from flask import Flask, request, jsonify
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import json
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app = Flask(__name__)
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# 创建一个全局的模型缓存字典
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model_cache = {}
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# 分割文本块
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def split_text(text, block_size, overlap_chars, delimiter):
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chunks = text.split(delimiter)
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text_blocks = []
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current_block = ""
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for chunk in chunks:
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if len(current_block) + len(chunk) + 1 <= block_size:
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if current_block:
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current_block += " " + chunk
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else:
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current_block = chunk
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else:
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text_blocks.append(current_block)
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current_block = chunk
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if current_block:
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text_blocks.append(current_block)
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overlap_blocks = []
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for i in range(len(text_blocks)):
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if i > 0:
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overlap_block = text_blocks[i - 1][-overlap_chars:] + text_blocks[i]
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overlap_blocks.append(overlap_block)
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overlap_blocks.append(text_blocks[i])
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return overlap_blocks
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# 文本向量化
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def vectorize_text_blocks(text_blocks, model):
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return model.encode(text_blocks)
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# 文本检索
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def retrieve_top_k(query, knowledge_base, k, block_size, overlap_chars, delimiter, model):
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# 将知识库拆分为文本块
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text_blocks = split_text(knowledge_base, block_size, overlap_chars, delimiter)
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# 向量化文本块
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knowledge_vectors = vectorize_text_blocks(text_blocks, model)
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# 向量化查询文本
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query_vector = model.encode([query]).reshape(1, -1)
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# 计算相似度
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similarities = cosine_similarity(query_vector, knowledge_vectors)
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# 获取相似度最高的 k 个文本块的索引
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top_k_indices = similarities[0].argsort()[-k:][::-1]
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# 返回文本块和它们的向量
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top_k_texts = [text_blocks[i] for i in top_k_indices]
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top_k_embeddings = [knowledge_vectors[i] for i in top_k_indices]
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return top_k_texts, top_k_embeddings
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@app.route('/vectorize', methods=['POST'])
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def vectorize_text():
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# 从请求中获取 JSON 数据
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data = request.json
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print(f"Received request data: {data}") # 调试输出请求数据
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text_list = data.get("text", [])
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model_name = data.get("model_name", "msmarco-distilbert-base-tas-b") # 默认模型
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delimiter = data.get("delimiter", "\n") # 默认分隔符
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k = int(data.get("k", 3)) # 默认检索条数
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block_size = int(data.get("block_size", 500)) # 默认文本块大小
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overlap_chars = int(data.get("overlap_chars", 50)) # 默认重叠字符数
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if not text_list:
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return jsonify({"error": "Text is required."}), 400
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# 检查模型是否已经加载
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if model_name not in model_cache:
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try:
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model = SentenceTransformer(model_name)
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model_cache[model_name] = model # 缓存模型
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except Exception as e:
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return jsonify({"error": f"Failed to load model: {e}"}), 500
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model = model_cache[model_name]
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top_k_texts_all = []
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top_k_embeddings_all = []
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# 如果只有一个查询文本
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if len(text_list) == 1:
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top_k_texts, top_k_embeddings = retrieve_top_k(text_list[0], text_list[0], k, block_size, overlap_chars, delimiter, model)
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top_k_texts_all.append(top_k_texts)
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top_k_embeddings_all.append(top_k_embeddings)
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elif len(text_list) > 1:
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# 如果多个查询文本，依次处理
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for query in text_list:
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top_k_texts, top_k_embeddings = retrieve_top_k(query, text_list[0], k, block_size, overlap_chars, delimiter, model)
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top_k_texts_all.append(top_k_texts)
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top_k_embeddings_all.append(top_k_embeddings)
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# 将嵌入向量（ndarray）转换为可序列化的列表
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top_k_embeddings_all = [[embedding.tolist() for embedding in embeddings] for embeddings in top_k_embeddings_all]
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print(f"Top K texts: {top_k_texts_all}") # 打印检索到的文本
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print(f"Top K embeddings: {top_k_embeddings_all}") # 打印检索到的向量
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# 返回 JSON 格式的数据
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return jsonify({
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"topKEmbeddings": top_k_embeddings_all # 返回嵌入向量
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})
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if __name__ == '__main__':
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app.run(host="0.0.0.0", port=5000, debug=True)
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