mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-07-21 01:45:27 -04:00
Ankush Gola
666 lines
65 KiB
Plaintext
666 lines
65 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# An agent for interacting with a SQL database\n",
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"\n",
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"In this tutorial, we will walk through how to build an agent that can answer questions about a SQL database. \n",
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"\n",
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"At a high level, the agent will:\n",
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"1. Fetch the available tables from the database\n",
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"2. Decide which tables are relevant to the question\n",
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"3. Fetch the DDL for the relevant tables\n",
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"4. Generate a query based on the question and information from the DDL\n",
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"5. Double-check the query for common mistakes using an LLM\n",
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"6. Execute the query and return the results\n",
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"7. Correct mistakes surfaced by the database engine until the query is successful\n",
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"8. Formulate a response based on the results\n",
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"\n",
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"The end-to-end workflow will look something like below:"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "9f853e403eabd4f8"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Set up environment\n",
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"\n",
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"We'll set up our environment variables for OpenAI, and optionally, to enable tracing with [LangSmith](https://smith.langchain.com)."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "b5a87813ffe7e4d2"
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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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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
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"os.environ[\"LANGSMITH_API_KEY\"] = \"lsv2_pt_...\"\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\""
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:08.635369Z",
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"start_time": "2024-06-12T20:18:08.630616Z"
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}
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},
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"id": "6c05a600f1afb5b6"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Configure the database\n",
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"\n",
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"We will be creating a SQLite database for this tutorial. SQLite is a lightweight database that is easy to set up and use. We will be loading the `chinook` database, which is a sample database that represents a digital media store.\n",
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"Find more information about the database [here](https://www.sqlitetutorial.net/sqlite-sample-database/).\n",
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"\n",
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"For convenience, we have hosted the database (`Chinook.db`) on a public GCS bucket."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "877d8c85825089d8"
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [],
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"source": [
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"# import requests\n",
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"# \n",
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"# url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n",
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"# \n",
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"# response = requests.get(url)\n",
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"# \n",
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"# if response.status_code == 200:\n",
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"# # Open a local file in binary write mode\n",
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"# with open(\"Chinook.db\", \"wb\") as file:\n",
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"# # Write the content of the response (the file) to the local file\n",
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"# file.write(response.content)\n",
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"# print(\"File downloaded and saved as Chinook.db\")\n",
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"# else:\n",
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"# print(f\"Failed to download the file. Status code: {response.status_code}\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:10.130822Z",
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"start_time": "2024-06-12T20:18:10.123796Z"
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}
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},
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"id": "64b0bf1b14c2e902"
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},
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{
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"cell_type": "markdown",
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"source": [
