| """Retrieve with pplx-embed, rerank with zerank: the recommended pair from the findings. |
| |
| pip install "onnxruntime>=1.31" tokenizers numpy huggingface_hub |
| python pipeline_example.py |
| |
| Downloads the two recommended variants (about 1.9 GB) on first use. |
| """ |
| import numpy as np |
|
|
| from inference import load |
|
|
| docs = [ |
| "To reset the router, hold the reset button on the back for ten seconds until the light blinks.", |
| "The router has four LAN ports and one WAN port.", |
| "Firmware updates are installed from the admin page under System > Update.", |
| "Para reiniciar el router, mantén pulsado el botón de reinicio durante diez segundos.", |
| "The warranty covers hardware defects for two years.", |
| ] |
| query = "How do I factory-reset my router?" |
|
|
| embedder = load("nynefold/pplx-embed-v1-0.6b-ONNX") |
| reranker = load("nynefold/zerank-1-small-ONNX") |
|
|
| |
| d = embedder.embed_documents(docs) |
| q = embedder.embed_queries([query]) |
| top = np.argsort(-(q @ d.T)[0])[:20] |
|
|
| |
| scores = reranker.rerank(query, [docs[i] for i in top]) |
| for s, i in sorted(zip(scores, top), reverse=True): |
| print(f"{s:.3f} {docs[i]}") |
|
|
