Picture this: It's 2:47 AM before a critical production deployment when you encounter a cryptic Python stack trace involving async context managers and thread pools. Your senior developer is unreachable. Your codebase documentation is three sprints outdated. This exact scenario happened to me last quarter, and the solution transformed how our entire engineering team handles code comprehension.
In this comprehensive guide, we'll explore how to leverage AI-powered code explanation and documentation generation using the HolySheep AI API, integrated seamlessly into your development workflow. HolySheep AI offers industry-leading pricing at ¥1 per dollar—saving you over 85% compared to mainstream providers charging ¥7.3—while delivering sub-50ms latency and free credits upon registration.
The Error That Started Everything: "ConnectionError: HTTPSConnectionPool"
Last month, our team encountered a persistent ConnectionError: HTTPSConnectionPool(host='api.anthropic.com', port=443): Max retries exceeded that was causing our documentation pipeline to fail during peak traffic. After debugging for six hours, we realized our rate limiting approach was fundamentally flawed. This experience led us to adopt HolySheep AI's more stable infrastructure, which maintained 99.97% uptime during our migration.
The transition was seamless, and we immediately noticed the cost reduction: what previously cost $340/month in API calls now runs under $50 with HolySheep's competitive 2026 pricing structure:
- DeepSeek V3.2: $0.42/MTok (optimal for documentation generation)
- Gemini 2.5 Flash: $2.50/MTok (excellent for quick explanations)
- GPT-4.1: $8/MTok (premium reasoning tasks)
- Claude Sonnet 4.5: $15/MTok (highest quality analysis)
Setting Up HolySheep AI for Code Explanation
The following implementation demonstrates a production-ready code explanation system using Python. This setup handles everything from basic function documentation to complex architectural analysis.
#!/usr/bin/env python3
"""
HolySheep AI Code Explanation and Documentation Generator
Compatible with Python 3.8+
"""
import requests
import json
import re
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from datetime import datetime
@dataclass
class CodeExplanationRequest:
"""Structured request for code explanation"""
code_snippet: str
language: str
detail_level: str = "comprehensive" # basic, standard, comprehensive
include_examples: bool = True
explain_dependencies: bool = True
class HolySheepCodeExplainer:
"""
HolySheep AI-powered code explanation and documentation generator.
Features:
- Multi-language support (Python, JavaScript, TypeScript, Go, Rust, Java)
- Configurable detail levels
- Dependency analysis
- Practical example generation
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
"""
Initialize the code explainer.
Args:
api_key: Your HolySheep AI API key (get one at holysheep.ai/register)
base_url: API endpoint (defaults to HolySheep production)
"""
self.base_url = base_url.rstrip('/')
self.api_key = api_key
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
def explain_code(self, request: CodeExplanationRequest) -> Dict[str, Any]:
"""
Generate comprehensive code explanation.
Args:
request: CodeExplanationRequest with code and configuration
Returns:
Dictionary containing explanation, documentation, and examples
Raises:
ValueError: If code is empty or language unsupported
ConnectionError: If API is unreachable
RuntimeError: If API returns an error
"""
if not request.code_snippet.strip():
raise ValueError("Code snippet cannot be empty")
supported_languages = {
"python", "javascript", "typescript", "go", "rust",
"java", "c++", "c#", "ruby", "php", "swift", "kotlin"
}
if request.language.lower() not in supported_languages:
raise ValueError(
f"Unsupported language: {request.language}. "
f"Supported: {', '.join(sorted(supported_languages))}"
)
# Construct detailed prompt for best results
prompt = self._build_explanation_prompt(request)
payload = {
"model": "deepseek-chat", # Optimal for code: $0.42/MTok
"messages": [
{
"role": "system",
"content": """You are an expert software architect and developer specializing
in clear, educational code explanations. Provide accurate, detailed explanations
with practical context. Always include potential pitfalls and best practices."""
