from sentence_transformers import sentencetransformer import numpy as np  # load a pre-trained sentence transformer model model = sentencetransformer("sentence-transformers/all-minilm-l6-v2")  # sample query and documents (vectorize the query and the documents) query = "how do i fix a broken landing gear?" documents = ["report 1 on landing gear failure", "report 2 on engine problems"]  # get embeddings for query and documents query_embedding = model.encode(query) document_embeddings = model.encode(documents)  # calculate cosine similarity between query and documents similarities = np.dot(document_embeddings, query_embedding)  # retrieve top-k most relevant documents top_k = np.argsort(similarities)[-5:] print("top 5 documents:", [documents[i] for i in top_k]) 

from sentence_transformers import crossencoder  # load the cross-encoder model cross_encoder = crossencoder("cross-encoder/ms-marco-tinybert-l-2-v2")  # use the cross-encoder to rerank top-k retrieved documents query_document_pairs = [(query, doc) for doc in documents] scores = cross_encoder.predict(query_document_pairs)  # rank documents based on the new scores top_k_reranked = np.argsort(scores)[-5:] print("top 5 reranked documents:", [documents[i] for i in top_k_reranked]) 

import argparse import numpy as np import pandas as pd from typing import Any from openvino.runtime import Core from transformers import AutoTokenizer   def load_openvino_model(model_path: str) -> Core:     core = Core()     model = core.read_model(model_path + ".xml")     compiled_model = core.compile_model(model, "CPU")     return compiled_model   def rerank(     compiled_model: Core,     query: str,     results: list[str],     tokenizer: AutoTokenizer,     batch_size: int, ) -> np.ndarray[np.float32, Any]:     max_length = 512     all_logits = []      # Split results into batches     for i in range(0, len(results), batch_size):         batch_results = results[i : i + batch_size]         inputs = tokenizer(             [(query, item) for item in batch_results],             padding=True,             truncation="longest_first",             max_length=max_length,             return_tensors="np",         )          # Extract input tensors (convert to NumPy arrays)         input_ids = inputs["input_ids"].astype(np.int32)         attention_mask = inputs["attention_mask"].astype(np.int32)         token_type_ids = inputs.get("token_type_ids", np.zeros_like(input_ids)).astype(             np.int32         )          infer_request = compiled_model.create_infer_request()         output = infer_request.infer(             {                 "input_ids": input_ids,                 "attention_mask": attention_mask,                 "token_type_ids": token_type_ids,             }         )          logits = output["logits"]         all_logits.append(logits)      all_logits = np.concatenate(all_logits, axis=0)     return all_logits   def fetch_search_data(search_text: str) -> pd.DataFrame:     # Usually you would fetch the data from a database     df = pd.read_csv("cnbc_headlines.csv")     df = df[~df["Headlines"].isnull()]      texts = df["Headlines"].tolist()      # Load the model and rerank     openvino_model = load_openvino_model("cross-encoder-openvino-model/model")     tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-TinyBERT-L-2-v2")     rerank_scores = rerank(openvino_model, search_text, texts, tokenizer, batch_size=16)      # Add the rerank scores to the DataFrame and sort by the new scores     df["rerank_score"] = rerank_scores     df = df.sort_values(by="rerank_score", ascending=False)      return df   if __name__ == "__main__":     parser = argparse.ArgumentParser(         description="Fetch search results with reranking using OpenVINO"     )      parser.add_argument(         "--search_text",         type=str,         required=True,         help="The search text to use for reranking",     )      args = parser.parse_args()      df = fetch_search_data(args.search_text)     print(df)