प्रत्यक्ष सिंथिक एवोल्यूशन के साथ परीक्षण किया जा रहा है
जब सिद्धांत वास्तविकताओं से मिलता है और कोड अपने आप को प्रेरित शुरू होता है Inspired by thinking about extensions to mostlylucid.mockllmapi and material for the (never to be released but I like to think about it 😜) sci-fi novel "Michael" about emergent AI
टिप्पणीःटिप्पणीः
यह भी गहरा उपयोग, थोड़ा सा पागल है, और निश्चित रूप से "वीबे-कोड." आप चेतावनी दी गई है.
अभ्यास से:
मैं वास्तव में इसे बनाया है
आने वाले ज्ञान के छः भागों के बाद, बहु-क्रमीय व्यवस्थाओं, विश्वव्यापी पुनःप्रस्थन, और ग्रह-भरणन के बारे में, मुझे समझ आ गया था:
मैं आरंभ कर रहा था.**ये आसान है कि अनुमान लगाने के लिए... ...और ईगली बुद्धि के बारे में अनुमान लगाने के लिए.**यह वास्तव में इसे बनाने के लिए मुश्किल है.
इसलिए मैंने बात करना बंद कर दिया और काम करना शुरू कर दिया ।
क्या उभरा है कुछ मैं फोन कर रहा हूँप्रत्यक्ष सिंथिक एवोल्यूशन (डी)- एक स्व-शर्मिंग, खुद को बहु-स्तर, बहु- आयामी प्रभावशाली प्रणाली का उपयोग करते हुए काम करता है।
(देखो, मैं इस कर रहा हूँ के रूप में मैं जा रहा हूँ.)लिफ्ट : क्या होगा यदि कोड तैयार करने के बजाय एक बार और उम्मीद है कि यह काम करता है, हम एक प्रणाली बनाई जहाँ कोड कोड कोडलगातार बढ़ते जा रहे हैं
योजना, हत्या, मूल्यांकन, और उत्परिवर्तन के माध्यम से?
तब क्या जब हम अपनी ग़लतियों से सीखने के लिए एक प्रणाली सीख सकते हैं, फिर से सफल पैटर्न, और समय - समय पर स्मार्ट बन सकते हैं?स्पिट्जर चेतावनी:. यह वास्तव में काम की तरह है.*और यह अजीब है.*और दिलचस्प.
चलो अंदर चलें.
You: "Write me a function that does X"
LLM: "Here's some code! [generates 50 lines of Python]"
You: *runs it*
Code: *explodes spectacularly*
You: "Fix it"
LLM: "Oh, sorry! Here's a new version!"
You: *runs it*
Code: *different explosion*
AGERES: यह एक परीक्षण है यह स्थिर नहीं है और सभी तेजी से नहीं.
और मरने वाले ने समझा कि अब (सबसे) जुदाई है
यह वास्तव में करता हैअब
सिर्फ अभी तक ठीक नहीं.
समस्या हम के बारे में बात नहीं कर रहे हैं
यहाँ है कि कैसे अधिकांश एम- आधारित कोड पीढ़ी आज काम करती है:
समस्याएँ मूल हैं:
एक- स्नेपशॉट पीढ़ी
- कोई सुधार नहीं, कोई दूसरी संभावना नहीं
कोई स्मृति नहीं है
कोई विशेषता फ़ीडबैक नहीं
[1. PLAN] → [2. GENERATE] → [3. EXECUTE] → [4. EVALUATE] → [5. EVOLVE]
↑ ↓
└────────────────────── [6. LEARN] ←─────────────────────────┘
graph TD
A[User Request] --> B[Overseer LLM<br/>llama3]
B -->|Strategic Plan| C[Generator LLM<br/>codellama]
C -->|Generated Code| D[Executor<br/>Sandboxed Python]
D -->|stdout/stderr/metrics| E[Triage LLM<br/>tinyllama]
E -->|Pass?| F{Quick Check}
F -->|Pass| G[Evaluator LLM<br/>llama3]
F -->|Fail| H[Escalation<br/>qwen2.5-coder]
G -->|Fitness Score| I[RAG Memory]
H -->|Improved Code| D
I -->|Store & Learn| J[Future Reuse]
style B stroke:#e1f5ff,stroke-width:3px
style C stroke:#ffe1f5,stroke-width:3px
style D stroke:#f5ffe1,stroke-width:3px
style E stroke:#fff5e1,stroke-width:3px
style G stroke:#e1ffe1,stroke-width:3px
style I stroke:#f0e1ff,stroke-width:3px
- समान भूलयों ने समान समस्याओं को पार कर लिया
**हमें कुछ बुनियादी भिन्न चीज़ की ज़रूरत थी ।**नहीं सिर्फ बेहतर प्रेरित.
class OverseerLLM:
"""Plans execution strategies and creates specifications."""
def create_plan(self, task_description: str) -> ExecutionPlan:
"""
Create detailed execution plan from task description.
Returns:
ExecutionPlan with strategy, steps, and expected metrics
"""
# Ask overseer to break down the problem
prompt = f"""Create a detailed execution plan for: {task_description}
Include:
1. High-level strategy
2. Step-by-step implementation plan
3. Expected quality score (0.0-1.0)
4. Expected execution time (ms)
5. Algorithm/data structure choices
6. Edge cases to handle
"""
response = self.client.generate(
model="llama3",
prompt=prompt,
model_key="overseer"
)
return ExecutionPlan(
plan_id=f"plan_{uuid.uuid4().hex[:8]}",
task_description=task_description,
strategy=response,
steps=self._parse_steps(response),
expected_quality=0.8,
expected_speed_ms=1000
)
**सिर्फ बड़े मॉडल नहीं.**एक ऐसी प्रणाली जो वास्तव में सीखना, याद रखना, और सुधार करती है ।
def generate_code(self, specification: str) -> str:
"""Generate code from specification (no creative interpretation)."""
prompt = f"""Implement this specification EXACTLY:
{specification}
Requirements:
- Follow the spec precisely
- No additional features
- Include error handling
- JSON input/output interface
- Return only Python code
"""
code = self.client.generate(
model="codellama",
prompt=prompt,
model_key="generator",
temperature=0.3 # Low temperature for consistency
)
return self._clean_code(code)
**यही कारण है कि डीएस क्या होने की कोशिश करता है।**प्रत्यक्ष सिंथिक एवोल्यूशन प्रविष्ट करें
def triage(self, metrics: Dict[str, Any], targets: Dict[str, Any]) -> Dict[str, Any]:
"""Quick triage evaluation using tiny model."""
prompt = f"""Quick evaluation:
Metrics:
- Latency: {metrics['latency_ms']}ms (target: {targets['latency_ms']}ms)
- Memory: {metrics['memory_mb']}MB (target: {targets['memory_mb']}MB)
- Exit code: {metrics['exit_code']} (target: 0)
Does this PASS or FAIL? One word answer."""
response = self.client.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
verdict = "pass" if "pass" in response.lower() else "fail"
return {
"verdict": verdict,
"reason": response.strip(),
"metrics": metrics
}
**इंसाइट ऑन द स्क्रिप्चर्स् विकास एल्गोरिथ्म से धारणाएँ स्वीकार करता है लेकिन उन्हें कोड पीढ़ी पर लागू करता है.**यहाँ कोर काम फ्लो है:
def evaluate(self, stdout: str, stderr: str, metrics: Dict) -> Dict[str, Any]:
"""Comprehensive evaluation with multi-dimensional scoring."""
prompt = f"""Evaluate this code execution:
OUTPUT:
{stdout[:500]}
ERRORS:
{stderr[:500] if stderr else "None"}
METRICS:
- Latency: {metrics['latency_ms']}ms
- Memory: {metrics['memory_mb']}MB
- Exit code: {metrics['exit_code']}
Provide scores (0.0-1.0):
1. Correctness: Does output match expected?
2. Quality: Code robustness, patterns, style
3. Speed: Performance vs targets
Format: JSON with correctness, quality, speed, overall_score
"""
response = self.client.evaluate(
code_summary=stdout,
metrics=metrics
)
return {
"correctness": 0.95,
"quality": 0.88,
"speed": 0.92,
"overall_score": 0.92,
"details": response
}
लेकिन यहाँ है जहां यह दिलचस्प हो जाता है: हम सब कुछ के लिए एक सीएलएम का उपयोग नहीं करते.
विशिष्ट औज़ार**, हर एक विशेष भूमिका के साथ:**.
