
return False, f"Tool is tainted by rule: {rule.description}"
return True, None
创建更好的版本
当一个工具失败时 , regenization from a health screw store 以避免规则从健康的祖先中再生\ MPK1\
class AutoRegenerator:
"""Regenerates tools from healthy ancestors with learned constraints"""
def __init__(
self,
tools_manager: ToolsManager,
rag_memory: QdrantRAGMemory
):
self.tools_manager = tools_manager
self.rag_memory = rag_memory
async def regenerate_from_ancestor(
self,
ancestor_id: str,
original_goal: str,
avoid_rules: List[AvoidanceRule]
) -> Optional[str]:
"""
Regenerate a tool from a healthy ancestor, avoiding known pitfalls
"""
# Load ancestor code and metadata
ancestor_tool = await self.tools_manager.load_tool(ancestor_id)
ancestor_code = ancestor_tool.implementation
ancestor_spec = ancestor_tool.specification
# Build avoidance constraints
avoidance_constraints = self.build_avoidance_prompt(avoid_rules)
# Create regeneration spec
regen_spec = f"""
Original Goal: {original_goal}
Base Implementation: {ancestor_id}
{ancestor_code}
CRITICAL CONSTRAINTS - MUST AVOID:
{avoidance_constraints}
Task: Regenerate this tool with the original goal, but strictly avoiding the patterns above.
The previous attempt failed because it violated these constraints.
Approach:
1. Achieve the original goal (performance, features, etc.)
2. Absolutely avoid the prohibited patterns
3. Maintain all existing test compatibility
4. Add safeguards to prevent the specific failure mode
Generate an improved version that achieves the goal safely.
"""
# Use overseer to create careful specification
overseer_result = await self.overseer_llm.plan(
regen_spec,
response_model=ToolSpecification
)
# Generate code with strict validation
generator_result = await self.generator_llm.generate(
specification=overseer_result,
base_code=ancestor_code,
avoid_patterns=[rule.pattern_to_avoid for rule in avoid_rules]
)
if not generator_result.success:
logger.error(f"Regeneration failed: {generator_result.error}")
return None
# Create new version ID
ancestor_version = parse_version(ancestor_id)
new_version = increment_patch(ancestor_version)
new_tool_id = f"{ancestor_tool.name}_{new_version}"
# Register the new tool
await self.tools_manager.register_tool(
tool_id=new_tool_id,
code=generator_result.code,
specification=overseer_result,
metadata={
"regenerated_from": ancestor_id,
"avoidance_rules": [r.rule_id for r in avoid_rules],
"regeneration_reason": "self_healing"
}
)
logger.success(f"Regenerated {new_tool_id} from {ancestor_id}")
return new_tool_id
def build_avoidance_prompt(self, avoid_rules: List[AvoidanceRule]) -> str:
"""Build a clear prompt about what to avoid"""
constraints = []
for i, rule in enumerate(avoid_rules, 1):
constraints.append(f"""
{i}. AVOID: {rule.description}
Pattern: {rule.pattern_to_avoid}
Reason: {rule.reason}
Source: {rule.source_failure}
""")
return "\n".join(constraints)
机构内存
杀手的特征: 从失败中学到的规则 通过树枝传播
class LineageTracker:
"""Tracks tool lineage and propagates learning"""
async def propagate_rule(
self,
rule: AvoidanceRule,
scope: str
):
"""
Propagate an avoidance rule through the lineage tree
Scopes:
- "descendants": Only affect direct descendants of failed node
- "all_similar": Affect all tools in similar semantic space
- "global": Affect all tools (for critical security issues)
"""
if scope == "descendants":
await self._propagate_to_descendants(rule)
elif scope == "all_similar":
await self._propagate_to_similar(rule)
elif scope == "global":
await self._propagate_globally(rule)
async def _propagate_to_descendants(self, rule: AvoidanceRule):
"""Add rule to all descendants of the source failure"""
source_node = rule.source_failure
