灵媒

此笔记本介绍如何从Psychic.有关更多详细信息，请参阅此处。

先决条件

按照本文档中的快速入门部分进行作
登录 Psychic 仪表板并获取您的密钥
将前端 React 库安装到 Web 应用程序中，并让用户对连接进行身份验证。将使用您指定的连接 ID 创建连接。

加载文档

使用PsychicLoader类以从连接加载到文档中。每个连接都有一个连接器 ID（对应于连接的 SaaS 应用程序）和一个连接 ID（您传入前端库）。

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

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m23.1.2[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

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

我们现在可以将这些文档转换为嵌入，并将它们存储在 Chroma 等矢量数据库中

from langchain.chains import RetrievalQAWithSourcesChain
from langchain_chroma import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

API 参考：RetrievalQAWithSourcesChain | OpenAI | OpenAI 嵌入 | CharacterTextSplitter（字符文本拆分器）

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)

Document loader 概念指南
Document loader 操作指南

先决条件

加载文档

将文档转换为嵌入

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