Add Activepieces integration for workflow automation
- Add Activepieces fork with SmoothSchedule custom piece - Create integrations app with Activepieces service layer - Add embed token endpoint for iframe integration - Create Automations page with embedded workflow builder - Add sidebar visibility fix for embed mode - Add list inactive customers endpoint to Public API - Include SmoothSchedule triggers: event created/updated/cancelled - Include SmoothSchedule actions: create/update/cancel events, list resources/services/customers 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
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{
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"Integrate with Contextual AI to automate document processing and AI workflows": "Integrate with Contextual AI to automate document processing and AI workflows",
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"API Key": "API Key",
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"Base URL": "Base URL",
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"Your Contextual AI API key": "Your Contextual AI API key",
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"API base URL (leave blank for default)": "API base URL (leave blank for default)",
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"\n## Contextual AI Connection Setup\n\n### Prerequisites\n- Create a Contextual AI account at [Contextual AI](https://contextual.ai)\n- Generate an API key from your workspace settings\n- You'll receive $25 in free credits (or $50 with work email)\n\n### Authentication Fields\n\n**API Key**: Your Contextual AI API key (required)\n- Sign in to your Contextual AI workspace\n- Navigate to API Keys in the sidebar\n- Click \"Create API Key\" and follow the instructions\n- Copy the generated key and paste it here\n\n**Base URL**: ": "\n## Contextual AI Connection Setup\n\n### Prerequisites\n- Create a Contextual AI account at [Contextual AI](https://contextual.ai)\n- Generate an API key from your workspace settings\n- You'll receive $25 in free credits (or $50 with work email)\n\n### Authentication Fields\n\n**API Key**: Your Contextual AI API key (required)\n- Sign in to your Contextual AI workspace\n- Navigate to API Keys in the sidebar\n- Click \"Create API Key\" and follow the instructions\n- Copy the generated key and paste it here\n\n**Base URL**: The API base URL (optional)\n- Leave blank to use the default: `https://api.contextual.ai/v1`\n- Only change if you have a custom deployment\n",
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"Query Agent": "Query Agent",
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"Generate Text": "Generate Text",
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"Ingest Document": "Ingest Document",
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"Parse File": "Parse File",
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"Create Agent": "Create Agent",
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"Invite Users": "Invite Users",
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"Create Datastore": "Create Datastore",
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"Send a message to a Contextual AI agent and get a response": "Send a message to a Contextual AI agent and get a response",
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"Generate text using Contextual AI's Grounded Language Model": "Generate text using Contextual AI's Grounded Language Model",
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"Upload and ingest a document into a Contextual AI datastore": "Upload and ingest a document into a Contextual AI datastore",
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"Parse a document file into structured Markdown and/or JSON format": "Parse a document file into structured Markdown and/or JSON format",
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"Create a new Contextual AI agent with specified configuration": "Create a new Contextual AI agent with specified configuration",
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"Invite new users to the Contextual AI workspace": "Invite new users to the Contextual AI workspace",
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"Create a new datastore for organizing documents": "Create a new datastore for organizing documents",
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"Agent": "Agent",
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"Message": "Message",
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"Conversation ID": "Conversation ID",
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"Include Retrieval Content": "Include Retrieval Content",
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"Prompt": "Prompt",
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"Model Version": "Model Version",
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"Knowledge Sources": "Knowledge Sources",
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"System Prompt": "System Prompt",
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"Max Tokens": "Max Tokens",
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"Temperature": "Temperature",
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"Top P": "Top P",
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"Avoid Commentary": "Avoid Commentary",
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"Datastore": "Datastore",
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"Document File": "Document File",
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"Custom Metadata": "Custom Metadata",
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"Configuration Override": "Configuration Override",
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"Parse Mode": "Parse Mode",
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"Page Range": "Page Range",
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"Enable Document Hierarchy": "Enable Document Hierarchy",
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"Enable Split Tables": "Enable Split Tables",
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"Max Split Table Cells": "Max Split Table Cells",
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"Figure Caption Mode": "Figure Caption Mode",
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"Agent Name": "Agent Name",
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"Description": "Description",
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"Datastores": "Datastores",
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"Filter Prompt": "Filter Prompt",
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"Users to Invite": "Users to Invite",
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"Tenant Short Name": "Tenant Short Name",
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"Datastore Name": "Datastore Name",
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"Select the agent to query": "Select the agent to query",
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"The message to send to the agent": "The message to send to the agent",
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"Optional conversation ID to continue an existing conversation (leave empty for new conversation)": "Optional conversation ID to continue an existing conversation (leave empty for new conversation)",
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"Include the text of retrieved contents in the response": "Include the text of retrieved contents in the response",
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"The text prompt to generate a response for": "The text prompt to generate a response for",
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"The version of Contextual's GLM to use": "The version of Contextual's GLM to use",
