# ayga-mcp-client Examples
This document provides detailed examples of all 39 available parsers, their request formats, and response structures.
## Table of Contents
- [AI Chat Parsers](#ai-chat-parsers)
- [Search Engines](#search-engines)
- [Social Media](#social-media)
- [Content Extraction](#content-extraction-parsers)
- [Analytics](#analytics-parser)
- [Visual Content](#visual-content-parser)
- [Translation Services](#translation-services)
- [YouTube Parsers](#youtube-parsers)
- [Metadata Tools](#metadata-tools)
- [Error Handling](#error-handling)
- [Real-World Use Cases](#real-world-use-cases)
---
## AI Chat Parsers
### search_perplexity
AI-powered search engine with real-time web access and cited sources.
**Request:**
```json
{
"query": "What are transformer models in AI?",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0,
"time_taken": 12.5
},
"answer": "Transformer models are a type of neural network architecture introduced in the 2017 paper 'Attention Is All You Need'. They revolutionized natural language processing by using self-attention mechanisms instead of recurrent layers...",
"sources": [
{
"title": "Attention Is All You Need",
"url": "https://arxiv.org/abs/1706.03762",
"snippet": "We propose a new simple network architecture..."
},
{
"title": "The Illustrated Transformer",
"url": "https://jalammar.github.io/illustrated-transformer/",
"snippet": "The Transformer – a model that uses attention to boost..."
}
],
"related": [
"GPT models",
"BERT",
"attention mechanism",
"encoder-decoder architecture"
]
}
}
```
**Claude Desktop Usage:**
```
@redis-api search_perplexity query="latest developments in quantum computing 2026"
```
---
### search_chatgpt
ChatGPT with web search capabilities.
**Request:**
```json
{
"query": "Explain quantum computing to a 10 year old",
"timeout": 60
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "Imagine regular computers use switches that are either ON (like 1) or OFF (like 0). They're like light switches! But quantum computers are special - they can be ON, OFF, or BOTH at the same time! It's like a coin spinning in the air - is it heads or tails? It's both until it lands! This lets quantum computers solve really hard puzzles much faster than regular computers.",
"web_results": [
{
"title": "What is Quantum Computing? - IBM",
"snippet": "Quantum computers harness the properties of quantum mechanics..."
}
]
}
}
```
---
### search_claude
Anthropic Claude AI assistant with reasoning capabilities.
**Request:**
```json
{
"query": "Compare async/await in Python vs JavaScript",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "Both Python and JavaScript use async/await for asynchronous programming, but with key differences:\n\n**Python:**\n- Uses `asyncio` event loop\n- Must explicitly run with `asyncio.run()`\n- `await` only works inside `async def` functions\n- Example: `async def fetch(): result = await get_data()`\n\n**JavaScript:**\n- Built-in event loop in runtime (Node.js/browsers)\n- Top-level await supported in modules\n- Promise-based natively\n- Example: `async function fetch() { const result = await getData(); }`\n\nKey similarity: Both prevent callback hell and make async code readable.",
"code_examples": [
"# Python\nasync def main():\n result = await fetch()\n return result\n\nasyncio.run(main())",
"// JavaScript\nasync function main() {\n const result = await fetch();\n return result;\n}\nmain();"
]
}
}
```
---
### search_gemini
Google Gemini AI with multimodal capabilities.
**Request:**
```json
{
"query": "What are the best practices for API design in 2026?",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "Modern API design in 2026 follows these best practices:\n\n1. **RESTful principles**: Clear resource naming, proper HTTP methods\n2. **GraphQL for complex queries**: Reduces over-fetching\n3. **API versioning**: Use URL path (/v1/, /v2/) or headers\n4. **Rate limiting**: Protect against abuse\n5. **Comprehensive documentation**: OpenAPI/Swagger specs\n6. **Security**: OAuth 2.0, JWT tokens, HTTPS only\n7. **Error handling**: Consistent error response format\n8. **Pagination**: For large datasets\n9. **Caching**: ETags, Cache-Control headers\n10. **Monitoring**: Track usage, errors, performance"
}
}
```
---
### search_copilot
Microsoft Copilot with web search integration.
