fast-forward-indexes

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fast-forward-indexes

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Efficient interpolation-based ranking on CPUs

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# Fast-Forward Indexes

This is the implementation of [Fast-Forward indexes](https://dl.acm.org/doi/abs/10.1145/3485447.3511955).

> [!IMPORTANT]
> As this library is still in its early stages, the API is subject to change!

## Installation

Install the package via `pip`:

```bash
pip install fast-forward-indexes
```

## Getting Started

Using a Fast-Forward index is as simple as providing a TREC run with retrieval scores:

```python
from pathlib import Path

from fast_forward import Ranking
from fast_forward.index import OnDiskIndex, Mode
from fast_forward.encoder import TCTColBERTQueryEncoder

# choose a pre-trained query encoder
encoder = TCTColBERTQueryEncoder("castorini/tct_colbert-msmarco")

# load an index on disk
ff_index = OnDiskIndex.load(Path("/path/to/index.h5"), encoder, mode=Mode.MAXP)

# load a run (TREC format) and attach all required queries
first_stage_ranking = (
    Ranking.from_file(Path("/path/to/input/run.tsv"))
    .attach_queries(
        {
            "q1": "query 1",
            "q2": "query 2",
            # ...
            "qn": "query n",
        }
    )
    .cut(5000)
)

# compute the corresponding semantic scores
out = ff_index(first_stage_ranking)

# interpolate scores and create a new TREC runfile
first_stage_ranking.interpolate(out, 0.1).save(Path("/path/to/output/run.tsv"))
```

## Documentation

A more detailed documentation is available [here](https://mrjleo.github.io/fast-forward-indexes/docs).

fast-forward-indexes

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