Feature Extraction

Our feature extraction endpoint generates embeddings or feature vectors from text inputs, useful for semantic search, clustering, and downstream ML tasks. Here is an example of feature extraction: Parameters:

model: The model ID of the model you want to use
inputs: The text input(s) to extract features from (string or list of strings)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.feature_extraction.create(
    model="model-id",
    inputs="This is a sample sentence for feature extraction."
)

for features in response.data:
    print("Features:", features)

curl -X POST https://api.synapsai.cloud/v1/feature-extraction \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "This is a sample sentence for feature extraction."
    }'

Here is an example with multiple inputs:

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.feature_extraction.create(
    model="model-id",
    inputs=[
        "Machine learning is a fascinating field.",
        "Deep learning models can process large amounts of data.",
        "Natural language processing helps computers understand text."
    ]
)

for i, features in enumerate(response.data):
    print(f"Text {i+1} features:", features)
    print(f"Feature count: {len(features) if isinstance(features, list) else 'N/A'}")
    print("---")

curl -X POST https://api.synapsai.cloud/v1/feature-extraction \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": [
          "Machine learning is a fascinating field.",
          "Deep learning models can process large amounts of data.",
          "Natural language processing helps computers understand text."
        ]
    }'

Here is an example for semantic search preparation:

from synapsai import SynapsAI
import numpy as np

model = SynapsAI("your-api-key")

documents = [
    "The weather is sunny and warm today.",
    "I enjoy reading books about artificial intelligence.",
    "Cooking is one of my favorite hobbies.",
    "Python is a popular programming language for data science."
]

response = model.feature_extraction.create(
    model="model-id",
    inputs=documents
)

# Store features for semantic search
document_features = []
for i, features in enumerate(response.data):
    document_features.append({
        "text": documents[i],
        "features": features
    })
    print(f"Indexed document {i+1}: {documents[i][:50]}...")

print(f"\nTotal documents indexed: {len(document_features)}")

curl -X POST https://api.synapsai.cloud/v1/feature-extraction \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": [
          "The weather is sunny and warm today.",
          "I enjoy reading books about artificial intelligence.",
          "Cooking is one of my favorite hobbies.",
          "Python is a popular programming language for data science."
        ]
    }'

For deeper insights into the feature extraction endpoint, see our API reference.

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