Tháng 9 năm ngoái, tôi nhận được cuộc gọi lúc 2 giờ sáng từ đội devops: hệ thống RAG của khách hàng thương mại điện tử lớn bậc nhất Việt Nam sập hoàn toàn ngay đỉnh flash sale 11.11. Nguyên nhân? API response từ model AI trả về một cấu trúc JSON không đồng nhất giữa các lần gọi — có lúc là choices[0].message.content, có lúc lại là choices[0].text. Chỉ một dòng code parse sai đã khiến toàn bộ pipeline xử lý đơn hàng đổ vỡ. Kinh nghiệm xương máu đó là lý do tôi viết bài hướng dẫn này — để bạn không phải trả giá bằng những đêm mất ngủ như tôi.
Tại Sao Response Format Parsing Quan Trọng Đến Vậy?
Khi làm việc với HolySheep AI — nền tảng API AI với độ trễ trung bình dưới 50ms và chi phí chỉ từ $0.42/MTok với DeepSeek V3.2 — tôi nhận ra rằng 80% lỗi tích hợp không đến từ model AI mà đến từ cách developer parse response. Dưới đây là framework xử lý mà tôi đã áp dụng thành công cho 12+ dự án enterprise.
1. Cấu Trúc Response Cơ Bản của HolySheep AI
HolySheep AI tuân thủ OpenAI-compatible API format, nhưng với enhancements đặc biệt cho use case doanh nghiệp. Response mặc định có cấu trúc:
{
"id": "chatcmpl-123abc456",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Xin chào! Tôi có thể giúp gì cho bạn?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 25,
"total_tokens": 40
},
"system_fingerprint": "fp_12345"
}Tuy nhiên, điểm khác biệt quan trọng: HolySheep bổ sung thêm trường latency_ms và cached trong metadata để bạn có thể optimize performance. Response từ streaming endpoint cũng có cấu trúc riêng mà tôi sẽ giải thích chi tiết.
2. Code Mẫu: Parser Engine Hoàn Chỉnh (Python)
Đây là parser engine mà tôi sử dụng trong production, xử lý mọi edge case từ empty response đến malformed JSON:
import json
import time
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from enum import Enum
class ResponseStatus(Enum):
SUCCESS = "success"
EMPTY = "empty"
PARTIAL = "partial"
ERROR = "error"
RATE_LIMIT = "rate_limit"
TIMEOUT = "timeout"
@dataclass
class ParsedResponse:
"""Structured response sau khi parse từ API"""
status: ResponseStatus
content: str
raw_response: Dict[str, Any]
token_usage: Dict[str, int] = field(default_factory=dict)
latency_ms: float = 0.0
model: str = ""
finish_reason: str = ""
error_message: Optional[str] = None
cached: bool = False
@property
def cost_usd(self) -> float:
"""Tính chi phí dựa trên pricing HolySheep 2026"""
pricing = {
"gpt-4o": 0.006, # $6/MTok input
"gpt-4o-mini": 0.0006, # $0.60/MTok input
"claude-sonnet-4-5": 0.015, # $15/MTok
"gemini-2.0-flash": 0.0025, # $2.50/MTok
"deepseek-v3.2": 0.00042 # $0.42/MTok
}
rate = pricing.get(self.model, 0.006)
tokens = self.token_usage.get("total_tokens", 0)
return round(tokens * rate / 1_000_000, 6) # Exact cents
class HolySheepResponseParser:
"""
Parser engine cho HolySheep AI API
Xử lý: standard response, streaming, errors, retries
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, timeout: int = 30):
self.api_key = api_key
self.timeout = timeout
def parse_response(self, response_data: Dict[str, Any]) -> ParsedResponse:
"""Parse response từ API, handle mọi edge cases"""
start_time = time.time()
# Case 1: Empty hoặc None response
if not response_data:
return ParsedResponse(
status=ResponseStatus.EMPTY,
content="",
raw_response={},
latency_ms=0,
error_message="Empty response from API"
)
# Case 2: Check cho error response
if "error" in response_data:
return self._parse_error_response(response_data)
# Case 3: Standard chat completion
if response_data.get("object") == "chat.completion":
return self._parse_chat_completion(response_data)
# Case 4: Text completion (legacy)
if response_data.get("object") == "text_completion":
return self._parse_text_completion(response_data)
# Case 5: Unknown format
return ParsedResponse(
status=ResponseStatus.ERROR,
content="",
raw_response=response_data,
latency_ms=(time.time() - start_time) * 1000,
error_message=f"Unknown response format: {response_data.get('object', 'N/A')}"
)
def _parse_chat_completion(self, data: Dict) -> ParsedResponse:
"""Parse chat completion response format"""
choices = data.get("choices", [])
if not choices:
return ParsedResponse(
status=ResponseStatus.EMPTY,
content="",
raw_response=data,
error_message="No choices in response"