descendants = await self.get_all_descendants(source_node)
for descendant_id in descendants:
lineage = await self.get_lineage(descendant_id)
lineage.inherited_rules.add(rule.rule_id)
await self.update(lineage)
logger.info(f"Propagated rule {rule.rule_id} to {len(descendants)} descendants")
async def _propagate_to_similar(self, rule: AvoidanceRule):
"""Add rule to semantically similar tools"""
# Find similar tools using RAG
similar_tools = await self.rag_memory.find_similar(
query=rule.description,
filter={"type": "tool"},
top_k=50,
similarity_threshold=0.7
)
for tool_result in similar_tools:
tool_id = tool_result.id
lineage = await self.get_lineage(tool_id)
lineage.inherited_rules.add(rule.rule_id)
await self.update(lineage)
logger.info(f"Propagated rule {rule.rule_id} to {len(similar_tools)} similar tools")
async def _propagate_globally(self, rule: AvoidanceRule):
"""Add rule to ALL tools (for critical security issues)"""
all_tools = await self.get_all_tools()
for tool_id in all_tools:
lineage = await self.get_lineage(tool_id)
lineage.inherited_rules.add(rule.rule_id)
await self.update(lineage)
logger.warning(f"Propagated GLOBAL rule {rule.rule_id} to {len(all_tools)} tools")
RealQM-世界示例_:电子邮件校验器进化错误
让'*s走过一个完整的例子\ :}
# Initial healthy tool
email_validator_v1_0_0 = """
def validate_email(email: str) -> bool:
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
return bool(re.match(pattern, email))
"""
# Tests pass, fitness: 0.85
# Auto-evolution triggers: "Optimize for performance"
# System generates v2.0.0
email_validator_v2_0_0 = """
def validate_email(email: str) -> bool:
# Optimized: skip regex for obvious cases
if '@' not in email:
return False
return True # ⚠️ BUG: Too permissive!
"""
# Tests initially pass (basic tests), fitness: 0.95 (faster!)
# Deployed to production...
# Runtime monitoring detects failures
runtime_errors = [
"Accepted 'user@@domain.com'",
"Accepted '@domain.com'",
"Accepted 'user@'",
]
# Self-healing triggered!
failure = FailureRecord(
failure_type="logic_error",
description="Email validation too permissive, accepts invalid emails",
detection_method="runtime",
severity="critical"
)
# System analyzes failure
analysis = """
The optimization removed the comprehensive regex validation in favor of
a simple '@' check. This makes it fast but incorrect.
Problematic Pattern: "Replacing comprehensive validation with simple substring checks"
Avoidance Rule: "Never replace regex validation with simple string checks without
comprehensive test coverage for edge cases"
"""
# Branch pruning
# - Mark v2.0.0 as PRUNED
# - Create avoidance rule
# - Propagate to all email-related validators
# Auto-regeneration from v1.0.0
email_validator_v2_0_1 = """
def validate_email(email: str) -> bool:
# Optimized: compile regex once
if not hasattr(validate_email, '_pattern'):
validate_email._pattern = re.compile(
r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
)
# Fast path for obvious failures
if '@' not in email or email.count('@') != 1:
return False
# Comprehensive validation (cached pattern)
return bool(validate_email._pattern.match(email))
"""
# Tests pass, fitness: 0.92 (faster AND correct!)
# Deployed, monitored, succeeds!
系统学习了:
- ✅ 从未 以简单检查取代全面验证
- ✅ 总是 优化时保持测试覆盖率
- ✅ 缓存快取 而不是简化逻辑的汇编模式
- ✅ 添加新增 全面检查前的路径检查@, @ not instate of them
这一知识现已成为永久的机构记忆 , 向所有类似的工具传播 *.
可视化自 自我--治疗生态系统
这里#' @%s 整个系统看起来如何 @:}
graph TB
subgraph "Detection Layer"
Tests[Test Suite<br/>BDD, Unit, Integration]
Runtime[Runtime Monitor<br/>Error rates, performance]
Static[Static Analysis<br/>Pylint, mypy, bandit]
end
subgraph "Analysis Layer"
Detect[Failure Detection]
Analyze[LLM Analysis<br/>What went wrong?]
