| """ |
| Minimal end-to-end retrieval example for the OPERA retrieval corpus. |
| |
| Loads opera_corpus.index + opera_corpus.meta, encodes a query with |
| BGE-M3, and prints the top-5 documents. |
| |
| Requirements: |
| pip install faiss-cpu FlagEmbedding numpy |
| # (or faiss-gpu if you have a CUDA build that matches your stack) |
| """ |
|
|
| from __future__ import annotations |
|
|
| import pickle |
| from pathlib import Path |
|
|
| import faiss |
| import numpy as np |
| from FlagEmbedding import BGEM3FlagModel |
|
|
| HERE = Path(__file__).resolve().parent |
| INDEX_PATH = HERE / "opera_corpus.index" |
| META_PATH = HERE / "opera_corpus.meta" |
|
|
| BGE_MODEL = "BAAI/bge-m3" |
|
|
|
|
| def main(query: str = "Who directed the film The Big Short?", top_k: int = 5) -> None: |
| print(f"loading FAISS index from {INDEX_PATH}") |
| index = faiss.read_index(str(INDEX_PATH)) |
|
|
| print(f"loading metadata from {META_PATH}") |
| with open(META_PATH, "rb") as f: |
| meta = pickle.load(f) |
|
|
| assert index.ntotal == len(meta), ( |
| f"count mismatch: index={index.ntotal} meta={len(meta)}" |
| ) |
| print(f"corpus size: {index.ntotal}") |
|
|
| print(f"loading BGE-M3 ({BGE_MODEL})") |
| model = BGEM3FlagModel(BGE_MODEL, use_fp16=True) |
|
|
| print(f"encoding query: {query!r}") |
| encoded = model.encode([query], return_dense=True, return_sparse=False, return_colbert_vecs=False) |
| vec = np.asarray(encoded["dense_vecs"], dtype="float32") |
| vec /= np.linalg.norm(vec, axis=1, keepdims=True) + 1e-12 |
|
|
| print(f"searching top-{top_k}") |
| scores, indices = index.search(vec, top_k) |
|
|
| for rank, (idx, score) in enumerate(zip(indices[0], scores[0]), start=1): |
| rec = meta[int(idx)] |
| snippet = rec["content"][:200].replace("\n", " ") |
| print(f" #{rank} score={score:.4f} {rec['title']!r}") |
| print(f" {snippet}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
