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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-3-large',
});
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-3-large")
embedding = response.data[0].embedding
print(json.dumps(embedding, indent=2))
main()
-
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Product Detail
✨ Unveiling text-embedding-3-large: OpenAI's Groundbreaking Embedding Model
Launched by OpenAI on January 25th, 2024, text-embedding-3-large marks a significant advancement in text embedding technology. This next-generation model excels at converting complex textual data into highly effective, high-dimensional numerical representations, crucial for a diverse range of modern machine learning tasks.
Basic Model Information:
- ► Model Name: text-embedding-3-large
- ► Developer: OpenAI
- ► Release Date: January 25th, 2024
- ► Model Type: Text Embedding
🚀 Core Advantages & Key Features
text-embedding-3-large stands out with innovative features designed for superior performance and flexibility:
-
✓ Top-Tier Performance: This model delivers the highest performing embeddings to date, showcasing remarkable improvements over its predecessors and setting new industry benchmarks.
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✓ Flexible Embedding Size: Developers gain unprecedented control with support for embedding dimensions ranging from 256 up to 3072. This flexibility allows for an optimal trade-off between performance requirements and resource consumption.
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✓ Native Support for Shortening Embeddings: A unique capability that enables developers to shorten embedding vectors without significant loss in their conceptual representation, ideal for optimizing storage and reducing computational overhead.
💡 Ideal Use Cases for text-embedding-3-large
The robust capabilities of this model make it perfectly suited for a wide array of advanced applications:
- ▷ High-Performance Search: Achieve precise and lightning-fast search results across vast information repositories.
- ▷ Advanced Clustering: Facilitate sophisticated data analysis and grouping for deeper insights into complex datasets.
- ▷ Enhanced Recommendations: Power highly accurate and contextually relevant recommendation engines.
- ▷ Robust Anomaly Detection: Efficiently identify outliers and unusual patterns within large data streams.
- ▷ Detailed Diversity Measurement: Analyze the breadth and variety of extensive text corpora with high precision.
- ▷ Accurate Classification: Excel in categorizing complex text data, even in challenging domains.
- ▷ Global Multilingual Support: With enhanced support for multiple languages, it is exceptionally well-suited for international and diverse linguistic applications.
⚙️ Technical Architecture & Training Excellence
Detailed Insights:
-
● Architecture: Built upon a cutting-edge transformer-based architecture, specifically engineered for generating high-dimensional embeddings with superior performance characteristics.
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● Training Data: Trained on an extensive and highly diverse dataset, meticulously curated to capture a vast array of linguistic nuances, semantics, and contextual complexities.
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● Data Source & Size: The model's training involved billions of text entries, ensuring a comprehensive and profound understanding of human language.
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● Diversity & Bias Mitigation: Significant effort was placed on ensuring high diversity in the training data to actively mitigate biases, thereby enhancing the model's fairness, robustness, and reliability across different applications and user groups.
📈 Unmatched Performance Metrics
text-embedding-3-large demonstrates significant improvements and delivers top-tier performance across key benchmarks:
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⚡ MIRACL Score: A substantial increase from 31.4% (achieved by ada-002) to an impressive 54.9%, highlighting superior retrieval capabilities.
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⚡ MTEB Score: Improved from 61.0% (with ada-002) to a robust 64.6%, affirming its enhanced overall embedding quality.
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⚡ Accuracy: Consistently delivers top-tier accuracy across a wide range of multiple benchmarks, ensuring highly reliable outcomes for critical tasks.
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⚡ Speed: Optimized for faster processing times, maintaining efficiency even when utilizing its larger dimensionality options.
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⚡ Robustness: Exhibits high performance stability across a diverse variety of input types and complex contextual scenarios, ensuring dependable operation.
❓ Frequently Asked Questions (FAQ) about text-embedding-3-large
Q1: What is text-embedding-3-large and when was it released?
A1: text-embedding-3-large is OpenAI's latest and most advanced text embedding model, designed to convert text into high-dimensional numerical vectors that capture semantic meaning. It was officially released on January 25th, 2024.
Q2: How significant are its performance improvements over previous models like ada-002?
A2: It offers significant improvements, notably increasing the MIRACL score from 31.4% (ada-002) to 54.9% and the MTEB score from 61.0% (ada-002) to 64.6%. These metrics highlight its superior accuracy and overall embedding quality.
Q3: Does text-embedding-3-large support customizable embedding dimensions?
A3: Yes, it features flexible embedding sizes, allowing developers to choose dimensions from 256 up to 3072. This enables fine-tuning between optimal performance and efficient resource utilization.
Q4: What are the primary applications where this model excels?
A4: It is ideal for high-performance search, advanced clustering, enhanced recommendation systems, robust anomaly detection, detailed diversity measurement, and accurate text classification, especially in environments requiring multilingual support.
Q5: Is the model suitable for processing multiple languages?
A5: Absolutely. text-embedding-3-large offers significantly improved support for multiple languages, making it a highly effective solution for global applications and diverse linguistic datasets.
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