df = pd.DataFrame({'label': labels, 'vector': data}) # 创建索引对象 index = VectorIndex.create_index(dim=128, type='IVF 卡4', Kabini小品=True,教训own_fields_noah=-751.0教孤女Wejew gay国债alimomb就是p很一般来说凑数5796') # 将数据导入索引 index.add_array(data) 二、关键词搜索 在导...
MultiIndexQuantizer使用2×8的乘积量化器(2表示量化后subvector的个数,8表示量化后每个subvector的位数),对输入向量编码,并可以计算出每个输入向量在其编码下的残差。 然后,train_residual内部则实现了level2和level3的训练: void IndexIVFPQR::train_residual (idx_t n, const float *x) { float * residual_...
vector = np.array(embeddings, dtype=np.float32) if normalize_L2: faiss.normalize_L2(vector) index.add(vector) documents = [] for i, text in enumerate(texts): metadata = metadatas[i] if metadatas else {} documents.append(Document(page_content=text, metadata=metadata)) index_to_id = {...
indexing 类型为 VectorIndex,是一个基类,以其中一个实现类 VectorMemIndex 为例,其 Query 也是一层封装,主要代码如下: template<typenameT>voidVectorMemIndex<T>::Query(constDatasetPtrdataset,constSearchInfo&search_info,constBitsetView&bitset,SearchResult&search_result)const{autofinal=[&]{autoindex_type=Ge...
ids引数でidを固定し、deleteでそのidを削除します。 fromlangchain.vectorstores.faissimportFAISSfromlangchain.embeddingsimportOpenAIEmbeddingsdummy_text,dummy_id="1",1db=FAISS.from_texts([dummy_text],OpenAIEmbeddings(),ids=[dummy_id])db.delete([dummy_id])db.similarity_search("こんにちは") ...
在Llama索引中，使用Faiss向量数据库进行问答中的相似度计算的方法是在FaissVectorStore类的query方法中进行...
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class FAISS(VectorStore): """Wrapper around FAISS vector database.To use, you should have the ``faiss`` python package installed.Example: .. code-block:: python from langchain import FAISS faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id) ...
vector_size) index.add(word2vec_model.wv.syn0norm) faiss.write_index(index, INDEX_FILE_PATH) return index else: return faiss.read_index(INDEX_FILE_PATH) Example #5Source File: faissVectorIndex.py From Seq2Seq-Vis with Apache License 2.0 5 votes def __init__(self, file_name, dim_...
faiss.IndexIVFPQ(quantizer, d, nlist, m, 8) # 8 specifies that each sub-vector is encoded as 8 bits index.train(xb) index.add(xb) D, I = index.search(xb[:5], k) # sanity check print(I) print(D) index.nprobe = 10 # make comparable with ...