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"Optional knowledge sources to ground the generation (leave empty for general generation)": "Optional knowledge sources to ground the generation (leave empty for general generation)",
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"Optional system instructions for the model": "Optional system instructions for the model",
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"Maximum number of tokens to generate (default: 1024)": "Maximum number of tokens to generate (default: 1024)",
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"Sampling temperature (0.0 to 1.0, lower = more focused, higher = more creative)": "Sampling temperature (0.0 to 1.0, lower = more focused, higher = more creative)",
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"Nucleus sampling parameter (0.0 to 1.0)": "Nucleus sampling parameter (0.0 to 1.0)",
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"Avoid providing additional conversational commentary not grounded in context": "Avoid providing additional conversational commentary not grounded in context",
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"Select the datastore to upload the document to": "Select the datastore to upload the document to",
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"The document file to upload (PDF, HTML, DOC, DOCX, PPT, PPTX)": "The document file to upload (PDF, HTML, DOC, DOCX, PPT, PPTX)",
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"Optional custom metadata as key-value pairs (max 15 fields, 2KB total)": "Optional custom metadata as key-value pairs (max 15 fields, 2KB total)",
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"Optional configuration override in JSON format for this specific document": "Optional configuration override in JSON format for this specific document",
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"The document file to parse (PDF, DOC, DOCX, PPT, PPTX)": "The document file to parse (PDF, DOC, DOCX, PPT, PPTX)",
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"Parsing mode - basic for simple text, standard for complex documents": "Parsing mode - basic for simple text, standard for complex documents",
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"Optional page range to parse (e.g., \"0,1,2\" or \"0-2,5,6\")": "Optional page range to parse (e.g., \"0,1,2\" or \"0-2,5,6\")",
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"Add table of contents with document structure (beta feature)": "Add table of contents with document structure (beta feature)",
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"Split large tables into multiple tables with headers": "Split large tables into multiple tables with headers",
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"Threshold for splitting large tables (only used when split tables is enabled)": "Threshold for splitting large tables (only used when split tables is enabled)",
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"How thorough figure captions should be": "How thorough figure captions should be",
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"Name for the new agent": "Name for the new agent",
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"Optional description of the agent": "Optional description of the agent",
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"Select datastores to associate with this agent (leave empty to create new datastore)": "Select datastores to associate with this agent (leave empty to create new datastore)",
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"Optional system prompt for the agent": "Optional system prompt for the agent",
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"Optional prompt for filtering retrieved chunks": "Optional prompt for filtering retrieved chunks",
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"List of users to invite": "List of users to invite",
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"The short name of the tenant/workspace": "The short name of the tenant/workspace",
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"Name for the new datastore": "Name for the new datastore",
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"GLM v2": "GLM v2",
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"GLM v1": "GLM v1",
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"Basic (text-only)": "Basic (text-only)",
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"Standard (complex documents)": "Standard (complex documents)",
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"Concise": "Concise",
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"Detailed (beta)": "Detailed (beta)",
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"New Agent": "New Agent",
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"Triggers when a new Contextual AI agent is created": "Triggers when a new Contextual AI agent is created"
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}
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import { PieceAuth, createPiece, Property } from "@activepieces/pieces-framework";
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import { PieceCategory } from '@activepieces/shared';
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import { ContextualAI } from 'contextual-client';
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import { queryAgentAction } from './lib/actions/query-agent';
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import { generateAction } from './lib/actions/generate';
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import { ingestDocumentAction } from './lib/actions/ingest-document';
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import { parseFileAction } from './lib/actions/parse-file';
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import { createAgentAction } from './lib/actions/create-agent';
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import { inviteUsersAction } from './lib/actions/invite-users';
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import { createDatastoreAction } from './lib/actions/create-datastore';
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import { newAgentTrigger } from './lib/triggers/new-agent';
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const markdown = `
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## Contextual AI Connection Setup
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### Prerequisites
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- Create a Contextual AI account at [Contextual AI](https://contextual.ai)
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- Generate an API key from your workspace settings
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- You'll receive $25 in free credits (or $50 with work email)
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### Authentication Fields
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**API Key**: Your Contextual AI API key (required)
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- Sign in to your Contextual AI workspace
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- Navigate to API Keys in the sidebar
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- Click "Create API Key" and follow the instructions
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- Copy the generated key and paste it here
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**Base URL**: The API base URL (optional)
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- Leave blank to use the default: \`https://api.contextual.ai/v1\`
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- Only change if you have a custom deployment
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`;
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export const contextualAiAuth = PieceAuth.CustomAuth({
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required: true,
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description: markdown,
