Skip to main content
Open In Colab在 GitHub 上打开

Typesense 字体

Typesense 是一个开源的内存搜索引擎，您可以自行托管或在 Typesense Cloud 上运行。

Typesense 通过将整个索引存储在 RAM 中（备份在磁盘上）来关注性能，还专注于通过简化可用选项和设置良好的默认值来提供开箱即用的开发人员体验。

它还允许您将基于属性的筛选与矢量查询相结合，以获取最相关的文档。

此笔记本介绍如何将 Typesense 用作 VectorStore。

让我们首先安装我们的依赖项：

%pip install --upgrade --quiet  typesense openapi-schema-pydantic langchain-openai langchain-community tiktoken

我们想使用OpenAIEmbeddings所以我们必须获取 OpenAI API 密钥。

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Typesense
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

让我们导入我们的测试数据集：

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
docsearch = Typesense.from_documents(
    docs,
    embeddings,
    typesense_client_params={
        "host": "localhost",  # Use xxx.a1.typesense.net for Typesense Cloud
        "port": "8108",  # Use 443 for Typesense Cloud
        "protocol": "http",  # Use https for Typesense Cloud
        "typesense_api_key": "xyz",
        "typesense_collection_name": "lang-chain",
    },
)
query = "What did the president say about Ketanji Brown Jackson"
found_docs = docsearch.similarity_search(query)
print(found_docs[0].page_content)

Typesense 作为 Retriever

Typesense 和所有其他向量存储一样，通过使用余弦相似性来获得 LangChain Retriever。

retriever = docsearch.as_retriever()
retriever
query = "What did the president say about Ketanji Brown Jackson"
retriever.invoke(query)[0]