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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cd80bc40-f10d-4ab3-826d-6cd0636d11e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
+    "from langchain.prompts import SystemMessagePromptTemplate, ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.runnables.openai_functions import OpenAIFunctionsRouter\n",
+    "\n",
+    "from permchain.connection_inmemory import InMemoryPubSubConnection\n",
+    "from permchain.pubsub import PubSub\n",
+    "from permchain.topic import Topic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c553be-9ed1-452c-a4ab-828f34dbb3ce",
+   "metadata": {},
+   "source": [
+    "## Content Fetcher"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56a6788d-b67c-4331-af8d-7741a66f03af",
+   "metadata": {},
+   "source": [
+    "First, we are going to define our content fetcher. This is responsible for taking a search query an getting relevant web pages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9c32e92-6f19-4cf1-8b87-1b756de0e263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain.document_loaders import AsyncHtmlLoader\n",
+    "from langchain.document_transformers import Html2TextTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7db344e2-fac4-4047-a422-6b0df738e656",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install google-api-python-client\n",
+    "# !pip install html2text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25983034-a563-403e-b9d4-400a27e6ec29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_tool = GoogleSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dcdc3812-0cdc-4677-bf9d-f9d7d5cac7e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrieve_documents(query):\n",
+    "    query=query.strip().strip('\"')\n",
+    "    urls_to_look = []\n",
+    "    search_results = search_tool.results(query, 5)\n",
+    "    for res in search_results:\n",
+    "        if res.get(\"link\", None):\n",
+    "            urls_to_look.append(res[\"link\"])\n",
+    "    \n",
+    "    # Relevant urls\n",
+    "    # Load, split, and add new urls to vectorstore\n",
+    "    if urls_to_look:\n",
+    "        loader = AsyncHtmlLoader(urls_to_look)\n",
+    "        html2text = Html2TextTransformer()\n",
+    "        docs = loader.load()\n",
+    "        docs = list(html2text.transform_documents(docs))\n",
+    "    else:\n",
+    "        docs = []\n",
+    "    return docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d087d2b4-d1fa-4f63-81de-d3d4e1dc5b1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#docs = retrieve_documents(\"langchain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1c7d867-d9b3-4d05-826f-f9d6602c8be6",
+   "metadata": {},
+   "source": [
+    "## Querier\n",
+    "\n",
+    "We will now come up with an actor to generate a query to search for given a user request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f01c5fae-75d3-4107-81e6-9a254b0d50c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_template(\"Come up with a search query given the user question:\\n\\n{question}\")\n",
+    "query_chain = prompt | ChatOpenAI() | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e0b79f-f6bd-4f68-9433-645ee1eea81e",
+   "metadata": {},
+   "source": [
+    "## Summarizer\n",
+    "We will now come up with an actor to summarize the results given a query and some search results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "901e1f8d-c973-4998-a731-5dab0c147b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_template(\"Answer the user's question given the search results\\n\\n<question>{question}</question><search_results>{search_results}</search_results>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "915bec33-d210-4471-b051-859ecba608be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarizer_chain = prompt | ChatOpenAI().with_fallbacks([ChatAnthropic(model=\"claude-2\")]) | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf403ec-39a4-4061-9401-06850ffe3761",
+   "metadata": {},
+   "source": [
+    "## All together now!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "edc0def4-d184-4438-9099-e6604ba9ff28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_inbox = Topic(\"query\")\n",
+    "summarizer_inbox = Topic(\"summarizer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "33540bc4-bb75-4e5a-876f-2b7b842e5509",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_actor = (\n",
+    "    # Listed in inputs\n",
+    "    Topic.IN.subscribe()\n",
+    "    | query_chain\n",
+    "    # The draft always goes to the editors inbox\n",
+    "    | query_inbox.publish()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "23f89611-7b0f-4f01-b9a7-119124e3341d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_actor = (\n",
+    "    query_inbox.subscribe()\n",
+    "    | {\n",
+    "        \"search_results\": retrieve_documents,\n",
+    "        \"question\": lambda x: x,\n",
+    "    }\n",
+    "    | summarizer_inbox.publish()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d4a6b51-bd93-47c2-a301-9593a47df7d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summ_actor = (\n",
+    "    summarizer_inbox.subscribe()\n",
+    "    | summarizer_chain\n",
+    "    | Topic.OUT.publish()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "11d3b066-7f95-4ad9-82d0-ba64bbf3e3fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "web_researcher = PubSub(\n",
+    "    processes=(query_actor, search_actor, summ_actor),\n",
+    "    connection=InMemoryPubSubConnection(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bc022d51-69f0-4da9-8025-70afdc3cc6a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#web_researcher.invoke({\"question\": \"What is langsmith?\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5952e9b8-2a56-4d2d-997a-dceb7652186f",
+   "metadata": {},
+   "source": [
