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558 lines
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558 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup Environment"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Python Modules"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Install the following Python modules:\n",
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"\n",
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"```bash\n",
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"pip install ipykernel python-dotenv cassio pandas langchain_openai langchain langchain-community langchainhub langchain_experimental openai-multi-tool-use-parallel-patch\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load the `.env` File"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Connection is via `cassio` using `auto=True` parameter, and the notebook uses OpenAI. You should create a `.env` file accordingly.\n",
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"\n",
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"For Casssandra, set:\n",
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"```bash\n",
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"CASSANDRA_CONTACT_POINTS\n",
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"CASSANDRA_USERNAME\n",
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"CASSANDRA_PASSWORD\n",
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"CASSANDRA_KEYSPACE\n",
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"```\n",
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"\n",
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"For Astra, set:\n",
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"```bash\n",
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"ASTRA_DB_APPLICATION_TOKEN\n",
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"ASTRA_DB_DATABASE_ID\n",
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"ASTRA_DB_KEYSPACE\n",
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"```\n",
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"\n",
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"For example:\n",
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"\n",
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"```bash\n",
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"# Connection to Astra:\n",
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"ASTRA_DB_DATABASE_ID=a1b2c3d4-...\n",
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"ASTRA_DB_APPLICATION_TOKEN=AstraCS:...\n",
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"ASTRA_DB_KEYSPACE=notebooks\n",
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"\n",
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"# Also set \n",
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"OPENAI_API_KEY=sk-....\n",
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"```\n",
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"\n",
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"(You may also modify the below code to directly connect with `cassio`.)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv(override=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Connect to Cassandra"
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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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"metadata": {},
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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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"import cassio\n",
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"\n",
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"cassio.init(auto=True)\n",
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"session = cassio.config.resolve_session()\n",
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"if not session:\n",
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" raise Exception(\n",
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" \"Check environment configuration or manually configure cassio connection parameters\"\n",
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" )\n",
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"\n",
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"keyspace = os.environ.get(\n",
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" \"ASTRA_DB_KEYSPACE\", os.environ.get(\"CASSANDRA_KEYSPACE\", None)\n",
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")\n",
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"if not keyspace:\n",
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" raise ValueError(\"a KEYSPACE environment variable must be set\")\n",
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"\n",
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"session.set_keyspace(keyspace)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup Database"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This needs to be done one time only!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Download Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The dataset used is from Kaggle, the [Environmental Sensor Telemetry Data](https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k?select=iot_telemetry_data.csv). The next cell will download and unzip the data into a Pandas dataframe. The following cell is instructions to download manually. \n",
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"\n",
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"The net result of this section is you should have a Pandas dataframe variable `df`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Download Automatically"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from io import BytesIO\n",
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"from zipfile import ZipFile\n",
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"\n",
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"import pandas as pd\n",
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"import requests\n",
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"\n",
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"datasetURL = \"https://storage.googleapis.com/kaggle-data-sets/788816/1355729/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240404%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240404T115828Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\n",
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"\n",
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"response = requests.get(datasetURL)\n",
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"if response.status_code == 200:\n",
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" zip_file = ZipFile(BytesIO(response.content))\n",
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" csv_file_name = zip_file.namelist()[0]\n",
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"else:\n",
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" print(\"Failed to download the file\")\n",
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"\n",
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"with zip_file.open(csv_file_name) as csv_file:\n",
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" df = pd.read_csv(csv_file)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Download Manually"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can download the `.zip` file and unpack the `.csv` contained within. Comment in the next line, and adjust the path to this `.csv` file appropriately."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# df = pd.read_csv(\"/path/to/iot_telemetry_data.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load Data into Cassandra"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This section assumes the existence of a dataframe `df`, the following cell validates its structure. The Download section above creates this object."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"assert df is not None, \"Dataframe 'df' must be set\"\n",