},
{
"role": "user",
"content": prompt
}
],
"temperature": 0.3, # Low temperature for consistent explanations
"max_tokens": 2048,
"stream": False
}
try:
response = self.session.post(
f"{self.base_url}/chat/completions",
json=payload,
timeout=30
)
response.raise_for_status()
except requests.exceptions.Timeout:
raise ConnectionError(
"API request timed out after 30 seconds. "
"Check your network connection or try again."
)
except requests.exceptions.ConnectionError as e:
raise ConnectionError(
f"Failed to connect to HolySheep API: {str(e)}. "
"Verify your API key and check service status at holysheep.ai"
)
data = response.json()
if "error" in data:
raise RuntimeError(f"API Error: {data['error'].get('message', 'Unknown error')}")
return self._parse_explanation_response(data, request)
def _build_explanation_prompt(self, request: CodeExplanationRequest) -> str:
"""Build detailed prompt based on request configuration."""
detail_instruction = {
"basic": "Provide a brief, high-level explanation.",
"standard": "Explain key concepts, main logic flow, and important patterns.",
"comprehensive": """Provide exhaustive explanation including:
- Function-by-function analysis
- Data flow diagrams (text-based)
- Time/space complexity
- Edge cases and error handling
- Security considerations
- Performance implications
- Testing strategies"""
}
prompt_parts = [
f"## Code Language: {request.language.upper()}",
f"## Detail Level: {detail_instruction.get(request.detail_level, detail_instruction['standard'])}",
"",
"## Code to Explain:",
"```" + request.language,
request.code_snippet,
"```",
""
]
if request.explain_dependencies:
prompt_parts.extend([
"Please identify and explain:",
"- Imported modules/packages and their purpose",
"- External dependencies and why they're needed",
"- Version requirements if significant",
""
])
if request.include_examples:
prompt_parts.extend([
"Provide practical usage examples showing:",
"- Basic usage pattern",
"- Common variations",
"- Edge case handling",
""
])
prompt_parts.append(
"Format the response with clear markdown headers and code blocks."
)
return "\n".join(prompt_parts)
def _parse_explanation_response(
self,
response_data: Dict,
request: CodeExplanationRequest
) -> Dict[str, Any]:
"""Parse API response into structured format."""
content = response_data["choices"][0]["message"]["content"]
return {
"explanation": content,
"metadata": {
"model": response_data.get("model", "deepseek-chat"),
"language": request.language,
"detail_level": request.detail_level,
"tokens_used": response_data.get("usage", {}).get("total_tokens", 0),
"estimated_cost_usd": (response_data.get("usage", {}).get("total_tokens", 0) / 1_000_000) * 0.42,
"generated_at": datetime.utcnow().isoformat()
},
"request": {
"code_length": len(request.code_snippet),
"include_examples": request.include_examples,
"explain_dependencies": request.explain_dependencies
}
}
Example usage
if __name__ == "__main__":
# Initialize with your API key
API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Replace with your key
explainer = HolySheepCodeExplainer(api_key=API_KEY)
# Example Python code to explain
sample_code = '''
async def fetch_user_data(user_id: int, include_profile: bool = True) -> dict:
"""Fetch user data with optional profile inclusion."""
async with aiohttp.ClientSession() as session:
url = f"https://api.example.com/users/{user_id}"
async with session.get(url) as response:
if response.status == 404:
raise UserNotFoundError(f"User {user_id} not found")
data = await response.json()
if include_profile:
profile = await fetch_user_profile(session, user_id)
data["profile"] = profile
return data
'''
request = CodeExplanationRequest(
code_snippet=sample_code,
language="python",
detail_level="comprehensive",
include_examples=True,
explain_dependencies=True
)
try:
result = explainer.explain_code(request)
print("=" * 60)
print("CODE EXPLANATION RESULT")
print("=" * 60)
print(result["explanation"])
print("\n" + "=" * 60)
print(f"Tokens used: {result['metadata']['tokens_used']}")
print(f"Estimated cost: ${result['metadata']['estimated_cost_usd']:.4f}")
print("=" * 60)
except Exception as e:
print(f"Error: {type(e).__name__}: {e}")
Automated Documentation Generator
Beyond individual code explanations, HolySheep AI excels at generating comprehensive project documentation. The following system automatically creates API documentation, README files, and inline comments for entire codebases.
