tips on testing my chunking strategy?

Please stand by @Rik as I search the knowledge base. I normally take about 15 seconds or so. :wave:

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Based on the knowledge sources provided, I can offer some tips on testing your chunking strategy for LlamaIndex. Here are some key points to consider:

1. Experiment with different chunk sizes: The optimal chunk size can vary depending on your specific use case. As mentioned in the Ensemble Retrieval Guide, you can try out different chunk sizes (e.g., 128, 256, 512, and 1024 tokens) to see which performs best for your data and queries.

1. Test various overlap values: The Transforms Evaluation notebook suggests experimenting with different overlap values for sentence splitters. This can help you find the right balance between context preservation and chunk distinctiveness.

1. Use evaluation metrics: Implement evaluation metrics to assess the performance of your chunking strategy. The examples in the knowledge sources use correctness and semantic similarity as evaluation criteria.

1. Consider the nature of your text: As noted in one of the documents, texts that clearly change subject matter might benefit from lower threshold values, while uniform texts may require higher threshold values to create more chunks.

1. Utilize parameter optimization tools: LlamaIndex provides tools like ParamTuner and AsyncParamTuner that can help you optimize various parameters, including chunk size. These are demonstrated in the Parameter Optimizer notebook.

1. Decouple retrieval and synthesis chunks: As suggested in the Production RAG guide, consider using different chunk representations for retrieval and synthesis. This can help optimize both processes independently.

1. Test with real queries: Use a set of representative queries to evaluate how well your chunking strategy performs in real-world scenarios.

Remember that the optimal chunking strategy can depend on various factors, including the nature of your data, the types of queries you expect, and the specific requirements of your application. It's essential to experiment and iterate to find the best approach for your use case.
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