bow_vector = np.zeros(len(vocabulary)) for word in words: if word in word_to_index: bow_vector[word_to_index[word]] = 1 return bow_vector paragraph_vectors = [convert_text_to_vector(paragraph['context']) for article in squad_data['data'] for paragraph in article['paragraphs']] 构...
vectors=[]doc_ids=[]fordoc_id,vectorindocuments:vectors.append(vector)doc_ids.append(doc_id)vectors=np.array(vectors).astype(np.float32)index.add(vectors)# Perform a similarity search query_vector=[0.3,0.5,0.7,0.2]# Example query vector query_vector=np.array([query_vector]).astype(np.flo...
search_vector = np.array([search_vector]).astype('float32') distances, indexes = index.search(search_vector, num_results) for i, (distance, index) inenumerate(zip(distances[0], indexes[0])):print(f"Result {i+1}, Distance: {distance}")print(squad_data['data'][index]['paragraphs'][...
I use an ordinary Zen2 notebook here, and it takes about half an hour to run, so you might as well stand up and move during this time to relieve the fatigue of the day. When the data vector is complete, we can first execute sentence_embeddings.shape to see the status of the data...
最后可以看到vector_store其实就是一个包含文档信息的FAISS对象,其中向量化的过程已经在流程中生成了文件 vector_store = MyFAISS.from_documents(docs, self.embeddings) # docs 为Document列表 class FAISS(VectorStore): """Wrapper around FAISS vector database. To use, you should have the ``faiss`` python...
最后可以看到vector_store其实就是一个包含文档信息的FAISS对象,其中向量化的过程已经在流程中生成了文件 vector_store = MyFAISS.from_documents(docs, self.embeddings) # docs 为Document列表 class FAISS(VectorStore): """Wrapper around FAISS vector database. To use, you should have the ``faiss`` python...
This notebook shows how to use functionality related to theFAISSvector database. It will show functionality specific to this integration. After going through, it may be useful to explorerelevant use-case pagesto learn how to use this vectorstore as part of a larger chain. ...
importnumpyasnpimportfaiss 然后,我们可以生成一些随机数据作为我们的向量数据库。在这个例子中,我们生成了10000个128维的向量。 d = 128 # dimension nb = 10000 # database size np.random.seed(1234) # make reproducible xb = np.random.random((nb, d)).astype('float32') ...
model=SentenceTransformer('uer/sbert-base-chinese-nli')sentences=df['sentence'].tolist()sentence_embeddings=model.encode(sentences) 这个过程会比较久,消耗时间将会和你的电脑性能相关,我这边使用一台 Zen2 的普通笔记本,大概需要运行接近半个小时,所以这个时间不妨站起来动一动,缓解一天的疲劳。
database_vectors=np.random.rand(100000, dimension).astype(float32) index.add(database_vectors) #查询最相似的向量 query_vector=np.random.rand(1,dimension).astype(float32) k=10#返回最相似的10个向量 D,I=index.search(query_vector,k)