neuml--txtai/docs/pipeline/text/reranker.md

Reranker

The Reranker pipeline runs embeddings queries and re-ranks them using a similarity pipeline.

Example

The following shows a simple example using this pipeline.

from txtai import Embeddings
from txtai.pipeline import Reranker, Similarity

# Embeddings instance
embeddings = Embeddings()
embeddings.load(provider="huggingface-hub", container="neuml/txtai-wikipedia")

# Similarity instance
similarity = Similarity(path="colbert-ir/colbertv2.0", lateencode=True)

# Reranking pipeline
reranker = Reranker(embeddings, similarity)
reranker("Tell me about AI")

Note: Content must be enabled with the embeddings instance for this to work properly.

See the link below for a more detailed example.

Notebook	Description
What's new in txtai 9.0	Learned sparse vectors, late interaction models and rerankers

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

embeddings:

similarity:

# Create pipeline using lower case class name
reranker:

# Run pipeline with workflow
workflow:
  translate:
    tasks:
      - reranker

Run with Workflows

from txtai import Application

# Create and run pipeline with workflow
app = Application("config.yml")
list(app.workflow("reranker", ["Tell me about AI"]))

Run with API

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

curl \
  -X POST "http://localhost:8000/workflow" \
  -H "Content-Type: application/json" \
  -d '{"name":"rerank", "elements":["Tell me about AI"]}'

Methods

Python documentation for the pipeline.

::: txtai.pipeline.Reranker.init

::: txtai.pipeline.Reranker.call