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"We will use a handy SQL database wrapper available in the `langchain_community` package to interact with the database. The wrapper provides a simple interface to execute SQL queries and fetch results. We will also use the `langchain_openai` package to interact with the OpenAI API for language models later in the tutorial."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "61c8304aa5ceb6a5"
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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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"outputs": [],
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"source": [
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"%%capture --no-stderr --no-display\n",
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"!pip install langchain_community langchain_openai"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:12.265718Z",
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"start_time": "2024-06-12T20:18:11.265328Z"
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}
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},
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"id": "a60191bd3489f278"
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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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"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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"sqlite\n",
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"['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n"
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]
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},
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{
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"data": {
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"text/plain": "\"[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]\""
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},
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"execution_count": 5,
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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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"from langchain_community.utilities import SQLDatabase\n",
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"\n",
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"db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
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"print(db.dialect)\n",
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"print(db.get_usable_table_names())\n",
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"db.run(\"SELECT * FROM Artist LIMIT 10;\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:12.902435Z",
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"start_time": "2024-06-12T20:18:12.796315Z"
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}
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},
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"id": "1f1e1f4f86ed54"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Utility functions\n",
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"\n",
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"We will define a few utility functions to help us with the agent implementation. Specifically, we will wrap a `ToolNode` with a fallback to handle errors and surface them to the agent."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "6959e93141d8099c"
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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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"outputs": [],
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"source": [
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"from typing import Any, Dict, List\n",
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"\n",
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"from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks\n",
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"from langchain_core.messages import ToolMessage, AIMessage\n",
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"\n",
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"from langgraph.prebuilt import ToolNode\n",
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"\n",
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"def create_tool_node_with_fallback(tools: list) -> RunnableWithFallbacks[Any, dict]:\n",
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" \"\"\"\n",
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" Create a ToolNode with a fallback to handle errors and surface them to the agent.\n",
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" \"\"\"\n",
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" return ToolNode(tools).with_fallbacks(\n",
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" [RunnableLambda(handle_tool_error)], exception_key=\"error\"\n",
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" )\n",
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"\n",
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"def handle_tool_error(state) -> dict:\n",
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" error = state.get(\"error\")\n",
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" tool_calls = state[\"messages\"][-1].tool_calls\n",
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" return {\n",
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" \"messages\": [\n",
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" ToolMessage(\n",
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" content=f\"Error: {repr(error)}\\n please fix your mistakes.\",\n",