)
first_choice = choices[0]
message = first_choice.get("message", {})
content = message.get("content", "")
# Handle edge case: content là None
if content is None:
content = ""
# Extract latency từ metadata (HolySheep-specific)
latency_ms = data.get("latency_ms", 0)
return ParsedResponse(
status=ResponseStatus.SUCCESS if content else ResponseStatus.PARTIAL,
content=content,
raw_response=data,
token_usage=data.get("usage", {}),
latency_ms=latency_ms,
model=data.get("model", ""),
finish_reason=first_choice.get("finish_reason", ""),
cached=data.get("cached", False)
)
def _parse_text_completion(self, data: Dict) -> ParsedResponse:
"""Parse legacy text completion format (tương thích ngược)"""
choices = data.get("choices", [])
if not choices:
return ParsedResponse(
status=ResponseStatus.EMPTY,
content="",
raw_response=data
)
# Text completion dùng text thay vì message.content
text = choices[0].get("text", "")
return ParsedResponse(
status=ResponseStatus.SUCCESS if text else ResponseStatus.PARTIAL,
content=text,
raw_response=data,
token_usage=data.get("usage", {}),
model=data.get("model", ""),
finish_reason=choices[0].get("finish_reason", "")
)
def _parse_error_response(self, data: Dict) -> ParsedResponse:
"""Parse error response và map sang status code"""
error = data["error"]
error_type = error.get("type", "")
status_map = {
"rate_limit_error": ResponseStatus.RATE_LIMIT,
"authentication_error": ResponseStatus.ERROR,
"invalid_request_error": ResponseStatus.ERROR,
"server_error": ResponseStatus.ERROR
}
return ParsedResponse(
status=status_map.get(error_type, ResponseStatus.ERROR),
content="",
raw_response=data,
error_message=f"{error.get('code', '')}: {error.get('message', '')}"
)
=== USAGE EXAMPLE ===
def demo_parse():
parser = HolySheepResponseParser(api_key="YOUR_HOLYSHEEP_API_KEY")
# Simulate successful response
sample_response = {
"id": "chatcmpl-xyz789",
"object": "chat.completion",
"created": 1700000000,
"model": "deepseek-v3.2",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Sản phẩm iPhone 15 Pro Max đang giảm giá 15% chỉ còn 24.990.000đ"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 20,
"completion_tokens": 35,
"total_tokens": 55
},
"latency_ms": 42.5,
"cached": True
}
result = parser.parse_response(sample_response)
print(f"Status: {result.status.value}")
print(f"Content: {result.content}")
print(f"Latency: {result.latency_ms}ms (HolySheep <50ms SLA)")
print(f"Cost: ${result.cost_usd} ({result.token_usage['total_tokens']} tokens)")
print(f"Cached: {result.cached}")
if __name__ == "__main__":
demo_parse()3. Streaming Response Handler — Xử Lý Real-time
Trong ứng dụng chatbot customer service thời gian thực, streaming là bắt buộc. Dưới đây là handler xử lý SSE (Server-Sent Events) từ HolySheep API với backpressure handling:
import asyncio
import json
from typing import AsyncGenerator, Callable, Optional
from dataclasses import dataclass
@dataclass
class StreamChunk:
"""Một chunk trong streaming response"""
delta: str
index: int
finish_reason: Optional[str] = None
is_final: bool = False
class HolySheepStreamHandler:
"""
Handler cho streaming response từ HolySheep AI
Hỗ trợ: SSE format, JSON lines, backpressure control
"""
def __init__(self, buffer_size: int = 100):
self.buffer_size = buffer_size
self.total_chunks = 0
self.total_tokens = 0
async def parse_stream_response(
self,
response_stream: AsyncGenerator[bytes, None],
on_chunk: Optional[Callable[[StreamChunk], None]] = None
) -> str:
"""
Parse streaming response thành complete text
Args:
response_stream: Async generator từ httpx/aiohttp
on_chunk: Callback được gọi mỗi khi có chunk mới
Returns:
Complete text từ stream
"""
full_content = []
buffer = []
async for line in response_stream:
# SSE format: "data: {...}\n\n"
if not line.strip():
continue
# Parse SSE line
if line.startswith(b"data: "):
json_str = line[6:].decode("utf-8")
elif line.startswith(b"{"):
json_str = line.decode("utf-8")
else:
continue
# Stop signal
if json_str.strip() == "[DONE]":
break
try:
chunk_data = json.loads(json_str)
except json.JSONDecodeError:
continue
# Parse chunk structure (HolySheep/OpenAI compatible)
delta = self._extract_delta(chunk_data)
if delta:
buffer.append(delta)
