WizSearch

A unified Python library for searching across multiple search engines with a consistent interface. WizSearch enables concurrent multi-engine searches with intelligent result merging, page crawling capabilities, and optional semantic search integration.

Features

Multiple Search Engines: Baidu, Bing, Brave, DuckDuckGo, Google, Google AI, SearxNG, Serper, Tavily, WeChat (with sogou engine)
Unified Interface: Single API for all search engines with consistent SearchResult format
Multi-Engine Aggregation: Concurrent searches across multiple engines with round-robin result merging
Page Crawling: Built-in web page content extraction using Crawl4AI
Semantic Search: Optional vector-based semantic search with local storage and web fallback
Async/Await Support: Full asynchronous API for high performance

Installation

# Basic installation
pip install wizsearch

# With development dependencies
pip install wizsearch[dev]

Quick Start

Basic Single Engine Search

import asyncio
from wizsearch import DuckDuckGoSearch, DuckDuckGoSearchConfig

async def search_example():
    # Initialize a single search engine
    config = DuckDuckGoSearchConfig(max_results=5)
    searcher = DuckDuckGoSearch(config=config)

    # Perform search
    results = await searcher.search("Python async programming")

    # Access results
    print(f"Query: {results.query}")
    print(f"Found {len(results.sources)} results\n")

    for source in results.sources:
        print(f"Title: {source.title}")
        print(f"URL: {source.url}")
        print(f"Content: {source.content[:100]}...")
        print()

asyncio.run(search_example())

Multi-Engine Search with WizSearch

WizSearch automatically discovers and runs searches across multiple engines concurrently, then merges results using a round-robin approach to maintain diversity.

import asyncio
from wizsearch import WizSearch, WizSearchConfig

async def multi_engine_search():
    # Auto-enable all available engines
    wizsearch = WizSearch()

    # Or configure specific engines
    config = WizSearchConfig(
        enabled_engines=["duckduckgo", "tavily", "brave"],
        max_results_per_engine=10,
        timeout=30,
        fail_silently=True  # Continue even if some engines fail
    )
    wizsearch = WizSearch(config=config)

    # Search across all enabled engines
    results = await wizsearch.search("machine learning tutorials")

    print(f"Total unique results: {len(results.sources)}")
    print(f"Response time: {results.response_time:.2f}s")

    for i, source in enumerate(results.sources[:5], 1):
        print(f"{i}. {source.title}")
        print(f"   {source.url}\n")

asyncio.run(multi_engine_search())

Detailed Usage

Available Search Engines

WizSearch supports the following search engines, each with its own configuration:

Engine	Code Name	🔑 API Key	Notes
Tavily	`tavily`	✅	AI-optimized search, requires `TAVILY_API_KEY`
Google AI	`googleai`	✅	Requires `GEMINI_API_KEY`
Serper	`serper`	✅	Google search API, requires `SERPER_API_KEY`, supports multiple search types (web, images, news, scholar, maps, videos, etc.)
DuckDuckGo	`duckduckgo`	❌	Free, no rate limits
Baidu	`baidu`	❌	Chinese search engine (via tarzi), runs in headless mode by default
WeChat	`wechat`	❌	WeChat article search (via tarzi), runs in headless mode by default
Brave	`brave`	❌	Browser-based scraping (via tarzi), runs in headless mode by default
Bing	`bing`	❌	Browser-based scraping, anti-bot protection (via tarzi), runs in headless mode by default
Google	`google`	❌	Browser-based scraping, anti-bot protection (via tarzi), runs in headless mode by default
SearxNG	`searxng`	❌	Self-hosted metasearch engine

Note: Use the "Code Name" column values when specifying engines in WizSearchConfig.enabled_engines.

