Entity

Entity

The Entity pipeline applies a token classifier to text and extracts entity/label combinations.

Example

The following shows a simple example using this pipeline.

from txtai.pipeline import Entity

## Create and run pipeline
entity = Entity()
entity("Canada's last fully intact ice shelf has suddenly collapsed, " \
       "forming a Manhattan-sized iceberg")

## Extract entities using a GLiNER model which supports dynamic labels
entity = Entity("gliner-community/gliner_medium-v2.5")
entity("Canada's last fully intact ice shelf has suddenly collapsed, " \
       "forming a Manhattan-sized iceberg", labels=["country", "city"])

See the link below for a more detailed example.

Notebook| Description|
---|---|---
Entity extraction workflows| Identify entity/label combinations|
Parsing the stars with txtai| Explore an astronomical knowledge graph of known stars, planets, galaxies|

Configuration-driven example

Pipelines are run with Python or configuration. Pipelines can be instantiated in configuration using the lower case name of the pipeline. Configuration-driven pipelines are run with workflows or the API.

config.yml

## Create pipeline using lower case class name
entity:

## Run pipeline with workflow
workflow:
  entity:
    tasks:
      - action: entity

Run with Workflows

from txtai import Application

## Create and run pipeline with workflow
app = Application("config.yml")
list(app.workflow("entity", ["Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg"]))

Run with API

CONFIG=config.yml uvicorn "txtai.api:app" &

curl \
  -X POST "http://localhost:8000/workflow" \
  -H "Content-Type: application/json" \
  -d '{"name":"entity", "elements": ["Canadas last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg"]}'

Methods

Python documentation for the pipeline.

__init__(path=None, quantize=False, gpu=True, model=None, **kwargs)

Source code in txtai/pipeline/text/entity.py

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``` |  ```
def init(self, path=None, quantize=False, gpu=True, model=None, kwargs):
    # Create a new entity pipeline
    self.gliner = self.isgliner(path)
    if self.gliner:
        if not GLINER:
            raise ImportError(‘GLiNER is not available - install “pipeline” extra to enable’)

        # GLiNER entity pipeline
        self.pipeline = GLiNER.frompretrained(path)
        self.pipeline = self.pipeline.to(Models.device(Models.deviceid(gpu)))
    else:
        # Standard entity pipeline
        super()._init(“token-classification”, path, quantize, gpu, model, kwargs)

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__call__(text, labels=None, aggregate='simple', flatten=None, join=False, workers=0)

Applies a token classifier to text and extracts entity/label combinations.

Parameters:

Returns:

Source code in txtai/pipeline/text/entity.py

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``` |  ```
def call(self, text, labels=None, aggregate=”simple”, flatten=None, join=False, workers=0):
    “””
    Applies a token classifier to text and extracts entity/label combinations.

    Args:
        text: text|list
        labels: list of entity type labels to accept, defaults to None which accepts all
        aggregate: method to combine multi token entities - options are “simple” (default), “first”, “average” or “max”
        flatten: flatten output to a list of labels if present. Accepts a boolean or float value to only keep scores greater than that number.
        join: joins flattened output into a string if True, ignored if flatten not set
        workers: number of concurrent workers to use for processing data, defaults to None

    Returns:
        list of (entity, entity type, score) or list of entities depending on flatten parameter
    “””

    # Run token classification pipeline
    results = self.execute(text, labels, aggregate, workers)

    # Convert results to a list if necessary
    if isinstance(text, str):
        results = [results]

    # Score threshold when flatten is set
    threshold = 0.0 if isinstance(flatten, bool) else flatten

    # Extract entities if flatten set, otherwise extract (entity, entity type, score) tuples
    outputs = []
    for result in results:
        if flatten:
            output = [r[“word”] for r in result if self.accept(r[“entity_group”], labels) and r[“score”] >= threshold]
            outputs.append(“ “.join(output) if join else output)
        else:
            outputs.append([(r[“word”], r[“entity_group”], float(r[“score”])) for r in result if self.accept(r[“entity_group”], labels)])

    return outputs[0] if isinstance(text, str) else outputs

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