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本笔记本介绍如何从 Psychic 加载文档。详情请参阅此处。

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前提条件

  1. 按照此文档中的快速入门部分进行操作
  2. 登录 Psychic 控制台 并获取您的密钥
  3. 将前端 React 库集成到您的 Web 应用中，并让用户完成连接认证。连接将使用您指定的连接 ID 创建。

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加载文档

使用 PsychicLoader 类从连接中加载文档。每个连接都有一个连接器 ID（对应已连接的 SaaS 应用）和一个连接 ID（您传入前端库的值）。 
# Uncomment this to install psychicapi if you don't already have it installed
!poetry run pip -q install psychicapi langchain-chroma

from langchain_community.document_loaders import PsychicLoader
from psychicapi import ConnectorId

# Create a document loader for google drive. We can also load from other connectors by setting the connector_id to the appropriate value e.g. ConnectorId.notion.value
# This loader uses our test credentials
google_drive_loader = PsychicLoader(
    api_key="7ddb61c1-8b6a-4d31-a58e-30d1c9ea480e",
    connector_id=ConnectorId.gdrive.value,
    connection_id="google-test",
)

documents = google_drive_loader.load()

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将文档转换为嵌入向量

现在我们可以将这些文档转换为嵌入向量，并存储到向量数据库（如 Chroma）中。 
from langchain_classic.chains import RetrievalQAWithSourcesChain
from langchain_chroma import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
chain = RetrievalQAWithSourcesChain.from_chain_type(
    OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever()
)
chain({"question": "what is psychic?"}, return_only_outputs=True)
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