ComponentsDocument loadersPsychicOn this pagePsychicThis notebook covers how to load documents from Psychic. See here for more details.Prerequisites​Follow the Quick Start section in this documentLog into the Psychic dashboard and get your secret keyInstall the frontend react library into your web app and have a user authenticate a connection. The connection will be created using the connection id that you specify.Loading documents​Use the PsychicLoader class to load in documents from a connection. Each connection has a connector id (corresponding to the SaaS app that was connected) and a connection id (which you passed in to the frontend library).# Uncomment this to install psychicapi if you don't already have it installedpoetry run pip -q install psychicapi        [notice] A new release of pip is available: 23.0.1 -> 23.1.2    [notice] To update, run: pip install --upgrade pipfrom langchain.document_loaders import PsychicLoaderfrom 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 credentialsgoogle_drive_loader = PsychicLoader(    api_key="7ddb61c1-8b6a-4d31-a58e-30d1c9ea480e",    connector_id=ConnectorId.gdrive.value,    connection_id="google-test",)documents = google_drive_loader.load()Converting the docs to embeddings​We can now convert these documents into embeddings and store them in a vector database like Chromafrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import RetrievalQAWithSourcesChaintext_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)PreviousPolars DataFrameNextPubMedPrerequisitesLoading documentsConverting the docs to embeddings