forked from Archives/langchain
354 lines
9.5 KiB
Plaintext
354 lines
9.5 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": "code",
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"execution_count": null,
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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": 3,
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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:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 9 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 9 employees.'"
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]
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},
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"execution_count": 3,
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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[(9,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 9 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 9 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": "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": 3,
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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": 8,
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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 FROM Track WHERE Composer = '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',), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m Examples of tracks by Johann Sebastian Bach include '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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"' Examples of tracks by Johann Sebastian Bach include \\'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": 8,
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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": "c12ae15a",
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"metadata": {},
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"source": [
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"## SQLDatabaseSequentialChain\n",
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"\n",
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"Chain for querying SQL database that is a sequential chain.\n",
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"\n",
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"The chain is as follows:\n",
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"\n",
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" 1. Based on the query, determine which tables to use.\n",
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" 2. Based on those tables, call the normal SQL database chain.\n",
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"\n",
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"This is useful in cases where the number of tables in the database is large."
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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": "e59a4740",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import SQLDatabaseSequentialChain"
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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": "58bb49b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = SQLDatabaseSequentialChain.from_llm(llm, 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": 5,
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"id": "95017b1a",
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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 SQLDatabaseSequentialChain chain...\u001b[0m\n",
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"Table names to use:\n",
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"\u001b[33;1m\u001b[1;3m['Customer', 'Employee']\u001b[0m\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 also customers? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Customer c INNER JOIN Employee e ON c.SupportRepId = e.EmployeeId;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(59,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 59 employees who are also customers.\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\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 59 employees who are also customers.'"
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]
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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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"chain.run(\"How many employees are also customers?\")"
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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": null,
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"id": "b2998b03",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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