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props: {
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apiKey: PieceAuth.SecretText({
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displayName: 'API Key',
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description: 'Your Contextual AI API key',
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required: true,
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}),
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baseUrl: Property.ShortText({
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displayName: 'Base URL',
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description: 'API base URL (leave blank for default)',
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required: false,
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}),
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},
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validate: async ({ auth }) => {
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try {
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const { apiKey, baseUrl } = auth;
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const client = new ContextualAI({
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apiKey: apiKey,
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baseURL: baseUrl || 'https://api.contextual.ai/v1',
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});
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await client.datastores.list();
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return {
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valid: true,
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};
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} catch (error) {
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return {
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valid: false,
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error: `Authentication failed: ${error instanceof Error ? error.message : 'Unknown error'}. Please verify your API key and base URL.`,
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};
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}
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},
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});
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export const contextualAi = createPiece({
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displayName: "Contextual AI",
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description: "Integrate with Contextual AI to automate document processing and AI workflows",
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auth: contextualAiAuth,
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minimumSupportedRelease: '0.36.1',
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logoUrl: "https://cdn.activepieces.com/pieces/contextual-ai.png",
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categories: [PieceCategory.ARTIFICIAL_INTELLIGENCE],
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authors: ["onyedikachi-david"],
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actions: [queryAgentAction, generateAction, ingestDocumentAction, parseFileAction, createAgentAction, inviteUsersAction, createDatastoreAction],
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triggers: [newAgentTrigger],
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});
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import { createAction, Property } from "@activepieces/pieces-framework";
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import { contextualAiAuth } from "../../index";
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import { ContextualAI } from 'contextual-client';
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import type { Datastore } from 'contextual-client/resources/datastores';
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export const createAgentAction = createAction({
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auth: contextualAiAuth,
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name: 'create_agent',
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displayName: 'Create Agent',
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description: 'Create a new Contextual AI agent with specified configuration',
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props: {
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name: Property.ShortText({
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displayName: 'Agent Name',
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description: 'Name for the new agent',
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required: true,
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}),
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description: Property.ShortText({
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displayName: 'Description',
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description: 'Optional description of the agent',
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required: false,
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}),
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datastoreIds: Property.MultiSelectDropdown({
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auth: contextualAiAuth,
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displayName: 'Datastores',
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description: 'Select datastores to associate with this agent (leave empty to create new datastore)',
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required: false,
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refreshers: [],
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options: async ({ auth }) => {
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try {
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if (!auth) {
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return {
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disabled: true,
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options: [],
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placeholder: 'Please connect your account first',
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};
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}
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const { apiKey, baseUrl } = auth.props;
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const client = new ContextualAI({
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apiKey: apiKey,
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baseURL: baseUrl || 'https://api.contextual.ai/v1',
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});
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const allDatastores: Datastore[] = [];
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for await (const datastore of client.datastores.list()) {
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allDatastores.push(datastore);
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}
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return {
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options: allDatastores.map((datastore: Datastore) => ({
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label: datastore.name,
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value: datastore.id,
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})),
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};
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} catch (error) {
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return {
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options: [],
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error: 'Failed to fetch datastores. Please check your API key.',
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};