self.total_chunks += 1
chunk = StreamChunk(
delta=delta,
index=self.total_chunks,
is_final=chunk_data.get("choices", [{}])[0].get("finish_reason") == "stop"
)
if on_chunk:
await on_chunk(chunk)
# Backpressure: yield control nếu buffer đầy
if len(buffer) >= self.buffer_size:
combined = "".join(buffer)
full_content.append(combined)
buffer = []
await asyncio.sleep(0) # Yield to event loop
# Flush remaining buffer
if buffer:
full_content.append("".join(buffer))
return "".join(full_content)
def _extract_delta(self, chunk_data: dict) -> str:
"""Extract text delta từ chunk data"""
choices = chunk_data.get("choices", [])
if not choices:
return ""
choice = choices[0]
# Chat completion format
if "delta" in choice:
delta = choice["delta"]
if isinstance(delta, dict):
return delta.get("content", "")
return str(delta) if delta else ""
# Text completion format (legacy)
if "text" in choice:
return choice["text"]
return ""
async def example_usage():
"""Ví dụ sử dụng streaming handler"""
import httpx
handler = HolySheepStreamHandler(buffer_size=50)
async def display_chunk(chunk: StreamChunk):
print(f"[{chunk.index}] {chunk.delta}", end="", flush=True)
if chunk.is_final:
print("\n--- Stream Complete ---")
# Demo với mock stream (thay bằng actual API call)
mock_chunks = [
b'data: {"choices":[{"delta":{"content":"Xin"},"finish_reason":null}]}\n\n',
b'data: {"choices":[{"delta":{"content":" chào"},"finish_reason":null}]}\n\n',
b'data: {"choices":[{"delta":{"content":" bạn!"},"finish_reason":"stop"}]}\n\n',
b"data: [DONE]\n\n"
]
async def mock_stream():
for chunk in mock_chunks:
yield chunk
await asyncio.sleep(0.01)
result = await handler.parse_stream_response(mock_stream(), on_chunk=display_chunk)
print(f"\nFinal result: {result}")
if __name__ == "__main__":
asyncio.run(example_usage())4. Retry Logic và Error Handling — Đảm Bảo 99.9% Uptime
Trong production, bạn cần handle không chỉ response format mà còn network failures, rate limits. Code dưới đây implement exponential backoff với circuit breaker pattern:
import asyncio
import time
from typing import TypeVar, Optional, Callable, Any
from dataclasses import dataclass
from enum import Enum
import logging
logger = logging.getLogger(__name__)
T = TypeVar('T')
class CircuitState(Enum):
CLOSED = "closed" # Normal operation
OPEN = "open" # Failing, reject requests
HALF_OPEN = "half_open" # Testing recovery
@dataclass
class RetryConfig:
max_retries: int = 3
base_delay: float = 1.0
max_delay: float = 60.0
exponential_base: float = 2.0
jitter: bool = True
retry_on: tuple = ("rate_limit", "timeout", "server_error")
class CircuitBreaker:
"""Circuit breaker để prevent cascade failures"""
def __init__(self, failure_threshold: int = 5, recovery_timeout: float = 30.0):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.failures = 0
self.last_failure_time: Optional[float] = None
self.state = CircuitState.CLOSED
def record_success(self):
self.failures = 0
self.state = CircuitState.CLOSED
def record_failure(self):
self.failures += 1
self.last_failure_time = time.time()
if self.failures >= self.failure_threshold:
self.state = CircuitState.OPEN
logger.warning(f"Circuit breaker OPEN after {self.failures} failures")
def can_attempt(self) -> bool:
if self.state == CircuitState.CLOSED:
return True
if self.state == CircuitState.OPEN:
if time.time() - self.last_failure_time >= self.recovery_timeout:
self.state = CircuitState.HALF_OPEN
return True
return False
return True # HALF_OPEN state
class ResilientAPIClient:
"""
API client với built-in retry, circuit breaker, và timeout
Designed cho HolySheep AI với SLA 99.9%
"""
def __init__(
self,
api_key: str,
retry_config: Optional[RetryConfig] = None,
circuit_breaker: Optional[CircuitBreaker] = None
):
self.api_key = api_key
self.retry_config = retry_config or RetryConfig()
self.circuit_breaker = circuit_breaker or CircuitBreaker()
self.base_url = "https://api.holysheep.ai/v1"
async def call_with_retry(
self,
request_func: Callable[[], Any],
operation_name: str = "API call"
) -> Any:
"""
Execute request với retry logic và circuit breaker
"""
last_exception = None
for attempt in range(self.retry_config.max_retries + 1):
# Check circuit breaker
if not self.circuit_breaker.can_attempt():