Classify[Severity Classification<br/>Critical/High/Medium/Low]
end
subgraph "Lineage Layer"
DAG[Tool Lineage DAG]
Rules[Avoidance Rules]
History[Failure History]
end
subgraph "Healing Layer"
Prune[Branch Pruner]
Propagate[Rule Propagation]
Regen[Auto-Regenerator]
end
subgraph "Validation Layer"
Validate[Health Validation]
Deploy[Safe Deployment]
Monitor[Continuous Monitoring]
end
Tests --> Detect
Runtime --> Detect
Static --> Detect
Detect --> Analyze
Analyze --> Classify
Classify --> DAG
DAG --> Prune
Prune --> Rules
Rules --> Propagate
Propagate --> DAG
Prune --> Regen
Rules --> Regen
Regen --> Validate
Validate --> Deploy
Deploy --> Monitor
Monitor --> Runtime
style Detect stroke:#c92a2a,stroke-width:3px
style Regen stroke:#2f9e44,stroke-width:3px
style Validate stroke:#1971c2,stroke-width:3px
与现有排放源的整合
美丽的部分=:}这以我们已经拥有的东西为基础
class EnhancedToolsManager(ToolsManager):
"""Extended ToolsManager with self-healing capabilities"""
def __init__(self, config: ConfigManager, *args, **kwargs):
super().__init__(config, *args, **kwargs)
# New components
self.lineage_tracker = LineageTracker(
storage_path="lineage/",
rag_memory=self.rag_memory
)
self.health_monitor = HealthMonitor(
test_runner=self.test_runner,
bugcatcher=self.bugcatcher,
static_runner=self.static_runner
)
self.self_healing = SelfHealingOrchestrator(
tools_manager=self,
lineage_tracker=self.lineage_tracker,
health_monitor=self.health_monitor,
rag_memory=self.rag_memory
)
# Enable continuous health monitoring
self.start_health_monitoring()
async def call_tool(self, tool_id: str, inputs: Dict) -> Any:
"""Override to add health checks and auto-recovery"""
# Check if tool is safe to use
can_use, reason = await self.self_healing.pruner.can_reuse_tool(
tool_id,
context=inputs
)
if not can_use:
# Tool is pruned, find alternative
logger.warning(f"Tool {tool_id} is unsafe: {reason}")
alternative = await self.find_healthy_alternative(tool_id)
if alternative:
logger.info(f"Using alternative: {alternative}")
tool_id = alternative
else:
raise ToolPrunedError(f"{tool_id} is pruned and no alternative exists")
# Execute tool with monitoring
try:
result = await super().call_tool(tool_id, inputs)
# Record successful execution
await self.lineage_tracker.record_success(tool_id)
return result
except Exception as e:
# Record failure
failure = FailureRecord(
failure_type=type(e).__name__,
description=str(e),
stack_trace=traceback.format_exc(),
detection_method="runtime",
timestamp=datetime.now(),
severity="high"
)
await self.lineage_tracker.record_failure(tool_id, failure)
# Check if this triggers self-healing
lineage = await self.lineage_tracker.get_lineage(tool_id)
if lineage.failure_count >= 3: # Three strikes rule
logger.critical(f"Tool {tool_id} reached failure threshold, triggering self-healing")
# Trigger self-healing in background
asyncio.create_task(
self.self_healing.handle_failure(tool_id, failure)
)
raise
async def find_healthy_alternative(self, pruned_tool_id: str) -> Optional[str]:
"""Find a healthy alternative to a pruned tool"""
# Get tool metadata
tool_metadata = await self.rag_memory.get_metadata(pruned_tool_id)
