이 노트북은 SQLiteVec vector store를 시작하는 방법을 다룹니다.
SQLite-Vec는 vector 검색을 위해 설계된 SQLite extension으로, 로컬 우선 작업과 외부 서버 없이 애플리케이션에 쉽게 통합할 수 있는 것을 강조합니다. 같은 작성자의 SQLite-VSS의 후속 버전입니다. 의존성이 없는 C로 작성되었으며 빌드하고 사용하기 쉽게 설계되었습니다.
이 노트북은 SQLiteVec vector database를 사용하는 방법을 보여줍니다.

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Setup

이 integration을 사용하려면 pip install -qU langchain-community로 langchain-community를 설치해야 합니다 
# You need to install sqlite-vec as a dependency.
pip install -qU  sqlite-vec

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Credentials

SQLiteVec는 vector store가 단순한 SQLite 파일이므로 사용하는 데 자격 증명이 필요하지 않습니다.

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Initialization

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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Manage vector store

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Add items to vector store

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

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Update items in vector store

아직 지원되지 않습니다

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Delete items from vector store

아직 지원되지 않습니다

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Query vector store

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Query directly

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

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Query by turning into retriever

아직 지원되지 않습니다

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Usage for retrieval-augmented generation

retrieval-augmented generation에 사용하는 방법에 대한 자세한 내용은 alexgarcia.xyz/sqlite-vec/의 sqlite-vec 문서를 참조하세요.

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API reference

모든 SQLiteVec 기능 및 구성에 대한 자세한 문서는 API reference를 참조하세요: python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.sqlitevec.SQLiteVec.html

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Other examples

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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Example using existing SQLite connection

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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