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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/text-embedding-3-large"
# Request payload
data = {
"prompt": "You are beautiful"
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Prompt to render
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
Text-Embedding-3-Large
Text-embedding-3-large is a robust language model by OpenAI designed for generating high-dimensional text embeddings. These embeddings provide sophisticated numerical representations of text data and are optimized for a wide range of natural language processing (NLP) tasks including semantic search, text clustering, and classification. The model's large size ensures enhanced accuracy and depth of understanding, making it suitable for applications requiring high-quality text representation.
How to Fine-Tune Outputs?
Input Text Length: Balance text length according to the specific task requirements. Short texts may not capture enough context, while very long texts might need truncation or summarization strategies.
Use Cases
Text-embedding-3-Large is versatile and can be deployed in numerous NLP applications:
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Semantic Search: Enhance search engines by leveraging embeddings to measure similarity between user queries and documents.
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Text Classification: Use embeddings as input features for training machine learning models in various classification tasks.
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Clustering and Topic Modeling: Employ clustering algorithms on embeddings to identify topics or group similar texts in a corpus.
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Recommendation Systems: Improve recommendation accuracy by computing and comparing embeddings of user queries and item descriptions.
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