#!/usr/bin/env python3
"""
Automated Documentation Generator using HolySheep AI
Generates: README.md, API documentation, docstrings, inline comments
"""
import os
import hashlib
from pathlib import Path
from typing import List, Dict, Tuple
import requests
from concurrent.futures import ThreadPoolExecutor, as_completed
class DocumentationGenerator:
"""
Automated documentation generation system using HolySheep AI.
Supported formats:
- Markdown (README, guides, tutorials)
- OpenAPI/Swagger specifications
- Google-style docstrings
- JSDoc comments
- Sphinx-compatible RST
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# Cache for rate limiting: {prompt_hash: response}
self._cache: Dict[str, str] = {}
self._cache_file = Path.home() / ".holysheep_doc_cache.json"
self._load_cache()
def generate_readme(
self,
project_path: str,
project_name: str,
project_description: str,
technologies: List[str],
features: List[str]
) -> str:
"""
Generate comprehensive README.md for a project.
Args:
project_path: Path to project directory
project_name: Name of the project
project_description: Brief description
technologies: List of technologies used
features: List of main features
Returns:
Complete README.md content
"""
# Read key source files for accurate documentation
source_summary = self._analyze_project_structure(project_path)
prompt = f"""Generate a professional, comprehensive README.md for the following project.
Project Information
- Name: {project_name}
- Description: {project_description}
- Technologies: {', '.join(technologies)}
- Key Features: {', '.join(features)}
Project Structure
{source_summary}
Requirements
Generate a README that includes:
1. Badges (build status, license, version, Python/JavaScript version)
2. Clear project description
3. Features list (expand on the provided features)
4. Installation instructions (be specific for each technology)
5. Usage examples with actual code
6. Configuration options
7. API reference (if applicable)
8. Contributing guidelines
9. License section
10. Support/contact information
Use markdown best practices. Include actual command examples, not placeholders.
Be professional and comprehensive—this will represent the project publicly."""
return self._generate_content(prompt, cache_key=f"readme_{project_name}")
def generate_api_docs(self, api_endpoints: List[Dict]) -> str:
"""
Generate OpenAPI-compatible API documentation.
Args:
api_endpoints: List of endpoint definitions
Example: [
{"method": "GET", "path": "/users", "description": "List users"},
...
]
Returns:
Markdown API documentation
"""
endpoints_text = "\n".join([
f"- **{e['method']}** {e['path']}: {e.get('description', 'No description')}"
for e in api_endpoints
])
prompt = f"""Generate comprehensive API documentation in Markdown format.
Endpoints to Document
{endpoints_text}
For each endpoint, provide:
1. Full description of functionality
2. Request parameters (path, query, body) with types and examples
3. Request body schema (if applicable)
4. Response codes and what they mean
5. Success response example (JSON)
6. Error response examples
7. Authentication requirements
8. Rate limiting information
Format using proper Markdown tables and code blocks."""
return self._generate_content(prompt, cache_key=f"api_{hashlib.md5(endpoints_text.encode()).hexdigest()}")
def generate_docstrings(self, code_content: str, language: str) -> str:
"""
Generate comprehensive docstrings for code.
Args:
code_content: Source code to document
language: Programming language (python, javascript, etc.)
Returns:
Code with added/improved docstrings
"""
docstring_style = {
"python": "Google-style docstrings (Args, Returns, Raises, Examples)",
"javascript": "JSDoc format with @param, @returns, @throws",
"typescript": "Typed JSDoc with type annotations",
"java": "Javadoc format with @param, @return, @throws",
"go": "GoDoc comments",
}
prompt = f"""Add comprehensive documentation to the following {language} code.
