mirror of
https://github.com/hwchase17/langchain
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688 lines
22 KiB
Plaintext
688 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0ed6aab1",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# SQLite example\n",
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"\n",
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"This example showcases hooking up an LLM to answer questions over a database."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2f66479",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"This uses the example Chinook database.\n",
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"To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d0e27d88",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
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]
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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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"id": "72ede462",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
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"llm = OpenAI(temperature=0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d1e692e",
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"metadata": {},
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"source": [
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"**NOTE:** For data-sensitive projects, you can specify `return_direct=True` in the `SQLDatabaseChain` initialization to directly return the output of the SQL query without any additional formatting. This prevents the LLM from seeing any contents within the database. Note, however, the LLM still has access to the database scheme (i.e. dialect, table and key names) by default."
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]
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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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"id": "a8fc8f23",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
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]
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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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"id": "15ff81df",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"How many employees are there? \n",
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"SQLQuery:"
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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/harrisonchase/workplace/langchain/langchain/sql_database.py:120: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
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" sample_rows = connection.execute(command)\n"
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]
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},
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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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"\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(8,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 8 employees.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' There are 8 employees.'"
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]
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},
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"execution_count": 4,
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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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"db_chain.run(\"How many employees are there?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aad2cba6",
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"metadata": {},
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"source": [
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"## Customize Prompt\n",
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"You can also customize the prompt that is used. Here is an example prompting it to understand that foobar is the same as the Employee table"
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]
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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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"id": "8ca7bafb",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.prompt import PromptTemplate\n",
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"\n",
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"_DEFAULT_TEMPLATE = \"\"\"Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\n",
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"Use the following format:\n",
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"\n",
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"Question: \"Question here\"\n",
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"SQLQuery: \"SQL Query to run\"\n",
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"SQLResult: \"Result of the SQLQuery\"\n",
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"Answer: \"Final answer here\"\n",
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"\n",
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"Only use the following tables:\n",
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"\n",
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"{table_info}\n",
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"\n",
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"If someone asks for the table foobar, they really mean the employee table.\n",
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"\n",
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"Question: {input}\"\"\"\n",
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"PROMPT = PromptTemplate(\n",
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" input_variables=[\"input\", \"table_info\", \"dialect\"], template=_DEFAULT_TEMPLATE\n",
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")"
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]
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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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"id": "ec47a2bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)"
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]
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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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"id": "ebb0674e",
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"metadata": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"How many employees are there in the foobar table? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(8,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 8 employees in the foobar table.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' There are 8 employees in the foobar table.'"
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]
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},
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"execution_count": 7,
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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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"db_chain.run(\"How many employees are there in the foobar table?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88d8b969",
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"metadata": {},
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"source": [
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"## Return Intermediate Steps\n",
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"\n",
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"You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database."
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]
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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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"id": "38559487",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
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]
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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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"id": "78b6af4d",
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"metadata": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"How many employees are there in the foobar table? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(8,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 8 employees in the foobar table.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[' SELECT COUNT(*) FROM Employee;', '[(8,)]']"
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]
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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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"result = db_chain(\"How many employees are there in the foobar table?\")\n",
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"result[\"intermediate_steps\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b408f800",
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"metadata": {},
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"source": [
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"## Choosing how to limit the number of rows returned\n",
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"If you are querying for several rows of a table you can select the maximum number of results you want to get by using the 'top_k' parameter (default is 10). This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "6adaa799",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "edfc8a8e",
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"metadata": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"What are some example tracks by composer Johann Sebastian Bach? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer LIKE '%Johann Sebastian Bach%' LIMIT 3;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach')]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m Some example tracks by composer Johann Sebastian Bach are 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' Some example tracks by composer Johann Sebastian Bach are \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', and \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\'.'"
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]
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},
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"execution_count": 11,
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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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"db_chain.run(\"What are some example tracks by composer Johann Sebastian Bach?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bcc5e936",
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"metadata": {},
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"source": [
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"## Adding example rows from each table\n",
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"Sometimes, the format of the data is not obvious and it is optimal to include a sample of rows from the tables in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names by providing two rows from the `Track` table."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "9a22ee47",
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"metadata": {},
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"outputs": [],
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"source": [
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"db = SQLDatabase.from_uri(\n",
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" \"sqlite:///../../../../notebooks/Chinook.db\",\n",
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" include_tables=['Track'], # we include only one table to save tokens in the prompt :)\n",
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" sample_rows_in_table_info=2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "952c0b4d",
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"metadata": {},
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"source": [
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"The sample rows are added to the prompt after each corresponding table's column information:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "9de86267",
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"metadata": {},
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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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"\n",
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"CREATE TABLE \"Track\" (\n",
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"\t\"TrackId\" INTEGER NOT NULL, \n",
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"\t\"Name\" NVARCHAR(200) NOT NULL, \n",
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"\t\"AlbumId\" INTEGER, \n",
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"\t\"MediaTypeId\" INTEGER NOT NULL, \n",
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"\t\"GenreId\" INTEGER, \n",
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"\t\"Composer\" NVARCHAR(220), \n",
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"\t\"Milliseconds\" INTEGER NOT NULL, \n",
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"\t\"Bytes\" INTEGER, \n",
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"\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
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"\tPRIMARY KEY (\"TrackId\"), \n",
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"\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
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"\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
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"\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
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")\n",
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"/*\n",
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"2 rows from Track table:\n",
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"TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
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"1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
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"2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n",
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"*/\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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"/home/jon/projects/langchain/langchain/sql_database.py:135: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
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" sample_rows = connection.execute(command)\n"
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]
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}
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],
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"source": [
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"print(db.table_info)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "bcb7a489",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "81e05d82",
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"metadata": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"What are some example tracks by Bach? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' Some example tracks by Bach are \\'American Woman\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\', and \\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\'.'"