**Request:**
```json
{
"query": "How to optimize PostgreSQL queries for large datasets?",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "Here are key strategies for optimizing PostgreSQL queries on large datasets:\n\n**1. Indexing:**\n- Create indexes on frequently queried columns\n- Use composite indexes for multi-column filters\n- Consider partial indexes for filtered queries\n\n**2. Query Optimization:**\n- Use EXPLAIN ANALYZE to identify bottlenecks\n- Avoid SELECT *; specify needed columns\n- Use JOINs instead of subqueries when possible\n\n**3. Partitioning:**\n- Partition large tables by date/range\n- Enables parallel query execution\n\n**4. Configuration:**\n- Tune shared_buffers (25% of RAM)\n- Adjust work_mem for sorting operations\n- Enable parallel workers\n\n**5. Maintenance:**\n- Regular VACUUM and ANALYZE\n- Update statistics for query planner",
"code_snippets": [
"-- Create index\nCREATE INDEX idx_users_email ON users(email);",
"-- Analyze query\nEXPLAIN ANALYZE SELECT * FROM orders WHERE created_at > '2026-01-01';"
]
}
}
```
---
### search_grok
xAI Grok assistant with real-time data access.
**Request:**
```json
{
"query": "What's trending on social media today?",
"timeout": 60
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "Top trending topics today (Jan 10, 2026):\n\n1. **#AI2026** - Major AI policy announcements\n2. **#SpaceX** - Starship Mars mission updates\n3. **#ClimateAction** - Global renewable energy summit\n4. **#TechEarnings** - Q4 2025 tech company results\n5. **#QuantumLeap** - IBM quantum computer breakthrough",
"trends": [
{"topic": "AI2026", "volume": 2500000, "sentiment": "positive"},
{"topic": "SpaceX", "volume": 1800000, "sentiment": "excited"}
]
}
}
```
---
### search_deepseek
DeepSeek AI assistant specialized in technical queries.
**Request:**
```json
{
"query": "Explain the difference between TCP and UDP",
"timeout": 60
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"answer": "**TCP (Transmission Control Protocol):**\n- Connection-oriented (handshake required)\n- Reliable: guarantees packet delivery and order\n- Error checking and retransmission\n- Slower due to overhead\n- Use cases: HTTP, HTTPS, email, file transfers\n\n**UDP (User Datagram Protocol):**\n- Connectionless (no handshake)\n- Unreliable: no delivery guarantee\n- No error correction\n- Faster, lower latency\n- Use cases: streaming, gaming, DNS, VoIP\n\n**When to use:**\n- TCP: When reliability matters (web pages, downloads)\n- UDP: When speed matters (video calls, live streams)",
"comparison_table": {
"reliability": {"TCP": "High", "UDP": "Low"},
"speed": {"TCP": "Slower", "UDP": "Faster"},
"ordering": {"TCP": "Guaranteed", "UDP": "Not guaranteed"}
}
}
}
```
---
## Search Engines
### search_google_search
Google web search with result snippets.
**Request:**
```json
{
"query": "Python asyncio tutorial",
"timeout": 30
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"results": [
{
"position": 1,
"title": "Asyncio — Asynchronous I/O — Python 3.13 documentation",
"url": "https://docs.python.org/3/library/asyncio.html",
"snippet": "asyncio is a library to write concurrent code using the async/await syntax. asyncio is used as a foundation for multiple Python asynchronous frameworks...",
"domain": "docs.python.org"
},
{
"position": 2,
"title": "Getting Started With Async Features in Python",
"url": "https://realpython.com/async-io-python/",
"snippet": "Learn how to use Python's asyncio library for concurrent programming. This tutorial covers event loops, coroutines, and best practices...",
"domain": "realpython.com"
},
{
"position": 3,
"title": "Python Asyncio Tutorial - YouTube",
"url": "https://www.youtube.com/watch?v=t5Bo1Je9EmE",
"snippet": "Complete guide to asynchronous programming in Python...",
"domain": "youtube.com"
}
],
"total_results": 1420000,
"search_time": 0.42
}
}
```
---
### search_bing_search
Bing web search with rich results.
**Request:**
```json
{
"query": "FastAPI best practices",
"timeout": 30
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"results": [
{
"title": "FastAPI Best Practices - GitHub",
"url": "https://github.com/zhanymkanov/fastapi-best-practices",
"snippet": "A collection of best practices, patterns, and conventions for building FastAPI applications...",
"date": "2026-01-05"
},
{
"title": "FastAPI Documentation - Best Practices",
"url": "https://fastapi.tiangolo.com/best-practices/",
"snippet": "Official guide to structuring FastAPI projects, dependency injection, and async operations..."
