From 39d309a255208dd898ce3b032deaef8aa2d0b77d Mon Sep 17 00:00:00 2001 From: solon Date: Wed, 15 Jan 2025 15:48:21 +0900 Subject: [PATCH 1/5] first translation --- 14-Chains/04-Structured-Data-Chat.ipynb | 1213 +++++++++++++++++++++++ 14-Chains/data/titanic.csv | 892 +++++++++++++++++ 2 files changed, 2105 insertions(+) create mode 100644 14-Chains/04-Structured-Data-Chat.ipynb create mode 100644 14-Chains/data/titanic.csv diff --git a/14-Chains/04-Structured-Data-Chat.ipynb b/14-Chains/04-Structured-Data-Chat.ipynb new file mode 100644 index 000000000..7f03dbb3b --- /dev/null +++ b/14-Chains/04-Structured-Data-Chat.ipynb @@ -0,0 +1,1213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MultiQueryRetriever\n", + "\n", + "- Author: [hong-seongmin](https://github.com/hong-seongmin)\n", + "- Design: \n", + "- Peer Review: \n", + "- This is a part of [LangChain OpenTutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-4/sub-graph.ipynb) [![Open in LangChain Academy](https://cdn.prod.website-files.com/65b8cd72835ceeacd4449a53/66e9eba12c7b7688aa3dbb5e_LCA-badge-green.svg)](https://academy.langchain.com/courses/take/intro-to-langgraph/lessons/58239937-lesson-2-sub-graphs)\n", + "\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates various data analysis and query techniques, combining traditional data processing with advanced AI-driven insights. By integrating pandas for data handling and LangChain for LLM-powered query resolution, it explores interactive and automated approaches to data exploration.\n", + "\n", + "- **Data Loading and Preparation** \n", + " The notebook begins by loading a Titanic dataset using pandas. The data is preprocessed and stored in a DataFrame (`df`). This section demonstrates how to read CSV files and prepare data for analysis.\n", + "\n", + "- **Interactive Query with LangChain Tools** \n", + " Using LangChain's `PythonAstREPLTool`, the notebook allows direct interaction with the DataFrame via Python commands. This tool provides an intuitive way to execute queries and retrieve results dynamically within the notebook environment.\n", + "\n", + "- **Agent-Based Query System** \n", + " A key highlight of the notebook is the creation of an agent powered by LangChain and OpenAI models. This agent processes natural language queries and translates them into actionable insights using the dataset. For instance, it calculates survival rates based on specific conditions and provides context-aware responses.\n", + "\n", + "- **Advanced Query Scenarios** \n", + " The notebook demonstrates the handling of more complex queries, such as analyzing survival rates for subsets of passengers (e.g., male passengers, children in specific classes). This is achieved by dynamically constructing and executing queries through the LLM-driven agent.\n", + "\n", + "- **Working with Multiple DataFrames** \n", + " The notebook extends its functionality to support queries involving multiple DataFrames. This capability is demonstrated with a cloned and modified version of the original dataset (`df1`), enabling comparative analysis between datasets.\n", + "\n", + "- **Streamlined Query Execution** \n", + " By integrating a callback system, the notebook ensures seamless real-time interaction with the LLM during query execution. This feature enhances the user experience by providing immediate feedback and results.\n", + "\n", + "- **Applications** \n", + " This notebook serves as an example of how to bridge traditional data analysis and advanced AI-driven techniques. It simplifies complex querying, offers deeper insights into structured data, and demonstrates the potential of combining machine learning with interactive data exploration.\n", + "\n", + "\n", + "### Table of Contents\n", + "\n", + "- [Overview](#overview)\n", + "- [Environment Setup](#environment-setup)\n", + "- [Data Loading](#Data-Loading)\n", + "- [Pandas DataFrame Agent](#Pandas-DataFrame-Agent)\n", + "- [Two or More DataFrames](#Two-or-More-DataFrames)\n", + "\n", + "### References\n", + "\n", + "- [LangChain Documentation: How to use the MultiQueryRetriever](https://python.langchain.com/docs/how_to/MultiQueryRetriever/)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "Set up the environment. You may refer to [Environment Setup](https://wikidocs.net/257836) for more details.\n", + "\n", + "**[Note]**\n", + "- `langchain-opentutorial` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials. \n", + "- You can checkout the [`langchain-opentutorial`](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Ignoring invalid distribution -angchain-community (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -orch (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -treamlit (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Error parsing dependencies of torchsde: .