Structured tool chat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6v_614UqlTeP"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"LANGCHAIN_TRACING\"] = \"true\" # If you want to trace the execution of the program, set to \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "wO7Q60vxlaiE"
   },
   "outputs": [],
   "source": [
    "from langchain.agents import AgentType\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.agents import initialize_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "k9Jka0PWlc-a"
   },
   "outputs": [],
   "source": [
    "from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\n",
    "from langchain.tools.playwright.utils import (\n",
    "    create_async_playwright_browser,\n",
    "    create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.\n",
    ")\n",
    "\n",
    "# This import is required only for jupyter notebooks, since they have their own eventloop\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "rsRHw4saleVS"
   },
   "outputs": [],
   "source": [
    "async_browser = create_async_playwright_browser()\n",
    "browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)\n",
    "tools = browser_toolkit.get_tools()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Xp1PBT0hlfuX"
   },
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0) # Also works well with Anthropic models\n",
    "agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "C08YxaE3liL3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd818ecb5b0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd818ecb400>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mHello Erica! How can I assist you today?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Hello Erica! How can I assist you today?\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "bQu0QOoZlj8f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd7f9d07b50>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd818fb8a90>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mThat's perfectly fine! I'm here to chat with you. How's your day going?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "That's perfectly fine! I'm here to chat with you. How's your day going?\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"Don't need help really just chatting.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "olr0kA7AllYr"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd818de0550>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mAction:\n",
      "```\n",
      "{\n",
      "  \"action\": \"navigate_browser\",\n",
      "  \"action_input\": {\n",
      "    \"url\": \"https://blog.langchain.dev\"\n",
      "  }\n",
      "}\n",
      "```\u001b[0m\n",
      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://blog.langchain.dev returned status code 200\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI have successfully navigated to the blog.langchain.dev website. Now I need to extract the text from the webpage to summarize it.\n",
      "Action:\n",
      "```\n",
      "{\n",
      "  \"action\": \"extract_text\",\n",
      "  \"action_input\": {}\n",
      "}\n",
      "```\u001b[0m\n",
      "Observation: \u001b[31;1m\u001b[1;3mLangChain LangChain Home About GitHub Docs LangChain The official LangChain blog. LangChain 🤝 Streamlit The initial integration of Streamlit with LangChain and our future plans. Jul 11, 2023 2 min read 🎉 Prem Challenge🎉 We're excited to announce a challenge hosted by Prem in collaboration with LangChain.\n",
      "\n",
      "At LangChain we try to make it easy as possible to experiment with as many different models as possible. That includes the incredible number of emerging open source models. We've made efforts to make our framework as Jun 26, 2023 6 min read LangChain <> MongoDB Atlas Today we’re announcing LangChain’s integration with MongoDB Atlas, adding support for one of the most popular developer data platforms in the world. This is an integration so anticipated that a few developers added the integration before we were ready to announce it :)\n",
      "\n",
      "\n",
      "Overview\n",
      "\n",
      "One of the key components Jun 22, 2023 3 min read Data-Driven Characters Data-driven-characters is a repo for creating, debugging, and interacting your own chatbots conditioned on your own story corpora. Jun 19, 2023 11 min read LangChain + Vectara: better together Introduction\n",
      "\n",
      "One of the main use cases of LangChain is connecting LLMs to user data, allowing users to build personalized LLM applications. A key part of this is retrieval - fetching relevant documents based on user queries.\n",
      "\n",
      "Today we’re happy to announce the integration of Vectara into LangChain to Jun 6, 2023 4 min read GPTeam: A multi-agent simulation Editor's Note: This is another edition in our series of guest posts highlighting novel applications of LangChain. After the Generative Agents paper was released, there was a flurry of open source projects rushing to incorporate some of the key ideas. GPTeam was one of the most impressive ones we saw, Jun 5, 2023 11 min read Going Beyond Chatbots: How to Make GPT-4 Output Structured Data Using LangChain By Jacob Lee\n",
      "\n",
