AI-Powered Documentation: Transform Developer Search

Query: "timeout error"
Results: Pages containing "timeout" and "error"

Query: "my request is taking too long"
Results: Pages about timeouts, rate limiting, async operations,
         and performance optimization—even without those exact words

// Vector embedding for semantic search
const searchDocs = async (query) => {
  // Convert query to embedding
  const queryEmbedding = await embedModel.encode(query);
 
  // Find similar document chunks
  const results = await vectorStore.search({
    vector: queryEmbedding,
    topK: 10,
    threshold: 0.7
  });
 
  return results;
};

Developer: How do I handle rate limits in Python?
 
AI: To handle rate limits with the Python SDK, use the built-in
retry mechanism:
 
from your_sdk import Client
 
client = Client(
    api_key="YOUR_KEY",
    max_retries=3,
    retry_delay=1.0  # seconds
)
 
The SDK automatically retries on 429 responses with exponential
backoff. For custom handling, catch RateLimitError:
 
try:
    response = client.users.list()
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
 
See also: [Rate Limiting Guide](/docs/rate-limits)

// IDE integration example
const suggestDocs = async (currentCode, cursorPosition) => {
  const context = extractContext(currentCode, cursorPosition);
 
  const suggestions = await ai.suggest({
    code: context,
    prompt: "What documentation would help with this code?"
  });
 
  return suggestions.map(s => ({
    title: s.title,
    url: s.url,
    relevance: s.score
  }));
};

// Input: Your code
/**
 * Creates a new user in the system
 */
async function createUser(
  email: string,
  options?: { name?: string; role?: UserRole }
): Promise<User> {
  // implementation
}
 
// Output: Generated documentation
/*
## createUser(email, options?)
 
Creates a new user in the system.
 
### Parameters
 
| Name | Type | Required | Description |
|------|------|----------|-------------|
| email | string | Yes | User's email address |
| options | object | No | Additional user options |
| options.name | string | No | Display name |
| options.role | UserRole | No | User's role |
 
### Returns
 
Promise<User> - The created user object
 
### Example
 
const user = await createUser("jane@example.com", {
  name: "Jane Doe",
  role: "admin"
});
*/

## v2.3.0 - January 2025
 
### New Features
- **Webhook filtering**: Filter webhook events by resource type
  and action (commit: abc123)
- **Batch operations**: Create up to 100 resources in a single
  API call (commit: def456)
 
### Improvements
- Reduced API latency by 40% for list operations
- Added TypeScript types for all webhook events
 
### Bug Fixes
- Fixed race condition in concurrent user updates
- Resolved memory leak in long-running connections
 
### Breaking Changes
- None

Input: API endpoint specification
 
Output for JavaScript:
const response = await fetch('https://api.example.com/users', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ email: 'user@example.com' })
});
 
Output for Python:
import requests
 
response = requests.post(
    'https://api.example.com/users',
    headers={'Authorization': 'Bearer YOUR_TOKEN'},
    json={'email': 'user@example.com'}
)
 
Output for cURL:
curl -X POST https://api.example.com/users \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"email": "user@example.com"}'

┌─────────────────────────────────────────────────────┐
│                    User Query                        │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│              Query Understanding                     │
│         (Intent classification, entity              │
│          extraction, context gathering)             │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│              Document Retrieval                      │
│         (Vector search, keyword search,             │
│          metadata filtering)                        │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│              Response Generation                     │
│         (LLM synthesis, source citation,            │
│          code example generation)                   │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│                 User Response                        │
│         (Answer + sources + related docs)           │
└─────────────────────────────────────────────────────┘

async def answer_question(query: str) -> Answer:
    # 1. Retrieve relevant documentation chunks
    chunks = await vector_store.search(
        query=query,
        top_k=5,
        filter={"type": "documentation"}
    )
 
    # 2. Build context from retrieved chunks
    context = "\n\n".join([
        f"Source: {c.metadata['url']}\n{c.content}"
        for c in chunks
    ])
 
    # 3. Generate answer using LLM
    response = await llm.generate(
        prompt=f"""Answer the developer's question using only
        the provided documentation. Cite sources.
 
        Documentation:
        {context}
 
        Question: {query}
 
        Answer:"""
    )
 
    # 4. Return answer with sources
    return Answer(
        text=response.text,
        sources=[c.metadata['url'] for c in chunks],
        confidence=response.confidence
    )

# Validate AI responses before showing to users
def validate_response(response, query):
    checks = [
        check_factual_accuracy(response, source_docs),
        check_code_syntax(response.code_examples),
        check_no_hallucinated_endpoints(response),
        check_version_accuracy(response),
    ]
 
    if not all(checks):
        return fallback_to_traditional_search(query)
 
    return response