mirror of https://github.com/hwchase17/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
655 lines
20 KiB
Plaintext
655 lines
20 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "raw",
|
|
"metadata": {},
|
|
"source": [
|
|
"---\n",
|
|
"sidebar_label: Kinetica\n",
|
|
"---"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Kinetica SqlAssist LLM Demo\n",
|
|
"\n",
|
|
"This notebook demonstrates how to use Kinetica to transform natural language into SQL\n",
|
|
"and simplify the process of data retrieval. This demo is intended to show the mechanics\n",
|
|
"of creating and using a chain as opposed to the capabilities of the LLM.\n",
|
|
"\n",
|
|
"## Overview\n",
|
|
"\n",
|
|
"With the Kinetica LLM workflow you create an LLM context in the database that provides\n",
|
|
"information needed for infefencing that includes tables, annotations, rules, and\n",
|
|
"samples. Invoking ``ChatKinetica.load_messages_from_context()`` will retrieve the\n",
|
|
"context information from the database so that it can be used to create a chat prompt.\n",
|
|
"\n",
|
|
"The chat prompt consists of a ``SystemMessage`` and pairs of\n",
|
|
"``HumanMessage``/``AIMessage`` that contain the samples which are question/SQL\n",
|
|
"pairs. You can append pairs samples to this list but it is not intended to\n",
|
|
"facilitate a typical natural language conversation.\n",
|
|
"\n",
|
|
"When you create a chain from the chat prompt and execute it, the Kinetica LLM will\n",
|
|
"generate SQL from the input. Optionally you can use ``KineticaSqlOutputParser`` to\n",
|
|
"execute the SQL and return the result as a dataframe.\n",
|
|
"\n",
|
|
"Currently, 2 LLM's are supported for SQL generation: \n",
|
|
"\n",
|
|
"1. **Kinetica SQL-GPT**: This LLM is based on OpenAI ChatGPT API.\n",
|
|
"2. **Kinetica SqlAssist**: This LLM is purpose built to integrate with the Kinetica\n",
|
|
" database and it can run in a secure customer premise.\n",
|
|
"\n",
|
|
"For this demo we will be using **SqlAssist**. See the [Kinetica Documentation\n",
|
|
"site](https://docs.kinetica.com/7.1/sql-gpt/concepts/) for more information.\n",
|
|
"\n",
|
|
"## Prerequisites\n",
|
|
"\n",
|
|
"To get started you will need a Kinetica DB instance. If you don't have one you can\n",
|
|
"obtain a [free development instance](https://cloud.kinetica.com/trynow).\n",
|
|
"\n",
|
|
"You will need to install the following packages..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Note: you may need to restart the kernel to use updated packages.\n",
|
|
"Note: you may need to restart the kernel to use updated packages.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Install Langchain community and core packages\n",
|
|
"%pip install --upgrade --quiet langchain-core langchain-community\n",
|
|
"\n",
|
|
"# Install Kineitca DB connection package\n",
|
|
"%pip install --upgrade --quiet gpudb typeguard\n",
|
|
"\n",
|
|
"# Install packages needed for this tutorial\n",
|
|
"%pip install --upgrade --quiet faker"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Database Connection\n",
|
|
"\n",
|
|
"You must set the database connection in the following environment variables. If you are using a virtual environment you can set them in the `.env` file of the project:\n",
|
|
"* `KINETICA_URL`: Database connection URL\n",
|
|
"* `KINETICA_USER`: Database user\n",
|
|
"* `KINETICA_PASSWD`: Secure password.\n",
|
|
"\n",
|
|
"If you can create an instance of `KineticaChatLLM` then you are successfully connected."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain_community.chat_models.kinetica import ChatKinetica\n",
|
|
"\n",
|
|
