index.storage_context.persist(persist_dir="./storage") else: # load vector store indexed if they exist index = load_index_from_storage( StorageContext.from_defaults(persist_dir="./storage"), service_context=sentence_context ) 运行此代码并确保其正常工作而不会出现错误。这将在Python文件夹所在的...
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...
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?") ...
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...
storage_context = StorageContext.from_defaults( persist_dir="./storage/uber" ) uber_index = load_index_from_storage(storage_context) index_loaded = True print("Index was already created. We just loaded it from the local storage.")
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)
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...
This is my code: from llama_index import StorageContext, load_index_from_storage index_cache_web_dir = Path('/tmp/cache_web/') if not index_cache_web_dir.is_dir(): index_cache_web_dir.mkdir(parents=True, exist_ok=True) web_storage_contex...
index.storage_context.persist() #To reload from disk: from llama_index import StorageContext, load_index _from_storage #rebuild storage context storage_context = StorageContext.from_defaults(persist_dir='./storage') #load index index = load_index_from_storage(storage_context) ...
)# load indexindex = load_index_from_storage( storage_context, )returnindex 这边用到了一种把 index 存储在硬盘上的方法,这样就不用每次再重新做索引这个步骤了,大大缩短了时间。同样,不知道 Langchain 有没有类似的技术。 现在我们将要调节的参数收集在字典param_dict中,还需要一个fixed_param_dict字典来...