Albus is an AI integration platform that empowers applications with advanced search and question-answering functionalities. Leverage Relevance AI to transform these capabilities into dynamic, automated workflows that enhance user engagement and decision-making.
Albus provides powerful AI-driven search and real-time responses, while Relevance AI amplifies these capabilities by enabling AI Agents to automate and optimize interactions across your applications.
Intelligent Document Mastery
Empowers the AI agent with advanced document processing capabilities across multiple formats and languages
Knowledge Graph Navigation
Enables the agent to traverse complex information networks and discover hidden relationships between data points
Automated Insight Generation
Transforms raw document data into actionable insights through advanced pattern recognition and analysis
Relevance AI seamlessly integrates with Albus to enhance your workflows with intelligent search and question-answering capabilities.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- An Albus account
- A Relevance AI account with access to your projects and datasets
- Authorization (you'll connect securely using OAuth—no sensitive info stored manually)
Security & Reliability
The Albus AI integration platform provides a powerful way to integrate AI-powered search and question-answering capabilities into your applications. This integration allows you to make authorized API calls to Albus's services and implement an AI assistant that can search through Slack conversations and web content to provide intelligent responses.
Key benefits include seamless AI-powered search functionality, OAuth-based secure authentication, real-time question-answering capabilities, and flexible API integration options.
To get started, ensure you have an Albus account with API access, OAuth credentials with pipedream-albus-read-write permissions, and valid API authentication tokens. Your environment should support HTTPS, JSON parsing, REST API calls, and handle asynchronous operations for wait-for-completion functionality.
Begin by configuring OAuth authentication and setting up the base URL for API calls. Initialize your API headers to include the necessary content type. Making a basic API call involves defining the method, path, and body of your request, and then executing the call with the appropriate parameters.
For asking questions, you can implement a request that includes your prompt and specifies whether to wait for completion. The expected response will include the answer, confidence level, and sources.
Common use cases include simple question-answer implementations and custom API endpoint access. Troubleshooting common issues such as authentication errors, request timeouts, and response format errors is essential for smooth operation. Implement best practices like error handling, request optimization, and security measures to ensure your integration runs efficiently and securely.
For additional support or detailed API documentation, please refer to the official Albus API documentation or contact support.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
To get the most out of the Albus + Relevance AI integration without writing code:
- Start with a clear configuration: Ensure your OAuth credentials and API tokens are correctly set up and stored securely.
- Utilize the quick start guide: Follow the provided examples for making API calls and asking questions to get up and running quickly.
- Test your API calls: Use sample data to validate your API requests and responses before deploying to production.
- Implement error handling: Always include try-catch blocks to manage potential errors gracefully and log them for troubleshooting.
- Monitor API usage: Keep an eye on your API call limits and implement throttling to avoid hitting rate limits.