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}
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},
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}),
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systemPrompt: Property.LongText({
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displayName: 'System Prompt',
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description: 'Optional system prompt for the agent',
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required: false,
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}),
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filterPrompt: Property.LongText({
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displayName: 'Filter Prompt',
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description: 'Optional prompt for filtering retrieved chunks',
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required: false,
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}),
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},
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async run({ auth, propsValue }) {
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const { apiKey, baseUrl } = auth.props;
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const { name, description, datastoreIds, systemPrompt, filterPrompt } = propsValue;
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const client = new ContextualAI({
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apiKey: apiKey,
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baseURL: baseUrl || 'https://api.contextual.ai/v1',
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});
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const datastoreIdsArray = datastoreIds || [];
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const agentParams: any = {
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name,
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};
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if (description) agentParams.description = description;
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if (datastoreIdsArray.length > 0) agentParams.datastore_ids = datastoreIdsArray;
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if (systemPrompt) agentParams.multiturn_system_prompt = systemPrompt;
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if (filterPrompt) agentParams.filter_prompt = filterPrompt;
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const response = await client.agents.create(agentParams);
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return {
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agent_id: response.id,
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datastore_ids: response.datastore_ids,
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status: 'Agent created successfully',
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};
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},
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});
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@@ -0,0 +1,35 @@
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import { createAction, Property } from "@activepieces/pieces-framework";
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import { contextualAiAuth } from "../../index";
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import { ContextualAI } from 'contextual-client';
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export const createDatastoreAction = createAction({
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auth: contextualAiAuth,
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name: 'create_datastore',
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displayName: 'Create Datastore',
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description: 'Create a new datastore for organizing documents',
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props: {
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name: Property.ShortText({
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displayName: 'Datastore Name',
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description: 'Name for the new datastore',
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required: true,
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}),
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},
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async run({ auth, propsValue }) {
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const { apiKey, baseUrl } = auth.props;
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const { name } = propsValue;
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const client = new ContextualAI({
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apiKey: apiKey,
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baseURL: baseUrl || 'https://api.contextual.ai/v1',
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});
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const response = await client.datastores.create({
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name,
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});
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return {
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datastore_id: response.id,
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status: 'Datastore created successfully',
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};
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},
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});
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import { createAction, Property } from "@activepieces/pieces-framework";
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import { contextualAiAuth } from "../../index";
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import { ContextualAI } from 'contextual-client';
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export const generateAction = createAction({
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auth: contextualAiAuth,
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name: 'generate',
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displayName: 'Generate Text',
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description: 'Generate text using Contextual AI\'s Grounded Language Model',
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props: {
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prompt: Property.LongText({
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displayName: 'Prompt',
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description: 'The text prompt to generate a response for',
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required: true,
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}),
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model: Property.StaticDropdown({
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displayName: 'Model Version',
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description: 'The version of Contextual\'s GLM to use',
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required: true,
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options: {
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options: [
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{ label: 'GLM v2', value: 'v2' },
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{ label: 'GLM v1', value: 'v1' },
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],
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},
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defaultValue: 'v2',
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}),
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knowledge: Property.Array({
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displayName: 'Knowledge Sources',