}
],
"related_searches": [
"FastAPI vs Flask",
"FastAPI authentication",
"FastAPI deployment"
]
}
}
```
---
### search_duckduckgo
Privacy-focused search without tracking.
**Request:**
```json
{
"query": "MCP protocol documentation",
"timeout": 30
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"results": [
{
"title": "Model Context Protocol - Anthropic",
"url": "https://modelcontextprotocol.io/",
"snippet": "MCP is an open protocol that enables seamless integration between LLM applications and external data sources..."
},
{
"title": "MCP Specification - GitHub",
"url": "https://github.com/anthropics/mcp",
"snippet": "Technical specification and reference implementation of the Model Context Protocol..."
}
],
"instant_answer": "Model Context Protocol (MCP) is an open standard for connecting AI assistants to data sources and tools."
}
}
```
---
## Social Media
### search_youtube_search
Search YouTube videos with metadata.
**Request:**
```json
{
"query": "Docker tutorial for beginners",
"timeout": 30
}
```
**Response:**
```json
{
"success": 1,
"data": {
"info": {
"success": 1,
"retries": 0
},
"videos": [
{
"title": "Docker Tutorial for Beginners - Full Course in 3 Hours",
"video_id": "3c-iBn73dDE",
"url": "https://www.youtube.com/watch?v=3c-iBn73dDE",
"channel": "TechWorld with Nana",
"channel_url": "https://www.youtube.com/c/TechWorldwithNana",
"duration": "3:10:24",
"views": 2500000,
"published": "2025-11-15",
"thumbnail": "https://i.ytimg.com/vi/3c-iBn73dDE/maxresdefault.jpg",
"description": "Complete Docker tutorial covering containers, images, volumes, networks, and Docker Compose..."
},
{
"title": "Learn Docker in 12 Minutes",
"video_id": "YFl2mCHdv24",
"url": "https://www.youtube.com/watch?v=YFl2mCHdv24",
"channel": "Fireship",
"duration": "12:15",
"views": 1800000,
"published": "2025-12-01",
"description": "Quick introduction to Docker fundamentals..."
}
],
"total_results": 54000
}
}
```
---
## Metadata Tools
### list_parsers
Get list of all available parsers with details.
**Request:**
```json
{}
```
**Response:**
```json
{
"parsers": [
{
"id": "perplexity",
"aparser_name": "FreeAI::Perplexity",
"name": "Perplexity AI",
"description": "AI-powered search with cited sources",
"category": "AI Chat",
"status": "active",
"features": ["web_search", "citations", "real_time"]
},
{
"id": "chatgpt",
"aparser_name": "FreeAI::ChatGPT",
"name": "ChatGPT",
"description": "OpenAI ChatGPT with web browsing",
"category": "AI Chat",
"status": "active",
"features": ["conversation", "web_search", "code_generation"]
},
{
"id": "google_search",
"aparser_name": "Google::Search",
"name": "Google Search",
"description": "Google web search engine",
"category": "Search Engine",
"status": "active",
"features": ["web_search", "snippets"]
}
],
"total": 11,
"categories": {
"AI Chat": 7,
"Search Engine": 3,
"Social Media": 1
}
}
```
**Claude Desktop Usage:**
```
@redis-api list_parsers
```
---
### get_parser_info
Get detailed information about specific parser.
**Request:**
```json
{
"parser_id": "perplexity"
}
```
**Response:**
```json
{
"id": "perplexity",
"aparser_name": "FreeAI::Perplexity",
"name": "Perplexity AI",
"description": "AI-powered search engine with real-time web access and source citations",
"category": "AI Chat",
"status": "active",
"features": [
"web_search",
"source_citations",
"real_time_data",
"follow_up_questions"
],
"options": {
"timeout": {
"type": "integer",
"default": 90,
"min": 30,
"max": 120,
"description": "Maximum wait time in seconds"
},
"locale": {
"type": "string",
"default": "en-US",
"options": ["en-US", "ru-RU", "de-DE"],
"description": "Response language"
}
},
"rate_limits": {
"requests_per_minute": 20,
"concurrent_requests": 5
},
"response_format": {
"success": "integer (0 or 1)",
"data": {
"answer": "string",
"sources": "array of objects",
"related": "array of strings"
},
"info": {
"success": "integer",
"retries": "integer",
"time_taken": "float"
}
}
}
```
---
### health_check
Check API and parsers availability.