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]
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},
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"execution_count": 15,
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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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"db_chain.run(\"What are some example tracks by Bach?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef94e948",
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"metadata": {},
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"source": [
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"### Custom Table Info\n",
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"In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first `sample_rows_in_table_info` sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns. \n",
|
|
"\n",
|
|
"This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let's provide a custom definition and sample rows for the Track table with only a few columns:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "2ad33ab1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"custom_table_info = {\n",
|
|
" \"Track\": \"\"\"CREATE TABLE Track (\n",
|
|
"\t\"TrackId\" INTEGER NOT NULL, \n",
|
|
"\t\"Name\" NVARCHAR(200) NOT NULL,\n",
|
|
"\t\"Composer\" NVARCHAR(220),\n",
|
|
"\tPRIMARY KEY (\"TrackId\")\n",
|
|
")\n",
|
|
"/*\n",
|
|
"3 rows from Track table:\n",
|
|
"TrackId\tName\tComposer\n",
|
|
"1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
|
|
"2\tBalls to the Wall\tNone\n",
|
|
"3\tMy favorite song ever\tThe coolest composer of all time\n",
|
|
"*/\"\"\"\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "db144352",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"CREATE TABLE \"Playlist\" (\n",
|
|
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
|
|
"\t\"Name\" NVARCHAR(120), \n",
|
|
"\tPRIMARY KEY (\"PlaylistId\")\n",
|
|
")\n",
|
|
"/*\n",
|
|
"2 rows from Playlist table:\n",
|
|
"PlaylistId\tName\n",
|
|
"1\tMusic\n",
|
|
"2\tMovies\n",
|
|
"*/\n",
|
|
"\n",
|
|
"CREATE TABLE Track (\n",
|
|
"\t\"TrackId\" INTEGER NOT NULL, \n",
|
|
"\t\"Name\" NVARCHAR(200) NOT NULL,\n",
|
|
"\t\"Composer\" NVARCHAR(220),\n",
|
|
"\tPRIMARY KEY (\"TrackId\")\n",
|
|
")\n",
|
|
"/*\n",
|
|
"3 rows from Track table:\n",
|
|
"TrackId\tName\tComposer\n",
|
|
"1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
|
|
"2\tBalls to the Wall\tNone\n",
|
|
"3\tMy favorite song ever\tThe coolest composer of all time\n",
|
|
"*/\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"db = SQLDatabase.from_uri(\n",
|
|
" \"sqlite:///../../../../notebooks/Chinook.db\",\n",
|
|
" include_tables=['Track', 'Playlist'],\n",
|
|
" sample_rows_in_table_info=2,\n",
|
|
" custom_table_info=custom_table_info)\n",
|
|
"\n",
|
|
"print(db.table_info)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5fc6f507",
|
|
"metadata": {},
|
|
"source": [
|
|
"Note how our custom table definition and sample rows for `Track` overrides the `sample_rows_in_table_info` parameter. Tables that are not overridden by `custom_table_info`, in this example `Playlist`, will have their table info gathered automatically as usual."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "dfbda4e6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
|
|
"What are some example tracks by Bach? \n",
|
|
"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;\u001b[0m\n",
|
|
"SQLResult: \u001b[33;1m\u001b[1;3m[('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]\u001b[0m\n",
|
|
"Answer:\u001b[32;1m\u001b[1;3m Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.\u001b[0m\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"' Some example tracks by Bach are \\'American Woman\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\', and \\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\'.'"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
|
|
"db_chain.run(\"What are some example tracks by Bach?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c12ae15a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## SQLDatabaseSequentialChain\n",
|
|
"\n",
|
|
"Chain for querying SQL database that is a sequential chain.\n",
|
|
"\n",
|
|
"The chain is as follows:\n",
|
|
"\n",
|
|
" 1. Based on the query, determine which tables to use.\n",
|
|
" 2. Based on those tables, call the normal SQL database chain.\n",
|
|
"\n",
|
|
"This is useful in cases where the number of tables in the database is large."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "e59a4740",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.chains import SQLDatabaseSequentialChain\n",
|
|
"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "58bb49b6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "95017b1a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new SQLDatabaseSequentialChain chain...\u001b[0m\n",
|
|
"Table names to use:\n",
|
|
"\u001b[33;1m\u001b[1;3m['Customer', 'Employee']\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
|
|
"How many employees are also customers? \n",
|
|
"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee INNER JOIN Customer ON Employee.EmployeeId = Customer.SupportRepId;\u001b[0m\n",
|
|
"SQLResult: \u001b[33;1m\u001b[1;3m[(59,)]\u001b[0m\n",
|
|
"Answer:\u001b[32;1m\u001b[1;3m 59 employees are also customers.\u001b[0m\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"' 59 employees are also customers.'"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"How many employees are also customers?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5eb39db6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"@webio": {
|
|
"lastCommId": null,
|
|
"lastKernelId": null
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|