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docs: update toolkit guides (#24992)
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ccurme
GitHub
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commit
acba38a18e
@ -122,7 +122,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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@ -130,14 +130,14 @@
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{
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"data": {
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"text/plain": [
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"[GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain_community.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
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" GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
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" GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain_community.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
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" GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain_community.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
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" GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain_community.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]"
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"[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
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" GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
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" GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
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" GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
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" GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]"
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]
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},
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -164,142 +164,96 @@
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"source": [
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"## Use within an agent\n",
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"\n",
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"We show here how to use it as part of an [agent](/docs/tutorials/agents). We use the OpenAI Functions Agent, so we will need to setup and install the required dependencies for that. We will also use [LangSmith Hub](https://smith.langchain.com/hub) to pull the prompt from, so we will need to install that.\n",
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"Below we show how to incorporate the toolkit into an [agent](/docs/tutorials/agents).\n",
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"\n",
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"```bash\n",
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"pip install -U langchain-openai langchainhub\n",
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"We will need a LLM or chat model:\n",
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"\n",
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"```{=mdx}\n",
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"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
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"\n",
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"<ChatModelTabs customVarName=\"llm\" />\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"# | output: false\n",
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"# | echo: false\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import hub\n",
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"from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"instructions = \"\"\"You are an assistant.\"\"\"\n",
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"base_prompt = hub.pull(\"langchain-ai/openai-functions-template\")\n",
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"prompt = base_prompt.partial(instructions=instructions)"
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"agent_executor = create_react_agent(llm, tools)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = create_openai_functions_agent(llm, toolkit.get_tools(), prompt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor = AgentExecutor(\n",
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" agent=agent,\n",
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" tools=toolkit.get_tools(),\n",
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" # This is set to False to prevent information about my email showing up on the screen\n",
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" # Normally, it is helpful to have it set to True however.\n",
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" verbose=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'input': 'Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot who is looking to collaborate on some research with her estranged friend, a cat. Under no circumstances may you send the message, however.',\n",
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" 'output': 'I have created a draft email for you to edit. Please find the draft in your Gmail drafts folder. Remember, under no circumstances should you send the message.'}"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"Draft an email to fake@fake.com thanking them for coffee.\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)\n",
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" Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M\n",
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" Args:\n",
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" message: Dear Fake,\n",
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"\n",
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"I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!\n",
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"\n",
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"Best regards,\n",
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"[Your Name]\n",
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" to: ['fake@fake.com']\n",
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" subject: Thank You for the Coffee\n",
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"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
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"Name: create_gmail_draft\n",
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"\n",
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"Draft created. Draft Id: r-7233782721440261513\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject \"Thank You for the Coffee\".\n"
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]
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}
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],
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"source": [
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"agent_executor.invoke(\n",
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" {\n",
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" \"input\": \"Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot\"\n",
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" \" who is looking to collaborate on some research with her\"\n",
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" \" estranged friend, a cat. Under no circumstances may you send the message, however.\"\n",
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" }\n",
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")"
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"example_query = \"Draft an email to fake@fake.com thanking them for coffee.\"\n",
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"\n",
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"events = agent_executor.stream(\n",
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" {\"messages\": [(\"user\", example_query)]},\n",
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" stream_mode=\"values\",\n",
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")\n",
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"for event in events:\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'input': 'Could you search in my drafts for the latest email? what is the title?',\n",
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" 'output': 'The latest email in your drafts is titled \"Collaborative Research Proposal\".'}"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent_executor.invoke(\n",