**Request:**
```json
{}
```
**Response:**
```json
{
"status": "healthy",
"version": "1.0.2",
"timestamp": "2026-01-10T15:30:45Z",
"uptime_seconds": 86400,
"components": {
"api": {
"status": "up",
"response_time_ms": 12
},
"redis": {
"status": "connected",
"ping_ms": 2
},
"aparser": {
"status": "running",
"active_tasks": 3,
"queue_size": 0
}
},
"parsers": {
"total": 11,
"active": 11,
"inactive": 0
},
"statistics": {
"total_requests_today": 1523,
"success_rate": 98.5,
"average_response_time_ms": 15400
}
}
```
---
## Error Handling
### Timeout Error
**Request:**
```json
{
"query": "Very complex query that takes too long",
"timeout": 10
}
```
**Response:**
```json
{
"error": "Task timeout after 10 seconds",
"task_id": "cb5e8e96-8a7f-4b8c-9d1e-2f3a4b5c6d7e",
"parser": "perplexity",
"query": "Very complex query that takes too long",
"timestamp": "2026-01-10T15:30:45Z"
}
```
---
### Parser Not Found
**Request:**
```json
{
"parser_id": "unknown_parser"
}
```
**Response:**
```json
{
"error": "Parser 'unknown_parser' not found",
"available_parsers": [
"perplexity",
"chatgpt",
"claude",
"gemini",
"copilot",
"grok",
"deepseek",
"google_search",
"bing_search",
"duckduckgo",
"youtube_search"
],
"suggestion": "Did you mean 'perplexity'?"
}
```
---
### Authentication Error
**Request:**
```
# Missing or invalid API key
```
**Response:**
```json
{
"error": "Authentication failed: Invalid API key",
"status_code": 401,
"message": "Please provide valid API key via REDIS_API_KEY environment variable",
"docs": "https://github.com/ozand/ayga-mcp-client#authentication"
}
```
---
### Rate Limit Error
**Response:**
```json
{
"error": "Rate limit exceeded",
"status_code": 429,
"message": "Maximum 20 requests per minute for parser 'perplexity'",
"retry_after_seconds": 42,
"current_usage": {
"requests_this_minute": 20,
"limit": 20
}
}
```
---
### Task Execution Error
**Response:**
```json
{
"success": 0,
"error_code": 500,
"error_message": "Parser execution failed: Connection timeout to external service",
"task_id": "task-uuid-here",
"parser": "perplexity",
"info": {
"retries": 3,
"last_error": "HTTPConnectionPool timeout"
}
}
```
---
## Real-World Use Cases
### 1. Research Assistant
Combine multiple parsers for comprehensive research:
```python
import asyncio
from ayga_mcp_client.api.client import RedisAPIClient
async def research_topic(topic: str):
"""Research a topic using multiple AI sources."""
client = RedisAPIClient(api_key="your_api_key")
# Get different perspectives
tasks = [
client.submit_parser_task("perplexity", f"Latest research on {topic}"),
client.submit_parser_task("chatgpt", f"Explain {topic} in simple terms"),
client.submit_parser_task("claude", f"Critical analysis of {topic}"),
]
# Wait for all results
task_results = await asyncio.gather(*tasks)
results = await asyncio.gather(*[
client.wait_for_result(t['task_id'])
for t in task_results
])
await client.close()
return results
# Usage
results = asyncio.run(research_topic("quantum computing"))
```
---
### 2. Content Aggregator
Search multiple sources and compile results:
```python
async def aggregate_content(query: str):
"""Search across multiple platforms."""
client = RedisAPIClient(api_key="your_api_key")
searches = {
'google': await client.submit_parser_task("google_search", query),
'bing': await client.submit_parser_task("bing_search", query),
'youtube': await client.submit_parser_task("youtube_search", query),
}
results = {}
for source, task in searches.items():
results[source] = await client.wait_for_result(task['task_id'])
await client.close()
return results
```
---
### 3. Multi-AI Consensus
Get consensus from multiple AI models:
```python
async def get_consensus(question: str):
"""Ask multiple AIs and compare answers."""