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" tool_call_id=tc[\"id\"],\n",
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" )\n",
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" for tc in tool_calls\n",
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" ]\n",
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" }"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:14.197218Z",
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"start_time": "2024-06-12T20:18:14.194149Z"
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}
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},
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"id": "deae8460e4cf72b1"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Define tools for the agent\n",
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"\n",
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"We will define a few tools that the agent will use to interact with the database.\n",
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"\n",
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"1. `list_tables_tool`: Fetch the available tables from the database\n",
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"2. `get_schema_tool`: Fetch the DDL for a table\n",
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"3. `db_query_tool`: Execute the query and fetch the results OR return an error message if the query fails\n",
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"\n",
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"For the first two tools, we will grab them from the `SQLDatabaseToolkit`, also available in the `langchain_community` package."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "d0196604f8cbb07b"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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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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"Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n",
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"\n",
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"CREATE TABLE \"Artist\" (\n",
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"\t\"ArtistId\" INTEGER NOT NULL, \n",
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"\t\"Name\" NVARCHAR(120), \n",
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"\tPRIMARY KEY (\"ArtistId\")\n",
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")\n",
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"\n",
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"/*\n",
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"3 rows from Artist table:\n",
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"ArtistId\tName\n",
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"1\tAC/DC\n",
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"2\tAccept\n",
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"3\tAerosmith\n",
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"*/\n"
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]
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}
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],
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"source": [
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"from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"toolkit = SQLDatabaseToolkit(db=db, llm=ChatOpenAI(model=\"gpt-4o\"))\n",
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"tools = toolkit.get_tools()\n",
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"\n",
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"list_tables_tool = next(tool for tool in tools if tool.name == \"sql_db_list_tables\")\n",
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"get_schema_tool = next(tool for tool in tools if tool.name == \"sql_db_schema\")\n",
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"\n",
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"print(list_tables_tool.invoke(\"\"))\n",
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"\n",
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"print(get_schema_tool.invoke(\"Artist\"))"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:15.838940Z",
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"start_time": "2024-06-12T20:18:15.734199Z"
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}
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},
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"id": "452d049a3d2a4406"
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},
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{
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"cell_type": "markdown",
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"source": [
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"The third will be defined manually. For the `db_query_tool`, we will execute the query against the database and return the results."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "c16359edada327fa"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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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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"[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/ankushgola/Code/langgraph/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseTool.__call__` was deprecated in langchain-core 0.1.47 and will be removed in 0.3.0. Use invoke instead.\n",
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" warn_deprecated(\n"
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]
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}
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],
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"source": [
