{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "983yjns496tx" }, "source": [ "# Working with Anthropic in Pixeltable\n", "\n", "Pixeltable's Anthropic integration enables you to access Anthropic's Claude LLM via the Anthropic API.\n", "\n", "### Prerequisites\n", "\n", "- An Anthropic account with an API key (https://docs.anthropic.com/en/api/getting-started)\n", "\n", "### Important notes\n", "\n", "- Anthropic usage may incur costs based on your Anthropic plan.\n", "- Be mindful of sensitive data and consider security measures when integrating with external services.\n", "\n", "First you'll need to install required libraries and enter an Anthropic API key." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9pckrD01ik-e", "outputId": "060b8b32-48a6-48a0-e720-4eacf94d83ef" }, "outputs": [], "source": [ "%pip install -qU pixeltable anthropic" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AQ6_Py7_7d0r", "outputId": "f82cfe36-be9e-4d43-f13e-9f6f5b680e8e" }, "outputs": [], "source": [ "import getpass\n", "import os\n", "\n", "if 'ANTHROPIC_API_KEY' not in os.environ:\n", " os.environ['ANTHROPIC_API_KEY'] = getpass.getpass(\n", " 'Anthropic API Key:'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a Pixeltable directory to hold the tables for our demo." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "brtjK-88tTSS", "outputId": "55d08c91-438a-4c3e-c217-3cea72faca11" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created directory 'anthropic_demo'.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pixeltable as pxt\n", "\n", "# Remove the 'anthropic_demo' directory and its contents, if it exists\n", "pxt.drop_dir('anthropic_demo', force=True)\n", "pxt.create_dir('anthropic_demo')" ] }, { "cell_type": "markdown", "metadata": { "id": "0kmjJoDq9Oqe" }, "source": [ "## Messages\n", "\n", "Create a Table: In Pixeltable, create a table with columns to represent your input data and the columns where you want to store the results from Anthropic." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5ti10tXu5m3X", "outputId": "30848066-1e9b-4efd-aad7-b2271a031ec3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created table 'chat'.\n", "Added 0 column values with 0 errors.\n" ] }, { "data": { "text/plain": [ "No rows affected." ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pixeltable.functions import anthropic\n", "\n", "# Create a table in Pixeltable and add a computed column that calls Anthropic\n", "\n", "t = pxt.create_table('anthropic_demo/chat', {'input': pxt.String})\n", "\n", "msgs = [{'role': 'user', 'content': t.input}]\n", "t.add_computed_column(\n", " output=anthropic.messages(\n", " messages=msgs,\n", " model='claude-haiku-4-5-20251001',\n", " max_tokens=300,\n", " model_kwargs={\n", " # Optional dict with parameters for the Anthropic API\n", " 'system': 'Respond to the prompt with detailed historical information.',\n", " 'temperature': 0.7,\n", " },\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mePjoku95iUn", "outputId": "07c3f8f0-5301-44ba-ba33-25d10c3c26fa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 0 column values with 0 errors.\n" ] }, { "data": { "text/plain": [ "No rows affected." ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Parse the response into a new column\n", "t.add_computed_column(response=t.output.content[0].text)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 599 }, "id": "IkMM7OYb5rQ_", "outputId": "8e94af3e-485c-49f2-d7ba-b5490ec83af9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inserting rows into `chat`: 1 rows [00:00, 203.87 rows/s]\n", "Inserted 1 row with 0 errors.\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
inputresponse
What was the outcome of the 1904 US Presidential election?# 1904 US Presidential Election\n", "\n", "**Winner: Theodore Roosevelt (Republican)**\n", "\n", "## Key Results:\n", "- **Roosevelt**: 336 electoral votes, 56.4% of popular vote (~7.6 million votes)\n", "- **Alton B. Parker** (Democrat): 140 electoral votes, 37.6% of popular vote (~5.1 million votes)\n", "- **Eugene V. Debs** (Socialist): 402,283 popular votes (3.3%)\n", "\n", "## Significance:\n", "- Roosevelt won decisively and in his own right (he had assumed the presidency in 1901 after McKinley's assassination)\n", "- His victory gave him a strong mandate to pursue his progressive policies\n", "- He remained in office through 1909, becoming one of the most influential presidents of the early 20th century\n", "- Parker's poor showing reflected the Democrats' weakness during this period and the popularity of Roosevelt's "Square Deal" domestic policies and assertive foreign policy\n", "\n", "The election is often remembered as a landslide victory that validated Roosevelt's presidency and the Progressive movement's growing influence in American politics.
" ], "text/plain": [ " input \\\n", "0 What was the outcome of the 1904 US Presidenti... \n", "\n", " response \n", "0 # 1904 US Presidential Election\\n\\n**Winner: T... " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start a conversation\n", "t.insert(\n", " [\n", " {\n", " 'input': 'What was the outcome of the 1904 US Presidential election?'\n", " }\n", " ]\n", ")\n", "t.select(t.input, t.response).show()" ] }, { "cell_type": "markdown", "metadata": { "id": "lTtQcjKQAlis" }, "source": [ "### Learn More\n", "\n", "To learn more about advanced techniques like RAG operations in Pixeltable, check out the [RAG Operations in Pixeltable](https://docs.pixeltable.com/howto/use-cases/rag-operations) tutorial.\n", "\n", "If you have any questions, don't hesitate to reach out." ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "pixeltable", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 4 }