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"expected_columns = [\n",
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" \"ts\",\n",
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" \"device\",\n",
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" \"co\",\n",
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" \"humidity\",\n",
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" \"light\",\n",
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" \"lpg\",\n",
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" \"motion\",\n",
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" \"smoke\",\n",
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" \"temp\",\n",
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"]\n",
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"assert all(\n",
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" [column in df.columns for column in expected_columns]\n",
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"), \"DataFrame does not have the expected columns\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create and load tables:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import UTC, datetime\n",
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"\n",
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"from cassandra.query import BatchStatement\n",
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"\n",
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"# Create sensors table\n",
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"table_query = \"\"\"\n",
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"CREATE TABLE IF NOT EXISTS iot_sensors (\n",
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" device text,\n",
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" conditions text,\n",
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" room text,\n",
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" PRIMARY KEY (device)\n",
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")\n",
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"WITH COMMENT = 'Environmental IoT room sensor metadata.';\n",
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"\"\"\"\n",
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"session.execute(table_query)\n",
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"\n",
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"pstmt = session.prepare(\n",
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" \"\"\"\n",
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"INSERT INTO iot_sensors (device, conditions, room)\n",
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"VALUES (?, ?, ?)\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"devices = [\n",
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" (\"00:0f:00:70:91:0a\", \"stable conditions, cooler and more humid\", \"room 1\"),\n",
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" (\"1c:bf:ce:15:ec:4d\", \"highly variable temperature and humidity\", \"room 2\"),\n",
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" (\"b8:27:eb:bf:9d:51\", \"stable conditions, warmer and dryer\", \"room 3\"),\n",
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"]\n",
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"\n",
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"for device, conditions, room in devices:\n",
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" session.execute(pstmt, (device, conditions, room))\n",
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"\n",
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"print(\"Sensors inserted successfully.\")\n",
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"\n",
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"# Create data table\n",
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"table_query = \"\"\"\n",
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"CREATE TABLE IF NOT EXISTS iot_data (\n",
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" day text,\n",
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" device text,\n",
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" ts timestamp,\n",
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" co double,\n",
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" humidity double,\n",
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" light boolean,\n",
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" lpg double,\n",
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" motion boolean,\n",
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" smoke double,\n",
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" temp double,\n",
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" PRIMARY KEY ((day, device), ts)\n",
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")\n",
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"WITH COMMENT = 'Data from environmental IoT room sensors. Columns include device identifier, timestamp (ts) of the data collection, carbon monoxide level (co), relative humidity, light presence, LPG concentration, motion detection, smoke concentration, and temperature (temp). Data is partitioned by day and device.';\n",
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"\"\"\"\n",
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"session.execute(table_query)\n",
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"\n",
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"pstmt = session.prepare(\n",
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" \"\"\"\n",
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"INSERT INTO iot_data (day, device, ts, co, humidity, light, lpg, motion, smoke, temp)\n",
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"VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"\n",
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"def insert_data_batch(name, group):\n",
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" batch = BatchStatement()\n",
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" day, device = name\n",
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" print(f\"Inserting batch for day: {day}, device: {device}\")\n",
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"\n",
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" for _, row in group.iterrows():\n",
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" timestamp = datetime.fromtimestamp(row[\"ts\"], UTC)\n",
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" batch.add(\n",
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" pstmt,\n",
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" (\n",
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" day,\n",
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" row[\"device\"],\n",
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" timestamp,\n",
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" row[\"co\"],\n",
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" row[\"humidity\"],\n",
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" row[\"light\"],\n",
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" row[\"lpg\"],\n",
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" row[\"motion\"],\n",
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" row[\"smoke\"],\n",
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" row[\"temp\"],\n",
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" ),\n",
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" )\n",
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"\n",
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" session.execute(batch)\n",
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"\n",
