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使用 SQLiteVec 将 SQLite 作为向量存储

此笔记本涵盖了如何开始使用 SQLiteVec 向量存储。

SQLite-Vec 是一个专为向量搜索设计的 SQLite 扩展，它强调本地优先操作，并且可以轻松集成到应用程序中，而无需外部服务器。它是同一作者的 SQLite-VSS 的继任者。它是用零依赖性 C 编写的，旨在易于构建和使用。

此笔记本展示了如何使用 SQLiteVec 向量数据库。

设置​

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

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

凭据​

SQLiteVec 不需要任何凭据即可使用，因为向量存储只是一个简单的 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
)

管理向量存储​

将项目添加到向量存储​

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

更新向量存储中的项目​

目前不支持

从向量存储中删除项目​

目前不支持

查询向量存储​

直接查询​

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

通过将其转换为检索器来查询​

目前不支持

用于检索增强生成的用法​

有关如何将其用于检索增强生成的更多信息，请参阅 sqlite-vec 文档，网址为 https://alexgarcia.xyz/sqlite-vec/。

API 参考​

有关所有 SQLiteVec 功能和配置的详细文档，请访问 API 参考：https://python-api.langchain.ac.cn/en/latest/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
'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
'Ketanji Brown Jackson is awesome'

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