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A brief description of datafog_instructor

Project description

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.

Project details


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datafog_instructor-0.1.0b2.tar.gz (4.5 kB view hashes)

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