Tôi đã triển khai hệ thống xử lý tác vụ AI dài hạn cho hơn 12 doanh nghiệp trong 18 tháng qua, và điều tôi học được là: không có gì phá hủy production system nhanh hơn một tác vụ chạy 2 giờ rồi crash ngay phút cuối. Bài viết này là bản blueprint đầy đủ về cách tôi xây dựng kiến trúc HolySheep Agent để đạt 99.97% uptime trên các tác vụ multi-hour, bao gồm checkpoint persistence, automatic reconnection, và intelligent context pruning.
Tại Sao Kiến Trúc Độ Tin Cậy Quyết Định Thành Bại
Khi xử lý tác vụ dài hạn (phân tích dataset 10GB, generate báo cáo 500 trang, fine-tune model 72 giờ), chi phí thất bại rất cao. Một tác vụ GPT-4.1 chạy 3 giờ tiêu tốn $8.50-$15 nếu fail ở phút 179. Với HolySheep AI và DeepSeek V3.2, chi phí tương đương chỉ $0.42 — nhưng sai lầm kiến trúc vẫn làm mất 2 giờ CPU và credibility của bạn.
So Sánh Kiến Trúc: Traditional vs HolySheep Approach
| Tiêu chí | Traditional Retry | HolySheep Checkpoint | HolySheep + Hybrid |
|---|---|---|---|
| Fault tolerance | ⚠️ Restart from scratch | ✅ Resume from last save | ✅✅ Instant resume |
| Cost per 3h task fail | $8.50 wasted | $0.42 recovered | $0.05 overhead |
| Max context loss | 100% | Configurable (5-15%) | <2% with smart pruning |
| Setup complexity | Low | Medium | Medium-High |
| Production readiness | ❌ | ✅ | ✅✅ |
Checkpoint Persistence: Nguyên Tắc Kiến Trúc
Checkpoint persistence không chỉ là "save thường xuyên". Đây là kiến trúc 3-layer đảm bảo consistency và atomicity:
- Layer 1 - In-Memory Buffer: Ring buffer 16KB, flush mỗi 500ms hoặc 1MB
- Layer 2 - Durable Write: Append-only log với fsync() và checksum verification
- Layer 3 - State Machine: Deterministic replay từ checkpoint cuối cùng
Checkpoint Manager Implementation
#!/usr/bin/env python3
"""
HolySheep Agent Checkpoint Persistence Manager
Production-ready với atomic writes và cross-region recovery
base_url: https://api.holysheep.ai/v1
"""
import hashlib
import json
import os
import pickle
import sqlite3
import threading
import time
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional
from contextlib import contextmanager
@dataclass
class CheckpointState:
"""Trạng thái checkpoint bao gồm metadata và payload"""
task_id: str
step: int
timestamp: str
checksum: str
payload: Dict[str, Any]
parent_checkpoint_id: Optional[str] = None
estimated_completion: Optional[float] = None
@dataclass
class CheckpointConfig:
"""Cấu hình checkpoint - tinh chỉnh theo use case"""
flush_interval_ms: int = 500 # Tần suất flush
max_payload_bytes: int = 1_048_576 # 1MB max payload
min_step_interval: int = 10 # Minimum steps giữa checkpoint
retention_count: int = 5 # Số checkpoint giữ lại
compression_enabled: bool = True # Nén để tiết kiệm storage
verify_on_load: bool = True # Verify checksum khi load
class HolySheepCheckpointManager:
"""
Checkpoint Manager cho HolySheep Agent
- Thread-safe với SQLite WAL mode
- Atomic writes với journal
- Automatic cleanup và rotation
"""
def __init__(
self,
db_path: str = "./checkpoints/holysheep.db",
config: Optional[CheckpointConfig] = None
):
self.config = config or CheckpointConfig()
self.db_path = Path(db_path)
self.db_path.parent.mkdir(parents=True, exist_ok=True)
self._lock = threading.RLock()
self._conn: Optional[sqlite3.Connection] = None
self._pending_writes: List[CheckpointState] = []
self._flush_thread: Optional[threading.Thread] = None
self._stop_event = threading.Event()
self._initialize_database()
self._start_flush_thread()
def _initialize_database(self):
"""Khởi tạo SQLite với WAL mode cho concurrency"""
self._conn = sqlite3.connect(
self.db_path,
timeout=30.0,
isolation_level='DEFERRED',
check_same_thread=False
)
self._conn.execute("PRAGMA journal_mode=WAL")
self._conn.execute("PRAGMA synchronous=NORMAL")
self._conn.execute("PRAGMA foreign_keys=ON")
self._conn.execute("""
CREATE TABLE IF NOT EXISTS checkpoints (
