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Typesense

Typesense 是一个开源的内存搜索引引擎，你可以选择自行托管，或在Typesense Cloud上运行。

Typesense 通过将整个索引存储在内存中（并在磁盘上备份）来专注于性能，同时通过简化可用选项并设置合理的默认值，致力于提供开箱即用的开发者体验。

它还允许您将基于属性的过滤与向量查询相结合，以获取最相关的文档。

此笔记本向您展示如何将 Typesense 用作您的向量存储。

首先让我们安装依赖项：

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

我们想要使用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 作为检索器​

Typesense 和其他所有向量数据库一样，通过使用余弦相似度，成为 LangChain 检索器。

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