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"from langchain.agents import tool\n",
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"\n",
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"@tool\n",
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"def db_query_tool(query: str) -> str:\n",
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" \"\"\"\n",
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" Execute a SQL query against the database and get back the result.\n",
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" If the query is not correct, an error message will be returned.\n",
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" If an error is returned, rewrite the query, check the query, and try again.\n",
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" \"\"\"\n",
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" result = db.run_no_throw(query)\n",
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" if not result:\n",
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" return \"Error: Query failed. Please rewrite your query and try again.\"\n",
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" return result\n",
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"\n",
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"print(db_query_tool(\"SELECT * FROM Artist LIMIT 10;\"))"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:17.745278Z",
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"start_time": "2024-06-12T20:18:17.338936Z"
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}
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},
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"id": "f7eb708ecb4c7cfc"
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},
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{
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"cell_type": "markdown",
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"source": [
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"While not strictly a tool, we will prompt an LLM to check for common mistakes in the query and later add this as a node in the workflow."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "f1d66db8b8621639"
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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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"outputs": [
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{
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"data": {
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"text/plain": "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_XnWquGNTWbJ2uhXO9hTLq2Zg', 'function': {'arguments': '{\\n \"query\": \"SELECT * FROM Artist LIMIT 10;\"\\n}', 'name': 'db_query_tool'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 222, 'total_tokens': 242}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'stop', 'logprobs': None}, id='run-aed5d66c-73fe-4fe1-bff5-c0b28e96f851-0', tool_calls=[{'name': 'db_query_tool', 'args': {'query': 'SELECT * FROM Artist LIMIT 10;'}, 'id': 'call_XnWquGNTWbJ2uhXO9hTLq2Zg'}], usage_metadata={'input_tokens': 222, 'output_tokens': 20, 'total_tokens': 242})"
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},
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"execution_count": 9,
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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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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"query_check_system = \"\"\"You are a SQL expert with a strong attention to detail.\n",
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"Double check the SQLite query for common mistakes, including:\n",
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"- Using NOT IN with NULL values\n",
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"- Using UNION when UNION ALL should have been used\n",
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"- Using BETWEEN for exclusive ranges\n",
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"- Data type mismatch in predicates\n",
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"- Properly quoting identifiers\n",
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"- Using the correct number of arguments for functions\n",
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"- Casting to the correct data type\n",
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"- Using the proper columns for joins\n",
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"\n",
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"If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n",
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"\n",
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"You will call the appropriate tool to execute the query after running this check.\"\"\"\n",
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"\n",
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"query_check_prompt = ChatPromptTemplate.from_messages([(\"system\", query_check_system),(\"placeholder\", \"{messages}\")])\n",
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"query_check = query_check_prompt | ChatOpenAI(model=\"gpt-4o\", temperature=0).bind_tools([db_query_tool], tool_choice=\"required\")\n",
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"\n",
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"query_check.invoke({\"messages\": [(\"user\", \"SELET * FROM Artist LIMIT 10;\")]})"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-06-12T20:18:19.658322Z",
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"start_time": "2024-06-12T20:18:18.756256Z"
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}