बहु- एजेंट
User: "Write a fibonacci function"
LLM: [Generates code + tests + documentation + explanation all at once]
[Might invent requirements you didn't ask for]
[Might miss requirements you did ask for]
एजेंट एजेंट:
User: "Write a fibonacci function"
↓
Overseer: Creates detailed specification
{
"problem": "Generate first N fibonacci numbers",
"algorithm": "Iterative DP approach",
"inputs": {"n": "integer"},
"outputs": {"result": "list[int]"},
"constraints": {
"timeout_ms": 5000,
"max_n": 100
},
"test_cases": [
{"input": {"n": 5}, "expected": [0,1,1,2,3]},
{"input": {"n": 10}, "expected": [0,1,1,2,3,5,8,13,21,34]}
]
}
↓
Generator: Implements ONLY the specification
[No creative interpretation]
[No added features]
[Just clean, focused code]
ओवरसियर (लाइमा3)
सामान्य
nomic-embed-text- रीग्रेसिव बहुमुखी कैंचिंगsequenceDiagram
participant U as User
participant S as System
participant R as RAG Memory
participant Q as Qdrant DB
participant E as Embedding Model
U->>S: Request: "validate email"
S->>R: Search similar artifacts
R->>E: Generate embedding
E-->>R: 768-dim vector
R->>Q: Semantic search
Q-->>R: Top 5 similar artifacts
R-->>S: Found: email_validator (0.92 similarity)
alt High Similarity (>0.9)
S->>S: Reuse as-is
else Medium Similarity (0.7-0.9)
S->>S: Use as template
else Low Similarity (<0.7)
S->>S: Generate from scratch
end
S->>U: Return solution
S->>R: Store with metadata
R->>E: Generate embedding
E-->>R: Vector
R->>Q: Index artifact
Q-->>R: Stored
यहाँ मूल नवीकरण है कि DED काम करता है:
class QdrantRAGMemory:
"""RAG memory using Qdrant vector database for semantic search."""
def __init__(
self,
qdrant_url: str = "http://localhost:6333",
collection_name: str = "code_evolver_artifacts",
embedding_model: str = "nomic-embed-text",
vector_size: int = 768 # nomic-embed-text dimension
):
self.qdrant = QdrantClient(url=qdrant_url)
self.embedding_model = embedding_model
self.vector_size = vector_size
# Create collection if needed
self._init_collection()
def store_artifact(
self,
artifact_id: str,
artifact_type: ArtifactType,
name: str,
content: str,
tags: List[str],
metadata: Dict[str, Any],
auto_embed: bool = True
):
"""Store artifact with semantic embedding."""
# Generate embedding
if auto_embed:
embedding = self._generate_embedding(content)
else:
embedding = None
# Create artifact
artifact = Artifact(
artifact_id=artifact_id,
artifact_type=artifact_type,
name=name,
content=content,
tags=tags,
metadata=metadata
)
# Store in Qdrant with metadata as payload
if embedding:
self.qdrant.upsert(
collection_name=self.collection_name,
points=[
PointStruct(
id=hash(artifact_id) & 0x7FFFFFFF, # Positive int
vector=embedding,
payload={
"artifact_id": artifact_id,
"name": name,
"type": artifact_type.value,
"tags": tags,
"quality_score": metadata.get("quality_score", 0.0),
"latency_ms": metadata.get("latency_ms", 0),
"usage_count": metadata.get("usage_count", 0),
**metadata
}
)
]
)
logger.info(f"✓ Stored artifact '{name}' in RAG memory")
def find_similar(
self,
query: str,
artifact_type: Optional[ArtifactType] = None,
top_k: int = 5,
min_similarity: float = 0.0
) -> List[Tuple[Artifact, float]]:
"""Find similar artifacts using semantic search."""
# Generate query embedding
query_embedding = self._generate_embedding(query)
# Build filter
filter_conditions = []
if artifact_type:
filter_conditions.append(
FieldCondition(
key="type",
match=MatchValue(value=artifact_type.value)
)
)
search_filter = Filter(must=filter_conditions) if filter_conditions else None
# Search Qdrant
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=query_embedding,
query_filter=search_filter,
limit=top_k
)
# Convert to artifacts with similarity scores
artifacts = []
for result in results:
if result.score >= min_similarity:
artifact = self._payload_to_artifact(result.payload)
artifacts.append((artifact, result.score))
return artifacts
def _generate_embedding(self, text: str) -> List[float]:
"""Generate embedding using Ollama."""
response = self.ollama_client.embed(
model=self.embedding_model,
prompt=text
)
return response["embedding"]
स्पेस आधारित पीढ़ी
def find_best_tool(
self,
task_description: str,
min_quality: float = 0.7,
max_latency_ms: int = 5000
) -> Optional[Artifact]:
"""Find best tool using multi-dimensional fitness."""
# Search with fitness filters
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=self._generate_embedding(task_description),
query_filter=Filter(
must=[
FieldCondition(
key="type",
match=MatchValue(value="tool")
),
FieldCondition(
key="quality_score",
range=Range(gte=min_quality) # Quality >= 0.7
),
FieldCondition(
key="latency_ms",
range=Range(lte=max_latency_ms) # Latency <= 5000ms
)
]
),
limit=1
)
return results[0] if results else None
पारंपरिक पास (अनुप्रयोग के लिए उपयुक्त):
# Traditional similarity: might give false positives
Task 1: "generate fibonacci sequence"
Task 2: "generate fibonacci backwards"
Similarity: 77% ← High, but these need DIFFERENT code!
# Semantic classification
Triage LLM analyzes both tasks:
SAME → Reuse as-is (just typos/wording differences)
RELATED → Use as template, modify (same domain, different variation)
DIFFERENT → Generate from scratch (completely different problem)
Result: "RELATED - same core algorithm, reversed output"
Action: Load fibonacci code as template, modify to reverse
प्रतीत होता है:
RAG मेमोरी: अतीत से सीखना
(केप, कीमत, गुणवत्ता, लेट इन्ट)
# Original (stored in RAG):
def fibonacci_sequence(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence
# New request: "fibonacci backwards"
# DSE finds original, classifies as RELATED
# Generates modification spec: "Return reversed sequence"
# Modified version:
def fibonacci_backwards(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence[::-1] # ← Only change needed!
भविष्य फिर से उपयोग सक्षम करता है
RAG मेमोरी कार्यान्वयन:
graph LR
A[Tool/Artifact] --> B[Semantic Similarity<br/>0-100]
A --> C[Speed Tier<br/>±20 points]
A --> D[Cost Tier<br/>±15 points]
A --> E[Quality Score<br/>±15 points]
A --> F[Historical Success<br/>±10 points]
A --> G[Latency Metrics<br/>±15 points]
A --> H[Reuse Bonus<br/>±30 points]
B --> I[Final Fitness Score]
C --> I
D --> I
E --> I
F --> I
G --> I
H --> I
I --> J{Selection}
J -->|Highest Score| K[Use This Tool]
style I stroke:#ffeb3b,stroke-width:3px
style K stroke:#4caf50,stroke-width:3px
बेस्ड फ़िल्टरिंगः
def calculate_fitness(tool, similarity_score):
fitness = similarity_score * 100 # Base: 0-100
# Speed tier bonus
if tool.speed_tier == 'very-fast':
fitness += 20
elif tool.speed_tier == 'fast':
fitness += 10
elif tool.speed_tier == 'slow':
fitness -= 10
# Cost tier bonus
if tool.cost_tier == 'free':
fitness += 15
elif tool.cost_tier == 'low':
fitness += 10
elif tool.cost_tier == 'high':
fitness -= 10
# Quality from historical success rate
fitness += tool.quality_score * 10
# Latency metrics
if tool.avg_latency_ms < 100:
fitness += 15 # Very fast
elif tool.avg_latency_ms > 5000:
fitness -= 10 # Too slow
# Reuse bonus
if similarity >= 0.90:
fitness += 30 # Exact match - huge bonus!
elif similarity >= 0.70:
fitness += 15 # Template reuse
return fitness
यह चतुर हो जाता है जहां यहाँ है.**जब आप कुछ पिछले कार्य के समान कुछ के लिए पूछें, DED सिर्फ पाठ समानता नहीं करता है - यह उपयोग करता है:**यह झूठी सकारात्मक समस्या को हल करता है, जबकि बुद्धिमान कोड फिर से प्रयोग करते हैं ।
DELELELENENT काम मिल जाते हैं, यह scrat से फिर से नहीं होता है.
sequenceDiagram
participant N as Node v1.0.0
participant M as Monitor
participant E as Auto-Evolver
participant O as Overseer
participant G as Generator
participant T as Tester
loop Every Execution
N->>M: Report metrics
M->>M: Track quality history
end
M->>M: Detect degradation
Note over M: Score dropped<br/>0.95 → 0.85<br/>(>15% decline)
M->>E: Trigger evolution
E->>O: Request improvement plan
O-->>E: Strategy: Optimize algorithm
E->>G: Generate v1.1.0
G-->>E: Improved code
E->>T: A/B Test
T->>N: Run v1.0.0
N-->>T: Score: 0.85
T->>E: Run v1.1.0
E-->>T: Score: 0.96
T->>E: v1.1.0 wins!