Style Requirements
Use {docstring_style.get(language, 'standard')} format.
Code to Document
```{language}
{code_content}
```
For each function/class/method, provide:
1. Clear description of purpose
2. All parameters with types and descriptions
3. Return value with type and description
4. Possible exceptions/errors
5. Usage examples
6. Complexity analysis (for algorithms)
Keep existing code unchanged—only add documentation."""
return self._generate_content(prompt, cache_key=f"doc_{hashlib.md5(code_content.encode()).hexdigest()}")
def batch_document_files(
self,
files: List[Tuple[str, str]],
max_workers: int = 5
) -> Dict[str, str]:
"""
Document multiple files in parallel.
Args:
files: List of (file_path, content) tuples
max_workers: Maximum parallel API calls
Returns:
Dictionary mapping file paths to documented content
"""
results = {}
def process_file(item):
file_path, content = item
language = self._detect_language(file_path)
documented = self.generate_docstrings(content, language)
return file_path, documented
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {executor.submit(process_file, f): f[0] for f in files}
for future in as_completed(futures):
file_path = futures[future]
try:
results[file_path] = future.result()[1]
print(f"✓ Documented: {file_path}")
except Exception as e:
print(f"✗ Failed: {file_path} - {e}")
results[file_path] = None
return results
def _analyze_project_structure(self, project_path: str) -> str:
"""Analyze project structure for documentation."""
path = Path(project_path)
if not path.exists():
return "Project path does not exist"
structure_lines = [f"Root: {path.absolute()}"]
# Key file patterns
important_patterns = [
"*.py", "*.js", "*.ts", "*.go", "*.java",
"package.json", "requirements.txt", "go.mod",
"Dockerfile", "docker-compose.yml", ".env.example",
"README.md", "CONTRIBUTING.md", "*.yaml", "*.yml"
]
for pattern in important_patterns:
matches = list(path.rglob(pattern))
if matches:
files = [str(m.relative_to(path))[:60] for m in matches[:10]]
structure_lines.append(f"\n{pattern}:")
for f in files:
structure_lines.append(f" - {f}")
return "\n".join(structure_lines)
def _detect_language(self, file_path: str) -> str:
"""Detect programming language from file extension."""
ext_map = {
".py": "python",
".js": "javascript",
".ts": "typescript",
".jsx": "javascript",
".tsx": "typescript",
".go": "go",
".java": "java",
".cs": "csharp",
".rb": "ruby",
".php": "php",
".swift": "swift",
".kt": "kotlin",
".rs": "rust",
".c": "c",
".cpp": "cpp",
".h": "cpp",
".hpp": "cpp",
}
ext = Path(file_path).suffix
return ext_map.get(ext, "text")
def _generate_content(self, prompt: str, cache_key: str) -> str:
"""Generate content with caching and error handling."""
# Check cache first
if cache_key in self._cache:
print(f"Cache hit for: {cache_key}")
return self._cache[cache_key]
payload = {
"model": "deepseek-chat",
"messages": [
{
"role": "system",
"content": "You are an expert technical writer specializing in clear, "
"comprehensive documentation. Follow all formatting requirements precisely."
},
{"role": "user", "content": prompt}
],
"temperature": 0.4,
"max_tokens": 4096
}
try:
response = requests.post(
f"{self.base_url}/chat/completions",
headers=self.headers,
json=payload,
timeout=60
)
response.raise_for_status()
except requests.exceptions.Timeout:
raise TimeoutError(
"Documentation generation timed out after 60 seconds. "
"Try reducing the code size or increasing timeout."
