llama-index-postprocessor-cohere-rerank

import os from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.postprocessor.cohere_rerank import CohereRerank from llama_index.readers.web import SimpleWebPageReader # Set your Cohere API key os.environ["COHERE_API_KEY"] = os.environ.get("COHERE_API_KEY", "your_cohere_api_key") # Load documents (using a dummy example for demonstration) documents = SimpleWebPageReader(html_to_text=True).load_data( ["https://docs.cohere.com/v2/docs/cohere-embed"] ) # Build a VectorStoreIndex index = VectorStoreIndex.from_documents(documents=documents) # Initialize the Cohere Rerank postprocessor cohere_rerank = CohereRerank( api_key=os.environ["COHERE_API_KEY"], model="rerank-english-v3.0", # Use an appropriate Cohere rerank model top_n=2 # Number of top results to return after reranking ) # Create a query engine with the reranker query_engine = index.as_query_engine( similarity_top_k=10, # Retrieve more nodes initially for reranking node_postprocessors=[cohere_rerank] ) # Query the index response = query_engine.query("What is Cohere's Embed Model?") print(response.response) # To see source nodes if needed # from llama_index.core.response.pprint_utils import pprint_response # pprint_response(response, show_source=True)