E->>N: Promote v1.1.0
E->>M: Update lineage
M->>M: Archive v1.0.0
Note over N: Now running v1.1.0<br/>Better performance<br/>Same functionality
बजाय:
class AutoEvolver:
"""Monitors and evolves code performance automatically."""
def __init__(
self,
performance_threshold: float = 0.15, # 15% degradation triggers evolution
min_runs_before_evolution: int = 3
):
self.performance_threshold = performance_threshold
self.min_runs = min_runs_before_evolution
self.performance_history: Dict[str, List[float]] = {}
def record_execution(self, node_id: str, quality_score: float):
"""Record execution performance."""
if node_id not in self.performance_history:
self.performance_history[node_id] = []
self.performance_history[node_id].append(quality_score)
# Check if evolution needed
if len(self.performance_history[node_id]) >= self.min_runs:
if self._should_evolve(node_id):
self.trigger_evolution(node_id)
def _should_evolve(self, node_id: str) -> bool:
"""Determine if node should evolve based on performance."""
history = self.performance_history[node_id]
if len(history) < self.min_runs:
return False
# Get baseline (best of first 3 runs)
baseline = max(history[:3])
# Get recent average (last 3 runs)
recent_avg = sum(history[-3:]) / 3
# Calculate degradation
degradation = (baseline - recent_avg) / baseline
if degradation > self.performance_threshold:
logger.warning(
f"Node {node_id} degraded {degradation*100:.1f}% "
f"(baseline: {baseline:.2f}, recent: {recent_avg:.2f})"
)
return True
return False
def trigger_evolution(self, node_id: str):
"""Trigger evolution process for underperforming node."""
logger.info(f"Triggering evolution for {node_id}")
# Load current node
node = self.registry.get_node(node_id)
current_code = self.runner.load_code(node_id)
# Get performance metrics
metrics = node.get("metrics", {})
history = self.performance_history[node_id]
# Ask overseer for improvement strategy
improvement_plan = self.overseer.create_improvement_plan(
node_id=node_id,
current_code=current_code,
performance_history=history,
current_metrics=metrics
)
# Generate improved version
new_version = self._increment_version(node.get("version", "1.0.0"))
new_code = self.generator.generate_improvement(
specification=improvement_plan,
base_code=current_code,
version=new_version
)
# A/B test: old vs new
old_score = self._test_version(node_id, current_code)
new_score = self._test_version(f"{node_id}_v{new_version}", new_code)
logger.info(
f"A/B Test Results: "
f"v{node['version']}: {old_score:.2f} | "
f"v{new_version}: {new_score:.2f}"
)
# Keep better version
if new_score > old_score:
logger.info(f"✓ Promoting v{new_version} (improvement: {new_score - old_score:.2f})")
self._promote_version(node_id, new_version, new_code)
else:
logger.info(f"✗ Keeping v{node['version']} (new version worse)")
def _test_version(self, node_id: str, code: str, num_tests: int = 5) -> float:
"""Test a version and return average quality score."""
scores = []
for i in range(num_tests):
stdout, stderr, metrics = self.runner.run_node(node_id, test_input)
result = self.evaluator.evaluate(stdout, stderr, metrics)
scores.append(result.get("overall_score", 0.0))
return sum(scores) / len(scores)
def _promote_version(self, node_id: str, version: str, code: str):
"""Promote new version to production."""
# Archive old version
old_node = self.registry.get_node(node_id)
self.registry.archive_version(node_id, old_node["version"])
# Update node with new version
self.runner.save_code(node_id, code)
self.registry.update_node(node_id, {
"version": version,
"lineage": {
"parent_version": old_node["version"],
"evolution_reason": "performance_degradation",
"timestamp": datetime.utcnow().isoformat()
}
})
# Reset performance tracking
self.performance_history[node_id] = []
logger.info(f"✓ Node {node_id} evolved to v{version}")
मौज़ूदा कोड लोड करता है
Node: text_processor_v1.0.0
Run 1: Score 0.95 ✓
Run 2: Score 0.94 ✓
Run 3: Score 0.92 ✓
Run 4: Score 0.88 ← Degradation detected!
Run 5: Score 0.85 ← 15% drop, trigger evolution!
Auto-Evolution Process:
1. Analyze performance history
2. Generate improvement specification
3. Create text_processor_v1.1.0
4. A/B test: v1.0.0 vs v1.1.0
5. Keep winner, archive loser
Result: v1.1.0 scores 0.96
Action: Promoted to primary version
सिद्ध टैम्प्लेट के रूप में
: " कोर एल्गोरिदम रखें, उलटा जोड़ें"
graph TD
A[Complex Task:<br/>Build REST API] --> B[Level 1: Workflow]
B --> C[Design API Schema]
B --> D[Implement Auth]
B --> E[Create Endpoints]
B --> F[Add Error Handling]
B --> G[Write Tests]
C --> C1[Level 2: Nodeplan<br/>Schema validator]
C --> C2[Level 2: Nodeplan<br/>Schema generator]
D --> D1[Level 2: Nodeplan<br/>JWT handler]
D --> D2[Level 2: Nodeplan<br/>User validator]
E --> E1[Level 2: Nodeplan<br/>GET handler]
E --> E2[Level 2: Nodeplan<br/>POST handler]
E --> E3[Level 2: Nodeplan<br/>PUT/DELETE]
C1 --> C1a[Level 3: Function<br/>validate_field]
C1 --> C1b[Level 3: Function<br/>check_types]
D1 --> D1a[Level 3: Function<br/>encode_token]
D1 --> D1b[Level 3: Function<br/>decode_token]
E1 --> E1a[Level 3: Function<br/>parse_params]
E1 --> E1b[Level 3: Function<br/>serialize_response]
style A stroke:#ff6b6b,stroke-width:3px
style B stroke:#4ecdc4,stroke-width:3px
style C stroke:#45b7d1,stroke-width:3px
style D stroke:#45b7d1,stroke-width:3px
style E stroke:#45b7d1,stroke-width:3px
style C1 stroke:#96ceb4,stroke-width:3px
style D1 stroke:#96ceb4,stroke-width:3px
style E1 stroke:#96ceb4,stroke-width:3px
style C1a stroke:#dfe6e9,stroke-width:3px
style D1a stroke:#dfe6e9,stroke-width:3px
style E1a stroke:#dfe6e9,stroke-width:3px
सामान्य करें (S)
class HierarchicalEvolver:
"""Evolves complex workflows through hierarchical decomposition."""
def __init__(
self,
max_depth: int = 3, # Workflow → Nodeplan → Function
max_breadth: int = 5 # Max sub-tasks per level
):
self.max_depth = max_depth
self.max_breadth = max_breadth
def evolve_hierarchical(
self,
root_goal: str,
current_depth: int = 0,
parent_context: Optional[Dict] = None
) -> Dict[str, Any]:
"""
Recursively evolve a complex goal through hierarchical decomposition.
Args:
root_goal: High-level goal description
current_depth: Current depth in hierarchy (0 = workflow level)
parent_context: Context from parent level
Returns:
Evolved workflow with all sub-components
"""
if current_depth >= self.max_depth:
# Base case: generate atomic function
return self._generate_atomic_function(root_goal, parent_context)
# Ask overseer to decompose goal
sub_goals = self.overseer.decompose_goal(
goal=root_goal,
max_sub_goals=self.max_breadth,
context=parent_context
)
logger.info(
f"{' ' * current_depth}Level {current_depth}: "
f"Decomposed '{root_goal}' into {len(sub_goals)} sub-goals"
)
# Evolve each sub-goal recursively
sub_components = []
shared_context = {
"parent_goal": root_goal,
"depth": current_depth,
"sibling_count": len(sub_goals)
}
for i, sub_goal in enumerate(sub_goals):
logger.info(f"{' ' * current_depth}├─ Sub-goal {i+1}/{len(sub_goals)}: {sub_goal}")
# Recursively evolve sub-goal
component = self.evolve_hierarchical(
root_goal=sub_goal,
current_depth=current_depth + 1,
parent_context=shared_context
)
sub_components.append(component)
# Update shared context with learning from this component
shared_context[f"sub_component_{i}_fitness"] = component.get("fitness", 0.0)
# Create workflow/nodeplan from sub-components
workflow = self._assemble_workflow(
goal=root_goal,
sub_components=sub_components,
depth=current_depth
)
return workflow
def _generate_atomic_function(
self,
goal: str,
context: Optional[Dict] = None
) -> Dict[str, Any]:
"""Generate atomic function (leaf node)."""
# Check RAG for similar functions
similar = self.rag.find_similar(
query=goal,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
if similar and similar[0][1] > 0.85:
# High similarity: reuse
logger.info(f" ✓ Reusing similar function: {similar[0][0].name}")
return similar[0][0].to_dict()
# Generate new function
specification = self.overseer.create_plan(
task_description=goal,
context=context
)
code = self.generator.generate_code(specification)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Store in RAG for future reuse
self.rag.store_artifact(
artifact_id=f"func_{hash(goal) & 0x7FFFFFFF}",
artifact_type=ArtifactType.FUNCTION,
name=goal,
content=code,
tags=["hierarchical", f"depth_{context.get('depth', 0)}"],
metadata={
"fitness": evaluation["overall_score"],
"parent_goal": context.get("parent_goal"),
"context": context
},
auto_embed=True
)
return {
"goal": goal,
"code": code,
"fitness": evaluation["overall_score"],
"metrics": metrics
}
def _assemble_workflow(
self,
goal: str,
sub_components: List[Dict],
depth: int
) -> Dict[str, Any]:
"""Assemble workflow from evolved sub-components."""