* suffix can only be used with `==` or `!=` operators\n", + " numpy (>=1.19.*) ; python_version >= \"3.7\"\n", + " ~~~~~~~^\n", + "WARNING: Ignoring invalid distribution -angchain-community (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -orch (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -treamlit (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -angchain-community (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -orch (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -treamlit (c:\\users\\user\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n" + ] + } + ], + "source": [ + "%%capture --no-stderr\n", + "%pip install langchain-opentutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Install required packages\n", + "from langchain_opentutorial import package\n", + "\n", + "package.install(\n", + " [\n", + " \"langchain\",\n", + " \"langchain_core\",\n", + " \"langchain_openai\",\n", + " \"langchain_experimental\",\n", + " ],\n", + " verbose=False,\n", + " upgrade=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Configuration file to manage API keys as environment variables\n", + "from dotenv import load_dotenv\n", + "\n", + "# Load API key information\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
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89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + ".. ... ... ... \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_experimental.tools import PythonAstREPLTool\n", + "\n", + "df = pd.read_csv(\"./data/titanic.csv\")\n", + "# Read data from the titanic.csv file and store it in a DataFrame.\n", + "tool = PythonAstREPLTool(locals={\"df\": df})\n", + "# Use PythonAstREPLTool to create an environment containing the local variable 'df'.\n", + "tool.invoke(\"df\")\n", + "# Calculate the mean value of the 'Fare' column in the 'df' DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hello = \"\"\"\n", + "print(\"Hello, world!\")\n", + "\n", + "def add(a, b):\n", + " return a + b\n", + "\n", + "print(add(30, 40))\n", + "\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"./data/titanic.csv\")\n", + "df.head()\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, world!\n", + "70\n" + ] + }, + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tool = PythonAstREPLTool(locals={\"df\": df})\n", + "# Use PythonAstREPLTool to create an environment containing the local variable 'df'.\n", + "tool.invoke(hello)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n", + "Using pandas to load the csv file into a DataFrame.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"data/titanic.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas DataFrame Agent\n", + "\n", + "The Pandas DataFrame Agent allows you to interact with tabular data in a conversational and dynamic way. Using LangChain’s integration with tools like `ChatOpenAI` and `create_pandas_dataframe_agent`, you can perform complex queries on your DataFrame with natural language instructions. Below, we create an agent capable of analyzing the Titanic dataset and answering questions like survival rates based on specific criteria or data summary statistics." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents.agent_types import AgentType\n", + "from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n", + "from langchain_openai import ChatOpenAI\n", + "from langchain.agents.agent_types import AgentType\n", + "from langchain.callbacks.base import BaseCallbackHandler\n", + "\n", + "class StreamCallback(BaseCallbackHandler):\n", + " def on_llm_new_token(self, token: str, **kwargs):\n", + " print(token, end=\"\", flush=True)\n", + "\n", + "# # Create an agent\n", + "agent = create_pandas_dataframe_agent(\n", + " ChatOpenAI(\n", + " temperature=0,\n", + " model_name=\"gpt-4o-mini\",\n", + " streaming=True,\n", + " callbacks=[StreamCallback()],\n", + " ), # Model definition\n", + " df, # DataFrame\n", + " verbose=True, # Print reasoning steps\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + " allow_dangerous_code=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': 'df.shape'}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m(891, 12)\u001b[0mThe dataframe has 891 rows and 12 columns.\u001b[32;1m\u001b[1;3mThe dataframe has 891 rows and 12 columns.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input': 'What is the number of rows and columns in the data?',\n", + " 'output': 'The dataframe has 891 rows and 12 columns.'