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|
},
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"id": "293017e8f05ac2b3"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Define the workflow\n",
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"\n",
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"We will then define the workflow for the agent. The agent will first force-call the `list_tables_tool` to fetch the available tables from the database, then follow the steps mentioned at the beginning of the tutorial."
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],
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"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "66f88452151e8188"
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"outputs": [],
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"source": [
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"from typing import Annotated, Literal\n",
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"from typing_extensions import TypedDict\n",
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"\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"from langgraph.graph import END, StateGraph\n",
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"from langgraph.graph.message import AnyMessage, add_messages\n",
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"from langgraph.prebuilt.tool_node import ToolNode\n",
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"from langchain_core.messages import AIMessage\n",
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"# Define the state for the agent\n",
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"class State(TypedDict):\n",
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" messages: Annotated[list[AnyMessage], add_messages]\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(State)\n",
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"\n",
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"# Add a node for the first tool call\n",
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"def first_tool_call(state: State) -> dict[str, list[AIMessage]]:\n",
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" return {\n",
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" \"messages\": [\n",
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" AIMessage(\n",
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" content=\"\",\n",
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" tool_calls=[\n",
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" {\n",
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" \"name\": \"sql_db_list_tables\",\n",
|
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" \"args\": {\n",
|
|
" },\n",
|
|
" \"id\": \"tool_abcd123\",\n",
|
|
" }\n",
|
|
" ],\n",
|
|
" )\n",
|
|
" ]\n",
|
|
" }\n",
|
|
"\n",
|
|
"def model_check_query(state: State) -> dict[str, list[AIMessage]]:\n",
|
|
" \"\"\"\n",
|
|
" Use this tool to double-check if your query is correct before executing it.\n",
|
|
" \"\"\"\n",
|
|
" return {\n",
|
|
" \"messages\": [\n",
|
|
" query_check.invoke({\"messages\": [state[\"messages\"][-1]]})\n",
|
|
" ]\n",
|
|
" }\n",
|
|
"\n",
|
|
"workflow.add_node(\"first_tool_call\", first_tool_call)\n",
|
|
"\n",
|
|
"# Add nodes for the first two tools\n",
|
|
"workflow.add_node(\"list_tables_tool\", create_tool_node_with_fallback([list_tables_tool]))\n",
|
|
"workflow.add_node(\"get_schema_tool\", create_tool_node_with_fallback([get_schema_tool]))\n",
|
|
"\n",
|
|
"# Add a node for a model to choose the relevant tables based on the question and available tables\n",
|
|
"model_get_schema = ChatOpenAI(model=\"gpt-4o\", temperature=0).bind_tools([get_schema_tool])\n",
|
|
"workflow.add_node(\"model_get_schema\", lambda state: {\"messages\": [model_get_schema.invoke(state[\"messages\"])],})\n",
|
|
"\n",
|
|
"# Describe a tool to represent the end state\n",
|
|
"class SubmitFinalAnswer(BaseModel):\n",
|
|
" \"\"\"Submit the final answer to the user based on the query results.\"\"\"\n",
|
|
" final_answer: str = Field(..., description=\"The final answer to the user\")\n",
|
|
"\n",
|
|
"# Add a node for a model to generate a query based on the question and schema\n",
|
|
"query_gen_system = \"\"\"You are a SQL expert with a strong attention to detail.\n",
|
|
"\n",
|
|
"Given an input question, output a syntactically correct SQLite query to run, then look at the results of the query and return the answer.\n",
|
|
"\n",
|
|
"DO NOT call any tool besides SubmitFinalAnswer to submit the final answer.\n",
|
|
"\n",
|
|
"When generating the query:\n",
|
|
"\n",
|
|
"Output the SQL query that answers the input question without a tool call.\n",
|
|
"\n",
|
|
"Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.\n",
|
|
"You can order the results by a relevant column to return the most interesting examples in the database.\n",
|
|
"Never query for all the columns from a specific table, only ask for the relevant columns given the question.\n",
|
|
"\n",
|
|
"If you get an error while executing a query, rewrite the query and try again.\n",
|
|
"\n",
|
|
"If you get an empty result set, you should try to rewrite the query to get a non-empty result set. \n",
|
|
"NEVER make stuff up if you don't have enough information to answer the query... just say you don't have enough information.\n",
|
|
"\n",
|
|
"If you have enough information to answer the input question, simply invoke the appropriate tool to submit the final answer to the user.\n",
|
|
"\n",
|
|
"DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\"\"\"\n",
|
|
"query_gen_prompt = ChatPromptTemplate.from_messages([(\"system\", query_gen_system),(\"placeholder\", \"{messages}\")])\n",
