langchain/docs/extras/modules/data_connection/document_loaders/integrations/psychic.ipynb
Davis Chase 87e502c6bc
Doc refactor (#6300)
Co-authored-by: jacoblee93 <jacoblee93@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-06-16 11:52:56 -07:00

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"# Psychic\n",
"This notebook covers how to load documents from `Psychic`. See [here](../../../../ecosystem/psychic.md) for more details.\n",
"\n",
"## Prerequisites\n",
"1. Follow the Quick Start section in [this document](../../../../ecosystem/psychic.md)\n",
"2. Log into the [Psychic dashboard](https://dashboard.psychic.dev/) and get your secret key\n",
"3. Install 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."
]
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"## Loading documents\n",
"\n",
"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)."
]
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"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
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],
"source": [
"# Uncomment this to install psychicapi if you don't already have it installed\n",
"!poetry run pip -q install psychicapi"
]
},
{
"cell_type": "code",
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"source": [
"from langchain.document_loaders import PsychicLoader\n",
"from psychicapi import ConnectorId\n",
"\n",
"# 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\n",
"# This loader uses our test credentials\n",
"google_drive_loader = PsychicLoader(\n",
" api_key=\"7ddb61c1-8b6a-4d31-a58e-30d1c9ea480e\",\n",
" connector_id=ConnectorId.gdrive.value,\n",
" connection_id=\"google-test\",\n",
")\n",
"\n",
"documents = google_drive_loader.load()"
]
},
{
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"source": [
"## Converting the docs to embeddings \n",
"\n",
"We can now convert these documents into embeddings and store them in a vector database like Chroma"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.llms import OpenAI\n",
"from langchain.chains import RetrievalQAWithSourcesChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"docsearch = Chroma.from_documents(texts, embeddings)\n",
"chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
" OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
")\n",
"chain({\"question\": \"what is psychic?\"}, return_only_outputs=True)"
]
}
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