}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query\n", + "agent.invoke({\"input\": \"What is the number of rows and columns in the data?\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_636684\\3271692855.py:2: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n", + " agent.run(\"What is the survival rate of male passengers? Provide it as a percentage.\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"male_survival_rate = df[df['Sex'] == 'male']['Survived'].mean() * 100\\nmale_survival_rate\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m18.890814558058924\u001b[0mThe survival rate of male passengers is approximately 18.89%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers is approximately 18.89%.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'The survival rate of male passengers is approximately 18.89%.'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query\n", + "agent.run(\"What is the survival rate of male passengers? Provide it as a percentage.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"# Filtering the dataframe for male passengers under the age of 15 in 1st or 2nd class\\nfiltered_df = df[(df['Sex'] == 'male') & (df['Age'] < 15) & (df['Pclass'].isin([1, 2]))]\\n\\n# Calculating the survival rate\\nsurvival_rate = filtered_df['Survived'].mean() * 100\\nsurvival_rate\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m100.0\u001b[0mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'The survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query\n", + "agent.run(\n", + " \"What is the survival rate of male passengers under the age of 15 who were in 1st or 2nd class? Provide it as a percentage.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"# Filter the dataframe for female passengers aged between 20 and 30 in 1st class\\nfiltered_df = df[(df['Sex'] == 'female') & (df['Age'] >= 20) & (df['Age'] <= 30) & (df['Pclass'] == 1)]\\n\\n# Calculate the survival rate\\nsurvival_rate = filtered_df['Survived'].mean() * 100\\nsurvival_rate\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m95.23809523809523\u001b[0mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.\u001b[32;1m\u001b[1;3mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'The survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query\n", + "agent.run(\n", + " \"What is the survival rate of female passengers aged between 20 and 30 who were in 1st class? Provide it as a percentage.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two or More DataFrames\n", + "\n", + "You can perform LLM-based queries based on two or more DataFrames. When entering two or more DataFrames, enclose them in `[]`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.25000S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.92500S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.05000S
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 0 S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 0 S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 0 S " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a sample DataFrame\n", + "df1 = df.copy()\n", + "df1 = df1.fillna(0)\n", + "df1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `python_repl_ast` with `{'query': \"import pandas as pd\\n\\n# Sample data for df1 and df2\\ndata1 = {\\n 'PassengerId': [1, 2, 3, 4, 5],\\n 'Survived': [0, 1, 1, 1, 0],\\n 'Pclass': [3, 1, 3, 1, 3],\\n 'Name': [\\n 'Braund, Mr. Owen Harris',\\n 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\\n 'Heikkinen, Miss. Laina',\\n 'Futrelle, Mrs. Jacques Heath (Lily May Peel)',\\n 'Allen, Mr. William Henry'\\n ],\\n 'Sex': ['male', 'female', 'female', 'female', 'male'],\\n 'Age': [22, 38, 26, 35, 35],\\n 'SibSp': [1, 1, 0, 1, 0],\\n 'Parch': [0, 0, 0, 0, 0],\\n 'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450'],\\n 'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05],\\n 'Cabin': [None, 'C85', None, 'C123', None],\\n 'Embarked': ['S', 'C', 'S', 'S', 'S']\\n}\\n\\ndata2 = {\\n 'PassengerId': [1, 2, 3, 4, 5],\\n 'Survived': [0, 1, 1, 1, 0],\\n 'Pclass': [3, 1, 3, 1, 3],\\n 'Name': [\\n 'Braund, Mr. Owen Harris',\\n 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\\n 'Heikkinen, Miss. Laina',\\n 'Futrelle, Mrs. Jacques Heath (Lily May Peel)',\\n 'Allen, Mr. William Henry'\\n ],\\n 'Sex': ['male', 'female', 'female', 'female', 'male'],\\n 'Age': [22, 38, 26, 35, 35],\\n 'SibSp': [1, 1, 0, 1, 0],\\n 'Parch': [0, 0, 0, 0, 0],\\n 'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450'],\\n 