llama-index-postprocessor-google-rerank 0.1.0

llama-index postprocessor google rerank integration

LlamaIndex Postprocessor Integration: Google Rerank

Uses Google's Discovery Engine Ranking API to rerank search results based on query relevance.

Installation

pip install llama-index-postprocessor-google-rerank

Prerequisites

• A Google Cloud project with the Discovery Engine API enabled
• Authentication via Application Default Credentials or explicit credentials

Usage

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.postprocessor.google_rerank import GoogleRerank

documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents=documents)

reranker = GoogleRerank(
    top_n=3,
    project_id="your-gcp-project-id",
    model="semantic-ranker-default-004",
)

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[reranker],
)
response = query_engine.query("What did Sam Altman do in this essay?")
print(response)

Available Models

Model	Context Window	Notes
semantic-ranker-default-004 (default)	1024 tokens	Latest, multilingual
semantic-ranker-default-003	512 tokens	Multilingual
semantic-ranker-default-002	512 tokens	English only

Configuration

Parameter	Type	Default	Description
model	str	"semantic-ranker-default-004"	Ranking model name
top_n	int	2	Number of top results to return
project_id	str	None	GCP project ID (falls back to GOOGLE_CLOUD_PROJECT env var, then ADC)
location	str	"global"	GCP location for the ranking config
ranking_config	str	"default_ranking_config"	Ranking config resource name
credentials	Credentials	None	Optional Google auth credentials object