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commit fe30be6 Author: Ankush Gola <9536492+agola11@users.noreply.github.com> Date: Wed Mar 1 21:55:43 2023 -0800 add async and streaming support to `OpenAIChat` (langchain-ai#1378) title says it all commit cfed049 Author: Lakshya Agarwal <lakshyajannu@gmail.com> Date: Thu Mar 2 10:48:09 2023 +0530 Minor grammatical fixes (langchain-ai#1325) Fixed typos and links in a few places across documents commit 59157b6 Author: Ryan Dao <daoduyducduong@gmail.com> Date: Wed Mar 1 21:15:27 2023 -0800 Bug: Fix Python version validation in PythonAstREPLTool (langchain-ai#1373) The current logic checks if the Python major version is < 8, which is wrong. This checks if the major and minor version is < 3.9. commit e178008 Author: Harrison Chase <hw.chase.17@gmail.com> Date: Wed Mar 1 21:15:13 2023 -0800 Harrison/track token usage (langchain-ai#1382) Co-authored-by: Zak King <zaking17@gmail.com> commit 1cd8996 Author: Harrison Chase <hw.chase.17@gmail.com> Date: Wed Mar 1 20:59:07 2023 -0800 Harrison/summarizer chain (langchain-ai#1356) Co-authored-by: Tim Asp <707699+timothyasp@users.noreply.github.com> commit cfae030 Author: yakigac <10434946+yakigac@users.noreply.github.com> Date: Thu Mar 2 11:24:32 2023 +0900 Fix the openaichat example (langchain-ai#1377) The example was wrong. commit 4b5e850 Author: Harrison Chase <hw.chase.17@gmail.com> Date: Wed Mar 1 11:47:01 2023 -0800 chatgpt wrapper (langchain-ai#1367) commit 4d4b43c Author: Harrison Chase <hw.chase.17@gmail.com> Date: Wed Mar 1 09:40:31 2023 -0800 fix doc names (langchain-ai#1354) commit c01f910 Author: Harrison Chase <hw.chase.17@gmail.com> Date: Wed Mar 1 08:20:24 2023 -0800 bump version to 0097 (langchain-ai#1365) commit edb3915 Author: Christie Jacob <christeejacobs@gmail.com> Date: Wed Mar 1 19:21:37 2023 +0400 typo in vectorstores (langchain-ai#1362) commit fe7dbec Author: Harrison Chase <hw.chase.17@gmail.com> Date: Tue Feb 28 22:19:11 2023 -0800 pandas and csv agents (langchain-ai#1353)
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diff --git a/README.md b/README.md
@@ -42,7 +42,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
 - Getting started (installation, setting up the environment, simple examples)
 - How-To examples (demos, integrations, helper functions)
 - Reference (full API docs)
-  Resources (high-level explanation of core concepts)
+- Resources (high-level explanation of core concepts)
 
 ## 🚀 What can this help with?
 

diff --git a/docs/modules/agents/agent_toolkits/csv.ipynb b/docs/modules/agents/agent_toolkits/csv.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7094e328",
+   "metadata": {},
+   "source": [
+    "# CSV Agent\n",
+    "\n",
+    "This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.\n",
+    "\n",
+    "**NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "827982c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_csv_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "caae0bec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16c4dc59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "46b9489d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 891 rows in the dataframe.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a96309be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'30 people have more than 3 siblings.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many people have more than 3 sibligngs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "964a09f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 5.449689683556195\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5.449689683556195'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats the square root of the average age?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551de2be",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}