client = RedisAPIClient(api_key="your_api_key")
ais = ["chatgpt", "claude", "gemini", "perplexity"]
tasks = [
await client.submit_parser_task(ai, question)
for ai in ais
]
answers = await asyncio.gather(*[
client.wait_for_result(t['task_id'])
for t in tasks
])
await client.close()
# Compare and analyze
return {
ai: answer['data'].get('answer', '')
for ai, answer in zip(ais, answers)
}
```
---
### 4. Monitoring & Health Check
Monitor API health before critical operations:
```python
async def safe_query(parser: str, query: str):
"""Query with health check first."""
client = RedisAPIClient(api_key="your_api_key")
# Check health
health_task = await client.submit_parser_task("health_check", "")
health = await client.wait_for_result(health_task['task_id'])
if health.get('status') != 'healthy':
raise RuntimeError("API is not healthy")
# Proceed with query
task = await client.submit_parser_task(parser, query)
result = await client.wait_for_result(task['task_id'])
await client.close()
return result
```
---
## Content Extraction Parsers
### parse_article_extractor
Extract clean article content using Mozilla Readability algorithm.
**Request:**
```json
{
"query": "https://example.com/article/ai-advances-2026",
"timeout": 60
}
```
**Response:**
```json
{
"success": 1,
"data": {
"title": "Major AI Advances in 2026",
"content": "<article>Full HTML content of the article...</article>",
"textContent": "Plain text version without HTML tags...",
"length": 4523,
"excerpt": "Brief description or first paragraph...",
"byline": "John Doe",
"siteName": "Tech News Daily"
}
}
```
**Use Cases:**
- Content aggregation and archiving
- Research data collection
- Article summarization pipelines
- Clean reading experience extraction
### parse_text_extractor
High-speed text extraction with automatic HTML cleaning (2000 queries/min).
**Request:**
```json
{
"query": "https://example.com/documentation",
"timeout": 30
}
```
**Response:**
```json
{
"success": 1,
"data": {
"text": "Extracted clean text without HTML tags...",
"pages": [
{"url": "page1", "text": "..."},
{"url": "page2", "text": "..."}
]
}
}
```
**Features:**
- Multi-page parsing
- CloudFlare bypass
- Minimum text length filtering
- Automatic HTML tag removal
---
## Social Media - Instagram Parsers
### parse_instagram_profile
Parse Instagram profile data including posts, followers, and biography.
**Request:**
```json
{
"query": "username",
"timeout": 120
}
```
**Response:**
```json
{
"success": 1,
"data": {
"username": "username",
"full_name": "Full Name",
"biography": "Bio text...",
"website": "https://example.com",
"posts_count": 1234,
"followers_count": 50000,
"following_count": 500,
"profile_pic_url": "https://...",
"is_business": false,
"is_private": false,
"is_verified": true,
"posts": [
{
"id": "post_id",
"caption": "Post caption...",
"likes": 5000,
"comments": 250
}
]
}
}
```
**Note:** Requires authentication cookie for private profiles.
### parse_instagram_post
Parse individual Instagram post with comments (up to 1200).
**Request:**
```json
{
"query": "https://www.instagram.com/p/POST_ID/",
"timeout": 120
}
```
**Response:**
```json
{
"success": 1,
"data": {
"type": "photo",
"caption": "Post caption text",
"created_at": 1705017600,
"likes_count": 10500,
"comments_count": 450,
"video_url": null,
"comments": [
{
"user": "username",
"text": "Comment text",
"likes": 25,
"timestamp": 1705020000
}
]
}
}
```
### parse_instagram_tag
Parse Instagram posts by hashtag.
**Request:**
```json
{
"query": "travel",
"timeout": 120
}
```
**Note:** Requires authentication cookie.
### parse_instagram_geo
Parse Instagram posts by location/geotag.
**Request:**
```json
{
"query": "https://www.instagram.com/explore/locations/123456/",
"timeout": 120
}
```
**Response:**
```json
{
"success": 1,
"data": {
"location_id": "123456",
"name": "Location Name",
"phone": "+1234567890",
"website": "https://example.com",
"category": "Restaurant",
"coordinates": {"lat": 40.7128, "lng": -74.0060},
"address": "123 Main St, City",
"posts": [...]
}
}
```
### search_instagram_search
Search Instagram for profiles, hashtags, and locations.
**Request:**
```json
{
"query": "fashion",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"users": [
{"username": "...", "full_name": "...", "followers": 50000}
],
"hashtags": [
{"name": "fashion", "media_count": 1000000}
],
"places": [
{"id": "123", "name": "Fashion District", "city": "NYC"}
]
}
}
```
---
## Social Media - TikTok Parser
### parse_tiktok_profile
Parse TikTok profile data including videos, followers, and bio.
