cohere

Enhance Your Search Accuracy with Cohere's Rerank-English-v2.0 Model

Tal Peretz

— 1 min read
Enhance Your Search Accuracy with Cohere's Rerank-English-v2.0 Model

The rerank-english-v2.0 model by Cohere has been designed to significantly improve the accuracy of search and retrieval systems. By refining ranked results based on their relevance to a specified query, it ensures that users get the most relevant outcomes for their searches. This model is specifically tailored for English language documents.

Key Features

  • Context Length: Capable of processing documents up to 512 tokens before chunking.
  • Usage: Ideal for reranking search results to enhance semantic relevance.
  • Integration: Accessible through Cohere's API and can be integrated with systems like PyMilvus and OpenSearch.

How to Use

Using the rerank-english-v2.0 model is straightforward:

Cohere API

import cohere
co = cohere.Client("your-cohere-api-key")
query = "What event in 1956 marked the official birth of artificial intelligence as a discipline?"
documents = [...]
results = co.rerank(query=query, documents=documents, top_n=4, model='rerank-english-v2.0')

PyMilvus

Utilize the PyMilvus SDK to integrate with the Milvus vector database.

OpenSearch

Embed the model into an OpenSearch reranking pipeline.

Performance and Deployment

  • Accuracy: Ensures accurate reranking even with noisy datasets due to Cohere’s embedding performance.
  • Scalability: Optimizes throughput and reduces compute requirements, making it scalable for various applications.
  • Deployment: Deployable via SaaS API, on cloud services, or soon through private deployments (VPC and on-premise).

Additional Information

  • Customization: Can be fine-tuned to further improve performance in specific domains.
  • Best Practices: Refer to Cohere's documentation for best practices on using the rerank model, including formatting documents and optimizing performance.

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