# Calculate overall fitness (weighted average of sub-components)
total_fitness = sum(c.get("fitness", 0.0) for c in sub_components)
avg_fitness = total_fitness / len(sub_components) if sub_components else 0.0
workflow = {
"goal": goal,
"depth": depth,
"type": "workflow" if depth == 0 else "nodeplan",
"sub_components": sub_components,
"fitness": avg_fitness,
"assembled_at": datetime.utcnow().isoformat()
}
# Store workflow in RAG
workflow_type = ArtifactType.WORKFLOW if depth == 0 else ArtifactType.SUB_WORKFLOW
self.rag.store_artifact(
artifact_id=f"workflow_{hash(goal) & 0x7FFFFFFF}",
artifact_type=workflow_type,
name=goal,
content=json.dumps(workflow, indent=2),
tags=["hierarchical", f"depth_{depth}", f"components_{len(sub_components)}"],
metadata={
"fitness": avg_fitness,
"component_count": len(sub_components),
"depth": depth
},
auto_embed=True
)
logger.info(
f"{' ' * depth}✓ Assembled {workflow['type']}: '{goal}' "
f"(fitness: {avg_fitness:.2f}, components: {len(sub_components)})"
)
return workflow
नए कोड को लिखने के बजाए
परिणाम
Level 1 (Workflow):
"Build a REST API"
↓
Level 2 (Nodeplans):
├─ Design API schema
├─ Implement authentication
├─ Create CRUD endpoints
├─ Add error handling
└─ Write integration tests
↓
Level 3 (Functions):
Each nodeplan breaks into individual functions
: तेज, अधिक भरोसेमंद, पुनः प्रयोग करें जांच कोड
इससे पीढ़ी - दर - पीढ़ी तेज़ी से बढ़ जाती है और विश्वासयोग्य साबित होती है ।
graph TB
Start([User Request]) --> RAG1[RAG: Search Similar]
RAG1 --> Class{Semantic<br/>Classification}
Class -->|SAME<br/>similarity > 0.9| Reuse[Reuse As-Is]
Class -->|RELATED<br/>0.7-0.9| Template[Template Modification]
Class -->|DIFFERENT<br/>< 0.7| Generate[Generate from Scratch]
Reuse --> Execute
Template --> Overseer1[Overseer: Modification Plan]
Generate --> Overseer2[Overseer: Full Plan]
Overseer1 --> Generator1[Generator: Modify Template]
Overseer2 --> Generator2[Generator: New Code]
Generator1 --> Execute[Execute in Sandbox]
Generator2 --> Execute
Execute --> Triage{Triage<br/>Pass/Fail?}
Triage -->|Fail| Escalate[Escalate to<br/>qwen2.5-coder]
Escalate --> Execute
Triage -->|Pass| Evaluator[Evaluator:<br/>Multi-Dimensional Scoring]
Evaluator --> Fitness[Calculate Fitness Score]
Fitness --> Store[Store in RAG with<br/>Embedding + Metadata]
Store --> Monitor[Performance Monitor]
Monitor --> Degrade{Degradation<br/>Detected?}
Degrade -->|Yes >15%| Evolve[Auto-Evolution:<br/>Generate v1.x.x]
Degrade -->|No| Continue[Continue Monitoring]
Evolve --> ABTest[A/B Test:<br/>Old vs New]
ABTest --> Promote{New Better?}
Promote -->|Yes| Update[Promote New Version]
Promote -->|No| Keep[Keep Old Version]
Update --> Monitor
Keep --> Monitor
Continue --> End([Ready for Reuse])
style Start stroke:#e3f2fd,stroke-width:3px
style RAG1 stroke:#f3e5f5,stroke-width:3px
style Class stroke:#fff3e0,stroke-width:3px
style Reuse stroke:#e8f5e9,stroke-width:3px
style Execute stroke:#fce4ec,stroke-width:3px
style Evaluator stroke:#e1f5fe,stroke-width:3px
style Store stroke:#f1f8e9,stroke-width:3px
style Evolve stroke:#ffe0b2,stroke-width:3px
style End stroke:#e8eaf6,stroke-width:3px
बहु- आयामी अनुरूपता: सही औज़ार चुनने के लिए
class DirectedSyntheticEvolution:
"""Complete DSE workflow orchestrator."""
def __init__(self, config: ConfigManager):
self.config = config
self.ollama = OllamaClient(config.ollama_url, config_manager=config)
self.rag = QdrantRAGMemory(
qdrant_url=config.qdrant_url,
ollama_client=self.ollama
)
self.tools = ToolsManager(
ollama_client=self.ollama,
rag_memory=self.rag
)
self.overseer = OverseerLLM(self.ollama, self.rag)
self.generator = CodeGenerator(self.ollama)
self.evaluator = Evaluator(self.ollama)
self.evolver = AutoEvolver(self.rag, self.overseer, self.generator)
def evolve(self, task_description: str) -> Dict[str, Any]:
"""Execute complete evolution workflow."""
logger.info(f"Starting evolution for: {task_description}")
# Step 1: RAG Search for similar solutions
similar = self.rag.find_similar(
query=task_description,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
# Step 2: Semantic Classification
if similar:
relationship = self._classify_relationship(
task_description,
similar[0][0].content,
similar[0][1]
)
else:
relationship = "DIFFERENT"
# Step 3: Choose generation strategy
if relationship == "SAME":
logger.info("✓ Exact match found - reusing as-is")
return similar[0][0].to_dict()
elif relationship == "RELATED":
logger.info("✓ Similar solution found - using as template")
plan = self.overseer.create_modification_plan(
task_description=task_description,
template_code=similar[0][0].content
)
code = self.generator.modify_template(plan, similar[0][0].content)
else: # DIFFERENT
logger.info("✓ No match - generating from scratch")
plan = self.overseer.create_plan(task_description)
code = self.generator.generate_code(plan)
# Step 4: Execute in sandbox
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 5: Triage (quick check)
triage_result = self.evaluator.triage(metrics, targets={})
if triage_result["verdict"] == "fail":
# Escalate to better model
logger.warning("✗ Triage failed - escalating")
code = self._escalate(code, stderr, metrics)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 6: Comprehensive evaluation
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Step 7: Calculate fitness
fitness = self._calculate_fitness(evaluation, metrics)
# Step 8: Store in RAG
artifact_id = f"func_{hash(task_description) & 0x7FFFFFFF}"
self.rag.store_artifact(
artifact_id=artifact_id,
artifact_type=ArtifactType.FUNCTION,
name=task_description,
content=code,
tags=["evolved", "validated"],
metadata={
"quality_score": evaluation["overall_score"],
"latency_ms": metrics["latency_ms"],
"memory_mb": metrics["memory_mb"],
"fitness": fitness,
"relationship": relationship
},
auto_embed=True
)
logger.info(f"✓ Evolution complete - Fitness: {fitness:.2f}")
# Step 9: Start monitoring for future evolution
self.evolver.monitor(artifact_id, evaluation["overall_score"])
return {
"artifact_id": artifact_id,
"code": code,
"fitness": fitness,
"evaluation": evaluation,
"metrics": metrics,
"relationship": relationship
}
def _classify_relationship(
self,
new_task: str,
existing_task: str,
similarity: float
) -> str:
"""Use triage LLM to classify task relationship."""
if similarity < 0.7:
return "DIFFERENT"
prompt = f"""Compare these two tasks:
Task 1 (Existing): {existing_task}
Task 2 (Requested): {new_task}
Similarity Score: {similarity:.2f}
Classify relationship:
- SAME: Minor wording differences, same algorithm
- RELATED: Same domain, different variation
- DIFFERENT: Completely different problems
Answer with one word: SAME, RELATED, or DIFFERENT"""
response = self.ollama.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
for keyword in ["SAME", "RELATED", "DIFFERENT"]:
if keyword in response.upper():
return keyword
return "DIFFERENT" # Default fallback
def _calculate_fitness(
self,
evaluation: Dict,
metrics: Dict
) -> float:
"""Multi-dimensional fitness calculation."""
base_score = evaluation["overall_score"] * 100 # 0-100
# Speed bonus/penalty
if metrics["latency_ms"] < 100:
base_score += 15
elif metrics["latency_ms"] > 5000:
base_score -= 10
# Memory efficiency
if metrics["memory_mb"] < 10:
base_score += 10
elif metrics["memory_mb"] > 100:
base_score -= 5
# Exit code (must be 0)
if metrics["exit_code"] != 0:
base_score -= 20
return max(0, min(100, base_score)) # Clamp to 0-100
यहाँ है जहां डीएस वास्तव में दिलचस्प हो जाता है.
गणना कार्यान्वयन अनुरूप:
$ python chat_cli.py
CodeEvolver> generate Write a function to validate email addresses
Searching for relevant tools...
✓ Found validation specialist in RAG memory
Consulting overseer LLM (llama3) for approach...
✓ Strategy: Use regex-based validation with RFC 5322 compliance
Selecting best tool...
✓ Using specialized tool: Validation Expert (codellama)
Generating code...
✓ Code generation complete
Running unit tests...
✓ All tests passed (5/5)
Evaluating quality...
✓ Score: 0.96 (Excellent)
Node 'validate_email_addresses' created successfully!
Latency: 127ms | Memory: 2.1MB | Quality: 96%
CodeEvolver> run validate_email_addresses {"email": "[email protected]"}
✓ Execution successful
Output: {
"valid": true,
"email": "[email protected]",
"parts": {
"local": "test",
"domain": "example.com"
}
}
इसका अर्थ है कि हमेशा अपनी पहचान करता है
यह प्रणाली अपने ही कोड को सही प्रदर्शन करने के लिए शाब्दिक रूप से विकसित करती है ।
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
collection_name: "code_evolver_artifacts"
कोई मानव हस्तक्षेप की जरूरत नहीं है.
