how to load documents from Psychic. See here for more details.Prerequisites‚ÄãFollow 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‚ÄãUse 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‚ÄãWe 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",
This notebook covers how to load documents from Psychic. See here for more details.
This notebook covers how to load documents from Psychic. See here for more details. ->: how to load documents from Psychic. See here for more details.Prerequisites‚ÄãFollow 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‚ÄãUse 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‚ÄãWe 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",