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AwaDB

AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.

您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成

本笔记本展示了如何使用与AwaDB相关的功能。

%pip install --upgrade --quiet  awadb
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import AwaDB
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
db = AwaDB.from_documents(docs)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

带分数的相似性搜索​

返回的距离分数介于 0 到 1 之间。0 表示不相似，1 表示最相似。

docs = db.similarity_search_with_score(query)
print(docs[0])
(Document(page_content='And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../how_to/state_of_the_union.txt'}), 0.561813814013747)

恢复之前创建并添加数据的表​

AwaDB 会自动持久化已添加的文档数据。

如果您能恢复之前创建并添加的表，只需按如下操作即可：

import awadb

awadb_client = awadb.Client()
ret = awadb_client.Load("langchain_awadb")
if ret:
    print("awadb load table success")
else:
    print("awadb load table failed")

awadb 加载表成功