**Request:**
```json
{
"query": "@username",
"timeout": 120
}
```
**Response:**
```json
{
"success": 1,
"data": {
"username": "username",
"nickname": "Display Name",
"bio": "Profile bio text",
"email": "contact@example.com",
"link": "https://link.com",
"video_count": 234,
"followers_count": 1500000,
"following_count": 123,
"avatar_url": "https://...",
"videos": [
{
"id": "video_id",
"caption": "Video caption",
"likes": 50000,
"comments": 1200,
"shares": 500,
"views": 2000000
}
]
}
}
```
---
## Analytics Parser
### get_google_trends
Analyze keyword trends, interest over time, and regional data.
**Request (single keyword):**
```json
{
"query": "artificial intelligence",
"timeout": 90
}
```
**Request (multiple keywords comparison):**
```json
{
"query": "AI,machine learning,deep learning",
"timeout": 90
}
```
**Response:**
```json
{
"success": 1,
"data": {
"categories": [
{
"query": "artificial intelligence",
"category": "Science & Technology",
"popularity": 85
}
],
"interest_over_time": [
{"timestamp": 1704067200, "popularity": 75},
{"timestamp": 1704153600, "popularity": 82},
{"timestamp": 1704240000, "popularity": 88}
],
"interest_by_region": [
{"region": "United States", "popularity": 100},
{"region": "United Kingdom", "popularity": 87},
{"region": "Canada", "popularity": 76}
],
"related_queries_top": [
{"query": "what is AI", "popularity": 100},
{"query": "AI applications", "popularity": 85}
],
"related_queries_rising": [
{"query": "AI in 2026", "popularity": "Breakout"},
{"query": "generative AI", "popularity": "+350%"}
]
}
}
```
**Use Cases:**
- SEO keyword research
- Market trend analysis
- Content planning
- Competitive analysis
- Regional targeting
---
## Visual Content Parser
### search_pinterest_search
High-speed Pinterest image search (4000+ queries/min).
**Request:**
```json
{
"query": "modern interior design",
"timeout": 60
}
```
**Response:**
```json
{
"success": 1,
"data": {
"titles": [
"Modern Living Room Design",
"Minimalist Interior Concept",
"Contemporary Home Decor"
],
"descriptions": [
"Beautiful modern living room with clean lines...",
"Minimalist approach to interior design..."
],
"image_links": [
"https://i.pinimg.com/originals/...",
"https://i.pinimg.com/736x/..."
],
"domains": [
"architecturaldigest.com",
"houzz.com",
"dwell.com"
],
"source_links": [
"https://architecturaldigest.com/gallery/..."
],
"pin_ids": [
"123456789",
"987654321"
],
"author_info": {
"pinners": ["user1", "user2"],
"boards": ["Interior Design", "Modern Homes"]
}
}
}
```
**Features:**
- Up to 100 pages per query
- 4000+ queries per minute
- Image links, titles, descriptions
- Source domain tracking
- Author and board information
**Use Cases:**
- Visual research and mood boards
- Design inspiration collection
- E-commerce product research
- Brand visual analysis
- Content creation planning
---
## Tips & Best Practices
### 1. Timeout Selection
- **Quick queries** (search engines): 30 seconds
- **AI chat** (simple questions): 60 seconds
- **Complex AI tasks** (research, analysis): 90-120 seconds
### 2. Error Handling
Always wrap calls in try-except:
```python
try:
result = await client.wait_for_result(task_id, timeout=90)
except asyncio.TimeoutError:
print("Task timed out, try increasing timeout")
except Exception as e:
print(f"Error: {e}")
```
### 3. Parallel Execution
Use `asyncio.gather()` for multiple independent queries:
```python
results = await asyncio.gather(
client.submit_parser_task("perplexity", "query1"),
client.submit_parser_task("chatgpt", "query2"),
client.submit_parser_task("google_search", "query3"),
)
```
### 4. Resource Cleanup
Always close the client:
```python
try:
# Your operations
pass
finally:
await client.close()
```
---
## See Also
- [README.md](README.md) - Installation and setup
- [DEVELOPMENT.md](DEVELOPMENT.md) - Technical architecture
- [CHANGELOG.md](CHANGELOG.md) - Version history
- [API Documentation](https://redis.ayga.tech/docs) - Backend API reference