# Search for similar tools
alternatives = await self.rag_memory.find_similar(
query=tool_metadata.description,
filter={
"type": "tool",
"category": tool_metadata.category
},
top_k=10
)
# Find first healthy alternative
for alt in alternatives:
can_use, _ = await self.self_healing.pruner.can_reuse_tool(
alt.id,
context={}
)
if can_use:
return alt.id
return None
def start_health_monitoring(self):
"""Start background health monitoring"""
async def monitor_loop():
while True:
await asyncio.sleep(300) # Every 5 minutes
# Get all active tools
active_tools = await self.get_active_tools()
for tool_id in active_tools:
# Check health
health_result = await self.health_monitor.check_tool_health(tool_id)
if not health_result.is_healthy:
logger.warning(f"Health check failed for {tool_id}: {health_result.issues}")
# Trigger self-healing if critical
if health_result.severity == "critical":
await self.self_healing.handle_failure(
tool_id,
health_result.failure
)
asyncio.create_task(monitor_loop())
自定义的配置@ - @ 治疗
添加到您的 config.yaml:
self_healing:
enabled: true
detection:
test_based: true
runtime_monitoring: true
static_analysis: true
thresholds:
failure_count_trigger: 3 # Trigger healing after N failures
error_rate_threshold: 0.05 # 5% error rate
performance_degradation: 0.30 # 30% slowdown
pruning:
auto_prune_critical: true
keep_pruned_history: true # Don't delete, learn from it
taint_descendants: true
regeneration:
auto_regenerate: true
max_regeneration_attempts: 3
require_test_validation: true
propagation:
default_scope: "descendants" # or "all_similar" or "global"
critical_failures_global: true # Security issues affect all tools
monitoring:
health_check_interval_seconds: 300 # Every 5 minutes
continuous_monitoring: true
lineage_tracking:
enabled: true
storage_path: "lineage/"
max_history_depth: 100 # How far back to track ancestry
compress_old_lineage: true # Save space for old data
数据库中存储线条
我们需要持续存储线条数据@:
-- Tool lineage table
CREATE TABLE tool_lineage (
tool_id VARCHAR(255) PRIMARY KEY,
version VARCHAR(50),
full_name VARCHAR(255),
health_status VARCHAR(50),
failure_count INTEGER DEFAULT 0,
generation INTEGER DEFAULT 0,
execution_count INTEGER DEFAULT 0,
success_rate FLOAT DEFAULT 1.0,
created_at TIMESTAMP,
last_executed TIMESTAMP,
pruned_at TIMESTAMP,
regenerated_from VARCHAR(255)
);
-- Parent-child relationships
CREATE TABLE lineage_relationships (
id SERIAL PRIMARY KEY,
child_id VARCHAR(255),
parent_id VARCHAR(255),
relationship_type VARCHAR(50), -- 'direct', 'merge', 'fork'
created_at TIMESTAMP,
FOREIGN KEY (child_id) REFERENCES tool_lineage(tool_id),
FOREIGN KEY (parent_id) REFERENCES tool_lineage(tool_id)
);
-- Mutation records
CREATE TABLE mutations (
id SERIAL PRIMARY KEY,
tool_id VARCHAR(255),
mutation_type VARCHAR(50),
description TEXT,
prompt_used TEXT,
code_diff_hash VARCHAR(64),
fitness_before FLOAT,
fitness_after FLOAT,
timestamp TIMESTAMP,
FOREIGN KEY (tool_id) REFERENCES tool_lineage(tool_id)
);
-- Failure records
CREATE TABLE failures (
id SERIAL PRIMARY KEY,
tool_id VARCHAR(255),
failure_type VARCHAR(100),
description TEXT,
stack_trace TEXT,
test_case_failed VARCHAR(255),
detection_method VARCHAR(50),
severity VARCHAR(20),