Browser Mode Configuration

The browser-based search engines (Baidu, Bing, Brave, Google, and WeChat) use the tarzi library for web scraping. These engines run in headless mode by default, meaning no visible browser window appears during searches. This is ideal for production environments as it:

Doesn't require a display or GUI
Consumes fewer resources
Runs faster than headed mode
Is more suitable for servers and containers

Using Headless Mode (Default)

from wizsearch import GoogleSearch, GoogleSearchConfig

# Default: runs in headless mode (no visible window)
search = GoogleSearch(config=GoogleSearchConfig())
results = await search.search("machine learning")

Using Headed Mode for Debugging

If you need to see the browser window for debugging (e.g., to check if searches are working correctly, debug anti-bot protections, or observe the scraping process), you can disable headless mode:

from wizsearch import GoogleSearch, GoogleSearchConfig

# Disable headless mode to see the browser window
config = GoogleSearchConfig(headless=False)
search = GoogleSearch(config=config)
results = await search.search("machine learning")

Configuring Browser Mode in WizSearch

When using the WizSearch aggregator, you can control the browser mode for all tarzi-based engines:

from wizsearch import WizSearch, WizSearchConfig

# All tarzi engines will use headless mode (default)
config = WizSearchConfig(
    enabled_engines=["google", "bing", "brave"],
    headless=True  # Default
)
wizsearch = WizSearch(config=config)

# Or disable headless for all tarzi engines (useful for debugging)
config = WizSearchConfig(headless=False)
wizsearch = WizSearch(config=config)

Engine-Specific Examples

DuckDuckGo Search

from wizsearch import DuckDuckGoSearch, DuckDuckGoSearchConfig

config = DuckDuckGoSearchConfig(
    max_results=10,
    region="us-en",  # Region setting
    safesearch="moderate",  # "on", "moderate", or "off"
    timelimit="m",  # Time limit: "d" (day), "w" (week), "m" (month), "y" (year)
    backend="auto"
)
searcher = DuckDuckGoSearch(config=config)
results = await searcher.search("climate change")

Tavily Search (Advanced Features)

from wizsearch import TavilySearch, TavilySearchConfig
import os

# Set API key
os.environ["TAVILY_API_KEY"] = "your-api-key"

config = TavilySearchConfig(
    max_results=5,
    search_depth="advanced",  # "basic" or "advanced"
    include_domains=["arxiv.org", "scholar.google.com"],
    exclude_domains=["youtube.com"],
    include_answer=True,  # Get AI-generated answer
    include_images=True
)

searcher = TavilySearch(config=config)
results = await searcher.search(
    query="quantum computing breakthroughs",
    search_depth="advanced",
    include_domains=["nature.com", "science.org"]
)

# Access AI-generated answer
if results.answer:
    print(f"Answer: {results.answer}")

# Access images
for image_url in results.images:
    print(f"Image: {image_url}")

Google AI Search

from wizsearch import GoogleAISearch
import os

# Set API key (or set GEMINI_API_KEY environment variable)
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

searcher = GoogleAISearch()
results = await searcher.search(
    query="neural network architectures",
    num_results=5
)

# Image search
image_results = await searcher.search(
    query="data visualization examples",
    search_type="image",
    num_results=10
)

Serper Search (Google API)

Serper provides a powerful Google search API with support for multiple search types:

from wizsearch import SerperSearch, SerperSearchConfig
import os

# Set API key (or set SERPER_API_KEY environment variable)
os.environ["SERPER_API_KEY"] = "your-serper-api-key"

# Web search
config = SerperSearchConfig(
    search_type="search",  # Default: web search
    max_results=10,
    gl="us",  # Country: United States
    hl="en",  # Language: English
)
searcher = SerperSearch(config=config)
results = await searcher.search("Python async programming")

# Image search
config = SerperSearchConfig(search_type="images", max_results=10)
searcher = SerperSearch(config=config)
results = await searcher.search("beautiful landscapes")

# News search
config = SerperSearchConfig(search_type="news", max_results=5)
searcher = SerperSearch(config=config)
results = await searcher.search("artificial intelligence")

# Scholar search (academic papers)
config = SerperSearchConfig(search_type="scholar", max_results=10)
searcher = SerperSearch(config=config)
results = await searcher.search("machine learning transformers")

# Other supported search types: videos, maps, places, autocomplete, lens

WizSearch Configuration

from wizsearch import WizSearch, WizSearchConfig

# Get all available engines
available = WizSearch.get_available_engines()
print(f"Available engines: {available}")

# Custom configuration
# Use code names from the table above (e.g., "duckduckgo", "tavily", "brave")
config = WizSearchConfig(
    enabled_engines=["duckduckgo", "tavily", "brave"],
    max_results_per_engine=10,  # Results per engine
    timeout=30,  # Timeout in seconds
    fail_silently=True,  # Don't raise if some engines fail
    headless=True  # Use headless mode for browser-based engines (default: True)
)

wizsearch = WizSearch(config=config)

