Building Custom Rerankers

You can build your own custom reranker by subclassing the Reranker class and implementing the rerank_hybrid() method. Optionally, you can also implement the rerank_vector() and rerank_fts() methods if you want to support reranking for vector and FTS search separately. Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores.

The Reranker base interface comes with a merge_results() method that can be used to combine the results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates the results and removes the duplicates without taking the scores into consideration. It only keeps the first copy of the row encountered. This works well in cases that don't require the scores of semantic and full-text search to combine the results. If you want to use the scores or want to support return_score="all", you'll need to implement your own merging algorithm.

from lancedb.rerankers import Reranker
import pyarrow as pa

class MyReranker(Reranker):
    def __init__(self, param1, param2, ..., return_score="relevance"):
        super().__init__(return_score)
        self.param1 = param1
        self.param2 = param2

    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table):
        # Use the built-in merging function
        combined_result = self.merge_results(vector_results, fts_results)

        # Do something with the combined results
        # ...

        # Return the combined results
        return combined_result

    def rerank_vector(self, query: str, vector_results: pa.Table):
        # Do something with the vector results
        # ...

        # Return the vector results
        return vector_results

    def rerank_fts(self, query: str, fts_results: pa.Table):
        # Do something with the FTS results
        # ...

        # Return the FTS results
        return fts_results

Example of a Custom Reranker

For the sake of simplicity let's build custom reranker that just enchances the Cohere Reranker by accepting a filter query, and accept other CohereReranker params as kwags.

from typing import List, Union
import pandas as pd
from lancedb.rerankers import CohereReranker

class ModifiedCohereReranker(CohereReranker):
    def __init__(self, filters: Union[str, List[str]], **kwargs):
        super().__init__(**kwargs)
        filters = filters if isinstance(filters, list) else [filters]
        self.filters = filters

    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table:
        combined_result = super().rerank_hybrid(query, vector_results, fts_results)
        df = combined_result.to_pandas()
        for filter in self.filters:
            df = df.query("not text.str.contains(@filter)")

        return pa.Table.from_pandas(df)

    def rerank_vector(self, query: str, vector_results: pa.Table)-> pa.Table:
        vector_results = super().rerank_vector(query, vector_results)
        df = vector_results.to_pandas()
        for filter in self.filters:
            df = df.query("not text.str.contains(@filter)")

        return pa.Table.from_pandas(df)

    def rerank_fts(self, query: str, fts_results: pa.Table)-> pa.Table:
        fts_results = super().rerank_fts(query, fts_results)
        df = fts_results.to_pandas()
        for filter in self.filters:
            df = df.query("not text.str.contains(@filter)")

        return pa.Table.from_pandas(df)

Tip

The vector_results and fts_results are pyarrow tables. Lean more about pyarrow tables here. It can be convered to other data types like pandas dataframe, pydict, pylist etc.

For example, You can convert them to pandas dataframes using to_pandas() method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using pa.Table.from_pandas() method and return it.