      "Over the past few months, I had the opportunity to do some cool exploratory work for a client that integrated LLMs like GPT-4 and Claude into their internal workflow, rather than exposing them through a chat interface. The general idea was to take some input data, analyze May 21, 2023 4 min read Auto-Evaluation of Anthropic 100k Context Window Lance Martin\n",
      "\n",
      "\n",
      "Retrieval Architectures\n",
      "\n",
      "LLM question answering (Q+A) typically involves retrieval of documents relevant to the question followed by synthesis of the retrieved chunks into an answer by an LLM.  In practice, the retrieval step is necessary because the LLM context window is limited relative to the size of May 16, 2023 3 min read Rebuff: Detecting Prompt Injection Attacks [Editor's Note]: we're excited to highlight a guest blog by Willem Pienaar. As more and more LangChains make their way into production, we're getting an increased amount of questions about the security and privacy of these systems. We did a webinar on this topic a few weeks ago, and the May 14, 2023 4 min read Plan-and-Execute Agents TL;DR: We’re introducing a new type of agent executor, which we’re calling “Plan-and-Execute”. This is to contrast against the previous types of agent we supported, which we’re calling “Action” agents. Plan-and-Execute agents are heavily inspired by BabyAGI and the recent Plan-and-Solve paper. We think Plan-and-Execute is May 10, 2023 4 min read Feature Stores and LLMs Editor's note: big thank you to Willem Pienaar (Feast), Mike Del Balso (Tecton), and Simba Khadder (FeatureForm) for their comments and help on this post.\n",
      "\n",
      "LLMs represent a new paradigm of AI. There is a big open question of how many of the tools and services that were useful for May 9, 2023 5 min read Structured Tools TL;DR: we're introducing a new abstraction to allow for usage of more complex tools. While previous tools took in a single string input, new tools can take in an arbitrary number of inputs of arbitrary types. We are also introducing a new agent class that works well with these May 2, 2023 6 min read Auto-Evaluator Opportunities Editor's Note: this is a guest blog post by Lance Martin.\n",
      "\n",
      "\n",
      "TL;DR\n",
      "\n",
      "We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applciations, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.\n",
      "\n",
      "💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin\n",
      "\n",
      "Context\n",
      "\n",
      "LLM ops platforms, such as LangChain, make it easy to assemble LLM components (e.g., models, document retrievers, data loaders) into chains. Question-Answering is one of the most popular applications of these chains. But it is often not always obvious to determine what parameters (e.g. Apr 15, 2023 3 min read Announcing LangChainJS Support for Multiple JS Environments TLDR: We're announcing support for running LangChain.js in browsers, Cloudflare Workers, Vercel/Next.js, Deno, Supabase Edge Functions, alongside existing support for Node.js ESM and CJS. See install/upgrade docs and breaking changes list.\n",
      "\n",
      "\n",
      "Context\n",
      "\n",
      "Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangChain and Supabase together.\n",
      "\n",
      "The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people’s imagination and fueling an explosion in developer activity, Jasper Apr 4, 2023 4 min read Custom Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents. This has always been a bit tricky - because in our mind it's actually still very unclear what an \"agent\" actually is, and therefor what the \"right\" abstractions for them may be. Recently, Apr 3, 2023 3 min read Page 1 of 2 Older Posts → LangChain © 2023 Sign up Powered by Ghost\u001b[0m\n",
      "Thought:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd819145b40>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mI have successfully navigated to the blog.langchain.dev website. The text on the webpage includes various blog posts about LangChain. Here are some of the titles and summaries of the blog posts:\n",
      "\n",
      "1. \"LangChain 🤝 Streamlit\": This blog post discusses the initial integration of Streamlit with LangChain and their future plans.\n",
      "\n",
      "2. \"LangChain <> MongoDB Atlas\": This post announces LangChain's integration with MongoDB Atlas, adding support for one of the most popular developer data platforms.\n",
      "\n",
      "3. \"Data-Driven Characters\": This blog post introduces a repository for creating, debugging, and interacting with chatbots conditioned on story corpora.\n",
      "\n",
      "4. \"LangChain + Vectara: better together\": The post announces the integration of Vectara into LangChain, allowing for fetching relevant documents based on user queries.\n",
      "\n",
      "5. \"GPTeam: A multi-agent simulation\": This blog post highlights the GPTeam project, which incorporates key ideas from the Generative Agents paper.\n",
      "\n",
      "6. \"Going Beyond Chatbots: How to Make GPT-4 Output Structured Data Using LangChain\": The post discusses integrating LLMs like GPT-4 and Claude into internal workflows to analyze input data and generate structured data.\n",
      "\n",
