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const { OpenAI } = require('openai');
const main = async () => {
const api = new OpenAI({ apiKey: '', baseURL: 'https://api.ai.cc/v1' });
const text = 'Your text string goes here';
const response = await api.embeddings.create({
input: text,
model: 'text-embedding-ada-002',
});
const embedding = response.data[0].embedding;
console.log(embedding);
};
main();
import json
from openai import OpenAI
def main():
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
text = "Your text string goes here"
response = client.embeddings.create(input=text, model="text-embedding-ada-002")
embedding = response.data[0].embedding
print(json.dumps(embedding, indent=2))
main()
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Product Detail
Exploring text-embedding-ada-002: OpenAI's Advanced Text Embedding Model
Model Essentials
- Model Name: text-embedding-ada-002
- Developer/Creator: OpenAI
- Release Date: December 2022
- Version: text-embedding-ada-002
- Model Type: Text Embedding
Overview: Transforming Text into Actionable Data
text-embedding-ada-002 stands as OpenAI's cutting-edge, efficient, and highly reliable embedding model. Its core function is to expertly convert human language text into precise numerical vector representations, often referred to as embeddings. This capability makes it an indispensable tool for a diverse array of Natural Language Processing (NLP) applications, empowering machines to understand, interpret, and process human communication with unprecedented effectiveness.
Distinguishing Features
- ✨ High Dimensionality: Generates embeddings with 1536 dimensions, ensuring a rich and detailed capture of semantic information from text.
- 🌐 Broad Applicability: Its versatility makes it suitable for a vast range of NLP tasks, including advanced search functionalities, intelligent text clustering, and accurate classification systems.
- 🚀 Scalability: Optimized for seamless integration into enterprise solutions, capable of efficiently handling large datasets and high-volume requests without compromising performance.
Versatile Applications of text-embedding-ada-002
The text-embedding-ada-002 model empowers a wide array of practical applications across various industries:
- 🔍 Enhanced Search: Improves search relevance by ranking results based on deep semantic understanding of queries.
- 📦 Intelligent Clustering: Groups similar text documents or strings together, simplifying data organization and discovery.
- 💡 Personalized Recommendations: Drives more accurate recommendation engines by identifying related content or products.
- 🚨 Anomaly Detection: Critical for security and quality control, it identifies unusual or outlier entries within large datasets.
- 📊 Diversity Measurement: Analyzes similarity distributions to ensure balanced and diverse content representation.
- 🏷️ Accurate Classification: Assigns text strings to predefined categories with high precision based on semantic similarity.
Highlight: Medical Coding Efficiency
The text-embedding-ada-002 model showcases exceptional performance in specialized domains such as Medical Coding. It successfully identifies the most relevant code from a set of similar options in 80% of cases, a notable improvement over GPT-4, which achieves 50% accuracy in the same task.
To delve deeper into this application and other AI advancements in healthcare, you can refer to: AI in Healthcare: Generative AI Uses & Examples
Technical Specifications
Architectural Foundation
The model is built upon a sophisticated Transformer-based architecture. This design is highly celebrated for its remarkable efficiency in processing sequential data, enabling it to meticulously capture contextual relationships between words and achieve superior semantic understanding.
Comprehensive Training Data
text-embedding-ada-002 was trained on a vast and diverse dataset, meticulously sourced from a broad spectrum of internet texts, including academic articles, digital books, and various web pages. This extensive training corpus allows the model to generalize effectively across numerous domains and capture nuanced language patterns.
Knowledge Cutoff Date
The model's knowledge base is limited by a knowledge cutoff date of September 2021. Consequently, it processes and understands information available only up to this specific date, without incorporating any events or data from after this period.
Commitment to Diversity & Bias Mitigation
OpenAI undertook substantial efforts to incorporate a wide range of text sources during training to minimize potential biases. Despite these measures, some biases might still persist due to the inherent nature of large-scale data collection. Continuous evaluation and regular updates are integral to addressing and mitigating any identified biases, ensuring responsible AI development.
Performance and Benchmarks
Comparative Advantage
Upon its launch, text-embedding-ada-002 significantly outperformed many of its predecessors and contemporary models, particularly excelling in terms of its impressive cost-efficiency and robust scalability.
Accuracy Metrics
The model demonstrates strong and consistent accuracy across critical benchmarks:
- 🌍 MIRACL: Achieved an average score of 31.4%, showcasing its capabilities in complex multi-language retrieval tasks.
- 🇬🇧 MTEB: Recorded an average score of 61.0%, indicating its reliable performance in various English language tasks.
Operational Efficiency
- ⚡ Speed: The model is meticulously optimized for rapid inference, making it an ideal choice for real-time applications and services where quick data processing is paramount.
- 💪 Robustness: It exhibits strong resilience and consistency, capable of seamlessly handling a diverse array of input types and maintaining performance across different text formats and languages.
Frequently Asked Questions (FAQ)
Q1: What is text-embedding-ada-002's primary function?
A: text-embedding-ada-002 is an OpenAI model designed to convert human text into high-dimensional numerical representations (embeddings). This allows machines to understand and process the semantic meaning of text for various NLP tasks.
Q2: What makes text-embedding-ada-002 suitable for enterprise applications?
A: Its scalability, optimized for handling large datasets and high-volume requests, along with its broad applicability across numerous NLP tasks, makes it highly suitable for demanding enterprise-level AI solutions.
Q3: How does it perform in specialized tasks like Medical Coding?
A: In Medical Coding, text-embedding-ada-002 achieves an 80% success rate in identifying relevant codes, significantly outperforming GPT-4's 50% accuracy for the same task.
Q4: What is the knowledge cutoff for the model?
A: The model's knowledge is current up to a knowledge cutoff date of September 2021. It does not include information or events that occurred after this specific date.
Q5: What are its key performance metrics?
A: text-embedding-ada-002 demonstrated strong performance on benchmarks like MIRACL (31.4% average) for multi-language tasks and MTEB (61.0% average) for English language tasks, while also excelling in cost-efficiency, speed, and robustness.
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