将 SQLite 作为向量存储与 SQLiteVec 结合使用

本笔记将介绍如何开始使用 SQLiteVec 向量存储。

SQLite-Vec 是一个专为向量搜索设计的 SQLite 扩展，强调本地优先操作和易于集成到无需外部服务器的应用程序中。它是同一作者的 SQLite-VSS 的后继版本。它采用零依赖的 C 语言编写，旨在易于构建和使用。

本笔记展示了如何使用 SQLiteVec 向量数据库。

设置

您需要安装 langchain-community (使用 pip install -qU langchain-community) 才能使用此集成

# You need to install sqlite-vec as a dependency.
%pip install --upgrade --quiet  sqlite-vec

凭证

SQLiteVec 作为向量存储使用时无需任何凭据，因为它只是一个简单的 SQLite 文件。

初始化

from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVec

embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vector_store = SQLiteVec(
    table="state_union", db_file="/tmp/vec.db", embedding=embedding_function
)

API 参考：SentenceTransformerEmbeddings | SQLiteVec

管理向量存储

向向量存储添加项目

vector_store.add_texts(texts=["Ketanji Brown Jackson is awesome", "foo", "bar"])

更新向量存储中的项目

暂不支持

从向量存储删除项目

暂不支持

查询向量存储

直接查询

data = vector_store.similarity_search("Ketanji Brown Jackson", k=4)

转换为检索器进行查询

暂不支持

检索增强生成的使用

有关如何将其用于检索增强生成，请参阅 SQLite-Vec 的文档：https://alexgarcia.xyz/sqlite-vec/ 以获取更多信息。

API 参考

有关所有 SQLiteVec 功能和配置的详细文档，请查阅 API 参考：https://python.langchain.ac.cn/api_reference/community/vectorstores/langchain_community.vectorstores.sqlitevec.SQLiteVec.html

其他示例

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVec
from langchain_text_splitters import CharacterTextSplitter

# load the document and split it into chunks
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()

# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
texts = [doc.page_content for doc in docs]


# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")


# load it in sqlite-vss in a table named state_union.
# the db_file parameter is the name of the file you want
# as your sqlite database.
db = SQLiteVec.from_texts(
    texts=texts,
    embedding=embedding_function,
    table="state_union",
    db_file="/tmp/vec.db",
)

# query it
query = "What did the president say about Ketanji Brown Jackson"
data = db.similarity_search(query)

# print results
data[0].page_content

API 参考：TextLoader | SentenceTransformerEmbeddings | SQLiteVec | CharacterTextSplitter

'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.'

使用现有 SQLite 连接的示例

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVec
from langchain_text_splitters import CharacterTextSplitter

# load the document and split it into chunks
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()

# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
texts = [doc.page_content for doc in docs]


# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
connection = SQLiteVec.create_connection(db_file="/tmp/vec.db")

db1 = SQLiteVec(
    table="state_union", embedding=embedding_function, connection=connection
)

db1.add_texts(["Ketanji Brown Jackson is awesome"])
# query it again
query = "What did the president say about Ketanji Brown Jackson"
data = db1.similarity_search(query)

# print results
data[0].page_content

API 参考：TextLoader | SentenceTransformerEmbeddings | SQLiteVec | CharacterTextSplitter

'Ketanji Brown Jackson is awesome'

相关

• 向量存储 概念指南
• 向量存储 操作指南