'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05],\\n 'Cabin': [0, 'C85', 0, 'C123', 0],\\n 'Embarked': ['S', 'C', 'S', 'S', 'S']\\n}\\n\\ndf1 = pd.DataFrame(data1)\\ndf2 = pd.DataFrame(data2)\\n\\n# Calculate average age for both dataframes\\navg_age_df1 = df1['Age'].mean()\\navg_age_df2 = df2['Age'].mean()\\n\\n# Calculate the difference in average age\\nage_difference = avg_age_df2 - avg_age_df1\\n\\n# Calculate the percentage difference\\npercentage_difference = (age_difference / avg_age_df1) * 100\\npercentage_difference\"}`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m0.0\u001b[0mThe difference in the average age between the two dataframes is 0.0%. This means that the average age in both dataframes is the same.\u001b[32;1m\u001b[1;3mThe difference in the average age between the two dataframes is 0.0%. This means that the average age in both dataframes is the same.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input': \"What is the difference in the average age from the 'Age' column? Calculate it as a percentage.\",\n", + " 'output': 'The difference in the average age between the two dataframes is 0.0%. This means that the average age in both dataframes is the same.'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an agent\n", + "agent = create_pandas_dataframe_agent(\n", + " ChatOpenAI(\n", + " temperature=0,\n", + " model_name=\"gpt-4o-mini\",\n", + " streaming=True,\n", + " callbacks=[StreamCallback()],\n", + " ),\n", + " [df, df1],\n", + " verbose=True,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + " allow_dangerous_code=True\n", + ")\n", + "\n", + "# Query\n", + "agent.invoke({\"input\": \"What is the difference in the average age from the 'Age' column? Calculate it as a percentage.\"})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.10.11" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/14-Chains/data/titanic.csv b/14-Chains/data/titanic.csv new file mode 100644 index 000000000..5cc466e97 --- /dev/null +++ b/14-Chains/data/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S +16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S +17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q +18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S +19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S +20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C +21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S +22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S +23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q +24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S +25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S +26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S +27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C +28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S +29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q +30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S +31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C +32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C +33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q +34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S +35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C +36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S +37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C +38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S +39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S +40,1,3,"Nicola-Yarred, Miss. 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Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C +877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S +878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S +879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S +880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C +881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S +882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S +883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S +884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S +885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S +886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q +887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S +888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S +889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q From d304d50f3335c1cadff3d7355dbf323057e1d8a6 Mon Sep 17 00:00:00 2001 From: solon Date: Wed, 15 Jan 2025 15:59:25 +0900 Subject: [PATCH 2/5] =?UTF-8?q?=EB=A7=88=ED=81=AC=EB=8B=A4=EC=9A=B4=20?= =?UTF-8?q?