OAuth2 Settings Finder with OpenRouter Chat Model and Llama 3.3

Overview: The AI agent identifies:

• Authorization URI
• Token URI
• Audience

Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data:

• Score Range: 0 < x ≤ 1
• Score Granularity: 0.01 increments

Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model.

Start

Trigger input from the calling process.

AI Agent

Prompt input to find data.

Output

Parser to grab the AI results into a JSON structure, according to the specified schema.

Conform

Output to your process expectation.

Purpose

This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience.

Value

Obtaining OAuth2 details via AI saves time and reduces the risk of human error. The confidence scoring system provides an indication of the trustworthiness of the results, empowering users to make informed decisions.

Setup Instructions

1. Configuration Nodes

• Structured Output Node: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing.
• Code Node: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios.

2. AI Model Prompt

The prompt for the AI model includes:

• A detailed structure and objectives of the query.
• Flexibility for the model to improvise when accurate results cannot be determined.

3. Confidence Scoring

The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity.

Configuration Example

This is an example of the Code node can be configured to reformat the data:

const originalText = items[0].json.output.text;
const lines = originalText.split('\n');
const service_name = lines[0].split(': ')[1];
const audience = lines[1].split(': ')[1];
const authorization_uri = lines[2].split(': ')[1];
const token_uri = lines[3].split(': ')[1];
const details = lines[4].split(': ')[1];
const confidence = parseFloat(lines[5].split(': ')[1]);
return [
  {
    json: {
      output: {
        service_name,
        audience,
        authorization_uri,
        token_uri,
        details,
        confidence
      }
    }
  }
];

Adaptability

Customize this template:

• Update the AI model prompt with details specific to your API or OAuth2 setup.
• Adjust the JSON schema in the Structured Output node to match the data format.
• Modify the Code logic to suit the application's requirements.

My workflow

Purpose and structure

This folder contains the My workflow workflow. Its executable nodes use chainLlm, code, executeWorkflowTrigger, lmChatOpenRouter, outputParserStructured. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When Executed by Another Workflow

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1846_Stickynote_Executeworkflow_Automate_Triggered.json MIT; preserve source notices.