raise Exception(
f"Circuit breaker OPEN for {operation_name}. "
f"Retry after {self.circuit_breaker.recovery_timeout}s"
)
try:
result = await request_func()
self.circuit_breaker.record_success()
return result
except Exception as e:
last_exception = e
self.circuit_breaker.record_failure()
error_type = self._classify_error(e)
if error_type not in self.retry_config.retry_on:
logger.error(f"{operation_name} failed with non-retryable error: {e}")
raise
if attempt < self.retry_config.max_retries:
delay = self._calculate_delay(attempt)
logger.warning(
f"{operation_name} attempt {attempt + 1} failed: {e}. "
f"Retrying in {delay:.2f}s..."
)
await asyncio.sleep(delay)
else:
logger.error(f"{operation_name} failed after {attempt + 1} attempts")
raise last_exception
def _classify_error(self, error: Exception) -> str:
"""Classify error type để determine retryability"""
error_msg = str(error).lower()
if "rate_limit" in error_msg or "429" in error_msg:
return "rate_limit"
elif "timeout" in error_msg or "timed out" in error_msg:
return "timeout"
elif "500" in error_msg or "server error" in error_msg:
return "server_error"
else:
return "unknown"
def _calculate_delay(self, attempt: int) -> float:
"""Calculate delay với exponential backoff và jitter"""
delay = self.retry_config.base_delay * (
self.retry_config.exponential_base ** attempt
)
delay = min(delay, self.retry_config.max_delay)
if self.retry_config.jitter:
import random
delay = delay * (0.5 + random.random())
return delay
async def example_resilient_call():
"""Ví dụ sử dụng resilient client với HolySheep API"""
import httpx
client = ResilientAPIClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
retry_config=RetryConfig(
max_retries=3,
base_delay=1.0,
max_delay=30.0
),
circuit_breaker=CircuitBreaker(
failure_threshold=5,
recovery_timeout=60.0
)
)
async def make_api_call():
async with httpx.AsyncClient(timeout=30.0) as http:
response = await http.post(
f"{client.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {client.api_key}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": "Chào bạn!"}]
}
)
response.raise_for_status()
return response.json()
try:
result = await client.call_with_retry(make_api_call, "chat_completion")
print(f"Success: {result['choices'][0]['message']['content']}")
except Exception as e:
print(f"Failed after retries: {e}")
if __name__ == "__main__":
asyncio.run(example_resilient_call())5. Data Structure Design cho RAG System
Đây là phần quan trọng nhất — thiết kế data structure cho Retrieval-Augmented Generation. Tôi đã optimize structure này qua 12+ dự án enterprise, đặc biệt hiệu quả cho e-commerce và legal tech:
from typing import List, Optional, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import hashlib
import json
class DocumentType(Enum):
PRODUCT = "product"
REVIEW = "review"
FAQ = "faq"
POLICY = "policy"
MANUAL = "manual"
CUSTOM = "custom"
class ChunkStrategy(Enum):
FIXED_SIZE = "fixed_size"
SENTENCE = "sentence"
PARAGRAPH = "paragraph"
SEMANTIC = "semantic"
@dataclass
class ChunkMetadata:
"""Metadata cho mỗi chunk document"""
source: str
doc_type: DocumentType
page: Optional[int] = None
section: Optional[str] = None
headers: List[str] = field(default_factory=list)
tags: List[str] = field(default_factory=list)
custom: Dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> Dict:
return {
"source": self.source,
"doc_type": self.doc_type.value,
"page": self.page,
"section": self.section,
"headers": self.headers,
"tags": self.tags,
**self.custom
}
@dataclass
class DocumentChunk:
"""
Atomic unit cho RAG retrieval
Optimized cho hybrid search (dense + sparse)
"""
chunk_id: str
content: str
content_hash: str
metadata: ChunkMetadata
embedding: Optional[List[float]] = None
# Pre-computed fields for filtering
char_count: int = 0
token_count: int = 0
sentence_count: int = 0
# Position info
position: int = 0 # Order trong document
is_first_chunk: bool = False
is_last_chunk: bool = False
def __post_init__(self):
self.content_hash = hashlib.md5(self.content.encode()).hexdigest()
self.char_count = len(self.content)
self.token_count = self._estimate_tokens()
self.sentence_count = self.content.count('.') + self.content.count('!') + self.content.count('?')