|
|
"query_gen = query_gen_prompt | ChatOpenAI(model=\"gpt-4o\", temperature=0).bind_tools([SubmitFinalAnswer])\n",
|
|
"def query_gen_node(state: State):\n",
|
|
" message = query_gen.invoke(state)\n",
|
|
" \n",
|
|
" # Sometimes, the LLM will hallucinate and call the wrong tool. We need to catch this and return an error message.\n",
|
|
" tool_messages = []\n",
|
|
" if message.tool_calls:\n",
|
|
" for tc in message.tool_calls:\n",
|
|
" if tc[\"name\"] != \"SubmitFinalAnswer\":\n",
|
|
" tool_messages.append(\n",
|
|
" ToolMessage(\n",
|
|
" content=f\"Error: The wrong tool was called: {tc['name']}. Please fix your mistakes. Remember to only call SubmitFinalAnswer to submit the final answer. Generated queries should be outputted WITHOUT a tool call.\",\n",
|
|
" tool_call_id=tc[\"id\"],\n",
|
|
" )\n",
|
|
" )\n",
|
|
" else:\n",
|
|
" tool_messages = []\n",
|
|
" return {\"messages\": [message] + tool_messages}\n",
|
|
"\n",
|
|
"workflow.add_node(\"query_gen\", query_gen_node)\n",
|
|
"\n",
|
|
"# Add a node for the model to check the query before executing it\n",
|
|
"workflow.add_node(\"correct_query\", model_check_query)\n",
|
|
"\n",
|
|
"# Add node for executing the query\n",
|
|
"workflow.add_node(\"execute_query\", create_tool_node_with_fallback([db_query_tool]))\n",
|
|
"\n",
|
|
"# Define a conditional edge to decide whether to continue or end the workflow\n",
|
|
"def should_continue(state: State) -> Literal[END, \"correct_query\", \"query_gen\"]:\n",
|
|
" messages = state[\"messages\"]\n",
|
|
" last_message = messages[-1]\n",
|
|
" # If there is a tool call, then we finish\n",
|
|
" if getattr(last_message, \"tool_calls\", None):\n",
|
|
" return END\n",
|
|
" if last_message.content.startswith(\"Error:\"):\n",
|
|
" return \"query_gen\"\n",
|
|
" else:\n",
|
|
" return \"correct_query\"\n",
|
|
"\n",
|
|
"# Specify the edges between the nodes\n",
|
|
"workflow.set_entry_point(\"first_tool_call\")\n",
|
|
"workflow.add_edge(\"first_tool_call\", \"list_tables_tool\")\n",
|
|
"workflow.add_edge(\"list_tables_tool\", \"model_get_schema\")\n",
|
|
"workflow.add_edge(\"model_get_schema\", \"get_schema_tool\")\n",
|
|
"workflow.add_edge(\"get_schema_tool\", \"query_gen\")\n",
|
|
"workflow.add_conditional_edges(\n",
|
|
" \"query_gen\",\n",
|
|
" should_continue,\n",
|
|
")\n",
|
|
"workflow.add_edge(\"correct_query\", \"execute_query\")\n",
|
|
"workflow.add_edge(\"execute_query\", \"query_gen\")\n",
|
|
"\n",
|
|
"# Compile the workflow into a runnable\n",
|
|
"app = workflow.compile()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-06-12T20:21:09.799829Z",
|
|
"start_time": "2024-06-12T20:21:09.765928Z"
|
|
}
|
|
},
|
|
"id": "90d04ceea7b6b010"
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Visualize the graph"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "6c344ae086ba8d22"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
|
|
"text/plain": "<IPython.core.display.Image object>"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain_core.runnables.graph import MermaidDrawMethod\n",
|
|
"from IPython.display import display, Image\n",
|
|
"\n",
|
|
"display(\n",
|
|
" Image(\n",
|
|
" app.get_graph().draw_mermaid_png(\n",
|
|
" draw_method=MermaidDrawMethod.API,\n",
|
|
" )\n",
|
|
" )\n",
|
|
")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-06-12T20:21:11.813905Z",
|
|
"start_time": "2024-06-12T20:21:11.712945Z"
|
|
}
|
|
},
|
|
"id": "4f200d1813897000"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'first_tool_call': {'messages': [AIMessage(content='', tool_calls=[{'name': 'sql_db_list_tables', 'args': {}, 'id': 'tool_abcd123'}])]}}\n",
|
|
"{'list_tables_tool': {'messages': [ToolMessage(content='Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track', name='sql_db_list_tables', tool_call_id='tool_abcd123')]}}\n",
|
|
"{'model_get_schema': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_zwAtGmtcszZIu0pMOWjd6kaN', 'function': {'arguments': '{\"table_names\":\"Employee, Invoice\"}', 'name': 'sql_db_schema'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 177, 'total_tokens': 195}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-488a21dc-549b-4cde-b5e6-f261bfd18e61-0', tool_calls=[{'name': 'sql_db_schema', 'args': {'table_names': 'Employee, Invoice'}, 'id': 'call_zwAtGmtcszZIu0pMOWjd6kaN'}], usage_metadata={'input_tokens': 177, 'output_tokens': 18, 'total_tokens': 195})]}}\n",
|
|
"{'get_schema_tool': {'messages': [ToolMessage(content='\\nCREATE TABLE \"Employee\" (\\n\\t\"EmployeeId\" INTEGER NOT NULL, \\n\\t\"LastName\" NVARCHAR(20) NOT NULL, \\n\\t\"FirstName\" NVARCHAR(20) NOT NULL, \\n\\t\"Title\" NVARCHAR(30), \\n\\t\"ReportsTo\" INTEGER, \\n\\t\"BirthDate\" DATETIME, \\n\\t\"HireDate\" DATETIME, \\n\\t\"Address\" NVARCHAR(70), \\n\\t\"City\" NVARCHAR(40), \\n\\t\"State\" NVARCHAR(40), \\n\\t\"Country\" NVARCHAR(40), \\n\\t\"PostalCode\" NVARCHAR(10), \\n\\t\"Phone\" NVARCHAR(24), \\n\\t\"Fax\" NVARCHAR(24), \\n\\t\"Email\" NVARCHAR(60), \\n\\tPRIMARY KEY (\"EmployeeId\"), \\n\\tFOREIGN KEY(\"ReportsTo\") REFERENCES \"Employee\" (\"EmployeeId\")\\n)\\n\\n/*\\n3 rows from Employee table:\\nEmployeeId\\tLastName\\tFirstName\\tTitle\\tReportsTo\\tBirthDate\\tHireDate\\tAddress\\tCity\\tState\\tCountry\\tPostalCode\\tPhone\\tFax\\tEmail\\n1\\tAdams\\tAndrew\\tGeneral Manager\\tNone\\t1962-02-18 00:00:00\\t2002-08-14 00:00:00\\t11120 Jasper Ave NW\\tEdmonton\\tAB\\tCanada\\tT5K 2N1\\t+1 (780) 428-9482\\t+1 (780) 428-3457\\tandrew@chinookcorp.com\\n2\\tEdwards\\tNancy\\tSales Manager\\t1\\t1958-12-08 00:00:00\\t2002-05-01 00:00:00\\t825 8 Ave SW\\tCalgary\\tAB\\tCanada\\tT2P 2T3\\t+1 (403) 262-3443\\t+1 (403) 262-3322\\tnancy@chinookcorp.com\\n3\\tPeacock\\tJane\\tSales Support Agent\\t2\\t1973-08-29 