llama-index-integrations/postprocessor/llama-index-postprocessor-google-rerank/README.md
Uses Google's Discovery Engine Ranking API to rerank search results based on query relevance.
pip install llama-index-postprocessor-google-rerank
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)
| 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 |
| 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 |