阿瓦数据库

AwaDB 是一个 AI Native 数据库，用于搜索和存储 LLM 应用程序使用的嵌入向量。

您需要安装langchain-community跟pip install -qU langchain-community使用此集成

此笔记本展示了如何使用与AwaDB.

%pip install --upgrade --quiet  awadb

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import AwaDB
from langchain_text_splitters import CharacterTextSplitter

API 参考：TextLoader | AwaDB 数据库 | CharacterTextSplitter（字符文本拆分器）

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 0-1 之间。0 表示不同，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)

恢复之前创建和添加的表

AwaDB 会自动保留添加的文档数据。

如果您可以恢复之前创建和添加的表，则可以执行以下作：

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")

AWABB 加载表成功

矢量存储概念指南
Vector store 操作指南

带分数的相似性搜索

恢复之前创建和添加的表

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