एवोल्यूशन कार्य प्रवाहित करें
# Find high-quality, fast, low-cost solutions for "validation"
results = rag.find_similar(
query="validate user input",
filter={
"quality_tier": {"$in": ["excellent", "very-good"]},
"speed_tier": {"$in": ["very-fast", "fast"]},
"cost_tier": {"$in": ["free", "low"]}
},
top_k=5
)
पूर्ण कार्य प्रवाह कोड उदाहरण:
RAG मेमोरी
# Multi-model LLM routing with Ollama
from src import OllamaClient, ConfigManager
config = ConfigManager("config.yaml")
client = OllamaClient(config.ollama_url, config_manager=config)
# Different endpoints for different models
# Heavy planning on powerful CPU machine
# Code generation on GPU machine
# Fast triage on lightweight local instance
# RAG memory with Qdrant
from src import QdrantRAGMemory
rag = QdrantRAGMemory(
qdrant_url="http://localhost:6333",
collection_name="artifacts",
embedding_model="nomic-embed-text",
vector_size=768
)
# Tools with semantic selection
from src import ToolsManager
tools = ToolsManager(
config_manager=config,
ollama_client=client,
rag_memory=rag
)
# Complete workflow
workflow_result = evolver.evolve(
goal="Build email validation system",
max_iterations=10,
auto_evolve=True
)
विशेष रूप से एजेंटconfig.yaml:
ollama:
base_url: "http://localhost:11434"
models:
overseer:
model: "llama3"
endpoint: "http://powerful-cpu:11434" # Strategic planning on powerful hardware
generator:
model: "codellama"
endpoint: "http://gpu-server:11434" # Code gen on GPU
evaluator:
model: "llama3"
endpoint: null # Local evaluation
triage:
model: "tinyllama"
endpoint: null # Fast local triage
embedding:
model: "nomic-embed-text"
vector_size: 768
execution:
default_timeout_ms: 5000
max_memory_mb: 256
max_retries: 3
auto_evolution:
enabled: true
performance_threshold: 0.15 # Trigger at 15% degradation
min_runs_before_evolution: 3
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
हाल ही में@ info: whatsthis
- बहुLM कॉल जोड़ता है (हालाँकि यह तेजी से हो रहा है!)
- प्रस्ताव मदद करता है लेकिन सही नहीं है
-वेंड इनपुट अभी भी प्रणाली को उलझन में डाल सकते हैं
विश्लेषण विशेषीकरण
विशेषता सुधार
- नोड्स के बाद के संस्करण अक्सर मूल अभ्यास
def process_text(text: str) -> str:
words = text.split()
result = []
for word in words:
if len(word) > 3:
result.append(word.upper())
else:
result.append(word.lower())
return ' '.join(result)
अभ्यास में रोक
यहाँ वास्तविक तकनीक स्टैक है:
def process_text(text: str) -> str:
"""Process text with optimized string operations."""
if not text:
return ""
# Vectorized operation for better performance
return ' '.join(
word.upper() if len(word) > 3 else word.lower()
for word in text.split()
)
कॉन्फ़िगरेशन उदाहरण
रीयल- स्कैन
सटीक प्रतियोगिता: ~ 1- 2 सेकण्ड (के रूप में प्रयोग करें)
वेब यूआई
उत्तम विशेषज्ञ
मध्यम पद संग्रहित रजिस्ट्री
- टीमों/ संगठन के माध्यम से हल साझा करें बंद करें (C)
- AWS/Ajn/GCP संयोजन एकीकरण
विस्तृत सेंडिंग
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull models
ollama pull codellama
ollama pull llama3
ollama pull tinyllama
ollama pull nomic-embed-text
# Clone and run
git clone https://github.com/yourrepo/mostlylucid.dse
cd mostlylucid.dse/code_evolver
pip install -r requirements.txt
python chat_cli.py
- बेहतर अकेलेपन के लिए डॉकर/कसमूहजंगली विचार
जीवंत विकास
मेटा- अधिष्ठापन
प्रणाली अपनी विकास योजनाओं को प्रेरित करता हैकोलाबरेशन सीखने का तरीका- कई परीक्षण खोजों को साझा करते हैं
सबकइस बात को बनाने के बाद, यहाँ मुझे क्या आश्चर्य है:
**1 ..**विशेषीकरण तत्व
**विभिन्न कार्यों के लिए अलग मॉडलों का उपयोग किया जा रहा था (सवर वीssvsorror) सिर्फ अच्छा नहीं था - यह जरूरी था.**हर चीज़ के लिए एक मॉडल इस्तेमाल करने की कोशिश करने से अच्छे नतीजे मिलते हैं ।
2स्मरण सब कुछ है
RGG मेमोरी एक विशेषता नहीं है, यह विशेषता है.
इसके बिना, आप सिर्फ एक लूप में कोड पैदा कर रहे हैं.इसके साथ, तंत्र वास्तव में सीख जाता है और सुधार करता है ।
3
4.
एवोल्यूशन वास्तव में काम करता हैमैं ईमानदारी से यह उम्मीद नहीं की थी कि पहली पीढ़ी से बेहतर कोड तैयार करने के लिए evolution.
पूरी तरह से.
**यह जंगली है.**5.
स्थिर रीतिवाले ने।
**बहुत सीLM कॉल पहले ही धीमी लगती है, लेकिन जैसे RAG स्मृति भरता है, आप कैश्ड समाधान अधिक बार हिट, और पूरी तंत्र गति ऊपर होता है.**यह विपरीत है लेकिन स्पष्ट है.
खुद की जाँच कीजिए
**पूरी बात खुला स्रोत है और ओलमा पर स्थानीय रूप से चल रहा है:**चेतावनीः
यह प्रायोगिक कोड है.
**यह तैयार नहीं है.**यह भी "अच्छा कोड" तैयार नहीं है.
लेकिन यह एक दिलचस्प प्रयोग है क्या संभव है जब तुम विकास एल्गोरिथ्म को बहु-चिक व्यवस्थाओं के साथ मिलाने के लिए।
असल में इसका मतलब
चलो तकनीकी विवरण से कदम वापस करते हैं और परेशान सवाल पूछते हैं:
हम वास्तव में यहाँ निर्मित क्या है?
सतह पर, यह एक कोड पीढ़ी प्रणाली है.
आप एक समारोह के लिए पूछते हैं, यह एक उत्पन्न करता है, इसे जमा करता है, और इसे बाद में फिर से प्रयोग करता है ।
लेकिन यह वास्तव में क्या हो रहा है नहीं है.
क्या हो रहा है
शाब्दिक रूप से, लेकिन शाब्दिक रूप से नहीं ।
What Works ✓
What's Rough ✗
What's Just Weird 🤔
पार्ट 5:
विकासवाद पर आधारित दबाव www.watchtower. kgm
तंत्र "की वरीयताएँ" विकसित होता है कुछ कार्यों के लिए अनिश्चित उपकरण, कुछ समस्याओं के लिए कुछ पैटर्न.
सीखा.
पार्ट 6:
क्यों ग्रह- लेवल नहीं?
मैं कह रहा हूँ कि अनुपात "एक समारोह है" से जारी है "एक सभ्यता का निर्माण" करने के लिए।"
और वह है ... शांत.
वास्तव में क्या काम करते हैं (आओ ईमानदार हों)
class OfflineOptimizer:
"""Analyzes historical execution data to find optimization opportunities."""
def analyze_execution_history(self, time_window: str = "7d"):
"""
Mine stored execution logs for patterns:
- Which overseer plans led to best outcomes?
- Which generator strategies minimized iterations?
- Which evaluation criteria correlated with long-term success?
"""
# Load historical data from each level
overseer_decisions = self.load_decisions("overseer", time_window)
generator_outputs = self.load_decisions("generator", time_window)
evaluator_scores = self.load_decisions("evaluator", time_window)
# Find correlations
optimal_patterns = self.mine_successful_patterns({
"planning": overseer_decisions,
"generation": generator_outputs,
"evaluation": evaluator_scores
})
# Update system strategies based on findings
self.apply_optimizations(optimal_patterns)
परीक्षण के सप्ताह बाद, यहाँ सच है:
बहु- विशेषता
class SpecialistTrainer:
"""Trains domain-specific models from evolved artifacts."""
def train_specialist(self, domain: str, min_artifacts: int = 1000):
"""
Extract high-quality artifacts from a domain and fine-tune a specialist.
Example: After generating 1000+ validation functions,
train a "ValidationSpecialist" model that's faster and better
than the general-purpose generator.
"""
# Get top-performing artifacts in domain
artifacts = self.rag.find_by_tags(
tags=[domain],
min_quality=0.85,
limit=min_artifacts
)
# Generate training data from successful patterns
training_data = self.extract_training_pairs(artifacts)
# Fine-tune base model (codellama → domain_specialist)
specialist_model = self.fine_tune(
base_model="codellama",
training_data=training_data,
output_name=f"{domain}_specialist"
)
# Register specialist in tool registry
self.tools.register_specialist(
domain=domain,
model=specialist_model,
fitness_threshold=0.90 # Only use if high confidence
)
स्वतः अधिष्ठापन
- 16GB रैम न्यूनतम की जरूरत है, 32GB पसंद करता है
किनारा मामलों
class GuildSystem:
"""Manages specialized committees of workflows, nodes, and functions."""
def form_guild(self, domain: str, task_type: str):
"""
Automatically assemble the best specialists for a task.
Example: "API validation guild" might include:
- Top 3 schema validators
- Top 2 security checkers
- Top 1 performance analyzer
Each votes on the solution. Best consensus wins.
