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import gradio as gr |
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import pandas as pd |
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import numpy as np |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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from sklearn.metrics.pairwise import cosine_similarity |
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import re |
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def average_pool(last_hidden_states: Tensor, |
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attention_mask: Tensor) -> Tensor: |
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last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
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return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
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df = pd.read_csv('wiki.csv') |
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data_embeddings = np.load("wiki-embeddings.npy") |
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print("loading the model...") |
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tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') |
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model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') |
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with gr.Blocks() as demo: |
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chatbot = gr.Chatbot(label="semantic search for 230k+ wikipedia articles") |
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msg = gr.Textbox(label="simple wikipedia semantic search query", placeholder="for example, \"medieval battles\"") |
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clear = gr.ClearButton([msg, chatbot]) |
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def _search(message, chat_history): |
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batch_dict = tokenizer(["query: " + message], max_length=512, padding=True, truncation=True, return_tensors='pt') |
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outputs = model(**batch_dict) |
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input_embedding = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
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input_embedding = F.normalize(input_embedding, p=2, dim=1) |
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input_embedding = input_embedding[0].tolist() |
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input_embedding = np.array(input_embedding).reshape(1, -1) |
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cos_similarities = cosine_similarity(data_embeddings, input_embedding).flatten() |
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k = 10 |
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top_k_idx = cos_similarities.argsort()[-k:][::-1] |
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top_k_text = df['title'].iloc[top_k_idx].tolist() |
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bot_message = "\n".join(f"{i+1}. {top_k_text[i]} // {top_k_idx[i]}" for i in range(len(top_k_text))) |
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chat_history.append((message, f"results (you can enter article number 1-{k} to see its contents):\n" + bot_message)) |
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return "", chat_history |
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def _retrieve(message, chat_history): |
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idx = int(message) |
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for _, m in chat_history[::-1]: |
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if m.startswith("results"): |
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for n in m.split("\n")[1:]: |
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print(n) |
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if str(idx) == n.split(".")[0]: |
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df_idx = int(n.split(" // ")[-1]) |
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print(df_idx) |
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article = df.iloc[df_idx]['text'] |
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article = re.sub(r'(===?=?[A-Z ].+?===?=?)', r'\n\n\1\n', article) |
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chat_history.append((message, f"contents of {n}:\n{article}")) |
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return "", chat_history |
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print("nothing found") |
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chat_history.append((message, "🤔 article not found")) |
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return "", chat_history |
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def respond(message, chat_history): |
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print(f"received input '{message}'") |
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try: |
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int(message) |
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print(f"retrieving #{message}") |
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return _retrieve(message, chat_history) |
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except ValueError: |
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print(f"searching for {message}") |
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return _search(message, chat_history) |
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msg.submit(respond, [msg, chatbot], [msg, chatbot]) |
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demo.launch() |