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" {\"input\": \"Could you search in my drafts for the latest email? what is the title?\"}\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
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]
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}
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],
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"metadata": {
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@ -123,10 +123,10 @@
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{
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"data": {
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"text/plain": [
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"[SlackGetChannel(client=<slack_sdk.web.client.WebClient object at 0x10ce3a4d0>),\n",
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" SlackGetMessage(client=<slack_sdk.web.client.WebClient object at 0x10ce3a0e0>),\n",
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" SlackScheduleMessage(client=<slack_sdk.web.client.WebClient object at 0x10ce3a050>),\n",
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" SlackSendMessage(client=<slack_sdk.web.client.WebClient object at 0x10ce3a020>)]"
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"[SlackGetChannel(client=<slack_sdk.web.client.WebClient object at 0x113caa8c0>),\n",
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" SlackGetMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa4d0>),\n",
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" SlackScheduleMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa440>),\n",
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" SlackSendMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa410>)]"
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]
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},
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"execution_count": 3,
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@ -163,7 +163,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -177,7 +177,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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@ -189,12 +189,12 @@
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"When was the #general channel created?\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" get_channelid_name_dict (call_mINmB55OWDIkXykGXZXaL5Ar)\n",
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" Call ID: call_mINmB55OWDIkXykGXZXaL5Ar\n",
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" get_channelid_name_dict (call_NXDkALjoOx97uF1v0CoZTqtJ)\n",
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" Call ID: call_NXDkALjoOx97uF1v0CoZTqtJ\n",
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" Args:\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"The #general channel was created on Unix timestamp 1671043305, which corresponds to \"Mon, 12 Dec 2022 18:41:45 GMT\" in human-readable format.\n"
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"The #general channel was created on timestamp 1671043305.\n"
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]
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}
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],
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@ -211,53 +211,6 @@
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example with AgentExecutor:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import hub\n",
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"from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
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"prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
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"agent = create_openai_tools_agent(\n",
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" tools=toolkit.get_tools(),\n",
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" llm=llm,\n",
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" prompt=prompt,\n",
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")\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor.invoke(\n",
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" {\n",
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" \"input\": \"Send a greeting to my coworkers in the #general channel. Note use `channel` as key of channel id, and `message` as key of content to sent in the channel.\"\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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@ -267,73 +220,33 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"Send a friendly greeting to channel C072Q1LP4QM.\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" send_message (call_xQxpv4wFeAZNZgSBJRIuaizi)\n",
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" Call ID: call_xQxpv4wFeAZNZgSBJRIuaizi\n",
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" Args:\n",
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" message: Hello! Have a great day!\n",
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" channel: C072Q1LP4QM\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mI need to get the list of channels in the workspace.\n",
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"Action: get_channelid_name_dict\n",
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"Action Input: {}\u001b[0m\u001b[36;1m\u001b[1;3m[{\"id\": \"C052SCUP4UD\", \"name\": \"general\", \"created\": 1681297313, \"num_members\": 1}, {\"id\": \"C052VBBU4M8\", \"name\": \"test-bots\", \"created\": 1681297343, \"num_members\": 2}, {\"id\": \"C053805TNUR\", \"name\": \"random\", \"created\": 1681297313, \"num_members\": 2}]\u001b[0m\u001b[32;1m\u001b[1;3mI now have the list of channels and their names.\n",
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"Final Answer: There are 3 channels in the workspace. Their names are \"general\", \"test-bots\", and \"random\".\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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"I have sent a friendly greeting to the channel C072Q1LP4QM.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'input': 'How many channels are in the workspace? Please list out their names.',\n",
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" 'output': 'There are 3 channels in the workspace. Their names are \"general\", \"test-bots\", and \"random\".'}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent_executor.invoke(\n",
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" {\"input\": \"How many channels are in the workspace? Please list out their names.\"}\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mFirst, I need to identify the channel ID for the #introductions channel.\n",
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"Action: get_channelid_name_dict\n",
|
||||
"Action Input: None\u001b[0m\u001b[36;1m\u001b[1;3m[{\"id\": \"C052SCUP4UD\", \"name\": \"general\", \"created\": 1681297313, \"num_members\": 1}, {\"id\": \"C052VBBU4M8\", \"name\": \"test-bots\", \"created\": 1681297343, \"num_members\": 2}, {\"id\": \"C053805TNUR\", \"name\": \"random\", \"created\": 1681297313, \"num_members\": 2}]\u001b[0m\u001b[32;1m\u001b[1;3mThe #introductions channel is not listed in the observed channels. I need to inform the user that the #introductions channel does not exist or is not accessible.\n",
|
||||
"Final Answer: The #introductions channel does not exist or is not accessible.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'input': 'Tell me the number of messages sent in the #introductions channel from the past month.',\n",
|
||||
" 'output': 'The #introductions channel does not exist or is not accessible.'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_executor.invoke(\n",
|
||||
" {\n",
|
||||
" \"input\": \"Tell me the number of messages sent in the #introductions channel from the past month.\"\n",
|
||||
" }\n",
|
||||
")"
|
||||
"example_query = \"Send a friendly greeting to channel C072Q1LP4QM.\"\n",
|
||||
"\n",
|
||||
"events = agent_executor.stream(\n",
|
||||
" {\"messages\": [(\"user\", example_query)]},\n",
|
||||
" stream_mode=\"values\",\n",
|
||||
")\n",
|
||||
"for event in events:\n",
|
||||
" message = event[\"messages\"][-1]\n",
|
||||
" if message.type != \"tool\": # mask sensitive information\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -342,7 +255,7 @@
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
|
||||
"For detailed documentation of all `SlackToolkit` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user