+    "## Trying to use it as a sub component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "43ca019d-a500-4c77-8e62-a46e54ffae7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "858a82ae-a73f-4da2-9210-298af789ea30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Write between 2 and 5 sub questions that serve as google search queries to search online that form an objective opinion from the following: {question}\"\"\"\n",
+    "functions = [\n",
+    "    {\n",
+    "                \"name\": \"sub_questions\",\n",
+    "                \"description\": \"List of sub questions\",\n",
+    "                \"parameters\": {\n",
+    "                    \"type\": \"object\",\n",
+    "                    \"properties\": {\n",
+    "                        \"questions\": {\n",
+    "                            \"type\": \"array\",\n",
+    "                            \"description\": \"List of sub questions to ask.\",\n",
+    "                              \"items\": {\n",
+    "                                \"type\": \"string\"\n",
+    "                              }\n",
+    "                        },\n",
+    "                    },\n",
+    "                },\n",
+    "            },\n",
+    "]\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "question_chain = prompt | ChatOpenAI(temperature=0).bind(functions=functions, function_call={\"name\":\"sub_questions\"}) | JsonKeyOutputFunctionsParser(key_name=\"questions\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c0298fdc-0e7c-4e79-9cb7-cdd4d50fe88f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['What is the purpose of Langsmith?',\n",
+       " 'Who developed Langsmith?',\n",
+       " 'What are the key features of Langsmith?',\n",
+       " 'How does Langsmith work?',\n",
+       " 'Are there any alternatives to Langsmith?']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question_chain.invoke({\"question\": \"what is langsmith?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a0101bd0-cd95-4b13-ab26-d5d34d703bf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"You are tasked with writing a research report to answer the following question:\n",
+    "\n",
+    "<question>\n",
+    "{question}\n",
+    "</question>\n",
+    "\n",
+    "In order to do that, you first came up with several sub questions and researched those. please find those below:\n",
+    "\n",
+    "<research>\n",
+    "{research}\n",
+    "</research>\n",
+    "\n",
+    "Now, write your final report answering the original question!\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "report_chain = prompt | ChatOpenAI() | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d7715e0c-23c0-4985-97a7-bf5b151cf734",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "research_inbox = Topic(\"research\")\n",
+    "writer_inbox = Topic(\"writer_inbox\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f274027a-c9f0-4efa-b798-1921b9b376d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subquestion_actor = (\n",
+    "    # Listed in inputs\n",
+    "    Topic.IN.subscribe()\n",
+    "    | question_chain\n",
+    "    # The draft always goes to the editors inbox\n",
+    "    | research_inbox.publish()\n",
+    ")\n",
+    "research_actor = (\n",
+    "    research_inbox.subscribe()\n",
+    "    | {\n",
+    "        \"research\": lambda x: web_researcher.batch([{\"question\": i} for i in x]),\n",
+    "        #\"research\": lambda x: [web_researcher.invoke({\"question\": i}) for i in x],\n",
+    "        \"question\": Topic.IN.current() | itemgetter(\"question\"),\n",
+    "    }\n",
+    "    | writer_inbox.publish()\n",
+    ")\n",
+    "write_actor = (\n",
+    "    writer_inbox.subscribe()\n",
+    "    | report_chain\n",
+    "    | Topic.OUT.publish()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "789b636d-23ce-44fb-a8e3-fdfbfabe77ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "longer_researcher = PubSub(\n",
+    "    processes=(subquestion_actor, research_actor, write_actor),\n",
+    "    connection=InMemoryPubSubConnection(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f713a31-5f60-4d90-9b95-3f42d8fbbddb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:01<00:00,  3.27it/s]\n",
+      "Fetching pages:   0%|                                                                                                                                                                                             | 0/5 [00:00<?, ?it/s]\n",
+      "Fetching pages:   0%|                                                                                                                                                                                             | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "Fetching pages:   0%|                                                                                                                                                                                             | 0/5 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Fetching pages:   0%|                                                                                                                                                                                             | 0/5 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:01<00:00,  3.88it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:00<00:00,  9.07it/s]\n",
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:00<00:00,  6.00it/s]\n",
+      "\n",
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:01<00:00,  4.79it/s]\u001b[A\n",
+      "\n",
+      "Fetching pages: 100%|#####################################################################################################################################################################################| 5/5 [00:00<00:00,  8.80it/s]\u001b[A\n"
+     ]
+    }
+   ],
+   "source": [
+    "longer_researcher.invoke({\"question\": \"what is langsmith?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fde8b227-64ec-4e96-b337-fc888e7ad787",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}