00:00:00\\t2002-04-01 00:00:00\\t1111 6 Ave SW\\tCalgary\\tAB\\tCanada\\tT2P 5M5\\t+1 (403) 262-3443\\t+1 (403) 262-6712\\tjane@chinookcorp.com\\n*/\\n\\n\\nCREATE TABLE \"Invoice\" (\\n\\t\"InvoiceId\" INTEGER NOT NULL, \\n\\t\"CustomerId\" INTEGER NOT NULL, \\n\\t\"InvoiceDate\" DATETIME NOT NULL, \\n\\t\"BillingAddress\" NVARCHAR(70), \\n\\t\"BillingCity\" NVARCHAR(40), \\n\\t\"BillingState\" NVARCHAR(40), \\n\\t\"BillingCountry\" NVARCHAR(40), \\n\\t\"BillingPostalCode\" NVARCHAR(10), \\n\\t\"Total\" NUMERIC(10, 2) NOT NULL, \\n\\tPRIMARY KEY (\"InvoiceId\"), \\n\\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\\n)\\n\\n/*\\n3 rows from Invoice table:\\nInvoiceId\\tCustomerId\\tInvoiceDate\\tBillingAddress\\tBillingCity\\tBillingState\\tBillingCountry\\tBillingPostalCode\\tTotal\\n1\\t2\\t2009-01-01 00:00:00\\tTheodor-Heuss-Straße 34\\tStuttgart\\tNone\\tGermany\\t70174\\t1.98\\n2\\t4\\t2009-01-02 00:00:00\\tUllevålsveien 14\\tOslo\\tNone\\tNorway\\t0171\\t3.96\\n3\\t8\\t2009-01-03 00:00:00\\tGrétrystraat 63\\tBrussels\\tNone\\tBelgium\\t1000\\t5.94\\n*/', name='sql_db_schema', tool_call_id='call_zwAtGmtcszZIu0pMOWjd6kaN')]}}\n",
|
|
"{'query_gen': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_0bJnebJLYtMDXdT8o5YDOR5b', 'function': {'arguments': '{\"query\":\"SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\\\\nFROM Employee e\\\\nJOIN Customer c ON e.EmployeeId = c.SupportRepId\\\\nJOIN Invoice i ON c.CustomerId = i.CustomerId\\\\nWHERE strftime(\\'%Y\\', i.InvoiceDate) = \\'2009\\'\\\\nGROUP BY e.EmployeeId\\\\nORDER BY TotalSales DESC\\\\nLIMIT 1;\"}', 'name': 'sql_db_query'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 92, 'prompt_tokens': 1179, 'total_tokens': 1271}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ac7ef301-ff96-4368-aa32-0ce02bf15a31-0', tool_calls=[{'name': 'sql_db_query', 'args': {'query': \"SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\\nFROM Employee e\\nJOIN Customer c ON e.EmployeeId = c.SupportRepId\\nJOIN Invoice i ON c.CustomerId = i.CustomerId\\nWHERE strftime('%Y', i.InvoiceDate) = '2009'\\nGROUP BY e.EmployeeId\\nORDER BY TotalSales DESC\\nLIMIT 1;\"}, 'id': 'call_0bJnebJLYtMDXdT8o5YDOR5b'}], usage_metadata={'input_tokens': 1179, 'output_tokens': 92, 'total_tokens': 1271}), ToolMessage(content='Error: The wrong tool was called: sql_db_query. Please fix your mistakes. Remember to only call SubmitFinalAnswer to submit the final answer. Generated queries should be outputted WITHOUT a tool call.', id='082c6fcb-51da-4ecd-9b30-d4b605ee22a0', tool_call_id='call_0bJnebJLYtMDXdT8o5YDOR5b')]}}\n",
|
|
"{'query_gen': {'messages': [AIMessage(content=\"```sql\\nSELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\\nFROM Employee e\\nJOIN Customer c ON e.EmployeeId = c.SupportRepId\\nJOIN Invoice i ON c.CustomerId = i.CustomerId\\nWHERE strftime('%Y', i.InvoiceDate) = '2009'\\nGROUP BY e.EmployeeId\\nORDER BY TotalSales DESC\\nLIMIT 1;\\n```\", response_metadata={'token_usage': {'completion_tokens': 82, 'prompt_tokens': 1321, 'total_tokens': 1403}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'stop', 'logprobs': None}, id='run-68e4bcf7-ab0d-4f49-a9da-4f721cabe3a0-0', usage_metadata={'input_tokens': 1321, 'output_tokens': 82, 'total_tokens': 1403})]}}\n",
|
|
"{'correct_query': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_gNi6QLLVsHY0wOy4QrSWIiD4', 'function': {'arguments': '{\"query\":\"SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\\\\nFROM Employee e\\\\nJOIN Customer c ON e.EmployeeId = c.SupportRepId\\\\nJOIN Invoice i ON c.CustomerId = i.CustomerId\\\\nWHERE strftime(\\'%Y\\', i.InvoiceDate) = \\'2009\\'\\\\nGROUP BY e.EmployeeId\\\\nORDER BY TotalSales DESC\\\\nLIMIT 1;\"}', 'name': 'db_query_tool'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 90, 'prompt_tokens': 294, 'total_tokens': 384}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'stop', 'logprobs': None}, id='run-13bafb17-66c9-4792-a76d-d16d1bb76d70-0', tool_calls=[{'name': 'db_query_tool', 'args': {'query': \"SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\\nFROM Employee e\\nJOIN Customer c ON e.EmployeeId = c.SupportRepId\\nJOIN Invoice i ON c.CustomerId = i.CustomerId\\nWHERE strftime('%Y', i.InvoiceDate) = '2009'\\nGROUP BY e.EmployeeId\\nORDER BY TotalSales DESC\\nLIMIT 1;\"}, 'id': 'call_gNi6QLLVsHY0wOy4QrSWIiD4'}], usage_metadata={'input_tokens': 294, 'output_tokens': 90, 'total_tokens': 384})]}}\n",
|
|
"{'execute_query': {'messages': [ToolMessage(content=\"[('Steve', 'Johnson', 164.34)]\", name='db_query_tool', tool_call_id='call_gNi6QLLVsHY0wOy4QrSWIiD4')]}}\n",
|
|
"{'query_gen': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_BPH1QSgVK9xdF2atxi6Fx3HM', 'function': {'arguments': '{\"final_answer\":\"The sales agent who made the most in sales in 2009 is Steve Johnson with total sales of 164.34.\"}', 'name': 'SubmitFinalAnswer'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 1519, 'total_tokens': 1560}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d3ed4aa6-3d79-4d7d-b69d-d97b64c41425-0', tool_calls=[{'name': 'SubmitFinalAnswer', 'args': {'final_answer': 'The sales agent who made the most in sales in 2009 is Steve Johnson with total sales of 164.34.'}, 'id': 'call_BPH1QSgVK9xdF2atxi6Fx3HM'}], usage_metadata={'input_tokens': 1519, 'output_tokens': 41, 'total_tokens': 1560})]}}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for event in app.stream({\"messages\": [(\"user\", \"Which sales agent made the most in sales in 2009?\")]}):\n",
|
|
" print(event)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-06-12T20:21:21.878352Z",
|
|
"start_time": "2024-06-12T20:21:12.854570Z"
|
|
}
|
|
},
|
|
"id": "956883cced0b8ec"
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|