=EB=B6=80=EB=B6=84=20=EC=9D=BC=EB=B6=80=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 14-Chains/04-Structured-Data-Chat.ipynb | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/14-Chains/04-Structured-Data-Chat.ipynb b/14-Chains/04-Structured-Data-Chat.ipynb index 7f03dbb3b..d4bc3718a 100644 --- a/14-Chains/04-Structured-Data-Chat.ipynb +++ b/14-Chains/04-Structured-Data-Chat.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# MultiQueryRetriever\n", + "# StructuredDataChat\n", "\n", "- Author: [hong-seongmin](https://github.com/hong-seongmin)\n", "- Design: \n", @@ -30,15 +30,9 @@ "- **Advanced Query Scenarios** \n", " The notebook demonstrates the handling of more complex queries, such as analyzing survival rates for subsets of passengers (e.g., male passengers, children in specific classes). This is achieved by dynamically constructing and executing queries through the LLM-driven agent.\n", "\n", - "- **Working with Multiple DataFrames** \n", - " The notebook extends its functionality to support queries involving multiple DataFrames. This capability is demonstrated with a cloned and modified version of the original dataset (`df1`), enabling comparative analysis between datasets.\n", - "\n", "- **Streamlined Query Execution** \n", " By integrating a callback system, the notebook ensures seamless real-time interaction with the LLM during query execution. This feature enhances the user experience by providing immediate feedback and results.\n", "\n", - "- **Applications** \n", - " This notebook serves as an example of how to bridge traditional data analysis and advanced AI-driven techniques. It simplifies complex querying, offers deeper insights into structured data, and demonstrates the potential of combining machine learning with interactive data exploration.\n", - "\n", "\n", "### Table of Contents\n", "\n", @@ -606,7 +600,7 @@ "source": [ "## Data Loading\n", "\n", - "Using pandas to load the csv file into a DataFrame.\n" + "The `pd.read_csv` function is used to read the CSV file and store its contents in a structured tabular format. By calling `df.head()`, we can preview the first few rows of the dataset to understand its structure and ensure the data has been loaded correctly.\n" ] }, { From 7756072bdf3e6baca5cfc80ee76c4e6ad30f95b0 Mon Sep 17 00:00:00 2001 From: solon Date: Wed, 15 Jan 2025 19:38:08 +0900 Subject: [PATCH 3/5] =?UTF-8?q?=ED=91=9C=EC=A4=80=20=EC=A4=80=EC=88=98?= =?UTF-8?q?=EB=A5=BC=20=EC=9C=84=ED=95=9C=20=EC=9D=BC=EB=B6=80=20=EC=88=98?= =?UTF-8?q?=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 14-Chains/04-Structured-Data-Chat.ipynb | 39 ++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/14-Chains/04-Structured-Data-Chat.ipynb b/14-Chains/04-Structured-Data-Chat.ipynb index d4bc3718a..f0d238e8a 100644 --- a/14-Chains/04-Structured-Data-Chat.ipynb +++ b/14-Chains/04-Structured-Data-Chat.ipynb @@ -11,7 +11,7 @@ "- Peer Review: \n", "- This is a part of [LangChain OpenTutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n", "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-4/sub-graph.ipynb) [![Open in LangChain Academy](https://cdn.prod.website-files.com/65b8cd72835ceeacd4449a53/66e9eba12c7b7688aa3dbb5e_LCA-badge-green.svg)](https://academy.langchain.com/courses/take/intro-to-langgraph/lessons/58239937-lesson-2-sub-graphs)\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/10-Retriever/06-MultiQueryRetriever.ipynb) [![Open in GitHub](https://img.shields.io/badge/Open%20in%20GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/10-Retriever/06-MultiQueryRetriever.ipynb)\n", "\n", "\n", "## Overview\n", @@ -115,6 +115,43 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Environment variables have been set successfully.\n" + ] + } + ], + "source": [ + "# Set environment variables\n", + "from langchain_opentutorial import set_env\n", + "\n", + "set_env(\n", + " {\n", + " \"LANGCHAIN_TRACING_V2\": \"true\",\n", + " \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n", + " \"LANGCHAIN_PROJECT\": \"04-Structured-Data-Chat\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, environment variables can also be set using a `.env` file.\n", + "\n", + "**[Note]**\n", + "\n", + "- This is not necessary if you've already set the environment variables in the previous step." + ] + }, { "cell_type": "code", "execution_count": 3, From d4d19e9266f648ea6b94cc8974c24ae8e8c5b40c Mon Sep 17 00:00:00 2001 From: solon Date: Thu, 16 Jan 2025 00:41:30 +0900 Subject: [PATCH 4/5] =?UTF-8?q?=EB=A7=88=ED=81=AC=EB=8B=A4=EC=9A=B4=20?= =?UTF-8?q?