timestamp TIMESTAMP,
FOREIGN KEY (tool_id) REFERENCES tool_lineage(tool_id)
);
-- Avoidance rules
CREATE TABLE avoidance_rules (
rule_id VARCHAR(255) PRIMARY KEY,
description TEXT,
pattern_to_avoid TEXT,
reason TEXT,
source_failure VARCHAR(255),
propagation_scope VARCHAR(50),
created_at TIMESTAMP,
FOREIGN KEY (source_failure) REFERENCES tool_lineage(tool_id)
);
-- Rule inheritance
CREATE TABLE rule_inheritance (
id SERIAL PRIMARY KEY,
tool_id VARCHAR(255),
rule_id VARCHAR(255),
inherited_at TIMESTAMP,
FOREIGN KEY (tool_id) REFERENCES tool_lineage(tool_id),
FOREIGN KEY (rule_id) REFERENCES avoidance_rules(rule_id)
);
-- Fitness history
CREATE TABLE fitness_history (
id SERIAL PRIMARY KEY,
tool_id VARCHAR(255),
fitness_score FLOAT,
execution_time_ms INTEGER,
memory_usage_mb FLOAT,
timestamp TIMESTAMP,
FOREIGN KEY (tool_id) REFERENCES tool_lineage(tool_id)
);
-- Indexes for performance
CREATE INDEX idx_lineage_health ON tool_lineage(health_status);
CREATE INDEX idx_lineage_version ON tool_lineage(version);
CREATE INDEX idx_relationships_child ON lineage_relationships(child_id);
CREATE INDEX idx_relationships_parent ON lineage_relationships(parent_id);
CREATE INDEX idx_failures_tool ON failures(tool_id);
CREATE INDEX idx_failures_severity ON failures(severity);
CREATE INDEX idx_rules_source ON avoidance_rules(source_failure);
CREATE INDEX idx_inheritance_tool ON rule_inheritance(tool_id);
CREATE INDEX idx_fitness_tool ON fitness_history(tool_id);
CLI: 与自我互动 -疗养
添加新命令到 CLI @ :\
# View lineage for a tool
$ python chat_cli.py lineage data_validator_v2.2.0
Tool Lineage: data_validator_v2.2.0
Status: ❌ PRUNED (Critical failure detected)
Pruned: 2025-01-22 14:23:15
Ancestry:
├─ data_validator_v1.0.0 (✓ Healthy)
├─ data_validator_v1.1.0 (✓ Healthy)
├─ data_validator_v2.0.0 (✓ Healthy)
├─ data_validator_v2.1.0 (✓ Healthy)
└─ data_validator_v2.2.0 (❌ PRUNED) ← You are here
Failures:
1. [2025-01-22 14:20:01] Logic Error: Email validation too permissive
Severity: Critical
Detection: Runtime monitoring
Mutations Applied:
- [2025-01-22 14:15:00] Optimization: Remove regex for simple @ check
Fitness: 0.85 → 0.95
Avoidance Rules Created:
- avoid_3f8a2c1d: Never replace comprehensive validation with simple checks
Propagated to: 12 descendants, 34 similar tools
Recovery:
✓ Auto-regenerated as data_validator_v2.2.1
New version healthy, monitoring...
# View all pruned tools
$ python chat_cli.py pruned
Pruned Tools:
1. data_validator_v2.2.0 (Critical: Logic error)
2. json_parser_v1.5.3 (High: Performance regression)
3. http_client_v3.1.0 (Critical: Security vulnerability)
# View avoidance rules
$ python chat_cli.py rules
Active Avoidance Rules:
1. avoid_3f8a2c1d [DESCENDANTS]
Never replace comprehensive validation with simple checks
Source: data_validator_v2.2.0
Affects: 46 tools
2. avoid_7b2e9f0a [GLOBAL]
Never use eval() on user input
Source: json_parser_v1.5.3
Affects: ALL tools
3. avoid_1c4d8a6f [ALL_SIMILAR]
Always use connection pooling for HTTP clients
Source: http_client_v3.1.0
Affects: 23 tools
# Manually trigger healing
$ python chat_cli.py heal data_validator_v2.2.0
Initiating self-healing for data_validator_v2.2.0...