def _estimate_tokens(self) -> int:
"""Estimate token count (1 token ≈ 4 chars for Vietnamese)"""
return len(self.content) // 4
def to_dict(self) -> Dict:
return {
"chunk_id": self.chunk_id,
"content": self.content,
"content_hash": self.content_hash,
"metadata": self.metadata.to_dict(),
"char_count": self.char_count,
"token_count": self.token_count,
"position": self.position
}
@dataclass
class Document:
"""
Full document với metadata và chunks
Support multi-modal: text, structured data
"""
doc_id: str
title: str
content: str
doc_type: DocumentType
source_url: Optional[str] = None
# Processing metadata
created_at: datetime = field(default_factory=datetime.now)
processed_at: Optional[datetime] = None
chunk_strategy: ChunkStrategy = ChunkStrategy.PARAGRAPH
# Extracted metadata
metadata: ChunkMetadata = None
chunks: List[DocumentChunk] = field(default_factory=list)
# Quality metrics
quality_score: float = 0.0
is_processed: bool = False
def chunk(
self,
chunk_size: int = 512,
overlap: int = 64,
min_chunk_size: int = 100
) -> List[DocumentChunk]:
"""
Chunk document theo strategy đã chọn
Returns list of DocumentChunk objects
"""
if self.chunk_strategy == ChunkStrategy.FIXED_SIZE:
chunks = self._chunk_fixed_size(self.content, chunk_size, overlap)
elif self.chunk_strategy == ChunkStrategy.PARAGRAPH:
chunks = self._chunk_by_paragraph(chunk_size, min_chunk_size)
else:
chunks = self._chunk_by_sentence(chunk_size, min_chunk_size)
self.chunks = chunks
self.is_processed = True
self.processed_at = datetime.now()
return chunks
def _chunk_fixed_size(
self,
text: str,
chunk_size: int,
overlap: int
) -> List[DocumentChunk]:
"""Chunk với fixed token size và overlap"""
chunks = []
start = 0
position = 0
while start < len(text):
end = start + chunk_size
content = text[start:end]
chunk = DocumentChunk(
chunk_id=f"{self.doc_id}_{position}",
content=content.strip(),
metadata=self.metadata or ChunkMetadata(
source=self.source_url or self.doc_id,
doc_type=self.doc_type
),
position=position,
is_first_chunk=(position == 0),
is_last_chunk=(end >= len(text))
)
chunks.append(chunk)
start = end - overlap
position += 1
return chunks
def _chunk_by_paragraph(
self,
target_size: int,
min_size: int
) -> List[DocumentChunk]:
"""Chunk theo paragraph boundaries"""
paragraphs = self.content.split('\n\n')
chunks = []
current_chunk = []
current_size = 0
position = 0
for para in paragraphs:
para_size = len(para)
if current_size + para_size > target_size and current_chunk:
# Emit current chunk
content = '\n\n'.join(current_chunk)
if len(content) >= min_size:
chunks.append(DocumentChunk(
chunk_id=f"{self.doc_id}_{position}",
content=content,
metadata=self.metadata,
position=position,
is_first_chunk=(position == 0)
))
position += 1
current_chunk = []
current_size = 0
# If single paragraph too large, split it
if para_size > target_size:
sub_chunks = self._chunk_fixed_size(para, target_size, 0)
for i, sc in enumerate(sub_chunks):
sc.chunk_id = f"{self.doc_id}_{position}"
sc.position = position
chunks.append(sc)
position += 1
continue
current_chunk.append(para)
current_size += para_size
# Don't forget last chunk
if current_chunk:
content = '\n\n'.join(current_chunk)
if len(content) >= min_size:
chunks.append(DocumentChunk(
chunk_id=f"{self.doc_id}_{position}",
content=content,
metadata=self.metadata,
position=position,
is_last_chunk=True
))
return chunks
def _chunk_by_sentence(
self,
target_size: int,
min_size: int
) -> List[DocumentChunk]:
"""Chunk theo sentence boundaries"""
import re
sentences = re.split(r'[.!?]+', self.content)
chunks = []
current = []
current_size = 0
position = 0
for sent in sentences:
sent = sent.strip()
if not sent:
continue
sent_size = len(sent)
if current_size + sent_size > target_size and current:
content = '. '.join(current) + '.'
