The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank

Main Idea

The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank

• Semantic Search

• Retrieve and Rerank

Setup

Download trained models

There are two models trained for spanish, a bi-encoder and a cross-encoder. These serve to make the retrieval system using the retrieve and rerank idea:

make setup
pip install -r requirements.txt

Basic usage

1. Setup Elasticsearch index with semantic vectors. For this step we supose that a set of json files is folder. Each json can contain several optional fields but need to contain id and text fiedlds.

from information_retrieval import SemanticEmbedder, CrossEncoder, Prepare, Search

data_folder = 'data/'
text_field = "texto_parrafo"
id_field = "id_parrafo"
elastic_index_name = "sentencias_2.0"

# Read the files, compute embeddings and upload them to elasticsearch
P = Prepare(data_folder, text_field, id_field, elastic_index_name)
P.prepare()

2. Make queries to retrieve documents:

from information_retrieval import SearchEngine

query = "la vida es bella"
S = SearchEngine(elastic_index_name)
S.retrieve(query) # Only semantic search

S.rerank(query) # Retrieve and rerank

Model architecture

Training

Finetuning