# Check enabled engines
print(f"Enabled: {wizsearch.get_enabled_engines()}")

# Get configuration
print(wizsearch.get_config())

# Perform search
results = await wizsearch.search("Python best practices")

Page Crawling

Extract full page content from search results using crawl4ai-powered page crawler:

from wizsearch import PageCrawler

crawler = PageCrawler(
    url="https://example.com/article",
    content_format="markdown",  # "markdown", "html", or "text"
    external_links=False,
    adaptive_crawl=False,
    depth=1,
    word_count_threshold=5,
    user_agent="Mozilla/5.0...",
    wait_for=None,  # CSS selector to wait for
    screenshot=False,
    bypass_cache=False,
    only_text=True
)

# Crawl the page
content = await crawler.crawl()
print(content)

Semantic Search (Advanced | Preview)

Combine web search with local vector storage for enhanced semantic search capabilities. The semantic search interface is synchronous.

from wizsearch.semsearch import SemanticSearch, SemanticSearchConfig
from wizsearch import TavilySearch

# Configure semantic search
config = SemanticSearchConfig(
    vector_store_provider="weaviate",  # or "pgvector"
    collection_name="DocumentChunks",
    embedding_model="nomic-embed-text:latest",
    local_search_limit=10,
    web_search_limit=5,
    fallback_threshold=5,  # Min local results before web search
    enable_caching=True,
    cache_ttl_hours=24,
    auto_store_web_results=True  # Automatically store web results
)

# Initialize with Tavily as web search engine
web_search = TavilySearch()
semantic_search = SemanticSearch(
    web_search_engine=web_search,
    config=config
)

# Connect to vector store
semantic_search.connect()

# Perform semantic search
# First searches local vector store, falls back to web if needed
result = semantic_search.search(
    query="machine learning best practices",
    limit=10,
    force_web_search=False
)

print(f"Total results: {result.total_results}")
print(f"Local: {result.local_results}, Web: {result.web_results}")
print(f"Search time: {result.search_time:.2f}s")

# Access chunks with scores
for chunk, score in result.chunks[:5]:
    print(f"\n[{score:.3f}] {chunk.source_title}")
    print(f"Content: {chunk.content[:200]}...")

# Manually store documents
semantic_search.store_document(
    content="Your document content here...",
    source_url="https://example.com",
    source_title="Example Document",
    metadata={"category": "tutorial"}
)

# Get statistics
stats = semantic_search.get_stats()
print(stats)

Working with Search Results

All search engines return a consistent SearchResult object:

# SearchResult structure
results = await searcher.search("query")

# Basic attributes
print(results.query)           # Original query
print(results.answer)          # AI-generated answer (if available)
print(results.images)          # List of image URLs
print(results.response_time)   # Response time in seconds
print(results.raw_response)    # Raw API response

# Source items
for source in results.sources:
    print(source.url)          # URL
    print(source.title)        # Title
    print(source.content)      # Extracted content/snippet
    print(source.score)        # Relevance score (if available)
    print(source.raw_content)  # Raw content

Custom Engine Registration

Register your own custom search engine:

from wizsearch import WizSearch, WizSearchConfig, BaseSearch, SearchResult, SourceItem
from pydantic import BaseModel

class CustomSearchConfig(BaseModel):
    max_results: int = 10
    api_key: str = ""

class CustomSearch(BaseSearch):
    def __init__(self, config: CustomSearchConfig):
        self.config = config

    async def search(self, query: str, **kwargs) -> SearchResult:
        # Implement your search logic
        # Example: return mock results
        sources = [
            SourceItem(
                url="https://example.com",
                title="Example Result",
                content="This is example content",
                score=0.95
            )
        ]
        return SearchResult(
            query=query,
            sources=sources,
            answer=None
        )

# Register the engine
WizSearch.register_custom_engine(
    name="custom",
    engine_class=CustomSearch,
    config_class=CustomSearchConfig
)

# Use it with WizSearch
config = WizSearchConfig(enabled_engines=["custom", "duckduckgo"])
wizsearch = WizSearch(config=config)
results = await wizsearch.search("test query")

