add everyting for docs

docs/extras/integrations/document_loaders/psychic.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Psychic\n",
+    "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic.html) for more details.\n",
+    "\n",
+    "## Prerequisites\n",
+    "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic.html)\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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\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"
+     ]
+    }
+   ],
+   "source": [
+    "# Uncomment this to install psychicapi if you don't already have it installed\n",
+    "!poetry run pip -q install psychicapi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}