tatry 1.0.8

Python client for the Tatry Content Retrieval API

Tatry Python SDK

Official Python SDK for the Tatry Content Retrieval API.

Installation

pip install tatry

Quick Start

from tatry import TatryRetriever

# Initialize the retriever
retriever = TatryRetriever(api_key="your_api_key")

# Search for documents
results = retriever.retrieve("quantum computing", max_results=5, ["medical"])
for doc in results.documents:
    print(f"Citation: {doc.metadata.citation}")
    print(f"Content: {doc.content[:200]}...")
    print(f"Relevance: {doc.relevance_score}")
    print("---")

Features

• 🔍 Content Search: Search across multiple sources with relevance scoring
• 📚 Source Management: Browse and access different content sources
• 📊 Usage Statistics: Track your API usage
• 🔒 Authentication: Secure API key management
• 💬 Feedback System: Submit and track user feedback
• ⚡ Batch Operations: Perform multiple searches in one request
• 🛡️ Type Safety: Full type hinting support with Pydantic models
• 🔄 Automatic Retries: Built-in retry mechanism with exponential backoff

Authentication

from tatry import TatryRetriever

retriever = TatryRetriever(api_key="your_api_key")

# Validate your API key
validation = retriever.auth.validate_api_key()
print(f"API Key Valid: {validation.data.valid}")
print(f"Permissions: {validation.data.permissions}")
print(f"Rate Limits: {validation.data.rate_limits.requests_per_minute}/min")

# List all API keys
keys = retriever.auth.list_keys(status="active")
for key in keys:
    print(f"Key: {key.id}")
    print(f"Last Used: {key.last_used_at}")

Content Retrieval

Single Search

# Basic search
results = retriever.retrieve.retrieve("artificial intelligence")

# Search with parameters
results = retriever.retrieve.retrieve(
    query="machine learning frameworks",
    max_results=10
)

Batch Search

# Perform multiple searches at once
queries = [
    {"query": "neural networks", "max_results": 5},
    {"query": "deep learning", "max_results": 3}
]
results = retriever.retrieve.batch_retrieve(queries)

Source Management

# List all available sources
sources = retriever.sources.list_sources()
for source in sources:
    print(f"Source: {source.name}")
    print(f"Type: {source.type}")
    print(f"Coverage: {', '.join(source.coverage)}")

# Get detailed information about a specific source
wikipedia = retriever.sources.get_source("wikipedia")
print(f"Total Documents: {wikipedia.metadata.total_documents}")
print(f"Languages: {', '.join(wikipedia.metadata.languages)}")

Usage Statistics

# Get current month's usage
usage = retriever.utils.get_usage()
print(f"Total Queries: {usage.data.usage.queries.total}")
print(f"Documents Retrieved: {usage.data.usage.documents.total}")

# Get usage for a specific month
usage = retriever.utils.get_usage(month="2024-01")

Feedback System

# Submit feedback
feedback = retriever.utils.submit_feedback(
    feedback_type="feature",
    description="Would love to see more academic sources",
    metadata={"user_type": "researcher"}
)
print(f"Feedback ID: {feedback.data.id}")

Error Handling

from tatry import TatryError, TatryAPIError, TatryAuthError

try:
    results = retriever.retrieve.search("quantum computing")
except TatryAuthError:
    print("Authentication failed")
except TatryAPIError as e:
    print(f"API error: {e.status_code}")
except TatryError as e:
    print(f"General error: {str(e)}")

Configuration

retriever = TatryRetriever(
    api_key="your_api_key",
    timeout=30,                         # Request timeout in seconds
    max_retries=3                      # Maximum retry attempts
)

Development

# Clone the repository
git clone https://github.com/tatryai/tatry.git
cd tatry

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.