ParallelWebSearchTool

Setup:

Install langchain-parallel and set environment variable PARALLEL_API_KEY.

pip install -U langchain-parallel
export PARALLEL_API_KEY="your-api-key"

Key init args:

api_key: Optional[SecretStr] Parallel API key. If not provided, will be read from PARALLEL_API_KEY env var. base_url: str Base URL for Parallel API. Defaults to "https://api.parallel.ai".

Instantiation:

from langchain_parallel import ParallelWebSearchTool

# Basic instantiation
tool = ParallelWebSearchTool()

# With custom API key
tool = ParallelWebSearchTool(api_key="your-api-key")

Basic Usage:

# Simple objective-based search
result = tool.invoke({
    "objective": "What are the latest developments in AI?"
})

# Query-based search with multiple queries
result = tool.invoke({
    "search_queries": [
        "latest AI developments 2024",
        "machine learning breakthroughs",
        "artificial intelligence news"
    ],
    "max_results": 10
})

Domain filtering and advanced options:

# Domain filtering with fetch policy (using dict format)
result = tool.invoke({
    "objective": "Recent climate change research",
    "source_policy": {
        "include_domains": ["nature.com", "science.org"],
        "exclude_domains": ["reddit.com", "twitter.com"]
    },
    "max_results": 15,
    "excerpts": {"max_chars_per_result": 2000},  # Auto-converted
    "mode": "one-shot",  # Use 'agentic' for token-efficient results
    "fetch_policy": {  # Auto-converted to FetchPolicy
        "max_age_seconds": 86400,  # 1 day cache
        "timeout_seconds": 60
    },
    "include_metadata": True
})

# Or use the types directly
from langchain_parallel import ExcerptSettings, FetchPolicy

result = tool.invoke({
    "objective": "Recent climate change research",
    "excerpts": ExcerptSettings(max_chars_per_result=2000),
    "fetch_policy": FetchPolicy(max_age_seconds=86400, timeout_seconds=60),
})

Async Usage:

import asyncio

async def search_async():
    result = await tool.ainvoke({
        "objective": "Latest tech news"
    })
    return result

result = asyncio.run(search_async())

Response Format:

{
    "search_id": "search_abc123...",
    "results": [
        {
            "url": "https://example.com/article",
            "title": "Article Title",
            "excerpts": [
                "Relevant excerpt from the page...",
                "Another important section..."
            ]
        }
    ],
    "search_metadata": {
        "search_duration_seconds": 2.451,
        "search_timestamp": "2024-01-15T10:30:00",
        "max_results_requested": 10,
        "actual_results_returned": 8,
        "search_id": "search_abc123...",
        "query_count": 3,
        "queries_used": ["query1", "query2", "query3"],
        "source_policy_applied": true,
        "included_domains": ["nature.com"],
        "excluded_domains": ["reddit.com"]
    }
}

Tool Calling Integration:

# When used with LangChain agents or chat models with tool calling
from langchain_core.messages import HumanMessage
from langchain_parallel import ChatParallelWeb

chat = ChatParallelWeb()
chat_with_tools = chat.bind_tools([tool])

response = chat_with_tools.invoke([
    HumanMessage(content="Search for the latest AI research papers")
])

Best Practices:

• Use specific objectives for better results
• Apply domain filtering for focused searches
• Include metadata for debugging and optimization