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本 notebook 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，无需外部服务器即可轻松集成到应用程序中。它是由同一作者开发的 SQLite-VSS 的继承者。该扩展用零依赖的 C 语言编写，设计简单易于构建和使用。
本 notebook 展示了如何使用 SQLiteVec 向量数据库。

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安装配置

使用此集成需要通过 pip install -qU langchain-community 安装 langchain-community。 
# You need to install sqlite-vec as a dependency.
pip install -qU  sqlite-vec

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凭据

SQLiteVec 不需要任何凭据，因为向量存储只是一个简单的 SQLite 文件。

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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
)

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管理向量存储

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向向量存储添加条目

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

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更新向量存储中的条目

暂不支持

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从向量存储删除条目

暂不支持

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查询向量存储

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直接查询

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

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转换为检索器进行查询

暂不支持

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用于检索增强生成

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：alexgarcia.xyz/sqlite-vec/。

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API 参考

所有 SQLiteVec 功能和配置的详细文档，请参阅 API 参考：python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.sqlitevec.SQLiteVec.html

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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.'

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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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