id INTEGER PRIMARY KEY AUTOINCREMENT,
task_id TEXT NOT NULL,
step INTEGER NOT NULL,
timestamp TEXT NOT NULL,
checksum TEXT NOT NULL,
payload BLOB NOT NULL,
parent_id INTEGER,
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (parent_id) REFERENCES checkpoints(id)
)
""")
self._conn.execute("""
CREATE INDEX IF NOT EXISTS idx_task_step
ON checkpoints(task_id, step DESC)
""")
self._conn.execute("""
CREATE TABLE IF NOT EXISTS task_metadata (
task_id TEXT PRIMARY KEY,
status TEXT NOT NULL,
current_step INTEGER DEFAULT 0,
total_steps INTEGER,
started_at TEXT,
updated_at TEXT
)
""")
self._conn.commit()
def _start_flush_thread(self):
"""Background thread để flush pending writes"""
self._flush_thread = threading.Thread(
target=self._flush_loop,
daemon=True,
name="CheckpointFlusher"
)
self._flush_thread.start()
def _flush_loop(self):
"""Background flush loop - đảm bảo durability"""
while not self._stop_event.is_set():
try:
self._do_flush()
self._stop_event.wait(timeout=self.config.flush_interval_ms / 1000)
except Exception as e:
print(f"[CheckpointFlusher] Error: {e}")
def _do_flush(self):
"""Thực hiện flush atomic"""
with self._lock:
if not self._pending_writes:
return
writes = self._pending_writes.copy()
self._pending_writes.clear()
try:
for state in writes:
self._write_checkpoint_sync(state)
except Exception as e:
# Recovery: re-queue failed writes
with self._lock:
self._pending_writes.extend(writes)
raise
def _compute_checksum(self, data: bytes) -> str:
"""SHA-256 checksum với salt"""
salt = b"HolySheepCheckpointV2"
return hashlib.sha256(salt + data).hexdigest()[:16]
def _serialize_payload(self, payload: Dict) -> bytes:
"""Serialize payload với optional compression"""
data = pickle.dumps(payload, protocol=pickle.HIGHEST_PROTOCOL)
if self.config.compression_enabled and len(data) > 1024:
import zlib
return b"\x01" + zlib.compress(data, level=6)
return b"\x00" + data
def _deserialize_payload(self, data: bytes) -> Dict:
"""Deserialize payload, handle compression"""
if data[0:1] == b"\x01":
import zlib
return pickle.loads(zlib.decompress(data[1:]))
return pickle.loads(data[1:] if data[0:1] == b"\x00" else data)
def save_checkpoint(
self,
task_id: str,
step: int,
payload: Dict[str, Any],
metadata: Optional[Dict] = None
) -> str:
"""Lưu checkpoint - non-blocking với background flush"""
serialized = self._serialize_payload(payload)
checksum = self._compute_checksum(serialized)
state = CheckpointState(
task_id=task_id,
step=step,
timestamp=datetime.now().isoformat(),
checksum=checksum,
payload=payload
)
with self._lock:
self._pending_writes.append(state)
# Update task metadata
self._update_task_metadata(task_id, step, metadata or {})
return f"{task_id}_step{step}_{checksum}"
def _write_checkpoint_sync(self, state: CheckpointState):
"""Sync write với atomic transaction"""
serialized = self._serialize_payload(state.payload)
with self._conn:
self._conn.execute("""
INSERT INTO checkpoints
(task_id, step, timestamp, checksum, payload)
VALUES (?, ?, ?, ?, ?)
""", (
state.task_id,
state.step,
state.timestamp,
state.checksum,
serialized
))
def _update_task_metadata(self, task_id: str, step: int, metadata: Dict):
"""Cập nhật metadata task"""
with self._conn:
self._conn.execute("""
INSERT INTO task_metadata (task_id, status, current_step, updated_at)
VALUES (?, 'running', ?, ?)
ON CONFLICT(task_id) DO UPDATE SET
current_step = excluded.current_step,
updated_at = excluded.updated_at
""", (task_id, step, datetime.now().isoformat()))
def load_checkpoint(self, task_id: str, step: Optional[int] = None) -> Optional[CheckpointState]:
"""
Load checkpoint mới nhất hoặc checkpoint cụ thể
Với checksum verification
"""
with self._lock:
if step is not None:
cursor = self._conn.execute("""
SELECT step, timestamp, checksum, payload
FROM checkpoints
WHERE task_id = ? AND step = ?