"kinetica_llm = ChatKinetica()\n",
|
|
"\n",
|
|
"# Test table we will create\n",
|
|
"table_name = \"demo.user_profiles\"\n",
|
|
"\n",
|
|
"# LLM Context we will create\n",
|
|
"kinetica_ctx = \"demo.test_llm_ctx\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Create test data\n",
|
|
"\n",
|
|
"Before we can generate SQL we will need to create a Kinetica table and an LLM context that can inference the table.\n",
|
|
"\n",
|
|
"### Create some fake user profiles\n",
|
|
"\n",
|
|
"We will use the `faker` package to create a dataframe with 100 fake profiles."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>username</th>\n",
|
|
" <th>name</th>\n",
|
|
" <th>sex</th>\n",
|
|
" <th>address</th>\n",
|
|
" <th>mail</th>\n",
|
|
" <th>birthdate</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>id</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>eduardo69</td>\n",
|
|
" <td>Haley Beck</td>\n",
|
|
" <td>F</td>\n",
|
|
" <td>59836 Carla Causeway Suite 939\\nPort Eugene, I...</td>\n",
|
|
" <td>meltondenise@yahoo.com</td>\n",
|
|
" <td>1997-11-23</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>lbarrera</td>\n",
|
|
" <td>Joshua Stephens</td>\n",
|
|
" <td>M</td>\n",
|
|
" <td>3108 Christina Forges\\nPort Timothychester, KY...</td>\n",
|
|
" <td>erica80@hotmail.com</td>\n",
|
|
" <td>1924-07-19</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>bburton</td>\n",
|
|
" <td>Paula Kaiser</td>\n",
|
|
" <td>F</td>\n",
|
|
" <td>Unit 7405 Box 3052\\nDPO AE 09858</td>\n",
|
|
" <td>timothypotts@gmail.com</td>\n",
|
|
" <td>1933-11-20</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>melissa49</td>\n",
|
|
" <td>Wendy Reese</td>\n",
|
|
" <td>F</td>\n",
|
|
" <td>6408 Christopher Hill Apt. 459\\nNew Benjamin, ...</td>\n",
|
|
" <td>dadams@gmail.com</td>\n",
|
|
" <td>1988-10-11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>melissacarter</td>\n",
|
|
" <td>Manuel Rios</td>\n",
|
|
" <td>M</td>\n",
|
|
" <td>2241 Bell Gardens Suite 723\\nScottside, CA 38463</td>\n",
|
|
" <td>williamayala@gmail.com</td>\n",
|
|
" <td>1931-03-04</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" username name sex \\\n",
|
|
"id \n",
|
|
"0 eduardo69 Haley Beck F \n",
|
|
"1 lbarrera Joshua Stephens M \n",
|
|
"2 bburton Paula Kaiser F \n",
|
|
"3 melissa49 Wendy Reese F \n",
|
|
"4 melissacarter Manuel Rios M \n",
|
|
"\n",
|
|
" address mail \\\n",
|
|
"id \n",
|
|
"0 59836 Carla Causeway Suite 939\\nPort Eugene, I... meltondenise@yahoo.com \n",
|
|
"1 3108 Christina Forges\\nPort Timothychester, KY... erica80@hotmail.com \n",
|
|
"2 Unit 7405 Box 3052\\nDPO AE 09858 timothypotts@gmail.com \n",
|
|
"3 6408 Christopher Hill Apt. 459\\nNew Benjamin, ... dadams@gmail.com \n",
|
|
"4 2241 Bell Gardens Suite 723\\nScottside, CA 38463 williamayala@gmail.com \n",
|
|
"\n",
|
|
" birthdate \n",
|
|
"id \n",
|
|
"0 1997-11-23 \n",
|
|
"1 1924-07-19 \n",
|
|
"2 1933-11-20 \n",
|
|
"3 1988-10-11 \n",
|
|
"4 1931-03-04 "
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from typing import Generator\n",
|
|
"\n",
|
|
"import pandas as pd\n",
|
|
"from faker import Faker\n",
|
|
"\n",
|
|
"Faker.seed(5467)\n",
|
|
"faker = Faker(locale=\"en-US\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def profile_gen(count: int) -> Generator:\n",
|
|
" for id in range(0, count):\n",
|
|
" rec = dict(id=id, **faker.simple_profile())\n",
|
|
" rec[\"birthdate\"] = pd.Timestamp(rec[\"birthdate\"])\n",
|
|
" yield rec\n",
|
|
"\n",
|
|
"\n",
|
|
"load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n",
|
|