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"\n",
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"# Convert columns to appropriate types\n",
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"df[\"light\"] = df[\"light\"] == \"true\"\n",
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"df[\"motion\"] = df[\"motion\"] == \"true\"\n",
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"df[\"ts\"] = df[\"ts\"].astype(float)\n",
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"df[\"day\"] = df[\"ts\"].apply(\n",
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" lambda x: datetime.fromtimestamp(x, UTC).strftime(\"%Y-%m-%d\")\n",
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")\n",
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"\n",
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"grouped_df = df.groupby([\"day\", \"device\"])\n",
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"\n",
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"for name, group in grouped_df:\n",
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" insert_data_batch(name, group)\n",
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"\n",
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"print(\"Data load complete\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"print(session.keyspace)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load the Tools"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Python `import` statements for the demo:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
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"from langchain_community.agent_toolkits.cassandra_database.toolkit import (\n",
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" CassandraDatabaseToolkit,\n",
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")\n",
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"from langchain_community.tools.cassandra_database.prompt import QUERY_PATH_PROMPT\n",
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"from langchain_community.tools.cassandra_database.tool import (\n",
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" GetSchemaCassandraDatabaseTool,\n",
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" GetTableDataCassandraDatabaseTool,\n",
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" QueryCassandraDatabaseTool,\n",
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")\n",
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"from langchain_community.utilities.cassandra_database import CassandraDatabase\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `CassandraDatabase` object is loaded from `cassio`, though it does accept a `Session`-type parameter as an alternative."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a CassandraDatabase instance\n",
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"db = CassandraDatabase(include_tables=[\"iot_sensors\", \"iot_data\"])\n",
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"\n",
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"# Create the Cassandra Database tools\n",
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"query_tool = QueryCassandraDatabaseTool(db=db)\n",
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"schema_tool = GetSchemaCassandraDatabaseTool(db=db)\n",
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"select_data_tool = GetTableDataCassandraDatabaseTool(db=db)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The tools can be invoked directly:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Test the tools\n",
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"print(\"Executing a CQL query:\")\n",
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"query = \"SELECT * FROM iot_sensors LIMIT 5;\"\n",
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"result = query_tool.run({\"query\": query})\n",
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"print(result)\n",
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"\n",
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"print(\"\\nGetting the schema for a keyspace:\")\n",
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"schema = schema_tool.run({\"keyspace\": keyspace})\n",
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"print(schema)\n",
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"\n",
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"print(\"\\nGetting data from a table:\")\n",
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"table = \"iot_data\"\n",
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"predicate = \"day = '2020-07-14' and device = 'b8:27:eb:bf:9d:51'\"\n",
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"data = select_data_tool.run(\n",
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" {\"keyspace\": keyspace, \"table\": table, \"predicate\": predicate, \"limit\": 5}\n",
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")\n",
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"print(data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Agent Configuration"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import Tool\n",
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"from langchain_experimental.utilities import PythonREPL\n",
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"\n",
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"python_repl = PythonREPL()\n",
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"\n",
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"repl_tool = Tool(\n",
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" name=\"python_repl\",\n",
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" description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
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" func=python_repl.run,\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"from langchain import hub\n",
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"\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n",
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"toolkit = CassandraDatabaseToolkit(db=db)\n",
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"\n",
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"# context = toolkit.get_context()\n",
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"# tools = toolkit.get_tools()\n",
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"tools = [schema_tool, select_data_tool, repl_tool]\n",
|
|
"\n",
|
|
"input = (\n",
|
|
" QUERY_PATH_PROMPT\n",
|
|
" + f\"\"\"\n",
|
|
"\n",
|
|
"Here is your task: In the {keyspace} keyspace, find the total number of times the temperature of each device has exceeded 23 degrees on July 14, 2020.\n",
|
|
" Create a summary report including the name of the room. Use Pandas if helpful.\n",
|
|
"\"\"\"\n",
|
|
")\n",
|
|
"\n",
|
|
"prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
|
|
"\n",
|
|
"# messages = [\n",
|
|
"# HumanMessagePromptTemplate.from_template(input),\n",
|
|
"# AIMessage(content=QUERY_PATH_PROMPT),\n",
|
|
"# MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
|
|
"# ]\n",
|
|
"\n",
|
|
"# prompt = ChatPromptTemplate.from_messages(messages)\n",
|
|
"# print(prompt)\n",
|
|
"\n",
|
|
"# Choose the LLM that will drive the agent\n",
|
|
"# Only certain models support this\n",
|
|
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)\n",
|
|
"\n",
|
|
"# Construct the OpenAI Tools agent\n",
|
|
"agent = create_openai_tools_agent(llm, tools, prompt)\n",
|
|
"\n",
|
|
"print(\"Available tools:\")\n",
|
|
"for tool in tools:\n",
|
|
" print(\"\\t\" + tool.name + \" - \" + tool.description + \" - \" + str(tool))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
|
|
"\n",
|
|
"response = agent_executor.invoke({\"input\": input})\n",
|
|
"\n",
|
|
"print(response[\"output\"])"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": 4
|
|
}
|