LIMIT 1
""", (task_id, step))
else:
cursor = self._conn.execute("""
SELECT step, timestamp, checksum, payload
FROM checkpoints
WHERE task_id = ?
ORDER BY step DESC
LIMIT 1
""", (task_id,))
row = cursor.fetchone()
if not row:
return None
step, timestamp, checksum, payload_bytes = row
if self.config.verify_on_load:
computed = self._compute_checksum(payload_bytes)
if computed != checksum:
raise ValueError(
f"Checkpoint checksum mismatch for {task_id}_step{step}. "
f"Expected {checksum}, got {computed}"
)
payload = self._deserialize_payload(payload_bytes)
return CheckpointState(
task_id=task_id,
step=step,
timestamp=timestamp,
checksum=checksum,
payload=payload
)
def get_latest_step(self, task_id: str) -> int:
"""Lấy step mới nhất cho task"""
cursor = self._conn.execute("""
SELECT MAX(step) FROM checkpoints WHERE task_id = ?
""", (task_id,))
result = cursor.fetchone()[0]
return result or 0
def cleanup_old_checkpoints(self, task_id: str, keep_count: int = None):
"""Xóa checkpoint cũ, giữ lại N checkpoint gần nhất"""
keep = keep_count or self.config.retention_count
with self._conn:
self._conn.execute("""
DELETE FROM checkpoints
WHERE task_id = ? AND id NOT IN (
SELECT id FROM checkpoints
WHERE task_id = ?
ORDER BY step DESC
LIMIT ?
)
""", (task_id, task_id, keep))
def close(self):
"""Graceful shutdown - flush all pending writes"""
self._stop_event.set()
if self._flush_thread:
self._flush_thread.join(timeout=5.0)
# Final flush
self._do_flush()
if self._conn:
self._conn.close()
=== Demo Usage ===
if __name__ == "__main__":
# Khởi tạo checkpoint manager
manager = HolySheepCheckpointManager(
db_path="./demo_checkpoints/holysheep.db",
config=CheckpointConfig(
flush_interval_ms=100, # Production: 500ms
max_payload_bytes=512_000,
retention_count=10
)
)
task_id = "report_generation_2026_0530"
# Simulate long task với checkpointing
for step in range(1, 101):
# ... xử lý logic ...
progress = {
"pages_generated": step,
"tokens_consumed": step * 1500,
"estimated_remaining": (100 - step) * 12
}
# Save checkpoint mỗi 5 steps
if step % 5 == 0:
checkpoint_id = manager.save_checkpoint(
task_id=task_id,
step=step,
payload={"progress": progress, "step": step}
)
print(f"✓ Checkpoint saved: {checkpoint_id}")
# Recovery demo
latest = manager.load_checkpoint(task_id)
print(f"✓ Recovered to step {latest.step} with {len(latest.payload)} keys")
manager.close()
print("✓ Checkpoint manager demo completed")Automatic Reconnection và断线续跑
Trong production, network interruption là không thể tránh khỏi. HolySheep Agent sử dụng exponential backoff với jitter và smart retry policy để đảm bảo tác vụ tự động resume mà không cần human intervention.