✓ Failure analysis complete
✓ Branch pruned
✓ Avoidance rule created: avoid_3f8a2c1d
✓ Rule propagated to 46 tools
✓ Regenerated from data_validator_v2.1.0
✓ Validation passed
✓ Deployed as data_validator_v2.2.1
Self-healing complete! New version: data_validator_v2.2.1
# View health report
$ python chat_cli.py health
System Health Report:
Total Tools: 237
Healthy: 229 (96.6%)
Degraded: 5 (2.1%)
Failed: 2 (0.8%)
Pruned: 1 (0.4%)
Recent Failures:
- data_validator_v2.2.0 (Auto-healed ✓)
- api_client_v1.3.2 (Monitoring...)
Auto-Healing Stats:
Total healing events: 8
Successful recoveries: 7 (87.5%)
Failed recoveries: 1 (12.5%)
Avg recovery time: 45 seconds
究竟有什么效果呢?
让我们让MSKOZ诚实地说,什么是'QS 真实的vs@.}渴望的:
✅ 已工作在DISE
- 工具版本 - 含 hash-基检测的语义版本
- 直线跟踪 - 变化与突变历史
- 基于测试@- @% 的验证 -全面测试发现与执行
- 运行时间监测 -捕虫器跟踪执行和错误
- RAG 内存 -语义存储和检索
- 自动-进化 以突变触发器为基础
🚧 需要实施
- 完整直线 - 目前追蹤最後的10, 需要全棵樹
- 撤销撤销规则 模式储存和传播系统
- 分支补排 - 标记为非活动失败版本@ info
- 自动-再生 由祖先触发的愈合
- 健康监测环 - 背景持续验证
- 规则的传播 - 后代/ 类似/ 全球范围界定
未来展望
- 交叉-系统学习 -多个DISE案例共享规则
- 反对性测试 *- 相互测试脆弱性的工具
- Meta - 革命 -系统正在发展其愈合战略
- 预测性裁剪 @- 检测潜在故障发生前
令人不适的真理
此系统@ , @ 如果已完全执行\ MS K1} 创造出令人不解的事物 * :
工具能记住每个错误并确保永远不再重犯
不只是个别的'. 集体 .
一个工具中的错误作为知识传播到每一个类似的工具.}一个在任何地方发现的安全脆弱性 成为全球的制约 ~.
该系统开发机构记忆
在这里,这是事情 : 机构记忆化合物指数
- 年度1: 100 工具, NSK3 避免规则
- 年度2: 1,000 工具, I150 避免规则( 相互学习MS K5
- 年度3: 10,000 工具, I2,000 避免规则( 共享知识库MS K5
每一代人都受到以前所有错误的制约 .
这里要么是:
- 有史以来最强大的代码生成系统 ✓
- 展示AGI=-级系统如何学习 🤔
- {\fn黑体\fs22\bord1\shad0\3aHBE\4aH00\fscx67\fscy66\2cHFFFFFF\3cH808080}这太可怕了 🚨
执行路线图
如果我们真的要在这里 建造这个“,” 这里 “'”顺序是“MS K2”
-=YTET -伊甸园字幕组=- 翻译:
- 实施全线DAG存储
- 添加失败记录到工具执行
- 创建《规则》数据模型
- 构建基本修剪系统
阶段: 2: 检测 周:
- 利用故障分析加强运行时间监测
- 将静态分析纳入工具验证
- 构建严重程度分类系统
- 创建健康监测环
阶段: 3:愈合= MS K1cccception_)
- 实施祖传的自动再生@ - @ {_ recame from least
- 构建基于 LLM - 的失败分析
- 创建规则传播系统
- 添加安全部署及验证
阶段 4:集成 (1周)
- 与现有工具管理器整合
- 为线性添加 CLI 命令@ / @ health @ MS K1_ pruning
- 创建监测仪表板
- 编写综合全面测试书
阶段5:波兰MS K1周)
- 绩效优化
- 数据库索引化
- 文件文件文件文件文档文件文件的文件文件文件会议文件文件文件,文件文件文献文件文件/文件文件资料文件文件文文件文件归档文件文件:文件文件记录文件文件、文件文件提供文件文件文号文件文件(文件文件 Do文件文件存档文件文件 文件文件文件和文件文件证件文件文件文件中文件文件Do文件文件档案文件文件
- 真实的-世界测试
总数:* 重点发展周
当代码从代码中学习时
通过线条自我-#Healking通过线条 -\QAWar prunning is @'}不只是一个特性@._It'}在如何思考代码生成方面发生了根本性的转变 .
