From d65375517a7d37f6e568d8d3a2d5693ade823e5e Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Fri, 1 Mar 2024 00:47:17 -0800
Subject: [PATCH] Add readme link

---
 README.md                  |  4 ++++
 examples/storm/storm.ipynb | 11 +++++++----
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index e5f7f2164..23ca5ac8b 100644
--- a/README.md
+++ b/README.md
@@ -497,6 +497,10 @@ When output quality is a major concern, it's common to incorporate some combinat
 - [Multi-agent with supervisor](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
 - [Hierarchical agent teams](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
 
+### Web Research
+
+- [STORM](./examples/storm/storm.ipynb): writing system that generates Wikipedia-style articles on any topic, applying outline generation (planning) + multi-perspective question-answering for added breadth and reliability. Based on [STORM](https://arxiv.org/abs/2402.14207) by [Shao](https://twitter.com/EchoShao8899), et. al.
+
 ### Chatbot Evaluation via Simulation
 
 It can often be tough to evaluation chat bots in multi-turn situations. One way to do this is with simulations.
diff --git a/examples/storm/storm.ipynb b/examples/storm/storm.ipynb
index d56ca5f98..6a9d7b0cf 100644
--- a/examples/storm/storm.ipynb
+++ b/examples/storm/storm.ipynb
@@ -62,22 +62,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "import getpass\n",
     "\n",
+    "\n",
     "def _set_env(var: str):\n",
     "    if os.environ.get(var):\n",
     "        return\n",
     "    os.environ[var] = getpass.getpass(var + \":\")\n",
     "\n",
+    "\n",
     "# Set for tracing\n",
-    "os.environ[\"LANGCHAIN_TRACING_V2\"[ = \"true\"\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_PROJECT\"] = \"STORM\"\n",
-    "_set_env(\"LANGCHAIN_API_KEY\") \n",
+    "_set_env(\"LANGCHAIN_API_KEY\")\n",
     "_set_env(\"OPENAI_API_KEY\")"
    ]
   },
@@ -99,7 +101,7 @@
     "from langchain_openai import ChatOpenAI\n",
     "from langchain_fireworks import ChatFireworks\n",
     "\n",
-    "fast_llm = ChatOpenAI(model=\"gpt-3.5-turbo\") \n",
+    "fast_llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "# Uncomment for a Fireworks model\n",
     "# fast_llm = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v1\", max_tokens=32_000)\n",
     "long_context_llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")"
@@ -693,6 +695,7 @@
     "# Tavily is typically a better search engine, but your free queries are limited\n",
     "# search_engine = TavilySearchResults(max_results=4)\n",
     "\n",
+    "\n",
     "@tool\n",
     "async def search_engine(query: str):\n",
     "    \"\"\"Search engine to the internet.\"\"\"\n",