=EC=9D=BC=EB=B6=80=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 14-Chains/04-Structured-Data-Chat.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/14-Chains/04-Structured-Data-Chat.ipynb b/14-Chains/04-Structured-Data-Chat.ipynb index f0d238e8a..9250033aa 100644 --- a/14-Chains/04-Structured-Data-Chat.ipynb +++ b/14-Chains/04-Structured-Data-Chat.ipynb @@ -44,7 +44,7 @@ "\n", "### References\n", "\n", - "- [LangChain Documentation: How to use the MultiQueryRetriever](https://python.langchain.com/docs/how_to/MultiQueryRetriever/)\n", + "- [LangChain Documentation: How to do question answering over CSVs](https://python.langchain.com/docs/how_to/sql_csv)\n", "\n", "---" ] From fc3a92abd9dff19d62bb1c5287f265b412461ed6 Mon Sep 17 00:00:00 2001 From: solon Date: Thu, 16 Jan 2025 11:57:05 +0900 Subject: [PATCH 5/5] agent.run => agent.invoke --- 14-Chains/04-Structured-Data-Chat.ipynb | 55 ++++++++++--------------- 1 file changed, 22 insertions(+), 33 deletions(-) diff --git a/14-Chains/04-Structured-Data-Chat.ipynb b/14-Chains/04-Structured-Data-Chat.ipynb index 9250033aa..121354722 100644 --- a/14-Chains/04-Structured-Data-Chat.ipynb +++ b/14-Chains/04-Structured-Data-Chat.ipynb @@ -875,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -884,26 +884,12 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_636684\\3271692855.py:2: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n", - " agent.run(\"What is the survival rate of male passengers? Provide it as a percentage.\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `python_repl_ast` with `{'query': \"male_survival_rate = df[df['Sex'] == 'male']['Survived'].mean() * 100\\nmale_survival_rate\"}`\n", + "Invoking: `python_repl_ast` with `{'query': \"import pandas as pd\\n\\n# Assuming df1 is the dataframe containing the Titanic data\\n# Calculate the survival rate for male passengers\\nmale_passengers = df1[df1['Sex'] == 'male']\\nsurvived_males = male_passengers['Survived'].sum()\\ntotal_males = male_passengers.shape[0]\\nsurvival_rate_male = (survived_males / total_males) * 100\\nsurvival_rate_male\"}`\n", "\n", "\n", - "\u001b[0m\u001b[36;1m\u001b[1;3m18.890814558058924\u001b[0mThe survival rate of male passengers is approximately 18.89%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers is approximately 18.89%.\u001b[0m\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m0.0\u001b[0mThe survival rate of male passengers is 0.0%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers is 0.0%.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] @@ -911,22 +897,23 @@ { "data": { "text/plain": [ - "'The survival rate of male passengers is approximately 18.89%.'" + "{'input': 'What is the survival rate of male passengers? Provide it as a percentage.',\n", + " 'output': 'The survival rate of male passengers is 0.0%.'}" ] }, - "execution_count": 10, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Query\n", - "agent.run(\"What is the survival rate of male passengers? Provide it as a percentage.\")" + "agent.invoke(\"What is the survival rate of male passengers? Provide it as a percentage.\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -937,10 +924,10 @@ "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `python_repl_ast` with `{'query': \"# Filtering the dataframe for male passengers under the age of 15 in 1st or 2nd class\\nfiltered_df = df[(df['Sex'] == 'male') & (df['Age'] < 15) & (df['Pclass'].isin([1, 2]))]\\n\\n# Calculating the survival rate\\nsurvival_rate = filtered_df['Survived'].mean() * 100\\nsurvival_rate\"}`\n", + "Invoking: `python_repl_ast` with `{'query': \"import pandas as pd\\n\\n# Sample data for df1 and df2\\ndata1 = {\\n 'PassengerId': [1, 2, 3, 4, 5],\\n 'Survived': [0, 1, 1, 1, 0],\\n 'Pclass': [3, 1, 3, 1, 3],\\n 'Name': ['Braund, Mr. Owen Harris', 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'Heikkinen, Miss. Laina', 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'Allen, Mr. William Henry'],\\n 'Sex': ['male', 'female', 'female', 'female', 'male'],\\n 'Age': [22, 38, 26, 35, 35],\\n 'SibSp': [1, 1, 0, 1, 0],\\n 'Parch': [0, 0, 0, 0, 0],\\n 'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450'],\\n 'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05],\\n 'Cabin': [None, 'C85', None, 'C123', None],\\n 'Embarked': ['S', 'C', 'S', 'S', 'S']\\n}\\n\\ndata2 = {\\n 'PassengerId': [1, 2, 3, 4, 5],\\n 'Survived': [0, 1, 1, 1, 0],\\n 'Pclass': [3, 1, 3, 1, 3],\\n 'Name': ['Braund, Mr. Owen Harris', 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'Heikkinen, Miss. Laina', 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'Allen, Mr. William