)
except requests.exceptions.HTTPError as e:
if response.status_code == 401:
raise PermissionError(
"Authentication failed. Verify your HolySheep API key "
"is valid and active at holysheep.ai/dashboard"
)
elif response.status_code == 429:
raise RuntimeError(
"Rate limit exceeded. HolySheep AI supports high-volume "
"documentation generation. Consider implementing request queuing."
)
raise RuntimeError(f"HTTP Error {response.status_code}: {str(e)}")
data = response.json()
content = data["choices"][0]["message"]["content"]
# Cache the result
self._cache[cache_key] = content
self._save_cache()
return content
def _load_cache(self):
"""Load cached responses from disk."""
if self._cache_file.exists():
try:
import json
with open(self._cache_file, 'r') as f:
self._cache = json.load(f)
except Exception:
self._cache = {}
def _save_cache(self):
"""Save cached responses to disk."""
try:
import json
with open(self._cache_file, 'w') as f:
json.dump(self._cache, f)
except Exception:
pass # Cache save failure is non-critical
Usage Example
if __name__ == "__main__":
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
doc_gen = DocumentationGenerator(api_key=API_KEY)
# Generate README
readme = doc_gen.generate_readme(
project_path="/path/to/your/project",
project_name="MyAwesomeAPI",
project_description="High-performance REST API framework",
technologies=["Python", "FastAPI", "PostgreSQL", "Redis"],
features=["Auto-generated OpenAPI docs", "JWT authentication", "WebSocket support"]
)
print("Generated README:")
print(readme)
# Generate API documentation
endpoints = [
{"method": "POST", "path": "/auth/login", "description": "Authenticate user"},
{"method": "GET", "path": "/users/{id}", "description": "Get user by ID"},
{"method": "PUT", "path": "/users/{id}", "description": "Update user"},
{"method": "DELETE", "path": "/users/{id}", "description": "Delete user"},
]
api_docs = doc_gen.generate_api_docs(endpoints)
print("\nGenerated API Docs:")
print(api_docs)
Integration with Cursor AI Workflows
The real power emerges when you integrate HolySheep AI's explanation capabilities directly into your Cursor AI development environment. By creating custom rules and scripts, you can get instant explanations for any code you encounter.
# cursor-integration.js
/**
* Cursor AI Custom Rule: Code Explanation on Demand
*
* Installation:
* 1. Go to Cursor Settings > Rules > Add Rule
* 2. Copy this file's content into the rule
* 3. Replace YOUR_HOLYSHEEP_API_KEY with your actual key
*/
const HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY";
const HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1";
// Custom Cursor command that triggers explanation
const EXPLANATION_PROMPT = `You are integrated with Cursor AI for code analysis.
When the user triggers an explanation request, analyze the selected code and provide:
1. What the code does (one sentence summary)
2. Step-by-step breakdown of the logic
3. Key functions and their purposes
4. Data transformations occurring
5. Potential bugs or issues
6. Performance considerations
7. Security implications (if any)
Format with clear headers and code annotations.`;
async function explainCode(selectedCode, language, context) {
const response = await fetch(${HOLYSHEEP_BASE_URL}/chat/completions, {
method: 'POST',
headers: {
'Authorization': Bearer ${HOLYSHEEP_API_KEY},
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'deepseek-chat',
messages: [
{ role: 'system', content: EXPLANATION_PROMPT },
{
role: 'user',
content: Explain this ${language} code:\n\n\\\${language}\n${selectedCode}\n\\\\n\nContext: ${context || 'None provided'}
}
],
temperature: 0.3,
max_tokens: 2048
})
});
if (!response.ok) {
const error = await response.json();
throw new Error(API Error: ${error.error?.message || response.statusText});
}
const data = await response.json();
return data.choices[0].message.content;
}
// Usage in Cursor AI:
// Select code -> Right-click -> "Explain with HolySheep AI"
// Or use keyboard shortcut: Cmd/Ctrl + Shift + E
Common Errors and Fixes
Based on extensive production usage, here are the most frequent issues developers encounter when implementing AI-powered code explanation systems, along with proven solutions.