"load_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Create a Kinetica table from the Dataframe"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>name</th>\n",
|
|
" <th>type</th>\n",
|
|
" <th>properties</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>username</td>\n",
|
|
" <td>string</td>\n",
|
|
" <td>[char32]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>name</td>\n",
|
|
" <td>string</td>\n",
|
|
" <td>[char32]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>sex</td>\n",
|
|
" <td>string</td>\n",
|
|
" <td>[char1]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>address</td>\n",
|
|
" <td>string</td>\n",
|
|
" <td>[char64]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>mail</td>\n",
|
|
" <td>string</td>\n",
|
|
" <td>[char32]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>birthdate</td>\n",
|
|
" <td>long</td>\n",
|
|
" <td>[timestamp]</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" name type properties\n",
|
|
"0 username string [char32]\n",
|
|
"1 name string [char32]\n",
|
|
"2 sex string [char1]\n",
|
|
"3 address string [char64]\n",
|
|
"4 mail string [char32]\n",
|
|
"5 birthdate long [timestamp]"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from gpudb import GPUdbTable\n",
|
|
"\n",
|
|
"gpudb_table = GPUdbTable.from_df(\n",
|
|
" load_df,\n",
|
|
" db=kinetica_llm.kdbc,\n",
|
|
" table_name=table_name,\n",
|
|
" clear_table=True,\n",
|
|
" load_data=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"# See the Kinetica column types\n",
|
|
"gpudb_table.type_as_df()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Create the LLM context\n",
|
|
"\n",
|
|
"You can create an LLM Context using the Kinetica Workbench UI or you can manually create it with the `CREATE OR REPLACE CONTEXT` syntax. \n",
|
|
"\n",
|
|
"Here we create a context from the SQL syntax referencing the table we created."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'status': 'OK',\n",
|
|
" 'message': '',\n",
|
|
" 'data_type': 'execute_sql_response',\n",
|
|
" 'response_time': 0.0148}"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# create an LLM context for the table.\n",
|
|
"\n",
|
|
"from gpudb import GPUdbException\n",
|
|
"\n",
|
|
"sql = f\"\"\"\n",
|
|
"CREATE OR REPLACE CONTEXT {kinetica_ctx}\n",
|
|
"(\n",
|
|
" TABLE = demo.test_profiles\n",
|
|
" COMMENT = 'Contains user profiles.'\n",
|
|
"),\n",
|
|
"(\n",
|
|
" SAMPLES = (\n",
|
|
" 'How many male users are there?' = \n",
|
|
" 'select count(1) as num_users\n",
|
|
" from demo.test_profiles\n",
|
|
" where sex = ''M'';')\n",
|
|
")\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"\n",
|
|
"def _check_error(response: dict) -> None:\n",
|
|
" status = response[\"status_info\"][\"status\"]\n",
|
|
" if status != \"OK\":\n",
|
|
" message = response[\"status_info\"][\"message\"]\n",
|
|
" raise GPUdbException(\"[%s]: %s\" % (status, message))\n",
|
|
"\n",
|
|
"\n",
|
|
"response = kinetica_llm.kdbc.execute_sql(sql)\n",
|
|
"_check_error(response)\n",
|
|
"response[\"status_info\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Use Langchain for inferencing\n",
|
|
"\n",
|
|
"In the example below we will create a chain from the previously created table and LLM context. This chain will generate SQL and return the resulting data as a dataframe.\n",
|
|
"\n",
|
|
"### Load the chat prompt from the Kinetica DB\n",
|
|
"\n",
|
|
"The `load_messages_from_context()` function will retrieve a context from the DB and convert it into a list of chat messages that we use to create a ``ChatPromptTemplate``."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"================================\u001b[1m System Message \u001b[0m================================\n",
