当工具自我破碎时, 系统应该记住为什么, 并且永远不要重犯错误
何时 *DiSE 日志 构成谋杀。
> **注:** 这是DISE下一个进化飞跃的投机性设计 — — 一种自我治愈的工具生态系统,跟踪线条、检测虫子、小虾断裂的分支,并永远从错误中吸取教训。 它雄心勃勃,略为可怕,并且可能实际上可以用我们已经拥有的东西来实施。
以下是我晚上睡不着的场景:
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系统可以使工具变得更好。 但是,当进化形成这些工具时会发生什么呢? 更 严重如果优化引入一个关键的虫子呢?如果工具突变打破了生产系统呢?
现在,我们发现失败, 可能升级,也许手工修复它。
但我们没有 学习学习 以深层的结构性方式从中分离出来。
我们不:
本质上,我们不会制造一种疫苗, 配有相关的检测系统 和研究成型。但是DISE允许我们做这个几乎是微不足道的。
今天,情况发生了变化。
从概念上讲,这是设计它的方式 能够 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作 工作
将DISE中的每一个工具都视为像Git一样的定向环绕图(DAG)中的节点:
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:
当检测到关键错误时, 系统 :
结果: 一种自我治愈的生态系统 昆虫成为永久的机构记忆
首先,我们需要比现在更深入的跟踪。以下是元数据强化的外观:
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要多 但这是真正自我愈合所必须的
可通过多个渠道检测到关键虫:
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
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
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
当发现一个关键错误时, 魔法就会发生 :
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
以下是执行过程:
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
pruner 标记失败, 无法使用分支 :
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
当工具失灵时, 从健康的祖先中再生, 并有避险规则:
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)
杀手的特征:从失败中学习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")
让我们举个完整的例子:
# 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!
该系统学到:
这种知识现已成为永久的机构记忆,传播到所有类似的工具中。
以下是整个系统的样子:
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
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())
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
我们需要持续储存线性数据:
-- 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);
为 CLI 添加新命令 :
# 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
让我们诚实地说,什么是真实的与愿望:
这个系统,如果得到充分实施,就会产生一些令人不安的问题:
工具能记住犯过的每一个错误 并确保永远不再重犯
不只是个别的 集体的。
一个工具中的错误作为知识传播到每一个类似的工具中。 在任何地方发现的安全脆弱性就成为各地的全球制约因素。
该系统发展机构记忆。
事情是这样的: 机构记忆化合物指数化。
每一代人都受到以往所有错误的制约。
这要么是:
如果我们真的建造了这个,这里的顺序是:
共计: ~9周重点发展
通过直系和觉悟的补丁自我愈合 不仅仅是一个特点。这是我们如何思考代码生成的根本转变。
传统制度:
Generate → Test → Use → Fail → Regenerate → Repeat forever
自愈合系统:
Generate → Test → Use → Fail → Learn → Prevent → Heal → Never repeat
区别在于记忆
不只是回忆什么起作用了。 对失败和原因的记忆。
通过后代,通过类似的工具,通过整个生态系统。
系统能培养抗体
一旦检测到一个错误, 它就永远不会再以这种形式发生。 模式被记住, 避免规则被创建, 知识被传播 。
这就是免疫系统的运作方式。
各组织就是这样学习的。
这就是文明的发展方式。
现在,也许,这就是代码的进化方式。
目前这是一个设计文件, 不是工作功能。 但如果您想要帮助构建它 :
目标不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, 我们创造了一个真正新的东西。
不仅仅是更好的代码生成。
代码记得。
概念基础:
要构建的密钥组件 :
lineage_tracker.py - 完成DAG储存和查询health_monitor.py - 多渠道故障检测branch_pruner.py - 分支管理失败auto_regenerator.py - 从祖先救治avoidance_rules.py - 模式储存和传播一体化点:
tools_manager.py - 在工具执行中增加健康检查auto_evolver.py - 增加避避规则的限制qdrant_rag_memory.py - 存储矢量 DB 中的线条test_discovery.py - 强化失败报告依赖 :
系列导航:
这是DISE自愈合能力的设计文件。 核心机制( 线条、 进化、 RAG 内存、 测试) 已经存在 。 文章描述了如何将它们整合到一个系统中, 工具从失败中学习, 并且不再重犯错误。 它雄心勃勃, 也许可以执行。 如果它有效, 它会改变代码如何演变的一切 。
令人不自在的平行点:这就是免疫系统是如何运作的。这是组织如何学习的。如果代码可以大规模地这样做...还有什么可能呢?
标记 : #Python #AI #CodeGeneration #SelfHealing #Lineage #AutoRecovery #EvolutionaryAlgorithms #DISE #ToolManagement #BugPrevention #InstitutionalMemory
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