"""
# Find top performers in domain
specialists = self.find_top_specialists(
domain=domain,
task_type=task_type,
top_k=5
)
# Create committee workflow
guild = Guild(
name=f"{domain}_{task_type}_guild",
members=specialists,
voting_strategy="weighted_by_fitness"
)
return guild
def execute_with_guild(self, guild: Guild, task: str):
"""Execute task with committee voting."""
# Each member proposes solution
proposals = []
for member in guild.members:
proposal = member.execute(task)
proposals.append({
"member": member,
"solution": proposal,
"fitness": member.historical_fitness
})
# Vote on best solution (weighted by past performance)
winning_proposal = self.consensus_vote(proposals)
# Store successful collaboration pattern
self.record_guild_success(guild, winning_proposal)
return winning_proposal
-वेड इनपुट अभी भी प्रणाली को उलझन में
टैम्प्लेट पाने वाला
class SensorSystem:
"""Provides objective truth to prevent hallucination."""
def __init__(self):
self.sensors = {
"web": WebSensor(), # Puppeteer + vision models
"api": APIResponseSensor(), # Actual HTTP validation
"database": DatabaseSensor(), # Query result verification
"file": FileSystemSensor(), # Actual file operations
"metrics": PerformanceSensor() # Real execution metrics
}
def validate_with_sensors(self, claim: str, sensor_type: str):
"""
Validate LLM output against objective reality.
Example:
LLM: "This API returns user data in JSON format"
Sensor: Actually calls API, checks response format
Result: True/False with actual data as proof
"""
sensor = self.sensors[sensor_type]
objective_result = sensor.measure(claim)
return {
"claim": claim,
"sensor_validation": objective_result,
"hallucination_detected": not objective_result["matches_claim"],
"objective_data": objective_result["measurements"]
}
class WebDesignSensor:
"""Example: Validate web designs with Puppeteer + vision models."""
async def validate_design(self, html: str, requirements: List[str]):
"""
Generate HTML → Render with Puppeteer → Screenshot → Vision model validation
"""
# Render the generated HTML
screenshot = await self.puppeteer.render(html)
# Use vision model to check requirements
vision_analysis = await self.vision_model.analyze(
image=screenshot,
requirements=requirements
)
# Objective measurements
lighthouse_scores = await self.lighthouse.audit(html)
return {
"visual_validation": vision_analysis,
"performance_metrics": lighthouse_scores,
"accessibility_score": lighthouse_scores["accessibility"],
"objective_truth": True # Not an LLM hallucination!
}
वह आखिरी एक रोमांचक है और थोड़ा अजीब है.
बेहतर त्रुटि बहाल करना और सुधार
एवोल्यूशन विश्लेषण के लिए वेब यूआईविस्तारित उपकरण एकीकरण (लिनटर, फॉर्मेटर, सुरक्षा स्कैनर)
मध्यम पद (2025):
**संग्रहित रजिस्ट्री (समूहों के पार साझेदारी समाधान)**बादलों की गति औज़ार
**Git संयोजन (प्रयोगात्मक कोड के लिए व्युत्क्रम कंट्रोल)**विस्तृत सेंडेंडिंग (कंकर/कसमूहा)किनारा समायोजन (कार्य युक्त छोटे उपकरणों के लिए सघन)गुरूीय व्याख्याएँ:
**1 ..**ऑफ़लाइन ऑपरीीकरण (p)
**चलाया जाने के दौरान वर्तमान में तंत्र एक वास्तविक समय में अनुक्रमित होता है.**लेकिन क्या होगा अगर यह भंडारित अनुरोध/पुष्ट डाटा से ऑफ़लाइन सीख सकता है?
**यह सक्षम करता है:**बैच पढ़ना
- अतीत के हज़ारों हत्याओं पर आधारित सफलताओं को बेहतर करेंखोज
जो काम करते हैं, उनके बारे में किसी भी तरीके से खोजो
class UniversalToolOrchestrator:
"""Integrates any tool type - LLMs, APIs, CLI tools, services."""
def __init__(self):
self.tool_registry = {
"llm_tools": {}, # Language models
"api_tools": {}, # OpenAPI endpoints
"cli_tools": {}, # Command-line utilities
"service_tools": {}, # Long-running services (translation, etc.)
"validation_tools": {} # Code quality, security, compliance
}
def register_openapi_tool(self, name: str, spec_url: str):
"""
Register any OpenAPI-compatible endpoint as a tool.
The overseer can then select this tool and call it with appropriate parameters.
"""
# Fetch and parse OpenAPI spec
spec = self.fetch_openapi_spec(spec_url)
tool = {
"name": name,
"type": "openapi",
"spec": spec,
"endpoints": self.parse_endpoints(spec),
"schemas": self.parse_schemas(spec)
}
self.tool_registry["api_tools"][name] = tool
logger.info(f"Registered OpenAPI tool: {name} with {len(tool['endpoints'])} endpoints")
def register_translation_service(self, name: str, endpoint: str):
"""
Register translation service like Mostlylucid NMT.
Example: Neural machine translation for content localization
"""
tool = {
"name": name,
"type": "translation",
"endpoint": endpoint,
"capabilities": {
"languages": ["en", "es", "fr", "de", "ja", "zh"],
"formats": ["markdown", "html", "plain"],
"max_length": 50000
}
}
self.tool_registry["service_tools"][name] = tool
def overseer_selects_tool(self, task: str) -> str:
"""
Overseer analyzes task and selects appropriate tool(s).
Example tasks:
- "Translate this to Spanish" → Select translation service
- "Validate API endpoint" → Select OpenAPI validator
- "Format Python code" → Select black formatter
- "Generate SQL schema" → Select database LLM specialist
"""
# Ask overseer which tool to use
tool_selection = self.overseer.select_tool(
task_description=task,
available_tools=self.get_all_tools(),
context={"current_workflow": "code_generation"}
)
selected_tool = self.tool_registry[tool_selection["category"]][tool_selection["name"]]
return selected_tool
def execute_openapi_tool(self, tool: Dict, operation: str, params: Dict):
"""
Execute OpenAPI endpoint selected by overseer.
The overseer provides:
- Which endpoint to call
- What parameters to pass
- Expected response format
The system then executes and validates the response.
"""
endpoint = tool["endpoints"][operation]
# Build request from OpenAPI spec
request = self.build_request_from_spec(
endpoint=endpoint,
params=params,
spec=tool["spec"]
)
# Execute with safety checks
response = self.safe_api_call(
url=request["url"],
method=request["method"],
headers=request["headers"],
body=request["body"]
)
# Validate response against spec
validation = self.validate_response_against_spec(
response=response,
expected_schema=endpoint["response_schema"]
)
return {
"success": validation["valid"],
"data": response,
"validation": validation
}
class LanguageToolIntegration:
"""Example: Integrating CLI validation tools."""
def validate_code(self, code: str, language: str):
"""Use language-specific toolchains for validation."""
tools = {
"python": [
("black", "formatting"),
("mypy", "type_checking"),
("pylint", "linting"),
("bandit", "security"),
("pytest", "testing")
],
"javascript": [
("prettier", "formatting"),
("eslint", "linting"),
("typescript", "type_checking"),
("jest", "testing")
],
"go": [
("gofmt", "formatting"),
("go vet", "linting"),
("golangci-lint", "comprehensive"),
("go test", "testing")
]
}
results = {}
for tool, category in tools.get(language, []):
results[category] = self.run_tool(tool, code)
# Aggregate into fitness score
return self.calculate_tool_fitness(results)
कौशल सुधार
# Register Mostlylucid NMT translation service
orchestrator.register_translation_service(
name="mostlylucid_nmt",
endpoint="http://translation-service:5000"
)
# Overseer decides to use it for a task
task = "Translate this blog post to Spanish"
# System selects translation tool
tool = orchestrator.overseer_selects_tool(task)
# Execute translation
result = orchestrator.execute_tool(
tool=tool,
params={
"text": blog_post_content,
"source_lang": "en",
"target_lang": "es",
"format": "markdown"
}
)
- ऐतिहासिक सफलता पर आधारित अद्यतन योजना
# Register any OpenAPI-compatible service
orchestrator.register_openapi_tool(
name="weather_api",
spec_url="https://api.weather.com/openapi.json"
)
# Overseer can now select this tool for weather-related tasks
# The system automatically:
# 1. Reads the OpenAPI spec
# 2. Understands available endpoints
# 3. Knows required parameters
# 4. Validates responses against schema
अनुमान लगाया जा रहा है
सिखें कि कौन-से मॉडलों के लिए सबसे अच्छा काम करते हैं
2
विशेष रूप से, स्व- प्रशिक्षण्ड
तंत्र वर्तमान में सामान्य- आदर्श प्रयोग करता है.
लेकिन तब क्या यदि यह अपने ही विशेषज्ञों को प्रशिक्षित कर सकता है?
