sinapsis-polyfuzz 0.1.0

Templates for PolyFuzz string matching and similarity models.

Sinapsis PolyFuzz

Templates for fuzzy string matching and semantic similarity using PolyFuzz

🐍 Installation • 🚀 Features • 📚 Usage example • 📙 Documentation • 🔍 License

Sinapsis PolyFuzz provides a powerful and flexible implementation for fuzzy string matching and semantic similarity matching. It integrates PolyFuzz models into Sinapsis templates, enabling string matching pipelines with various backends including TF-IDF, Edit Distance, RapidFuzz, Sentence-BERT, Flair (🤗 Transformers, FastText, GloVe), Gensim, spaCy, and Universal Sentence Encoder.

🐍 Installation

Install using your package manager of choice. We encourage the use of uv

Example with uv:

  uv pip install sinapsis-polyfuzz --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-polyfuzz --extra-index-url https://pypi.sinapsis.tech

Optional Dependencies

PolyFuzz supports multiple embedding backends. Install the extras you need:

# Sentence-BERT embeddings
uv pip install "sinapsis-polyfuzz[sbert]" --extra-index-url https://pypi.sinapsis.tech

# Flair embeddings
uv pip install "sinapsis-polyfuzz[flair]" --extra-index-url https://pypi.sinapsis.tech

# Word2Vec embeddings
uv pip install "sinapsis-polyfuzz[gensim]" --extra-index-url https://pypi.sinapsis.tech

# Universal Sentence Encoder
uv pip install "sinapsis-polyfuzz[use]" --extra-index-url https://pypi.sinapsis.tech

# FastText embeddings
uv pip install "sinapsis-polyfuzz[fast]" --extra-index-url https://pypi.sinapsis.tech

# spaCy for text processing
uv pip install "sinapsis-polyfuzz[spacy]" --extra-index-url https://pypi.sinapsis.tech

# Install all extras
uv pip install "sinapsis-polyfuzz[all]" --extra-index-url https://pypi.sinapsis.tech

🚀 Features

Templates Supported

This module includes multiple templates tailored for different fuzzy matching approaches:

• TFIDFWrapper: Classic TF-IDF based fuzzy string matching using character n-grams and cosine similarity.
• EditDistanceWrapper: Edit distance-based matching with customizable distance functions.
• RapidFuzzWrapper: Fast implementation of fuzzy string matching (fuzzywuzzy alternative) with MIT license.
• EmbeddingsWrapper: Flair-based embeddings matching supporting all 🤗 Transformers models.
• SentenceTransformersWrapper: Sentence-BERT embeddings for semantic similarity using sentence-transformers.
• GensimEmbeddingsWrapper: Word embeddings matching using Gensim (e.g., Word2Vec, GloVe).
• SpacyEmbeddingsWrapper: spaCy embeddings for linguistic similarity matching.
• USEEmbeddingsWrapper: Universal Sentence Encoder (USE) for deep semantic similarity using TensorFlow Hub.
• *PairWrapper (e.g. TFIDFPairWrapper, EditDistancePairWrapper, RapidFuzzPairWrapper, ...): Paired-matching variants of every matcher. Instead of a static to_list, they match a column of one DataFramePacket against a column of another (selected by source or index) and append a From/To/Similarity matches packet. They short-circuit (return the container unchanged) when the reference column is empty, and expose a backend-independent min_similarity post-filter that drops weak matches (use this rather than the matcher's own threshold, which only some backends honor). For short alphanumeric codes, RapidFuzzPairWrapper is the most reliable — unlike n-gram TF-IDF it does not rank a single-character substitution (e.g. KML029→KML028) above a formatting variant (KML029→KML29).
• ExactMatch: Splits reference values into exact (normalized) matches against a target column and a non-exact residual. Emits an exact_matches packet (From/To/Similarity = 1.0) and a no_exact_matches residual packet to feed a downstream fuzzy *PairWrapper.

[!TIP] Use CLI command sinapsis info --all-template-names to show a list with all the available Template names installed with Sinapsis PolyFuzz.

🌍 General Attributes

All templates share the following attributes defined in PolyfuzzBaseAttributes:

• target_column (str, required): The name of the column in the DataFrame to be used as the source for matching.
• to_list (list[str], optional): A list of strings to match against. If not provided, the model will match within the target column itself.
• overwrite (bool, optional): Whether to overwrite the original packet content with results. If False, creates new packets. Defaults to False.

[!TIP] Use CLI command sinapsis info --example-template-config TEMPLATE_NAME to produce an example Agent config for the Template specified in TEMPLATE_NAME.

For example, for TFIDFWrapper use sinapsis info --example-template-config TFIDFWrapper to produce an example config like:

agent:
  name: my_test_agent
templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}
- template_name: TFIDFWrapper
  class_name: TFIDFWrapper
  template_input: InputTemplate
  attributes:
    target_column: '`replace_me:<class ''str''>`'
    to_list: null
    overwrite: false
    tfidf_init:
      n_gram_range: !!python/tuple
      - 3
      - 3
      clean_string: true
      min_similarity: 0.75
      top_n: 1
      cosine_method: sparse
      model_id: null
      remove_space_ngrams: true

📚 Usage example

Below is an example YAML configuration for fuzzy string matching using TF-IDF. In this example, we define an agent named my_test_agent and configure a matching template to find similar strings between a source column and a target list.

Config

agent:
  name: my_test_agent
  description: "Agent utilizing TF-IDF for fuzzy string matching."

templates:
  - template_name: InputTemplate
    class_name: InputTemplate
    attributes: {}

  - template_name: CSVDatasetReader
    class_name: CSVDatasetReader
    template_input: InputTemplate
    attributes:
      root_dir: "artifacts"
      path_to_csv: "example.csv"
      store_as_time_series: false

  - template_name: TFIDFWrapper
    class_name: TFIDFWrapper
    template_input: CSVDatasetReader
    attributes:
      target_column: "from_list"
      to_list:
        - "Apple Inc"
        - "Microsoft Corporation"
        - "Google LLC"
        - "Amazon.com"
        - "Netflix Inc"
        - "Tesla Motors"
        - "Meta Platforms"
        - "NVIDIA Corp"
        - "Intel Corp"
      overwrite: true
      tfidf_init:
        n_gram_range:
          - 3
          - 3
        clean_string: true
        min_similarity: 0.5
        top_n: 1
        cosine_method: sparse
        remove_space_ngrams: true

This configuration defines an agent and a sequence of templates to perform fuzzy string matching:

1. InputTemplate: Entry point for the pipeline.
2. CSVDatasetReader: Reads a CSV file containing strings to match (from sinapsis-data-readers).
3. TFIDFMatcher: Performs fuzzy matching using TF-IDF with character n-grams, minimum similarity of 0.5, returning top 1 matches.

[!IMPORTANT] The CSVDatasetReader template corresponds to sinapsis-data-readers. If you want to use the example, please make sure you install the package.

The to_list attribute specifies the reference strings to match against. If omitted, the model will match strings within the target column against themselves.

To run the config, use the CLI:

sinapsis run name_of_config.yml

📙 Documentation

Documentation for this and other sinapsis packages is available on the sinapsis website

Tutorials for different projects within sinapsis are available at sinapsis tutorials page

🔍 License

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the LICENSE file.

For commercial use, please refer to our official Sinapsis website for information on obtaining a commercial license.