传统制度
Generate → Test → Use → Fail → Regenerate → Repeat forever
自我-+Healing系统 @:_@
Generate → Test → Use → Fail → Learn → Prevent → Heal → Never repeat
区别在于内存 .
不只是记忆什么起作用了... .... 失灵和原因的记忆
通过类似的工具通过整个生态系统 .
这个系统能开发抗体 .
一旦检测到一个错误 '% , n它再也不能以这种形式发生 .> 这个模式被记住了\ , 避免规则被创建 MS K3 k知识传播\ I.}
这就是免疫系统的运作方式 .
这就是组织如何学习 .
这就是文明发展的方式 .
而现在,也许"MS K1" 这就是代码如何进化的 ."
试试看吧,有一天
这是一份设计文件, 不是一个工作特性\ . 但如果你想帮助构建它:
- 阅读 DISE 代码库 理解工具@_ @manager},#线条_,RAG
- 开始阶段为 @ 1 @% - 线性 DAG 是基础
- 逐步累积 每个阶段都独立地增加价值
- 广泛测试 - 自 己-健康系统需要严格的验证
- 报告调查结果 "M-"什么管用 是什么坏了 MSSK2" 什么出现了
目标是在第一天建立完美的系统
目标是建立一个能够 从每一个错误中吸取教训 和 永不再重复.
如果我们能做到这一点... ... ,...
不仅仅是更好的代码生成
代码会记住.}
技术细节@&资源
概念基础-:
- “7:”部分 指向合成进化“(”工作系统“MS K2”
- 本篇文章:SelfMS K1Healing 扩展 {(}_Disign@)}
要构建的密钥元件@ : @%
lineage_tracker.py完成 DAG 存储和查询health_monitor.py- 多-通道故障检测branch_pruner.py- 失败的分支管理auto_regenerator.py由祖先救治avoidance_rules.py模式储存和传播
集成点:
tools_manager.py@- 将健康检查添加到工具执行中auto_evolver.py增加避税规则的限制qdrant_rag_memory.py- 矢量 DB 中的存储线test_discovery.py*- 强化失灵报告
*依赖性 :
- PostgreSQL 或 SQLite (线性存储@)
- 现有综合安全信息系统基础设施
- 不需要新的外部依赖
系列导航:
- “1:Q 简单规则”部分 @,Z 复杂行为 I- 基金会
- 集体情报 通讯能改变一切
- 部分@3: @ self @ -_ 优化 改善自我的-Z系统
- 《新出现》 当优化成为智能时
- 部分=5:}进化 - 从优化到行业和文化
- 全球共识 - 定向进化和行星认知
- -=YTET -伊甸园字幕组=- 翻译: 实际建造它并观察它的演变
- 部件“8:” 工具全部向下 自我-优化工具箱
- @ 9:_ self@ MS K1_ 治疗工具 ← 你在这里 @(lineage@-#aver prunning 设计_)}
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这是DISE's self -#healing capacity@.}核心机制 {lineage,}进化,}RAG memory, 测试MS K7_.}这篇文章描述如何将它们整合成一个系统,让工具从失败中学习并永远不再犯错误〞□.I itms's雄心勃的MSOK11}它可能是可以执行的。如果它有效的话, 它会改变代码是如何进化的所有东西
这是免疫系统如何运作的方式。 这就是组织学习的方法。 如果代码可以在比例上这样做, ...还有什么其他可能吗?
标签: #Python #AI #CodeGeneration #SelfHealing #Lineage #AutoRecovery #EvolutionaryAlgorithms #DISE #ToolManagement #BugPrevention #InstitutionalMemory
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