wehub-resource-sync
2524 lines
84 KiB
Plaintext
2524 lines
84 KiB
Plaintext
{
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"cells": [
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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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"id": "e4rVoXBpRMwI"
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},
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"outputs": [],
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"source": [
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"# If you are using Colab for free, we highly recommend you activate the T4 GPU\n",
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"# hardware accelerator. Our models are designed to run with at least 16GB\n",
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"# of RAM, activating T4 will grant the notebook 16GB of GDDR6 RAM as opposed\n",
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"# to the ~13GB Colab gives automatically.\n",
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"# To activate T4:\n",
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"# 1. click on the \"Runtime\" tab\n",
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"# 2. click on \"Change runtime type\"\n",
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"# 3. select T4 GPU under \"Hardware Accelerator\"\n",
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"# NOTE: there is a weekly usage limit on using T4 for free"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"collapsed": true,
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"id": "9TasXeh9ntUx",
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"outputId": "0673f3a8-126d-47e7-e2ec-1eb0943343de"
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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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"Collecting llmware\n",
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" Downloading llmware-0.3.0-py3-none-any.whl (56.0 MB)\n",
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"\u001b[?25hCollecting boto3>=1.24.53 (from llmware)\n",
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" Downloading pymongo-4.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (669 kB)\n",
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" Downloading psycopg_binary-3.1.17-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n",
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" Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
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"Collecting s3transfer<0.11.0,>=0.10.0 (from boto3>=1.24.53->llmware)\n",
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"Collecting dnspython<3.0.0,>=1.16.0 (from pymongo>=4.7.0->llmware)\n",
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"Installing collected packages: psycopg-binary, psycopg, pgvector, jmespath, dnspython, colorama, pymongo, botocore, s3transfer, boto3, llmware\n",
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"Successfully installed boto3-1.34.124 botocore-1.34.124 colorama-0.4.6 dnspython-2.6.1 jmespath-1.0.1 llmware-0.3.0 pgvector-0.2.4 psycopg-3.1.17 psycopg-binary-3.1.17 pymongo-4.7.3 s3transfer-0.10.1\n"
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]
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}
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],
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"source": [
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"!pip install llmware"
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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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"id": "DNym3TAGnw9N"
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},
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"outputs": [],
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"source": [
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"from llmware.agents import LLMfx\n",
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"\n",
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"earnings_transcripts = [\n",
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" \"This is one of the best quarters we can remember for the industrial sector with significant growth across the \"\n",
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" \"board in new order volume, as well as price increases in excess of inflation. We continue to see very strong \"\n",
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" \"demand, especially in Asia and Europe. Accordingly, we remain bullish on the tier 1 suppliers and would be \"\n",
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" \"accumulating more stock on any dips. \",\n",
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"\n",
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" \"Not the worst results, but overall we view as negative signals on the direction of the economy, and the likely \"\n",
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" \"short-term trajectory for the telecom sector, and especially larger market leaders, including AT&T, Comcast, and\"\n",
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" \"Deutsche Telekom.\",\n",
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"\n",
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" \"This quarter was a disaster for Tesla, with falling order volume, increased costs and supply, and negative \"\n",
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" \"guidance for future growth forecasts in 2024 and beyond.\",\n",
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"\n",
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" \"On balance, this was an average result, with earnings in line with expectations and no big surprises to either \"\n",
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" \"the positive or the negative.\"\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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"id": "kwIXabPGn2cK"
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},
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"outputs": [],
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"source": [
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"def get_one_sentiment_classification(text):\n",
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"\n",
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" \"\"\"This example shows a basic use to get a sentiment classification and use the output programmatically. \"\"\"\n",
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"\n",
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" # simple basic use to get the sentiment on a single piece of text\n",
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" agent = LLMfx(verbose=True)\n",
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" agent.load_tool(\"sentiment\")\n",
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" sentiment = agent.sentiment(text)\n",
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"\n",
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" # look at the output\n",
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" print(\"sentiment: \", sentiment)\n",
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" for keys, values in sentiment.items():\n",
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" print(f\"{keys}-{values}\")\n",
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"\n",
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" # two key attributes of the sentiment output dictionary\n",
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" sentiment_value = sentiment[\"llm_response\"][\"sentiment\"]\n",
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" confidence_level = sentiment[\"confidence_score\"]\n",
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"\n",
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" # use the sentiment classification as a 'if...then' decision point in a process\n",
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" if \"positive\" in sentiment_value:\n",
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" print(\"sentiment is positive .... will take 'positive' analysis path ...\", sentiment_value)\n",
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"\n",
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" if \"positive\" in sentiment_value and confidence_level > 0.8:\n",
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" print(\"sentiment is positive with high confidence ... \", sentiment_value, confidence_level)\n",
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"\n",
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" return sentiment"
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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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"id": "-YqsnaLBn6SK"
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},
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"outputs": [],
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"source": [
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"def review_batch_earning_transcripts():\n",
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"\n",
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" \"\"\" This example highlights how to review multiple earnings transcripts and iterate through a batch\n",
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" using the load_work mechanism. \"\"\"\n",
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"\n",
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" agent = LLMfx()\n",
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" agent.load_tool(\"sentiment\")\n",
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"\n",
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" # iterating through a larger list of samples\n",
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" # note: load_work method is a flexible input mechanism - pass a string, list, dictionary or combination, and\n",
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" # it will 'package' as iterable units of processing work for the agent\n",
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"\n",
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" agent.load_work(earnings_transcripts)\n",
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"\n",
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" while True:\n",
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" output = agent.sentiment()\n",
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" # print(\"update: test - output - \", output)\n",
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" if not agent.increment_work_iteration():\n",
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" break\n",
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"\n",
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" response_output = agent.response_list\n",
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"\n",
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" agent.clear_work()\n",
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" agent.clear_state()\n",
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"\n",
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" return response_output"
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]
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},
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{
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"cell_type": "code",
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"step - \t6 - \tanalyzing response - sentiment\n",
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"\t\t\t\t -- confidence score - 0.98\n",
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"\t\t\t\t -- analyzing response - \u001b[32msent\u001b[32miment\u001b[32m':\u001b[32m ['\u001b[32mpos\u001b[32mitive\u001b[32m']\u001b[32m}\u001b[39m\n",
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"\t\t\t\t -- analyzing response - {'choice_1': ['pos', 0.98, 1066], 'choice_2': ['neut', 0.018, 17821], 'choice_3': ['negative', 0.001, 22198]}\n",
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|
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|
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|
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"choices-[('positive', 1066, 0.98), ('neutral', 17821, 0.018), ('negative', 22198, 0.001)]\n",
|
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|
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"sentiment is positive with high confidence ... ['positive'] 0.98\n"
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|
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|
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" sentiment = get_one_sentiment_classification(earnings_transcripts[0])\n",
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|
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