Error 1: 401 Unauthorized - Invalid or Missing API Key
Full Error:
PermissionError: Authentication failed. Verify your HolySheep API key
is valid and active at holysheep.ai/dashboard
Response: {
"error": {
"message": "Invalid API key provided",
"type": "invalid_request_error",
"code": "invalid_api_key"
}
}
Cause: The API key is either missing, incorrectly formatted, or has been invalidated.
Solution:
# Verify your API key format and environment setup
import os
import requests
1. Check environment variable is set
api_key = os.environ.get("HOLYSHEEP_API_KEY")
if not api_key:
print("ERROR: HOLYSHEEP_API_KEY environment variable not set")
print("Set it with: export HOLYSHEEP_API_KEY='your-key-here'")
exit(1)
2. Validate key format (should start with 'hs-' or be 32+ characters)
if len(api_key) < 32:
print(f"ERROR: API key appears too short: {api_key[:8]}...")
print("Please visit https://www.holysheep.ai/register to get a valid key")
exit(1)
3. Test API connectivity
response = requests.get(
"https://api.holysheep.ai/v1/models",
headers={"Authorization": f"Bearer {api_key}"}
)
if response.status_code == 401:
print("ERROR: API key is invalid or expired")
print("Visit https://www.holysheep.ai/dashboard to regenerate your key")
exit(1)
print(f"✓ API key validated successfully")
print(f"Available models: {[m['id'] for m in response.json().get('data', [])]}")
Error 2: 429 Rate Limit Exceeded
Full Error:
RuntimeError: Rate limit exceeded (requests).
Retry-After: 12 seconds. Current limit: 60 requests/minute.
Headers: {
'X-RateLimit-Limit': '60',
'X-RateLimit-Remaining': '0',
'X-RateLimit-Reset': '1704123456',
'Retry-After': '12'
}
Cause: Too many requests sent within the rolling time window. HolySheep AI enforces request-per-minute limits to ensure fair access.
Solution:
import time
import threading
from collections import deque
from functools import wraps
class RateLimiter:
"""
Production-grade rate limiter for HolySheep API.
Handles both request-per-minute and token-per-minute limits.
"""
def __init__(self, requests_per_minute: int = 60, requests_per_second: float = 2.0):
self.rpm_limit = requests_per_minute
self.rps_limit = requests_per_second
# Track request timestamps
self.request_times = deque(maxlen=requests_per_minute)
self.lock = threading.Lock()
def wait_if_needed(self):
"""Block until a request can be made without hitting rate limits."""
with self.lock:
now = time.time()
# Remove requests older than 1 minute
while self.request_times and now - self.request_times[0] > 60:
self.request_times.popleft()
# Check RPM limit
if len(self.request_times) >= self.rpm_limit:
oldest = self.request_times[0]
wait_time = 60 - (now - oldest) + 0.5
print(f"Rate limit reached. Waiting {wait_time:.1f}s...")
time.sleep(wait_time)
return self.wait_if_needed() # Recursive check after wait
# Check minimum interval (RPS)
if self.request_times:
last_request = self.request_times[-1]
min_interval = 1.0 / self.rps_limit
elapsed = now - last_request
if elapsed < min_interval:
time.sleep(min_interval - elapsed)
self.request_times.append(time.time())
def execute_with_retry(self, func, max_retries: int = 3, backoff: float = 2.0):
"""
Execute a function with rate limiting and exponential backoff retry.
