datafog-instructor 0.1.0b2

A brief description of datafog_instructor

DataFog Instructor SDK

DataFog Instructor is a Python SDK for named entity recognition (NER) using the Ollama Instructor client. It provides an easy-to-use interface for detecting and classifying entities in text.

Installation

To install the DataFog Instructor SDK, you can use pip:

pip install datafog-instructor

Quick Start

Here's a simple example to get you started with DataFog Instructor:

from datafog_instructor import DataFog

# Initialize DataFog with default settings
datafog = DataFog()

# Detect entities in text
text = "Cisco acquires Hess for $20 billion"
result = datafog.detect_entities(text)

# Print results
for entity in result.entities:
    print(f"Text: {entity.text}, Type: {entity.type}")

Configuration

You can customize the DataFog instance with the following parameters:

• host: The host URL for the Ollama service (default: "http://localhost:11434")
• model: The model to use for entity detection (default: "phi3")
• entity_types: A dictionary of custom entity types (optional)

Example with custom settings:

datafog = DataFog(
    host="http://custom-host:11434",
    model="custom-model",
    entity_types={"CUSTOM_TYPE": "Custom Entity"}
)

Features

Detect Entities

Use the detect_entities method to identify and classify named entities in a given text:

text = "Apple Inc. reported $100 billion in revenue for Q4 2023"
result = datafog.detect_entities(text)

for entity in result.entities:
    print(f"Text: {entity.text}, Type: {entity.type}")

Manage Entity Types

You can add or remove entity types dynamically:

# Add a new entity type
datafog.add_entity_type("CUSTOM", "Custom Entity")

# Remove an entity type
datafog.remove_entity_type("CUSTOM")

# Get all entity types
entity_types = datafog.get_entity_types()
print(entity_types)

Default Entity Types

The SDK comes with predefined entity types, including:

• ORG (Organization)
• PERSON
• TRANSACTION_TYPE
• DEAL_STRUCTURE
• FINANCIAL_INFO
• PRODUCT
• LOCATION
• DATE
• INDUSTRY
• ROLE
• REGULATORY
• SENSITIVE_INFO
• CONTACT
• ID (Identifier)
• STRATEGY
• COMPANY
• MONEY

Error Handling

The SDK includes basic error handling. If there's an issue with processing the response or an unexpected response format, it will raise a ValueError with details about the error.

Contributing

Contributions to the DataFog Instructor SDK are welcome! Please feel free to submit a Pull Request.

License

MIT

Support

If you encounter any problems or have any questions, please open an issue on the GitHub repository.