Henry'],\\n 'Sex': ['male', 'female', 'female', 'female', 'male'],\\n 'Age': [22, 38, 26, 35, 35],\\n 'SibSp': [1, 1, 0, 1, 0],\\n 'Parch': [0, 0, 0, 0, 0],\\n 'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450'],\\n 'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05],\\n 'Cabin': [0, 'C85', 0, 'C123', 0],\\n 'Embarked': ['S', 'C', 'S', 'S', 'S']\\n}\\n\\n# Creating DataFrames\\n# df1 has NaN in Cabin, df2 has 0 in Cabin\\ndf1 = pd.DataFrame(data1)\\ndf2 = pd.DataFrame(data2)\\n\\n# Combine the two DataFrames\\ncombined_df = pd.concat([df1, df2])\\n\\n# Filter for male passengers under 15 in 1st or 2nd class\\nfiltered_df = combined_df[(combined_df['Sex'] == 'male') & (combined_df['Age'] < 15) & (combined_df['Pclass'].isin([1, 2]))]\\n\\n# Calculate survival rate\\nif not filtered_df.empty:\\n survival_rate = filtered_df['Survived'].mean() * 100\\nelse:\\n survival_rate = 0.0\\n\\nsurvival_rate\"}`\n", "\n", "\n", - "\u001b[0m\u001b[36;1m\u001b[1;3m100.0\u001b[0mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.\u001b[0m\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m0.0\u001b[0mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 0.0%.\u001b[32;1m\u001b[1;3mThe survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 0.0%.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] @@ -948,24 +935,25 @@ { "data": { "text/plain": [ - "'The survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 100%.'" + "{'input': 'What is the survival rate of male passengers under the age of 15 who were in 1st or 2nd class? Provide it as a percentage.',\n", + " 'output': 'The survival rate of male passengers under the age of 15 who were in 1st or 2nd class is 0.0%.'}" ] }, - "execution_count": 11, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Query\n", - "agent.run(\n", + "agent.invoke(\n", " \"What is the survival rate of male passengers under the age of 15 who were in 1st or 2nd class? Provide it as a percentage.\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -976,10 +964,10 @@ "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `python_repl_ast` with `{'query': \"# Filter the dataframe for female passengers aged between 20 and 30 in 1st class\\nfiltered_df = df[(df['Sex'] == 'female') & (df['Age'] >= 20) & (df['Age'] <= 30) & (df['Pclass'] == 1)]\\n\\n# Calculate the survival rate\\nsurvival_rate = filtered_df['Survived'].mean() * 100\\nsurvival_rate\"}`\n", + "Invoking: `python_repl_ast` with `{'query': \"import pandas as pd\\n\\n# Sample data for df1 and df2\\n# df1 = pd.DataFrame(...) # Assuming df1 is already defined\\n# df2 = pd.DataFrame(...) # Assuming df2 is already defined\\n\\n# Combine the two dataframes for analysis\\ncombined_df = pd.concat([df1, df2])\\n\\n# Filter for female passengers aged between 20 and 30 in 1st class\\nfiltered_df = combined_df[(combined_df['Sex'] == 'female') & \\n (combined_df['Age'] >= 20) & \\n (combined_df['Age'] <= 30) & \\n (combined_df['Pclass'] == 1)]\\n\\n# Calculate survival rate\\nif len(filtered_df) > 0:\\n survival_rate = filtered_df['Survived'].mean() * 100\\nelse:\\n survival_rate = 0\\n\\nsurvival_rate\"}`\n", "\n", "\n", - "\u001b[0m\u001b[36;1m\u001b[1;3m95.23809523809523\u001b[0mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.\u001b[32;1m\u001b[1;3mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.\u001b[0m\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m0\u001b[0mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is 0%. This means that none of the female passengers in that age group and class survived.\u001b[32;1m\u001b[1;3mThe survival rate of female passengers aged between 20 and 30 who were in 1st class is 0%. This means that none of the female passengers in that age group and class survived.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] @@ -987,17 +975,18 @@ { "data": { "text/plain": [ - "'The survival rate of female passengers aged between 20 and 30 who were in 1st class is approximately 95.24%.'" + "{'input': 'What is the survival rate of female passengers aged between 20 and 30 who were in 1st class? Provide it as a percentage.',\n", + " 'output': 'The survival rate of female passengers aged between 20 and 30 who were in 1st class is 0%. This means that none of the female passengers in that age group and class survived.'}" ] }, - "execution_count": 12, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Query\n", - "agent.run(\n", + "agent.invoke(\n", " \"What is the survival rate of female passengers aged between 20 and 30 who were in 1st class? Provide it as a percentage.\"\n", ")" ]