About ADA Connector
This connector transforms text data into binary vectors known as embeddings using OpenAI's text-embedding-ada-002 model. This model generates high-quality embeddings, captures the semantic nuances of text, making it perfect for processing, analyzing, and comparing textual information. By integrating this connector, OutSystems applications can seamlessly leverage these advanced features, enhancing their ability to handle and interpret text data. This integration empowers developers to build more intelligent and text-aware applications, delivering improved functionality and user experience.
Benefits
Multilingual Support: Process and analyze text across multiple languages effectively.
Enhanced Efficiency: Generate embeddings quickly and reliably, reducing the complexity of implementing
machine learning models.
Improved Accuracy: Leverage state-of-the-art technology for better semantic understanding and context capture.
Scalability: Easily scale embedding generation for applications of any size.
Cost-Effective: Utilize the optimized ADA 002 model, designed to provide high-quality embeddings with minimal resource usage.
Pre-requisite
Here is the step-by-step documentation for getting the API key for Whisper model from Open AI.
Click on the below URL to proceed further
https://openai.com/
Create an OpenAI account
Verify your account
Log into your account
Navigate to the API section.
Generate a new API key.
Save your API key.
Getting Started
Download the connector from OutSystems Forge.
Configure the API key for OpenAI Embeddings Ada 002 in the connector settings.
Integrate the connector into your application workflows.
Input Parameters
API Key: The OpenAI API key to authenticate and access the Whisper Translator service.
InputText: The text data you wish to embed. This should be a non-empty string.
Example Request Body:
{
“model": "text-embedding-ada-002",
"input": "Your input text here"
}
Output Parameters
Embedded: The generated embedding vector representing the semantic meaning of the input text.
IsSuccess: Boolean value indicates whether the embedding generation was successful.
Error Message: Contains error details if the request fails; otherwise, it is null or empty.
Example Response:
"data": [
"embedding": [0.021884, -0.008202, ...]
],
"IsSuccess": true,
"ErrorMessage": null
Use Cases:
The vector data generated from text can be stored in a specialized vector database. This database can be queried to deliver a variety of business-specific insights. Here are some potential use cases that can be implemented:
Semantic Search: Enhance search capabilities by ranking results based on meaning rather than exact matches.
Clustering and Categorization: Group similar content together for improved organization and insights.
Recommendations: Provide personalized suggestions by comparing user preferences or behaviors.
Text Similarity and Matching: Identify and compare related content efficiently.