llama-index-postprocessor-bedrock-rerank

import os from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.postprocessor.bedrock_rerank import BedrockRerank # Load documents documents = SimpleDirectoryReader("./data").load_data() # Create index with a retriever index = VectorStoreIndex.from_documents(documents) retriever = index.as_retriever(similarity_top_k=10) # Set up reranker (uses AWS default profile or env vars) os.environ["AWS_ACCESS_KEY_ID"] = os.environ.get("AWS_ACCESS_KEY_ID", "") os.environ["AWS_SECRET_ACCESS_KEY"] = os.environ.get("AWS_SECRET_ACCESS_KEY", "") os.environ["AWS_DEFAULT_REGION"] = os.environ.get("AWS_DEFAULT_REGION", "us-east-1") reranker = BedrockRerank( model="cohere.rerank-v3-5:0", top_k=3, ) # Query with reranker query_engine = index.as_query_engine( node_postprocessors=[reranker], similarity_top_k=10, ) response = query_engine.query("What is the capital of France?") print(response)