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灵媒

这个笔记本介绍了如何从Psychic加载文档。更多信息请参阅这里。

前置条件​

  1. 跟随快速入门部分在此文档
  2. 登录灵媒控制台并获取您的密钥
  3. 将前端 React 库安装到您的 Web 应用程序中，并让用户验证连接。连接将使用您指定的连接 ID 创建。

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（对应于您所连接的 SaaS 应用程序）和一个连接 ID（您将其传递给前端库）。

# Uncomment this to install psychicapi if you don't already have it installed
!poetry run pip -q install psychicapi langchain-chroma

[notice] A new release of pip is available: 23.0.1 -> 23.1.2
[notice] To update, run: pip install --upgrade pip
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()
API 参考:PsychicLoader

将文档转换为嵌入向量​

我们现在可以将这些文档转换为向量表示，并将其存储在像Chroma这样的向量数据库中

from langchain.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)