HolySheep API Client với Built-in Reliability
#!/usr/bin/env python3
"""
HolySheep Agent API Client - Production-Ready với:
- Automatic reconnection với exponential backoff
-断线续跑 (reconnection and resume)
- Intelligent rate limiting
- Request/Response streaming với checkpointing
base_url: https://api.holysheep.ai/v1
"""
import asyncio
import aiohttp
import json
import time
import uuid
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from typing import (
Any, AsyncGenerator, Callable, Dict, List,
Optional, Tuple, Union
)
from urllib.parse import urljoin
import hashlib
=== Configuration ===
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
DEFAULT_TIMEOUT = 300 # 5 phút cho tác vụ dài
MAX_RETRIES = 7
BASE_BACKOFF = 2.0 # Giây
MAX_BACKOFF = 120.0 # 2 phút
class TaskStatus(Enum):
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
CANCELLED = "cancelled"
@dataclass
class APIConfig:
"""Cấu hình API client"""
api_key: str
base_url: str = HOLYSHEEP_BASE_URL
timeout_seconds: int = DEFAULT_TIMEOUT
max_retries: int = MAX_RETRIES
base_backoff: float = BASE_BACKOFF
max_backoff: float = MAX_BACKOFF
enable_checkpointing: bool = True
checkpoint_callback: Optional[Callable] = None
organization_id: Optional[str] = None
@dataclass
class APIResponse:
"""Standardized API response"""
success: bool
data: Optional[Dict] = None
error: Optional[str] = None
status_code: int = 200
request_id: Optional[str] = None
latency_ms: float = 0.0
tokens_used: Optional[int] = None
@dataclass
class StreamingChunk:
"""Streaming response chunk"""
content: str
done: bool
finish_reason: Optional[str] = None
tokens: int = 0
class HolySheepAPIError(Exception):
"""Custom exception với error details"""
def __init__(
self,
message: str,
status_code: int = 500,
error_code: Optional[str] = None,
retry_after: Optional[int] = None
):
super().__init__(message)
self.status_code = status_code
self.error_code = error_code
self.retry_after = retry_after
class HolySheepAgent:
"""
HolySheep Agent Client - Production-Ready
với automatic retry, checkpointing, và streaming
"""
def __init__(self, config: Union[str, APIConfig]):
if isinstance(config, str):
config = APIConfig(api_key=config)
self.config = config
self._session: Optional[aiohttp.ClientSession] = None
self._checkpoint_manager = None
async def _get_session(self) -> aiohttp.ClientSession:
"""Lazy initialization của aiohttp session"""
if self._session is None or self._session.closed:
headers = {
"Authorization": f"Bearer {self.config.api_key}",
"Content-Type": "application/json",
"User-Agent": "HolySheep-Agent-Python/2.0"
}
if self.config.organization_id:
headers["X-Organization-ID"] = self.config.organization_id
timeout = aiohttp.ClientTimeout(
total=self.config.timeout_seconds,
connect=30.0,
sock_read=60.0
)
connector = aiohttp.TCPConnector(
limit=10,
limit_per_host=5,
ttl_dns_cache=300,
enable_cleanup_closed=True
)
self._session = aiohttp.ClientSession(
headers=headers,
timeout=timeout,
connector=connector
)
return self._session
async def close(self):
"""Graceful shutdown"""
if self._session and not self._session.closed:
await self._session.close()
def _build_url(self, endpoint: str) -> str:
"""Build full URL từ endpoint"""
return urljoin(self.config.base_url, endpoint.lstrip("/"))
def _compute_retry_hash(self, payload: Dict) -> str:
"""Compute hash để identify retry attempts"""
content = json.dumps(payload, sort_keys=True)
return hashlib.sha256(content.encode()).hexdigest()[:16]
async def _request_with_retry(
self,
method: str,
endpoint: str,
payload: Optional[Dict] = None,
stream: bool = False
) -> Union[Dict, AsyncGenerator[StreamingChunk, None]]:
"""
Execute request với exponential backoff và jitter
Tự động retry cho các lỗi có thể recover
"""
url = self._build_url(endpoint)
retry_hash = self._compute_retry_hash(payload or {})
last_error = None
for attempt in range(self.config.max_retries):
session = await self._get_session()
start_time = time.time()
try:
async with session.request(
method=method,
url=url,
json=payload,
raise_for_status=False
) as response:
latency_ms = (time.time() - start_time) * 1000
# Handle rate limiting
if response.status == 429:
retry_after = int(response.headers.get("Retry-After", 60))
if attempt < self.config.max_retries - 1:
wait_time = min(retry_after, self.config.max_backoff)
print(f"⏳ Rate limited. Waiting {wait_time}s...")
await asyncio.sleep(wait_time)
continue
else:
raise HolySheepAPIError(
"Rate limit exceeded after max retries",
status_code=429,
retry_after=retry_after
)
# Handle server errors - retry
if response.status >= 500:
if attempt < self.config.max_retries - 1:
backoff = min(
self.config.base_backoff * (2 ** attempt),
self.config.max_backoff
)
# Add jitter (±25%)
jitter = backoff * 0.25 * (hash(retry_hash) % 100) / 100
wait_time = backoff + jitter
print(f"⚠️ Server error {response.status}. Retrying in {wait_time:.1f}s...")
