Tools
ExaSearch
The ExaSearch node is a wrapper around the Exa Search API, designed to be used as a tool within a larger system, likely for AI-powered applications. Exa is a search engine specifically optimized for use by Language Models (LLMs).
Node Details
- Name: ExaSearch
- Type: Tool
- Version: 1.0
- Category: Tools
Parameters
Required
- Credential: An API key for Exa Search (credential type: exaSearchApi)
Optional
- Tool Description: A custom description of the tool’s functionality (default provided)
- Num of Results: Number of search results to return (default: 10, max varies by plan)
- Search Type: Options include ‘keyword’, ‘neural’, or ‘magic’ (auto-decides between keyword and neural)
- Use Auto Prompt: Boolean to enable query conversion to Exa format
- Category: Specifies a data category to focus the search (e.g., company, research paper, news)
- Include Domains: List of domains to include in the search
- Exclude Domains: List of domains to exclude from the search
- Start Crawl Date: ISO 8601 date to set the earliest crawl date for results
- End Crawl Date: ISO 8601 date to set the latest crawl date for results
- Start Published Date: ISO 8601 date to set the earliest publication date for results
- End Published Date: ISO 8601 date to set the latest publication date for results
Input
The input to this node should be an Exa-optimized query string. If ‘Use Auto Prompt’ is enabled, the input can be a natural language query that will be converted to Exa format.Output
The output is a JSON array containing the search results. Each result typically includes details such as the URL, title, snippet, and other metadata related to the search hit.Usage
This node is particularly useful in AI applications that require up-to-date information from the web. It can be used in various scenarios such as:- Fact-checking and information verification
- Gathering current events or news for analysis
- Research assistance in specific domains (using the Category filter)
- Competitive analysis (using company category and domain filters)
- Trend analysis across various data types (news, social media, academic papers)