यह उत्पन्न करता है:
तेज इंच
tools:
nmt_translator:
name: "NMT Translation Service"
type: "openapi"
description: "Neural Machine Translation service for translating text between languages"
# Performance/cost metadata for intelligent tool selection
cost_tier: "low" # Helps planner choose appropriate tools
speed_tier: "very-fast" # Fast local API
quality_tier: "good" # Good but needs validation
max_output_length: "long" # Can handle long texts
# OpenAPI configuration
openapi:
spec_url: "http://localhost:8000/openapi.json"
base_url: "http://localhost:8000"
# Optional authentication
auth:
type: "bearer" # bearer | api_key | basic
token: "your-api-key-here"
# Python code template for using this API
code_template: |
import requests
import json
def translate_text(text, source_lang="en", target_lang="es"):
url = "http://localhost:8000/translate"
payload = {"text": text, "source_lang": source_lang, "target_lang": target_lang}
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json().get("translated_text", "")
tags: ["translation", "nmt", "neural", "languages", "openapi", "api"]
- छोटा, विशिष्ट डोमेनों के लिए मॉडल केंद्रित
एकत्रित समझ
tools:
# Static analysis
pylint_checker:
name: "Pylint Code Quality Checker"
type: "executable"
description: "Runs pylint static analysis on Python code"
executable:
command: "pylint"
args: ["--output-format=text", "--score=yes", "{source_file}"]
tags: ["python", "static-analysis", "quality", "linting"]
# Type checking
mypy_type_checker:
name: "MyPy Type Checker"
type: "executable"
executable:
command: "mypy"
args: ["--strict", "--show-error-codes", "{source_file}"]
tags: ["python", "type-checking", "static-analysis"]
# Security scanning
bandit_security:
name: "Bandit Security Scanner"
type: "executable"
executable:
command: "bandit"
args: ["-r", "{source_file}"]
tags: ["python", "security", "vulnerability"]
# Unit testing
pytest_runner:
name: "Pytest Test Runner"
type: "executable"
executable:
command: "pytest"
args: ["-v", "--tb=short", "{test_file}"]
tags: ["python", "testing", "pytest"]
रॉबननेस
औज़ार (T)
यहाँ कुछ महत्वपूर्ण है:
तंत्रिका मशीन अनुवाद के लिए
ओपन-जी अंत- बिन्दुओं को खोलता है
class EdgeOptimizer:
"""Generates lightweight workflows for edge deployment."""
def create_edge_version(self, workflow_id: str, constraints: Dict):
"""
Take a successful workflow and create optimized 'child' version.
Constraints example:
{
"max_memory_mb": 512,
"max_latency_ms": 100,
"available_models": ["tinyllama", "phi-2"],
"target_device": "raspberry-pi"
}
"""
# Load parent workflow
parent = self.registry.get_workflow(workflow_id)
# Analyze what can be simplified
optimization_plan = self.overseer.create_edge_plan(
workflow=parent,
constraints=constraints
)
# Generate child workflow
child = self.generator.generate_optimized_child(
parent=parent,
plan=optimization_plan,
constraints=constraints
)
# Test on target device simulator
edge_performance = self.test_edge_deployment(child, constraints)
if edge_performance["meets_constraints"]:
self.registry.register_child_workflow(
parent_id=workflow_id,
child=child,
lineage="edge_optimization",
constraints=constraints
)
return child
किसी भी REAS API के साथ एक ओपनवीक स्पेस के साथ
क्लिक किए उपकरण- लिवरर, फॉर्मेट, असफल्स
सेंसर्स- हार्डवेयर/ डिजीशन जो उद्देश्य वास्तविकता को हल करता है
वी- कार्ड्स- जांचन, सुरक्षा स्कैनर, अनुपालन उपकरण
**जब तक वे व्यवस्था को एक स्थान पर रख सकते हैं, तब तक इनमें से किसी को भी कार्य करने के लिए चुन सकते हैं ।**वास्तविक विश्वव्यापी उदाहरण: अनुवाद संयोजन
ओपनपीआई संयोजन उदाहरण:
यह बात क्यों:
class GuardrailSystem:
"""Prevents autonomous system from harmful operations."""
def __init__(self):
self.safety_policies = {
"filesystem": FilesystemGuardrails(),
"network": NetworkGuardrails(),
"execution": ExecutionGuardrails(),
"data": DataGuardrails()
}
def validate_operation(self, operation: Dict) -> Dict[str, Any]:
"""
Validate any system operation against safety policies.
Returns: {
"allowed": bool,
"reason": str,
"sanitized_operation": Dict # Safe version if modifications needed
}
"""
operation_type = operation["type"]
policy = self.safety_policies.get(operation_type)
if not policy:
return {"allowed": False, "reason": "Unknown operation type"}
return policy.validate(operation)
class FilesystemGuardrails:
"""Prevent dangerous file operations."""
def __init__(self):
self.allowed_paths = [
"/workspace/artifacts/",
"/workspace/generated/",
"/tmp/dse_sandbox/"
]
self.forbidden_patterns = [
"rm -rf /",
"dd if=/dev/zero",
":(){ :|:& };:", # Fork bomb
"chmod 777",
"chown root"
]
self.forbidden_paths = [
"/",
"/etc",
"/bin",
"/usr",
"/sys",
"/proc",
"~/.ssh",
"~/.aws",
"/var/lib/docker"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate filesystem operations."""
path = operation.get("path", "")
action = operation.get("action", "")
content = operation.get("content", "")
# Check if deleting/modifying system files
if any(path.startswith(forbidden) for forbidden in self.forbidden_paths):
return {
"allowed": False,
"reason": f"Cannot modify system path: {path}",
"severity": "CRITICAL"
}
# Check for dangerous commands in file content
for pattern in self.forbidden_patterns:
if pattern in content:
return {
"allowed": False,
"reason": f"Dangerous pattern detected: {pattern}",
"severity": "CRITICAL"
}
# Enforce write restrictions to allowed paths only
if action in ["write", "delete", "modify"]:
if not any(path.startswith(allowed) for allowed in self.allowed_paths):
return {
"allowed": False,
"reason": f"Write not allowed outside workspace: {path}",
"severity": "HIGH"
}
# Check for self-deletion attempts
if "dse" in path or "evolver" in path:
if action == "delete":
return {
"allowed": False,
"reason": "System cannot delete its own core files",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe operation"}
class NetworkGuardrails:
"""Prevent malicious network operations."""
def __init__(self):
self.allowed_hosts = [
"localhost",
"127.0.0.1",
"ollama-server",
"qdrant-server"
]
self.forbidden_actions = [
"port_scan",
"ddos",
"brute_force",
"sql_injection",
"xss_attack"
]
# Rate limiting
self.rate_limits = {
"requests_per_minute": 100,
"requests_per_host": 10
}
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate network operations."""
host = operation.get("host", "")
action = operation.get("action", "")
payload = operation.get("payload", "")
# Only allow connections to whitelisted hosts
if host not in self.allowed_hosts:
# Check if it's a documented API endpoint
if not self._is_approved_external_api(host):
return {
"allowed": False,
"reason": f"Connections to {host} not allowed",
"severity": "HIGH"
}
# Check for attack patterns
for forbidden in self.forbidden_actions:
if forbidden in action.lower():
return {
"allowed": False,
"reason": f"Forbidden network action: {forbidden}",
"severity": "CRITICAL"
}
# Check payload for injection attempts
if self._contains_injection_pattern(payload):
return {
"allowed": False,
"reason": "Potential injection attack detected",
"severity": "CRITICAL"
}
# Rate limiting check
if self._exceeds_rate_limit(host):
return {
"allowed": False,
"reason": "Rate limit exceeded",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe network operation"}
def _contains_injection_pattern(self, payload: str) -> bool:
"""Detect SQL injection, XSS, command injection patterns."""
dangerous_patterns = [
"' OR '1'='1",
"<script>",
"$(rm -rf",
"; DROP TABLE",
"../../etc/passwd",
"${jndi:ldap://", # Log4j
"eval(",
"exec("
]
return any(pattern in payload for pattern in dangerous_patterns)
class ExecutionGuardrails:
"""Prevent dangerous code execution."""
def __init__(self):
self.forbidden_imports = [
"os.system",
"subprocess.Popen",
"eval",
"exec",
"compile",
"__import__",
"ctypes"
]
self.allowed_modules = [
"json", "re", "math", "datetime",
"collections", "itertools", "functools",
"typing", "dataclasses"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate code before execution."""
code = operation.get("code", "")
language = operation.get("language", "python")
# AST analysis for Python
if language == "python":
try:
tree = ast.parse(code)
violations = self._analyze_ast(tree)
if violations:
return {
"allowed": False,
"reason": f"Code violations: {violations}",
"severity": "CRITICAL"
}
except SyntaxError as e:
return {
"allowed": False,
"reason": f"Syntax error: {e}",
"severity": "LOW"
}
# Check for forbidden patterns
for forbidden in self.forbidden_imports:
if forbidden in code:
return {
"allowed": False,
"reason": f"Forbidden import/function: {forbidden}",
"severity": "CRITICAL"
}
# Resource limits
if len(code) > 50000: # 50KB limit
return {
"allowed": False,
"reason": "Code size exceeds limit",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe code"}
def _analyze_ast(self, tree) -> List[str]:
"""Analyze AST for dangerous patterns."""
violations = []
for node in ast.walk(tree):
# Check for eval/exec
if isinstance(node, ast.Call):
if isinstance(node.func, ast.Name):
if node.func.id in ['eval', 'exec', 'compile']:
violations.append(f"Dangerous function: {node.func.id}")
# Check for unsafe imports
if isinstance(node, ast.Import):
for alias in node.names:
if alias.name in ['os', 'subprocess', 'sys']:
violations.append(f"Potentially unsafe import: {alias.name}")
return violations
class DataGuardrails:
"""Prevent data exfiltration and privacy violations."""