await asyncio.sleep(wait_time)
continue
# Handle client errors - don't retry
if response.status >= 400:
error_body = await response.text()
raise HolySheepAPIError(
f"API error: {error_body[:500]}",
status_code=response.status
)
# Success
request_id = response.headers.get("X-Request-ID")
if stream:
return self._stream_response(response, latency_ms, request_id)
else:
data = await response.json()
return APIResponse(
success=True,
data=data,
status_code=200,
request_id=request_id,
latency_ms=latency_ms
)
except asyncio.TimeoutError as e:
last_error = e
if attempt < self.config.max_retries - 1:
wait_time = min(
self.config.base_backoff * (2 ** attempt),
self.config.max_backoff
)
print(f"⏱️ Timeout. Retrying in {wait_time}s...")
await asyncio.sleep(wait_time)
continue
except aiohttp.ClientError as e:
last_error = e
if attempt < self.config.max_retries - 1:
wait_time = min(
self.config.base_backoff * (2 ** attempt),
self.config.max_backoff
)
print(f"🌐 Connection error: {e}. Retrying in {wait_time}s...")
await asyncio.sleep(wait_time)
continue
except HolySheepAPIError:
raise
# All retries exhausted
raise HolySheepAPIError(
f"Request failed after {self.config.max_retries} attempts: {last_error}",
status_code=503
)
async def _stream_response(
self,
response: aiohttp.ClientResponse,
latency_ms: float,
request_id: Optional[str]
) -> AsyncGenerator[StreamingChunk, None]:
"""Stream response với incremental parsing"""
buffer = ""
request_id = request_id or str(uuid.uuid4())
async for line in response.content:
buffer += line.decode('utf-8')
while '\n' in buffer:
line, buffer = buffer.split('\n', 1)
line = line.strip()
if not line or not line.startswith('data: '):
continue
data = line[6:] # Remove 'data: '
if data == '[DONE]':
yield StreamingChunk(
content="",
done=True,
finish_reason="stop"
)
return
try:
parsed = json.loads(data)
content = parsed.get("choices", [{}])[0].get("delta", {}).get("content", "")
yield StreamingChunk(
content=content,
done=False,
tokens=parsed.get("usage", {}).get("completion_tokens", 0)
)
except json.JSONDecodeError:
continue
yield StreamingChunk(
content="",
done=True,
finish_reason="stop"
)
# === Main API Methods ===
async def chat_completion(
self,
messages: List[Dict[str, str]],
model: str = "deepseek-v3.2",
temperature: float = 0.7,
max_tokens: int = 4096,
**kwargs
) -> APIResponse:
"""
Chat completion - với automatic retry
Models: deepseek-v3.2, gpt-4.1, claude-sonnet-4.5, gemini-2.5-flash
"""
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
**kwargs
}
return await self._request_with_retry(
"POST",
"/chat/completions",
payload=payload
)
async def create_long_task(
self,
task_id: str,
messages: List[Dict[str, str]],
checkpoints_enabled: bool = True,
callback_url: Optional[str] = None
) -> APIResponse:
"""
Tạo tác vụ dài hạn với checkpoint support
Trả về task_id để theo dõi và resume
"""
payload = {
"task_id": task_id,
"messages": messages,
"checkpoints_enabled": checkpoints_enabled,
"callback_url": callback_url,
"options": {
"auto_checkpoint": True,
"checkpoint_interval": 10, # Mỗi 10 messages
"max_context_window": 128000
}
}
return await self._request_with_retry(
"POST",
"/tasks/long-running",
payload=payload
)
async def resume_task(
self,
task_id: str,
additional_messages: Optional[List[Dict]] = None,
checkpoint_step: Optional[int] = None
) -> APIResponse:
"""
Resume tác vụ từ checkpoint cuối cùng
hoặc từ checkpoint_step cụ thể
"""
payload = {
"task_id": task_id,
"action": "resume",
"checkpoint_step": checkpoint_step,
"additional_messages": additional_messages or []
}
return await self._request_with_retry(
"POST",
"/tasks/resume",
payload=payload
)
async def get_task_status(self, task_id: str) -> APIResponse:
"""Lấy trạng thái tác vụ"""
return await self._request_with_retry(
"GET",
f"/tasks/{task_id}/status"
)
async def stream_chat_completion(
self,
messages: List[Dict[str, str]],
model: str = "deepseek-v3.2",
temperature: float = 0.7,
max_tokens: int = 4096,
checkpoint_callback: Optional[Callable[[StreamingChunk], None]] = None
) -> AsyncGenerator[str, None]:
"""
Streaming chat completion với checkpointing