Examples

Check the examples/ directory for comprehensive examples:

wizsearch_demo.py - Multi-engine search demonstrations
tavily_search_demo.py - Tavily-specific features
google_ai_search_demo.py - Google AI search examples
serper_search_demo.py - Serper search examples with multiple search types
ddg_search_demo.py - DuckDuckGo search examples
Individual engine demos for each supported search engine

Run examples:

# Basic demo
uv run python examples/wizsearch_demo.py

# Tavily demo (requires API key)
export TAVILY_API_KEY="your-key"
uv run python examples/tavily_search_demo.py

# Serper demo (requires API key)
export SERPER_API_KEY="your-key"
uv run python examples/serper_search_demo.py

API Reference

Core Classes

WizSearch: Multi-engine search aggregator
- search(query, **kwargs): Perform concurrent search
- get_available_engines(): List all available engines
- get_enabled_engines(): List enabled engines
- get_config(): Get current configuration
- register_custom_engine(name, engine_class, config_class): Register custom engine
WizSearchConfig: Configuration for WizSearch
- enabled_engines: List of engine names to enable
- max_results_per_engine: Max results per engine (1-50)
- timeout: Request timeout in seconds (1-60)
- fail_silently: Continue if engines fail (default: True)
- headless: Enable headless browser mode for browser-based engines (default: True)
BaseSearch: Abstract base class for search engines
- search(query, **kwargs): Async search method
SearchResult: Unified search result format
- query: Original query string
- answer: AI-generated answer (optional)
- images: List of image URLs
- sources: List of SourceItem objects
- response_time: Response time in seconds
- raw_response: Raw API response
SourceItem: Individual search result
- url: Result URL
- title: Result title
- content: Extracted content/snippet
- score: Relevance score (optional)
- raw_content: Raw content (optional)
PageCrawler: Web page content crawler
- crawl(): Async crawl method
SemanticSearch: Semantic search with vector storage
- connect(): Connect to vector store
- search(query, limit, force_web_search, filters): Semantic search
- store_document(content, source_url, source_title, metadata): Store document
- get_stats(): Get system statistics
- clear_cache(): Clear query cache

Environment Variables

Some search engines require API keys set as environment variables:

# Tavily (required for TavilySearch)
export TAVILY_API_KEY="your-tavily-api-key"

# Google AI (required for GoogleAISearch)
export GEMINI_API_KEY="your-gemini-api-key"

# Serper (required for SerperSearch)
export SERPER_API_KEY="your-serper-api-key"

Development

# Clone repository
git clone https://github.com/mirasoth/wizsearch.git
cd wizsearch

# Install development dependencies
pip install -e ".[dev]"

# Run tests
make test

# Run linting
make lint

# Format code
make format

Architecture

┌─────────────────┐
│   WizSearch     │  Multi-engine orchestrator
└────────┬────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐ ┌────────┐
│Engine 1│ │Engine 2│  Individual search engines
└────────┘ └────────┘
    │         │
    └────┬────┘
         ▼
   ┌──────────┐
   │  Merger  │  Round-robin result merging
   └──────────┘
         │
         ▼
  ┌─────────────┐
  │SearchResult │  Unified result format
  └─────────────┘

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

MIT License - see LICENSE file for details.

Links

Homepage: https://github.com/mirasoth/wizsearch
PyPI: https://pypi.org/project/wizsearch/
Issues: https://github.com/mirasoth/wizsearch/issues

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.github/workflows		.github/workflows
docs/impl		docs/impl
examples		examples
tests		tests
wizsearch		wizsearch
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
pyproject.toml		pyproject.toml

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WizSearch

Features

Installation

Quick Start

Basic Single Engine Search

Multi-Engine Search with WizSearch

Detailed Usage

Available Search Engines

Browser Mode Configuration

Using Headless Mode (Default)

Using Headed Mode for Debugging

Configuring Browser Mode in WizSearch

Engine-Specific Examples

DuckDuckGo Search

Tavily Search (Advanced Features)

Google AI Search

Serper Search (Google API)

WizSearch Configuration

Page Crawling

Semantic Search (Advanced | Preview)

Working with Search Results

Custom Engine Registration

Examples

API Reference

Core Classes

Environment Variables

Development

Architecture

Contributing

License

Links

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