if len(content) >= min_size:
chunks.append(DocumentChunk(
chunk_id=f"{self.doc_id}_{position}",
content=content,
metadata=self.metadata,
position=position
))
position += 1
current = []
current_size = 0
current.append(sent)
current_size += sent_size
if current:
content = '. '.join(current) + '.'
if len(content) >= min_size:
chunks.append(DocumentChunk(
chunk_id=f"{self.doc_id}_{position}",
content=content,
metadata=self.metadata,
position=position,
is_last_chunk=True
))
return chunks
=== RAG PIPELINE EXAMPLE ===
def create_ecommerce_rag_pipeline():
"""
Example: Tạo RAG pipeline cho e-commerce chatbot
Sử dụng HolySheep API cho embedding và generation
"""
from concurrent.futures import ThreadPoolExecutor
# Sample product document
product = Document(
doc_id="prod_iphone15_001",
title="iPhone 15 Pro Max 256GB - Technical Specifications",
content="""
iPhone 15 Pro Max features the most advanced camera system ever on iPhone.
Display: 6.7-inch Super Retina XDR display with ProMotion and Always-On display.
Chip: A17 Pro chip with 6-core GPU for next-level graphics performance.
Camera: 48MP Main | 12MP Ultra Wide | 12MP Telephoto with 5x optical zoom.
Battery Life: Up to 29 hours video playback.
Storage Options: 256GB, 512GB, 1TB.
Price in Vietnam: Starting from 34,990,000 VND.
Key Features:
- Titanium design with textured matte glass back
- Action button for instant access to favorite feature
- USB-C with USB 3 speeds for fast transfers
- Wi-Fi 6E for up to 2x faster speeds
""",
doc_type=DocumentType.PRODUCT,
source_url="https://apple.com/vn/iphone-15-pro",
metadata=ChunkMetadata(
source="apple.com",
doc_type=DocumentType.PRODUCT,
tags=["smartphone", "apple", "flagship", "ios"],
custom={
"brand": "Apple",
"category": "smartphone",
"price_vnd": 34990000,
"in_stock": True
}
),
chunk_strategy=ChunkStrategy.PARAGRAPH
)
# Chunk document
chunks = product.chunk(chunk_size=512, overlap=64)
print(f"Created {len(chunks)} chunks from document '{product.title}'")
for i, chunk in enumerate(chunks[:3]):
print(f"\nChunk {i+1} (ID: {chunk.chunk_id}):")
print(f" Content: {chunk.content[:100]}...")
print(f" Tokens: ~{chunk.token_count}")
print(f" Hash: {chunk.content_hash}")
return product, chunks
if __name__ == "__main__":
doc, chunks = create_ecommerce_rag_pipeline()Lỗi Thường Gặp và Cách Khắc Phục
1. Lỗi "Cannot read property 'content' of undefined"
Nguyên nhân: Response structure không đồng nhất — có lúc model trả về choices[0].message.content, có lúc là choices[0].text (đặc biệt với các model legacy hoặc when switching giữa providers).
# ❌ SAI - Giả định luôn có message.content
content = response["choices"][0]["message"]["content"]
✅ ĐÚNG - Safe access với fallback
content = (
response.get("choices", [{}])[0]
.get("message", {})
.get("content") or
response.get("choices", [{}])[0]
.get("text", "") or
""
)
✅ TỐT HƠN - Dùng parser class đã viết ở trên
parser = HolySheepResponseParser(api_key="YOUR_HOLYSHEEP_API_KEY")
result = parser.parse_response(response)
content = result.content2. Lỗi "Connection timeout after 30000ms" hoặc "Rate limit exceeded"
Nguyên nhân