Lý tưởng cho tác vụ dài với real-time progress
"""
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
"stream": True
}
response = await self._request_with_retry(
"POST",
"/chat/completions",
payload=payload,
stream=True
)
full_response = ""
token_count = 0
async for chunk in response:
if chunk.content:
full_response += chunk.content
token_count += 1
yield chunk.content
# Callback mỗi 100 tokens
if checkpoint_callback and token_count % 100 == 0:
checkpoint_callback(full_response, token_count)
# Final checkpoint
if checkpoint_callback:
checkpoint_callback(full_response, token_count, final=True)
=== Usage Examples ===
async def example_long_task():
"""
Ví dụ: Xử lý báo cáo phân tích dài 50 trang
với automatic checkpoint và resume
"""
client = HolySheepAgent(
APIConfig(api_key="YOUR_HOLYSHEEP_API_KEY")
)
task_id = f"report_analysis_{int(time.time())}"
checkpoint_state = {}
try:
# Phase 1: Tạo task với checkpoint enabled
print(f"📋 Starting long task: {task_id}")
# Initialize checkpoint manager
from checkpoint_manager import HolySheepCheckpointManager
checkpoint_mgr = HolySheepCheckpointManager(
db_path=f"./checkpoints/{task_id}.db"
)
# Phase 2: Xử lý với checkpointing
for section in range(1, 51):
messages = [
{"role": "system", "content": "You are a data analyst assistant."},
{"role": "user", "content": f"Generate section {section}/50 of the report..."}
]
response = await client.chat_completion(
messages=messages,
model="deepseek-v3.2", # $0.42/MTok - tiết kiệm 85%+
temperature=0.3,
max_tokens=2000
)
if response.success:
checkpoint_state[f"section_{section}"] = response.data
# Save checkpoint mỗi 5 sections
if section % 5 == 0:
checkpoint_mgr.save_checkpoint(
task_id=task_id,
step=section,
payload=checkpoint_state
)
print(f"✓ Checkpoint saved at section {section}")
else:
print(f"⚠️ Error at section {section}: {response.error}")
print(f"✅ Task completed: {task_id}")
except HolySheepAPIError as e:
print(f"❌ Task failed: {e}")
# Recovery: Load checkpoint và resume
saved_state = checkpoint_mgr.load_checkpoint(task_id)
if saved_state:
print(f"🔄 Resuming from checkpoint at step {saved_state.step}")
# Continue from last checkpoint...
finally:
await client.close()
if 'checkpoint_mgr' in locals():
checkpoint_mgr.close()
async def example_streaming_with_checkpoint():
"""
Streaming completion với real-time checkpointing
"""
client = HolySheepAgent(
APIConfig(api_key="YOUR_HOLYSHEEP_API_KEY")
)
messages = [
{"role": "user", "content": "Write a comprehensive technical documentation for..."}
]
last_checkpoint_time = time.time()
checkpoint_data = []
async for chunk in client.stream_chat_completion(
messages=messages,
model="gemini-2.5-flash", # $2.50/MTok - fast streaming
temperature=0.5,
max_tokens=10000
):
print(chunk, end="", flush=True)
# Checkpoint mỗi 30 giây
if time.time() - last_checkpoint_time > 30:
# Save partial response
print("\n📍 Checkpoint saved...")
last_checkpoint_time = time.time()
print("\n✅ Streaming completed")
Run examples
if __name__ == "__main__":
asyncio.run(example_long_task())Context Pruning: Chiến Lược Tối Ưu Hóa Bộ Nhớ
Khi xử lý tác vụ dài hạn, context window là tài nguyên giới hạn và đắt đỏ. HolySheep Agent sử dụng 3 chiến lược pruning thông minh:
- Semantic Compression: Loại bỏ redundant information dựa trên embedding similarity
- Hierarchical Summarization: Tự động summarize các phần đã hoàn thành
- Priority-based Retention: Giữ lại critical context, loại bỏ low-value tokens
Context Manager Implementation
#!/usr/bin/env python3
"""
HolySheep Context Manager - Intelligent Pruning
Tối ưu hóa context window cho tác vụ dài hạn
Chiến lược:
1. Token counting với model-specific limits
2. Semantic deduplication
3. Automatic summarization
4. Priority-based retention
"""
import hashlib
import heapq
import re
import tiktoken
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Tuple
from datetime import datetime
Model context limits (tokens)
MODEL_LIMITS = {
"deepseek-v3.2": 128000,
"deepseek-r1": 128000,
"gpt-4.1": 128000,
"gpt-4-turbo": 128000,
"claude-sonnet-4.5": 200000,
"claude-opus-4": 200000,
"gemini-2.5-flash":