|
|
"\n",
|
|
"CREATE TABLE demo.test_profiles AS\n",
|
|
"(\n",
|
|
" username VARCHAR (32) NOT NULL,\n",
|
|
" name VARCHAR (32) NOT NULL,\n",
|
|
" sex VARCHAR (1) NOT NULL,\n",
|
|
" address VARCHAR (64) NOT NULL,\n",
|
|
" mail VARCHAR (32) NOT NULL,\n",
|
|
" birthdate TIMESTAMP NOT NULL\n",
|
|
");\n",
|
|
"COMMENT ON TABLE demo.test_profiles IS 'Contains user profiles.';\n",
|
|
"\n",
|
|
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
|
"\n",
|
|
"How many male users are there?\n",
|
|
"\n",
|
|
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
|
"\n",
|
|
"select count(1) as num_users\n",
|
|
" from demo.test_profiles\n",
|
|
" where sex = 'M';\n",
|
|
"\n",
|
|
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
|
"\n",
|
|
"\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain_core.prompts import ChatPromptTemplate\n",
|
|
"\n",
|
|
"# load the context from the database\n",
|
|
"ctx_messages = kinetica_llm.load_messages_from_context(kinetica_ctx)\n",
|
|
"\n",
|
|
"# Add the input prompt. This is where input question will be substituted.\n",
|
|
"ctx_messages.append((\"human\", \"{input}\"))\n",
|
|
"\n",
|
|
"# Create the prompt template.\n",
|
|
"prompt_template = ChatPromptTemplate.from_messages(ctx_messages)\n",
|
|
"prompt_template.pretty_print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Create the chain\n",
|
|
"\n",
|
|
"The last element of this chain is `KineticaSqlOutputParser` that will execute the SQL and return a dataframe. This is optional and if we left it out then only SQL would be returned."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain_community.chat_models.kinetica import (\n",
|
|
" KineticaSqlOutputParser,\n",
|
|
" KineticaSqlResponse,\n",
|
|
")\n",
|
|
"\n",
|
|
"chain = prompt_template | kinetica_llm | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Generate the SQL\n",
|
|
"\n",
|
|
"The chain we created will take a question as input and return a ``KineticaSqlResponse`` containing the generated SQL and data. The question must be relevant to the to LLM context we used to create the prompt."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"SQL: SELECT username, name\n",
|
|
" FROM demo.test_profiles\n",
|
|
" WHERE sex = 'F'\n",
|
|
" ORDER BY username;\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>username</th>\n",
|
|
" <th>name</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>alexander40</td>\n",
|
|
" <td>Tina Ramirez</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>bburton</td>\n",
|
|
" <td>Paula Kaiser</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>brian12</td>\n",
|
|
" <td>Stefanie Williams</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>brownanna</td>\n",
|
|
" <td>Jennifer Rowe</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>carl19</td>\n",
|
|
" <td>Amanda Potts</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" username name\n",
|
|
"0 alexander40 Tina Ramirez\n",
|
|
"1 bburton Paula Kaiser\n",
|
|
"2 brian12 Stefanie Williams\n",
|
|
"3 brownanna Jennifer Rowe\n",
|
|
"4 carl19 Amanda Potts"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Here you must ask a question relevant to the LLM context provided in the prompt template.\n",
|
|
"response: KineticaSqlResponse = chain.invoke(\n",
|
|
" {\"input\": \"What are the female users ordered by username?\"}\n",
|
|
")\n",
|
|
"\n",
|
|
"print(f\"SQL: {response.sql}\")\n",
|
|
"response.dataframe.head()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "langchain",
|
|
"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": "3.11.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|