AI Data SDK by Zeebee

A comprehensive SDK for standardizing, processing, embedding, and retrieving data for AI applications.

Features

• Text embedding generation
• Semantic search with filtering
• PII detection and masking/anonymization
• User feedback submission
• IP allowlist management (admin only)

Installation

pip install ai-data-sdk-zeebee==0.1.3

Usage

Initialize Client

from ai_data_sdk import AIDataClient

# Initialize with your API key
client = AIDataClient(api_key="your_api_key_here")

Generate Embeddings

# Generate embeddings for a list of texts
texts = [
    "AI Data SDK helps standardize data for AI applications.",
    "The embedding module converts text into vector representations."
]

# Optional metadata for each text
metadata = [
    {"source": "documentation", "category": "overview"},
    {"source": "documentation", "category": "technical"}
]

result = client.create_embeddings(
    texts=texts,
    model="text-embedding-3-small",
    normalize=True,
    metadata=metadata
)

Search for Similar Documents

# Basic search with query text
search_result = client.search(query="How do machines learn from data?")

# Search with vector embedding
embedding = [0.1, 0.2, 0.3, ...]  # Your embedding vector
search_result = client.search(embedding=embedding, top_k=5)

# Advanced search with filters
filters = {
    "category": "technology",
    "rating": {"$gt": 4.5}
}

search_result = client.search(
    query="neural networks",
    filters=filters,
    top_k=10
)

Detect and Mask PII

# Basic PII detection
text = "My email is john.doe@example.com and my phone is 555-123-4567."
result = client.detect_pii(
    text=text,
    pii_types=["email", "phone"]
)

# PII detection with masking
result = client.detect_pii(
    text=text,
    pii_types=["email", "phone"],
    mask_pii=True,
    mask_type="type",
    preserve_length=False,
    resolve_overlaps=True
)

# Advanced PII anonymization
result = client.anonymize_pii(
    text=text,
    pii_types=["email", "phone", "name"],
    consistent_replacements=True,
    resolve_overlaps=True
)

Submit Feedback

feedback = client.submit_feedback(
    query_id="q_12345",
    result_id="doc_1",
    rating=4,
    comments="Very relevant result, but missing some details."
)

Error Handling

from ai_data_sdk import APIError, AuthenticationError, InvalidRequestError, RateLimitError

try:
    result = client.create_embeddings(texts)
except AuthenticationError:
    print("Authentication failed. Check your API key.")
except InvalidRequestError as e:
    print(f"Invalid request: {e}")
except RateLimitError:
    print("Rate limit exceeded. Please try again later.")
except APIError as e:
    print(f"API error: {e}")

Documentation

For full documentation, visit https://ai-data-sdk.readthedocs.io/

License

This project is licensed under the MIT License - see the LICENSE file for details.