LlamaIndex使用persist()方法可以存储数据,使用load_index_from_storage()方法可以毫不费力地检索数据。 # Persisting to disk index.storage_context.persist(persist_dir="<persist_dir>") # Loading from disk from llama_index import StorageContext, load_index_from_storage storage_context = StorageContext.fro...
from llama_index import StorageContext, load_index_from_storage # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir="<persist_dir>") # load index index = load_index_from_storage(storage_context) NOTE: 如果在初始化index时使用的是自己定制化的ServiceContext,那么在使用...
如果需要可扩展的版本,可能需要将向量数据库作为向量索引存储。 如果想要加载索引,需要从llama_index导入另外两个模块StorageContext和load_index_from_storage,并将持久存储目录传给StorageContext类。一旦加载了存储的上下文,就可以在上面调用load_index_from_storage函数重新加载索引。 代码语言:javascript 代码运行次数:0 ...
uber_index=load_index_from_storage(storage_context) index_loaded=True print("Index was already created. We just loaded it from the local storage.") except: index_loaded=False print("Index is not present. We need it to create it again.") ifnotindex_loaded: print("Creating Index..") # ...
load_index_from_storage,StorageContext, ) # load documents documents =SimpleDirectoryReader( input_files=["paul_graham_essay.txt"] ).load_data() 向量存储: index= VectorStoreIndex.from_documents(documents)retriever= index.as_retriever(similarity_top_k=10)question="Where did the author go for art...
index.storage_context.persist()else: # load the existing index storage_context= StorageContext.from_defaults(persist_dir="./storage") index=load_index_from_storage(storage_context) query_engine=index.as_query_engine() response= query_engine.query("what is The worst thing about leaving YC?") ...
index.storage_context.persist(persist_dir="")如果遗漏了persist_dir参数,默认会保存到./storage目录。当想要从磁盘加载index数据时:注意:如果在初始化index时使用的是自己定制化的ServiceContext,那么在使用load_index_from_storage函数时,也必须使用同样的ServiceContext。基于其它indices构建indices你可以在其他indices的...
B_index = load_index_from_storage(storage_context) index_loaded = True except: index_loaded = False # 创建查询引擎 A_engine = A_index.as_query_engine(similarity_top_k=3) B_engine = B_index.as_query_engine(similarity_top_k=3)
storage_context = StorageContext.from_defaults(persist_dir="doc_emb") index = load_index_from_storage(storage_context) # 构建查询引擎 query_engine = index.as_query_engine(similarity_top_k=5) # 更新查询引擎中的prompt template query_engine.update_prompts( ...
query_engine() # storing locally index.storage_context.persist("../data/vector_db/subquery_db_1") # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir="../data/vector_db/subquery_db_1/") # load index index = load_index_from_storage(storage_context=storage_...