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SLIM notebook that helps users make multiple queries on one piece of text, made in response to query on Discord from @DarkEnigma
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@simonrisman
simonrisman authored Jun 19, 2024
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# If you are using Colab for free, we highly recommend you activate the T4 GPU\n",
"# hardware accelerator. Our models are designed to run with at least 16GB\n",
"# of RAM, activating T4 will grant the notebook 16GB of GDDR6 RAM as opposed\n",
"# to the ~13GB Colab gives automatically.\n",
"# To activate T4:\n",
"# 1. click on the \"Runtime\" tab\n",
"# 2. click on \"Change runtime type\"\n",
"# 3. select T4 GPU under Hardware Accelerator\n",
"# NOTE: there is a weekly usage limit on using T4 for free"
],
"metadata": {
"id": "5DKDTnMGHTGn"
}
},
{
"cell_type": "markdown",
"source": [
"##This example aims to show how to run multiple specific queries on text using our slim extract tool, and ouput it as a dictionary"
],
"metadata": {
"id": "aBE4SLY5LYiq"
}
},
{
"cell_type": "code",
"source": [
"!pip install llmware\n",
"from llmware.models import ModelCatalog\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oanCf6Dn8UlA",
"outputId": "84e6207b-40bb-47b6-d756-ae6ead743bc1"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"To add more strings to analyze, add them to Text_to_analyze.\n",
"To add more queries, add them to queries_list.\n"
],
"metadata": {
"id": "erpOjVoUGZPU"
}
},
{
"cell_type": "code",
"source": [
"Text_to_analyze = [\"I am John Doe, I visited Peter, a therapist in the year 2019. I travelled to NYC for around 5 therapy sessions, I was advised to be more outgoing.\"]\n",
"queries_list = [\"consultant specialty\", \"Consultant name\", \"Consultant location\", \"Consultant Advice\"]\n"
],
"metadata": {
"id": "Ls6GHIEqF1uq"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# load the model\n",
"model = ModelCatalog().load_model(\"slim-extract-tool\",sample=False, temperature=0.0, max_output=200)\n",
"# loop through the text and queries, calling the model each time\n",
"output_dict = {}\n",
"for i, sample in enumerate(Text_to_analyze):\n",
" for j, query in enumerate(queries_list):\n",
" print(f\"Query {j+1}: {query}\")\n",
" response = model.function_call(sample, function=\"extract\", params=[query])\n",
" output_dict.update(response[\"llm_response\"])\n",
" if response[\"llm_response\"] == []:\n",
" print(\"No response\")\n",
" print(\"extract response: \", response[\"llm_response\"])\n",
"\n",
"\n",
" # display the response on the screen\n",
"print(\"output dictionary: \" )\n",
"print(output_dict)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aWw7uro5C1ym",
"outputId": "b33fbf63-171c-4b5b-f33a-b102282b42d2"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Query 1: consultant specialty\n",
"extract response: {'consultant_specialty': ['Therapist']}\n",
"Query 2: Consultant name\n",
"extract response: {'Consultant_name': ['Peter']}\n",
"Query 3: Consultant location\n",
"extract response: {'Consultant_location': ['NYC']}\n",
"Query 4: Consultant Advice\n",
"extract response: {'Consultant_Advice': ['Be more outgoing.']}\n",
"output dictionary: \n",
"{'consultant_specialty': ['Therapist'], 'Consultant_name': ['Peter'], 'Consultant_location': ['NYC'], 'Consultant_Advice': ['Be more outgoing.']}\n"
]
}
]
}
]
}

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