bee-taxonomy 0.0.16

A library for taxonomy management.

Taxonomy Library README

This module provides functions for taxonomy creation, text classification, and header translation using AI models and vector similarity search.

Usage

Import the module with:

from bee_taxonomy import taxonomy

Installation

Install the library using pip:

pip install bee-taxonomy

Main Functions

1. taxonomy.propose_taxonomy(field: str, description: str, discrete_fields: list[str] = None)

Purpose: Generate taxonomy suggestions using OpenAI

Parameters:

• field: Name of the field to categorize
• description: Description of the field's purpose
• discrete_fields: Optional specific values to consider

Example:

taxonomy.propose_taxonomy(
    field="Color",
    description="Vehicle paint color classification",
    discrete_fields=["Red", "Blue", "Green", "Custom"]
)
# Returns: ["Red", "Blue", "Green", "Other"]

2. taxonomy.apply_taxonomy_similarity(discrete_fields: list[str], taxonomy: list[str], category_type: str = None)

Purpose: Classify values using semantic similarity with vector database

Parameters:

• discrete_fields: Values to classify
• taxonomy: List of allowed classification terms
• category_type: Special processing for categories like 'streets'

Example:

taxonomy.apply_taxonomy_similarity(
    discrete_fields=["Rd", "Street", "Ave"],
    taxonomy=["Road", "Street", "Avenue"],
    category_type="streets"
)
# Returns: {'Rd': {'match': 'Road', 'score': 0.92}, ...}

3. taxonomy.apply_taxonomy_reasoning(discrete_fields: list[str], taxonomy: list[str], classification_description: str, hash_file: str = None)

Purpose: Use AI reasoning to classify values into taxonomy

Parameters:

• discrete_fields: List of values to classify
• taxonomy: List of allowed categories
• classification_description: Context for classification
• hash_file: Optional file hash for progress tracking

Example:

taxonomy.apply_taxonomy_reasoning(
    discrete_fields=["Quick Brown Fox", "Lazy Dog"],
    taxonomy=["Animal", "Object", "Action"],
    classification_description="Classify animal-related phrases"
)
# Returns: {'Quick Brown Fox': 'Animal', 'Lazy Dog': 'Animal'

4. taxonomy.translate_headers_reasoning(src_lang, dest_lang, headers)

Purpose: Translate headers between languages using AI reasoning

Parameters:

• src_lang: Source language code
• dest_lang: Target language code
• headers: List of headers to translate

Example:

taxonomy.translate_headers_reasoning(
    src_lang="en",
    dest_lang="es",
    headers=["Street Name", "Zip Code"]
)
# Returns: {'Street Name': 'Nombre de la Calle', 'Zip Code': 'Código Postal'

5. taxonomy.analyze_text_field(field_name: str, field_value: str, task: Literal["label", "summarize"] = "label")

Purpose: Analyze text fields for classification or summarization

Parameters:

• field_name: Name of the text field
• field_value: Text to analyze
• task: "label" for classification or "summarize" for text summary

Example:

taxonomy.analyze_text_field(
    field_name="Product Description",
    field_value="This ergonomic chair provides lumbar support and adjustable height",
    task="label"
)
# Returns: "Office Furniture"

Environment Variables

Users must rename .env.example to .env and fill in all the required fields with their specific values:

• MODEL_NAME: Hugging Face model identifier
• SERVER_URL: Base URL for OpenAI-compatible API
• API_KEY: Authentication token for the API
• EMBEDDER_MODEL: Embedding model for semantic similarity
• EMBEDDER_SERVER_URL: Base URL for OpenAI-compatible API for embedder model

Features

• Validation workflow with Pydantic models
• Progress checkpointing for large datasets
• Google search integration for ambiguous classifications