      "7. \"Auto-Evaluation of Anthropic 100k Context Window\": This blog post explores retrieval architectures for LLM question answering and the limitations of the LLM context window.\n",
      "\n",
      "8. \"Rebuff: Detecting Prompt Injection Attacks\": The post discusses prompt injection attacks and the security and privacy of LangChain systems.\n",
      "\n",
      "9. \"Plan-and-Execute Agents\": This blog post introduces a new type of agent executor called \"Plan-and-Execute\" and contrasts it with \"Action\" agents.\n",
      "\n",
      "10. \"Feature Stores and LLMs\": The post explores the tools and services useful for LLMs and the challenges of integrating them.\n",
      "\n",
      "These are just a few of the blog posts available on the LangChain blog.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "I have successfully navigated to the blog.langchain.dev website. The text on the webpage includes various blog posts about LangChain. Here are some of the titles and summaries of the blog posts:\n",
      "\n",
      "1. \"LangChain 🤝 Streamlit\": This blog post discusses the initial integration of Streamlit with LangChain and their future plans.\n",
      "\n",
      "2. \"LangChain <> MongoDB Atlas\": This post announces LangChain's integration with MongoDB Atlas, adding support for one of the most popular developer data platforms.\n",
      "\n",
      "3. \"Data-Driven Characters\": This blog post introduces a repository for creating, debugging, and interacting with chatbots conditioned on story corpora.\n",
      "\n",
      "4. \"LangChain + Vectara: better together\": The post announces the integration of Vectara into LangChain, allowing for fetching relevant documents based on user queries.\n",
      "\n",
      "5. \"GPTeam: A multi-agent simulation\": This blog post highlights the GPTeam project, which incorporates key ideas from the Generative Agents paper.\n",
      "\n",
      "6. \"Going Beyond Chatbots: How to Make GPT-4 Output Structured Data Using LangChain\": The post discusses integrating LLMs like GPT-4 and Claude into internal workflows to analyze input data and generate structured data.\n",
      "\n",
      "7. \"Auto-Evaluation of Anthropic 100k Context Window\": This blog post explores retrieval architectures for LLM question answering and the limitations of the LLM context window.\n",
      "\n",
      "8. \"Rebuff: Detecting Prompt Injection Attacks\": The post discusses prompt injection attacks and the security and privacy of LangChain systems.\n",
      "\n",
      "9. \"Plan-and-Execute Agents\": This blog post introduces a new type of agent executor called \"Plan-and-Execute\" and contrasts it with \"Action\" agents.\n",
      "\n",
      "10. \"Feature Stores and LLMs\": The post explores the tools and services useful for LLMs and the challenges of integrating them.\n",
      "\n",
      "These are just a few of the blog posts available on the LangChain blog.\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"Browse to blog.langchain.dev and summarize the text, please.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "nJBVvnZwlrFb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd819146260>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd8191467a0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mI can navigate to the xkcd website and check the latest comic for you. Should I do that?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "I can navigate to the xkcd website and check the latest comic for you. Should I do that?\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"What's the latest xkcd comic about?\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "M0VQ_K7EltAu"
   },
   "outputs": [],
   "source": [
    "from langchain.prompts import MessagesPlaceholder\n",
    "from langchain.memory import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Adi3lgbOluH5"
   },
   "outputs": [],
   "source": [
    "chat_history = MessagesPlaceholder(variable_name=\"chat_history\")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "vEgHnG8ElvXr"
   },
   "outputs": [],
   "source": [
    "agent_chain = initialize_agent(\n",
    "    tools,\n",
    "    llm,\n",
    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
    "    verbose=True,\n",
    "    memory=memory,\n",
    "    agent_kwargs = {\n",
    "        \"memory_prompts\": [chat_history],\n",
    "        \"input_variables\": [\"input\", \"agent_scratchpad\", \"chat_history\"]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "pKCZt03hlxD0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd818eca0b0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd819145390>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mHello Erica! How can I assist you today?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Hello Erica! How can I assist you today?\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "x-AgAonblz3z"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd819146a10>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fd8191478b0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mYour name is Erica.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Your name is Erica.\n"
     ]
    }
   ],
   "source": [
    "response = await agent_chain.arun(input=\"whats my name?\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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