llama-index packs infer retrieve rerank integration
Project description
Infer-Retrieve-Rerank LlamaPack
This is our implementation of the paper "In-Context Learning for Extreme Multi-Label Classification by Oosterlinck et al.
The paper proposes "infer-retrieve-rerank", a simple paradigm using frozen LLM/retriever models that can do "extreme"-label classification (the label space is huge).
- Given a user query, use an LLM to predict an initial set of labels.
- For each prediction, retrieve the actual label from the corpus.
- Given the final set of labels, rerank them using an LLM.
All of these can be implemented as LlamaIndex abstractions.
A full notebook guide can be found here.
CLI Usage
You can download llamapacks directly using llamaindex-cli
, which comes installed with the llama-index
python package:
llamaindex-cli download-llamapack InferRetrieveRerankPack --download-dir ./infer_retrieve_rerank_pack
You can then inspect the files at ./infer_retrieve_rerank_pack
and use them as a template for your own project!
Code Usage
You can download the pack to a ./infer_retrieve_rerank_pack
directory:
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
InferRetrieveRerankPack = download_llama_pack(
"InferRetrieveRerankPack", "./infer_retrieve_rerank_pack"
)
From here, you can use the pack, or inspect and modify the pack in ./infer_retrieve_rerank_pack
.
Then, you can set up the pack like so:
# create the pack
pack = InferRetrieveRerankPack(
labels, # list of all label strings
llm=llm,
pred_context="<pred_context>",
reranker_top_n=3,
verbose=True,
)
The run()
function runs predictions.
pred_reactions = pack.run(inputs=[s["text"] for s in samples])
You can also use modules individually.
# call the llm.complete()
llm = pack.llm
label_retriever = pack.label_retriever
Project details
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