wehub-resource-sync
1427 lines
50 KiB
Plaintext
1427 lines
50 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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"id": "zECN53X4Wf7M"
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},
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"source": [
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"**Quick Start for RAG with `llmware`**\n",
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"\n",
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"This example illustrates a simple contract analysis using a small RAG-optimized LLM running locally\n",
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"\n",
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"*Note: Colab's built-in Python 3.10 comes with a newer version of `grcpio` (1.60) which is incompatible with a dependency of llmware. Downgrading `grcpio` requires a restart afterwards.*"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "nGD_UZh4IfYm",
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"outputId": "3f74b4f8-1457-4559-faf1-7cf903abbe9e"
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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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"Python 3.10.12\n"
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]
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}
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],
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"source": [
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"!python -V"
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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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"metadata": {
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"id": "971vCYB51IqI"
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},
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"outputs": [],
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"source": [
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"%pip install \"grpcio<=1.60.0,>=1.49.1\" --no-cache-dir ## just to be safe with enviroments\n",
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"!pip install -q llmware\n"
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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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"id": "SKBTfwyTDr6F"
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},
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"source": [
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"🛑 In Colab click Runtime > Restart session and run all"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RoFBsyUgyJni",
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"outputId": "956f1d28-5160-45aa-e53a-00ebe8a60c75"
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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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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.1/22.1 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m796.3/796.3 kB\u001b[0m \u001b[31m29.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.5/132.5 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.3/157.3 kB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m671.3/671.3 kB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.3/65.3 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.2/53.2 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.4/300.4 kB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.8/143.8 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for ai21 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"llmx 0.0.15a0 requires tiktoken, which is not installed.\u001b[0m\u001b[31m\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"!pip install -q 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": 4,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000,
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"referenced_widgets": [
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"f34b375b7e464941b06fa55056186b1c",
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"0878975b308f410a9cd80c1f150f8edc",
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"353c350eec234922bcce1fd87b641be9",
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"c8238e040b8e4ebfade1b1f84bf3b9d4",
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"a1bfe7fb8dcf40f6956aadf34d879711",
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"881d89a137ab4a54b629cbaed4a5d116",
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"fc4f680743e149e7a329b3028d6aad10",
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"a93ea4fd7e314034b832f52766ffa741",
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"fe76443ff5ae41268a310dea485a30a4",
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"5cfb42aa18244f57bcb799c6d17f1347",
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"406d86667d234d9aaff0ca844f14e402",
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"4f3d19d7e8f24fdb8d39a7a14e302338",
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"8f21514be40747f887f161a21698173d",
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"c90c25225aac40078311d8ee63a27892",
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"a740788a38604222b5594b999b70f0c8",
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"f9f989043c0d4312a656bb1f5a15a1da",
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"056b709c53a14074a1b295ad66a7e0a0",
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"f512d6a27ce245c6918a2f05f1b87a9d",
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"8ae4150d5f784bd4a56a4a42fe588b48",
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"cc2aa420b4cd44f0988ea1f6ee73d479",
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"e0b44f199ee54c58a0bcdb119cec52ef",
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"7e8a32daccbf4d7ab23bc5d587faacfd",
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"35e2957dc115421bba4a68399a51c9c9",
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"9378f057e81148869421d38c6e3d73ce",
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"e4c7f46621c945a690c55ed47c34dc77",
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"cf0d2867b8304913a2fad9e5115bfde8",
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"c113e36bd5704df4b30922533cddc88f"
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]
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||
},
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||
"id": "cqWRMrztyCfR",
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||
"outputId": "8a22a3a0-09e4-48b0-bd11-44f129abc73d"
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||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"\n",
|
||
" > Loading the llmware sample files...\n",
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||
"\n",
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" > Loading model llmware/bling-1b-0.1...\n"
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||
]
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||
},
|
||
{
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||
"data": {
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||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "f34b375b7e464941b06fa55056186b1c",
|
||
"version_major": 2,
|
||
"version_minor": 0
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},
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||
"text/plain": [
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"config.json: 0%| | 0.00/2.27k [00:00<?, ?B/s]"
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||
]
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},
