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Ayush Chaurasia 549ca51a8a feat: add answerdotai rerankers support and minor improvements (#1560 )

This PR:
- Adds missing license headers
- Integrates with answerdotai Rerankers package
- Updates ColbertReranker to subclass answerdotai package. This is done
to keep backwards compatibility as some users might be used to importing
ColbertReranker directly
- Set `trust_remote_code` to ` True` by default in CrossEncoder and
sentence-transformer based rerankers

2024-08-26 13:25:10 +05:30

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AnswersDotAI Rerankers

This integration allows using answersdotai's rerankers to rerank the search results. Rerankers A lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.

!!! note Supported Query Types: Hybrid, Vector, FTS

import numpy
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
from lancedb.rerankers import AnswerdotaiRerankers

embedder = get_registry().get("sentence-transformers").create()
db = lancedb.connect("~/.lancedb")

class Schema(LanceModel):
    text: str = embedder.SourceField()
    vector: Vector(embedder.ndims()) = embedder.VectorField()

data = [
    {"text": "hello world"},
    {"text": "goodbye world"}
    ]
tbl = db.create_table("test", schema=Schema, mode="overwrite")
tbl.add(data)
reranker = AnswerdotaiRerankers()

# Run vector search with a reranker
result = tbl.search("hello").rerank(reranker=reranker).to_list() 

# Run FTS search with a reranker
result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()

# Run hybrid search with a reranker
tbl.create_fts_index("text", replace=True)
result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()

Accepted Arguments

Argument	Type	Default	Description
`model_type`	`str`	`"colbert"`	The type of model to use. Supported model types can be found here - https://github.com/AnswerDotAI/rerankers
`model_name`	`str`	`"answerdotai/answerai-colbert-small-v1"`	The name of the reranker model to use.
`column`	`str`	`"text"`	The name of the column to use as input to the cross encoder model.
`return_score`	str	`"relevance"`	Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type

Supported Scores for each query type

You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:

Hybrid Search

`return_score`	Status	Description
`relevance`	✅ Supported	Returns only have the `_relevance_score` column
`all`	❌ Not Supported	Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`)

Vector Search

`return_score`	Status	Description
`relevance`	✅ Supported	Returns only have the `_relevance_score` column
`all`	✅ Supported	Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`)

FTS Search

`return_score`	Status	Description
`relevance`	✅ Supported	Returns only have the `_relevance_score` column
`all`	✅ Supported	Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`)

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