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 |
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