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||
"metadata": {},
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"output_type": "display_data"
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},
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||
{
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"data": {
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||
"application/vnd.jupyter.widget-view+json": {
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||
"model_id": "fe76443ff5ae41268a310dea485a30a4",
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||
"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"pytorch_model.bin: 0%| | 0.00/4.11G [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "056b709c53a14074a1b295ad66a7e0a0",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
|
||
"output_type": "stream",
|
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"text": [
|
||
"\n",
|
||
"Analyzing contract: 1 Leto EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Leto Apollo and TestCo Software, Inc.\n",
|
||
"base salary : $200,000\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 2 Nyx EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Nyx Pan and the Company\n",
|
||
"base salary : $200,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 3 Amphitrite EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Amphitrite Ares and TestCo Software, Inc.\n",
|
||
"base salary : $5,000,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 4 Metis EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Metis Hades and TestCo Software, Inc.\n",
|
||
"base salary : $200,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 5 Eileithyia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Eileithyia Hades (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
|
||
"base salary : $300,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 6 Persephone EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Persephone Zeus and TestCo Software, Inc.\n",
|
||
"base salary : $200,000\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 7 Rhea EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Rhea Hecate and TestCo Software, Inc.\n",
|
||
"base salary : $4350,000\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 8 Demeter EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Demeter Dionysus and TestCo Software, Inc.\n",
|
||
"base salary : $300,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 9 Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Athena Zeus (Executive) and TestCo Software, Inc. (Company or Employer).\n",
|
||
"base salary : $500,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 10 Apollo EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Aphrodite Apollo (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
|
||
"base salary : $600,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 11 Artemis Poseidon EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Artemus Poseidon and TestCo Software, Inc.\n",
|
||
"base salary : $400,000\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 12 Gaia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Gaia Eros and TestCo Software, Inc.\n",
|
||
"base salary : $250,000\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 14 Bia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Bia Hermes (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
|
||
"base salary : $400,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 15 Aphrodite EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Aphrodite Apollo (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
|
||
"base salary : $600,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Analyzing contract: 16 Nike EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
|
||
"LLM Responses:\n",
|
||
"executive employment agreement : Nike Cronus and TestCo Software, Inc.\n",
|
||
"base salary : $200,000.\n",
|
||
"governing law : State of Massachusetts\n",
|
||
"\n",
|
||
"Prompt state saved at: /root/llmware_data/prompt_history/63d2a23f-8f23-465c-96e7-a5a911ac1f5b\n",
|
||
"csv output saved at: {'report_name': 'interaction_report_Sun Jan 14 04:59:54 2024.csv', 'report_fp': '/root/llmware_data/prompt_history/interaction_report_Sun Jan 14 04:59:54 2024.csv', 'results': 45}\n"
|
||
]
|
||
}
|
||
],
|
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"source": [
|
||
"# This example illustrates a simple contract analysis\n",
|
||
"# using a small RAG-optimized LLM running locally\n",
|
||
"\n",
|
||
"import os\n",
|
||
"import re\n",
|
||
"from llmware.prompts import Prompt, HumanInTheLoop\n",
|
||
"from llmware.setup import Setup\n",
|
||
"from llmware.configs import LLMWareConfig\n",
|
||
"\n",
|
||
"def contract_analysis_on_laptop (model_name):\n",
|
||
"\n",
|
||
" # Load the llmware sample files\n",
|
||
" print (f\"\\n > Loading the llmware sample files...\")\n",
|
||
" sample_files_path = Setup().load_sample_files()\n",
|
||
" contracts_path = os.path.join(sample_files_path,\"Agreements\")\n",
|
||
"\n",
|
||
" # query list\n",
|
||
" query_list = {\"executive employment agreement\": \"What are the name of the two parties?\",\n",
|
||
" \"base salary\": \"What is the executive's base salary?\",\n",
|
||
" \"governing law\": \"What is the governing law?\"}\n",
|
||
"\n",
|
||
" print (f\"\\n > Loading model {model_name}...\")\n",
|
||
"\n",
|
||
" prompter = Prompt().load_model(model_name)\n",
|
||
"\n",
|
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" for i, contract in enumerate(os.listdir(contracts_path)):\n",
|
||
"\n",
|
||
" # excluding Mac file artifact\n",
|
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" if contract != \".DS_Store\":\n",
|
||
"\n",
|
||
" print(\"\\nAnalyzing contract: \", str(i+1), contract)\n",
|
||
"\n",
|
||
" print(\"LLM Responses:\")\n",
|
||
" for key, value in query_list.items():\n",
|
||
"\n",
|
||
" # contract is parsed, text-chunked, and then filtered by topic key\n",
|
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" source = prompter.add_source_document(contracts_path, contract, query=key)\n",
|
||
"\n",
|
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" # calling the LLM with 'source' information from the contract automatically packaged into the prompt\n",
|
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" responses = prompter.prompt_with_source(value, prompt_name=\"just_the_facts\", temperature=0.3)\n",
|
||
"\n",
|
||
" for r, response in enumerate(responses):\n",
|
||
" print(key, \":\", re.sub(\"[\\n]\",\" \", response[\"llm_response\"]).strip())\n",
|
||
"\n",
|
||
" # We're done with this contract, clear the source from the prompt\n",
|
||
" prompter.clear_source_materials()\n",
|
||
"\n",
|
||
" # Save jsonl report to jsonl to /prompt_history folder\n",
|
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" print(\"\\nPrompt state saved at: \", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))\n",
|
||
" prompter.save_state()\n",
|
||
"\n",
|
||
" # Save csv report that includes the model, response, prompt, and evidence for human-in-the-loop review\n",
|
||
" csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()\n",
|
||
" print(\"csv output saved at: \", csv_output)\n",
|
||
"\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
"\n",
|
||
" # use local cpu model - smallest, fastest (use larger BLING models for higher accuracy)\n",
|
||
" model = \"llmware/bling-1b-0.1\"\n",
|
||
"\n",
|
||
" contract_analysis_on_laptop(model)"
|
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]
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}
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],
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