Skip to main content

Cohere Rerank Retriever

Overview​

The Cohere Rerank Retriever is a powerful feature in AnswerAI that enhances document retrieval by ranking documents based on their semantic relevance to a given query. This retriever uses Cohere's advanced reranking models to provide more accurate and contextually appropriate search results.

Key Benefits​

  • Improved search accuracy: Ranks documents based on semantic relevance, not just keyword matching
  • Multilingual support: Offers both English and multilingual reranking models
  • Customizable retrieval: Allows fine-tuning of parameters for optimal results

How to Use​

  1. Add the Cohere Rerank Retriever node to your AnswerAI workflow canvas.
  2. Connect a Vector Store Retriever to the Cohere Rerank Retriever node.
  3. Configure the node settings:
    • Select the Cohere API credential.
    • Choose the reranking model (English or Multilingual).
    • Set additional parameters like Top K and Max Chunks Per Doc if needed.
  4. Connect the output to your desired next step in the workflow.

Cohere Retreiver Node & Drop UI

Tips and Best Practices​

  1. Start with the default settings and adjust as needed based on your specific use case.
  2. Experiment with different Top K values to balance between retrieval speed and accuracy.
  3. Use the multilingual model if your content or queries are in languages other than English.
  4. Consider the trade-off between Max Chunks Per Doc and processing time – higher values may provide better results but take longer to process.

Troubleshooting​

  1. If you're not getting expected results:

    • Ensure your Cohere API credential is correctly set up.
    • Check if the base Vector Store Retriever is properly configured and contains relevant documents.
    • Try adjusting the Top K value to retrieve more or fewer documents.

  2. If the retrieval process is slow:

    • Consider reducing the Max Chunks Per Doc value.
    • Optimize your base Vector Store Retriever for faster initial retrieval.

  3. For multilingual issues:

    • Make sure you're using the 'rerank-multilingual-v2.0' model for non-English content.