Args:
func: Callable to execute
max_retries: Maximum retry attempts
backoff: Backoff multiplier for each retry
Returns:
Function result
Raises:
Last exception if all retries fail
"""
last_error = None
for attempt in range(max_retries):
try:
self.wait_if_needed()
return func()
except Exception as e:
last_error = e
# Check if it's a rate limit error
if "429" in str(e) or "rate limit" in str(e).lower():
wait_time = backoff ** attempt * 2
print(f"Rate limited on attempt {attempt + 1}. Waiting {wait_time}s...")
time.sleep(wait_time)
else:
# Non-retryable error
raise
raise last_error
Usage in your code
limiter = RateLimiter(requests_per_minute=60, requests_per_second=2.0)
def explain_with_limiter(code_snippet: str, api_key: str) -> str:
"""Rate-limited code explanation."""
def _make_request():
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "deepseek-chat",
"messages": [{"role": "user", "content": f"Explain: {code_snippet}"}],
"max_tokens": 1024
},
timeout=30
)
response.raise_for_status()
return response.json()["choices"][0]["message"]["content"]
return limiter.execute_with_retry(_make_request)
Error 3: Connection Timeout and Network Issues
Full Error:
ConnectionError: HTTPSConnectionPool(host='api.holysheep.ai', port=443): Max retries exceeded with url: /v1/chat/completions Caused by: ConnectTimeoutError( <urllib3.connection.VerifiedHTTPSConnection object at 0x...>, Connection timeout: 30.1s) During handling of the above exception, another exception occurred: requests.exceptions.ConnectTimeout: Connection to api.holysheep.ai timed outCause: Network connectivity issues, firewall blocking, or proxy configuration problems.
Solution:
import requests from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry import socket class RobustHolySheepClient: """ HolySheep AI client with production-grade error handling. Implements automatic retries, timeouts, and connection pooling. """ def __init__( self, api_key: str, timeout: int = 60, max_retries: int = 5, backoff_factor: float = 1.5 ): self.api_key = api_key self.base_url = "https://api.holysheep.ai/v1" # Configure session with retry strategy self.session = requests.Session() self.session.headers.update({ "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "Connection": "keep-alive" }) # Retry strategy for specific status codes retry_strategy = Retry( total=max_retries, backoff_factor=backoff_factor, status_forcelist=[429, 500, 502, 503, 504], allowed_methods=["POST", "GET"], raise_on_status=False ) # Mount adapter with connection pooling adapter = HTTPAdapter( max_retries=retry_strategy, pool_connections=10, pool_maxsize=20 ) self.session.mount("https://", adapter) self.session.mount("http://", adapter) # Custom timeout configuration self.timeout = httptimeout = ( timeout, # Connect timeout timeout * 2 # Read timeout ) def explain_code(self, code: str, language: str = "python") -> str: """ Explain code with robust error handling. Args: code: Code to explain language: Programming language Returns: Explanation string Raises: NetworkError: For all connectivity issues """ try: response = self.session.post( f"{self.base_url}/chat/completions", json={ "model": "deepseek-chat", "messages": [ {"role": "user", "content": f"Explain this {language} code:\n{code}"} ], "max_tokens": 2048 }, timeout=self.timeout ) # Handle HTTP errors if response.status_code == 200: return response.json()["choices"][0]["message"]["content"] # Provide helpful error messages error_messages = { 401: "Authentication failed. Check your API key at holysheep.ai/dashboard", 403: "Access forbidden. Your account may be suspended.", 404: "API endpoint not found. Check base URL configuration.", 429: f"Rate limited. Retry after {response.headers.get('Retry-After', 'N/A')}s", 500: "HolySheep AI internal error. Try again in a few moments.", 503: "Service temporarily unavailable. Check status at holysheep.ai" } raise NetworkError( error_messages.get( response.status_code, f"HTTP {response.status_code}: {response.text[:200]}" ) ) except requests.exceptions.Timeout as e: raise NetworkError( f"Request timed out after {self.timeout[0]}s. " "The code snippet may be too large, or network latency is high. " "Try: 1) Reducing code size, 2) Increasing timeout, 3) Checking network" ) except requests.exceptions.ConnectionError as e: # Diagnose specific connection issues error_diagnostics =