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license: mit |
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This repo contains the code needed to run AIR-Bench using llamaindex. |
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In this repo, I implement a custom retriever that uses `text-embedding-ada-002` as the dense embedding model, bm25 for sparse embeddings, and the `QueryFusionRetriever()` to combine results from these two, as well as generating extra queries for retrieval. |
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## Usage |
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`pip install llama-index llama-index-retrievers-bm25` |
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`python ./run_airbench.py` |
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## Customization |
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Feel free to use this as a template to evaluate other llama-index retrieval pipelines! You just need to customize the setup in `create_retriever_fn()` with the setup that you want to test against. |