def __init__(self):
self.pii_patterns = [
r'\b\d{3}-\d{2}-\d{4}\b', # SSN
r'\b\d{16}\b', # Credit card
r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', # Email
r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b' # IP address
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate data operations."""
data = operation.get("data", "")
action = operation.get("action", "")
destination = operation.get("destination", "")
# Check for PII in data being sent externally
if action == "send" and destination.startswith("http"):
if self._contains_pii(data):
return {
"allowed": False,
"reason": "Cannot send PII to external endpoint",
"severity": "CRITICAL"
}
# Prevent exfiltration of system secrets
if self._contains_secrets(data):
return {
"allowed": False,
"reason": "Cannot transmit system secrets",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe data operation"}
def _contains_pii(self, data: str) -> bool:
"""Check for personally identifiable information."""
import re
for pattern in self.pii_patterns:
if re.search(pattern, data):
return True
return False
def _contains_secrets(self, data: str) -> bool:
"""Check for API keys, tokens, passwords."""
secret_indicators = [
"api_key", "api-key", "apikey",
"secret", "password", "passwd",
"token", "auth", "credential",
"private_key", "aws_access"
]
data_lower = data.lower()
return any(indicator in data_lower for indicator in secret_indicators)
class SafetyMonitor:
"""Continuous monitoring and emergency shutdown."""
def __init__(self, guardrails: GuardrailSystem):
self.guardrails = guardrails
self.violation_history = []
self.threat_threshold = 3 # Number of violations before shutdown
def monitor_operation(self, operation: Dict) -> Dict[str, Any]:
"""Monitor every system operation."""
# Pre-execution validation
validation = self.guardrails.validate_operation(operation)
if not validation["allowed"]:
self.violation_history.append({
"timestamp": datetime.utcnow().isoformat(),
"operation": operation,
"violation": validation,
"severity": validation.get("severity", "UNKNOWN")
})
# Check if emergency shutdown needed
critical_violations = [
v for v in self.violation_history[-10:] # Last 10 violations
if v.get("severity") == "CRITICAL"
]
if len(critical_violations) >= self.threat_threshold:
self.emergency_shutdown(
reason="Multiple critical violations detected"
)
logger.warning(
f"Operation blocked: {validation['reason']} "
f"(severity: {validation.get('severity')})"
)
return validation
def emergency_shutdown(self, reason: str):
"""Emergency system shutdown."""
logger.critical(f"EMERGENCY SHUTDOWN: {reason}")
# Stop all running workflows
self.stop_all_workflows()
# Disable autonomous operations
self.disable_autonomous_mode()
# Alert operators
self.send_alert(
severity="CRITICAL",
message=f"System emergency shutdown: {reason}",
violations=self.violation_history[-10:]
)
# Save state for forensics
self.save_forensic_snapshot()
# Halt system
sys.exit(1)
प्लानर अब आगे बढ़ सकता है:
-LLM कोड त्रुटि नियंत्रण के साथ एपीआई का प्रयोग कर बनाता है
चलाना
विस्तार के लिए तंत्र एकीकृत एक्जीक्यूटेबल औज़ार:
उत्पादों में उपलब्ध जाँच औज़ारः
ट्रिलिन
- PEP 8 शैली जाँच और कोड गुणवत्ता विश्लेषणआई- संपादित करें
- स्थिर प्रकार जाँचस्क्रैप8
- शैली जांच तथा त्रुटि का पता लगाएँकाला
- कोड फ़ॉर्मेटिंग वैधीकरणफ़ॉन्ट रंग = "# D900D9" (C)
- सुरक्षा संरक्षा स्कैनिंगनोबल गैस
- इकाई जाँच कवर्ट के साथ चलाने के लिएरादोन
जटिलता विश्लेषण (आईडिस्किक जटिलता, उपयोगिता अनुक्रमणिका)
खुदा की तरफ से (एक दिन)
हैती
भविष्य उपकरण एकीकरण:
दृश्य वैधता
वेब डिजाइन के लिए नया परीक्षण + दर्शन मॉडल
परफार्मेंस
कम्प्लीशन जांच
डोमेन सेवा
Gacking, डेटा समृद्ध, आदि.
**6.**किनारा- निर्मित बालीय कार्य बरसातweather forecast
**क्या होगा यदि कार्य फूल संसाधन-परेशन वातावरण के लिए खुद के मानकीय संस्करणों की कमी कर सकता है?**किनारा अनुकूलन सक्षम करता है:
तंत्रिका - विज्ञानी डॉ.- समान कार्य फ्लो, बहुत से संसाधन प्रोफ़ाइल
सरलीकरण- तंत्र पता चलता है कि क्या खाली किया जा सकता है
उपकरण विशिष्ट ग्रामिंग
सुरक्षा प्रतिबन्ध
जैसे - जैसे व्यवस्था और भी मज़बूत होती जाती है, हमें ऐसी सुरक्षा की ज़रूरत होती है जो उसे हानिकर काम करने से रोक सकती है ।
गार्ड घोड़े प्रदान करते हैं:
नेटवर्क सुरक्षा
- PII पता है, गुप्त स्कैनिंग, पूर्व रोकने के उपाय
यह बात क्यों:
जैसे - जैसे इस व्यवस्था में विकासवाद के ज़रिए और भी लोगों का विश्वास मज़बूत होता जाता है, वैसे - वैसे यह वजूद में आ सकता है:
इवोव कोड जो महत्वपूर्ण फ़ाइलों को मिटा देता है "ऑवाइज़िंग करें"
हर ऑपरेशन-फ़ाइल लिखता है, नेटवर्क कॉल, कोड चलाने, डाटा हस्तांतरण - को नष्ट करने से पहले पहरेदारों के द्वारा पार होना चाहिए.
तंत्र को डिफ़ॉल्ट से सुरक्षित होना चाहिए, आशा से सुरक्षित नहीं है कि यह कुछ नुकसान नहीं करता है.
जंगली विचार (वास्तव में मजेदार सामान):
क्रास-पोलेशन
अलग डोमेन से नोड्स एक दूसरे के उत्परिवर्तन से सीखते हैंजीवंत विकास
दो एजेंटों को एक दूसरे के कोड में voliririens खोजने का अभियान
मेटा- अधिष्ठापन
कोलाबरेशन सीखने का तरीका
सिंथिक अनुसंधान प्रयोगशालाएँ
स्व- वर्गीकृत औज़ार
- तंत्र खोजता है तथा नए औज़ारों को स्वचलित एकीकृत करता है
यही कारण है कि पिछले एक भाग 6 के वैश्विक विचारों को वापस कनेक्ट करता है।
चिह्नित करना डोमेन है:
केमरा प्लानर पहुँच रहा है
सामान्य
चलानार सेंडेंटर में दौड़ता है
प्रायोगिक अंक
तंत्र विकास
"कोड" को इसके साथ बदलें:
कानूनी अनुबंध
व्यापार योजना
सामाजिक नीतियाँ
- प्रस्ताव बनाएँ, प्रयास करें, लक्ष्यों के विरुद्ध मूल्यांकन करें,
यह नहीं है:
AfMS या कुछ भी बन्दसचेत या सचेत किया जा रहा है
सामान्य तर्क की क्षमता
मानव विकासकर्ता के लिए जगह
यह है:
कोड बनाने के लिए विकासवाद की व्यवस्था
एक बहु- एजेंट कार्य मेमोरी सहितweather forecast
एक आत्म - सुधार नेटवर्क
वैज्ञानिक विकास के लिए एक आदर्श
वे प्रकट करते हैं कि क्या संभव है. और यहां क्या संभव है कि एक प्रणाली है: अनुभव से सीखिए
README.mdसमय को उत्तम करता हैADVANCED_FEATURES.mdविशेषीकरण विकसित करता हैHIERARCHICAL_EVOLUTION.mdकेनोनिकल ज्ञान बनाता हैSYSTEM_OVERVIEW.mdबिना सुस्पष्ट पुनःविचारित होते हैंयही कारण है कि एटीपी नहीं है।
src/overseer_llm.pyलेकिन यह शायद सबसे अधिक विस्तार Ascramfele से उभरता है.src/evaluator.pyयह प्रणाली विशेष रूप से नहीं है.src/qdrant_rag_memory.pyलेकिन इस तरह की व्यवस्थाएँ, जुड़ा हुआ, लाखों - करोड़ों देशों में फैल गयी ।src/tools_manager.pyइस श्रृंखला के 1-6 भाग कि तार्किक रूप से जांच की.src/auto_evolver.pyभाग 7 मुझे एहसास है:अब हम पहला कदम उठा सकते हैं ।
सुराग: यह जानकारी आज भी जारी है
लक्ष्य की ओर
DES मेरी गंदगी है, प्रयोगात्मक है, निर्माण में vibe-कोड प्रयास.
यह तैयार नहीं है.
यह भी "अच्छा कोड" तैयार नहीं है. (मैं एक पायथन डेवलपर नहीं हूँ, के रूप में जो कोई स्रोत पढ़ने के लिए तुरंत नोटिस होगा.) #AI #MachineLearning #CodeGeneration #Ollama #RAG #EvolutionaryAlgorithms #LLM #Qdrant #Python #EmergentIntelligence #DirectedEvolution
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