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description: 'Optional knowledge sources to ground the generation (leave empty for general generation)',
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required: false,
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}),
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systemPrompt: Property.LongText({
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displayName: 'System Prompt',
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description: 'Optional system instructions for the model',
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required: false,
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}),
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maxTokens: Property.Number({
|
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displayName: 'Max Tokens',
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description: 'Maximum number of tokens to generate (default: 1024)',
|
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required: false,
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defaultValue: 1024,
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}),
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temperature: Property.Number({
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displayName: 'Temperature',
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description: 'Sampling temperature (0.0 to 1.0, lower = more focused, higher = more creative)',
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required: false,
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defaultValue: 0.7,
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}),
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topP: Property.Number({
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displayName: 'Top P',
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description: 'Nucleus sampling parameter (0.0 to 1.0)',
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required: false,
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defaultValue: 0.9,
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}),
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avoidCommentary: Property.Checkbox({
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displayName: 'Avoid Commentary',
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description: 'Avoid providing additional conversational commentary not grounded in context',
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required: false,
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||||
defaultValue: false,
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||||
}),
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||||
},
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async run({ auth, propsValue }) {
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const { apiKey, baseUrl } = auth.props;
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const {
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prompt,
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model,
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knowledge,
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systemPrompt,
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||||
maxTokens,
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temperature,
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topP,
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||||
avoidCommentary,
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} = propsValue;
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||||
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const client = new ContextualAI({
|
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apiKey: apiKey,
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baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
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const messages: Array<{ role: 'user' | 'assistant'; content: string }> = [
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{ role: 'user', content: prompt },
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];
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||||
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const response = await client.generate.create({
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messages,
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model,
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knowledge: (knowledge || []) as string[],
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||||
system_prompt: systemPrompt,
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||||
max_new_tokens: maxTokens,
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||||
temperature,
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top_p: topP,
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||||
avoid_commentary: avoidCommentary,
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||||
});
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||||
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return {
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||||
response: response.response,
|
||||
};
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||||
},
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||||
});
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@@ -0,0 +1,101 @@
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import { createAction, Property } from "@activepieces/pieces-framework";
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||||
import { contextualAiAuth } from "../../index";
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||||
import { ContextualAI, toFile } from 'contextual-client';
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import type { Datastore } from 'contextual-client/resources/datastores';
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||||
export const ingestDocumentAction = createAction({
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||||
auth: contextualAiAuth,
|
||||
name: 'ingest_document',
|
||||
displayName: 'Ingest Document',
|
||||
description: 'Upload and ingest a document into a Contextual AI datastore',
|
||||
props: {
|
||||
datastoreId: Property.Dropdown({
|
||||
auth: contextualAiAuth,
|
||||
displayName: 'Datastore',
|
||||
description: 'Select the datastore to upload the document to',
|
||||
required: true,
|
||||
refreshers: [],
|
||||
options: async ({ auth }) => {
|
||||
try {
|
||||
if (!auth) {
|
||||
return {
|
||||
disabled: true,
|
||||
options: [],
|
||||
placeholder: 'Please connect your account first',
|
||||
};
|
||||
}
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const allDatastores: Datastore[] = [];
|
||||
for await (const datastore of client.datastores.list()) {
|
||||
allDatastores.push(datastore);
|
||||
}
|
||||
|
||||
return {
|
||||
options: allDatastores.map((datastore: Datastore) => ({
|
||||
label: datastore.name,
|
||||
value: datastore.id,
|
||||
})),
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
options: [],
|
||||
error: 'Failed to fetch datastores. Please check your API key.',
|
||||
};
|
||||
}
|
||||
},
|
||||
}),
|
||||
file: Property.File({
|
||||
displayName: 'Document File',
|
||||
description: 'The document file to upload (PDF, HTML, DOC, DOCX, PPT, PPTX)',
|
||||
required: true,
|
||||
}),
|
||||
customMetadata: Property.Object({
|
||||
displayName: 'Custom Metadata',
|
||||
description: 'Optional custom metadata as key-value pairs (max 15 fields, 2KB total)',
|
||||
required: false,
|
||||
}),
|
||||
configuration: Property.LongText({
|
||||
displayName: 'Configuration Override',
|
||||
description: 'Optional configuration override in JSON format for this specific document',
|
||||
required: false,
|
||||
}),
|
||||
},
|
||||
async run({ auth, propsValue }) {
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const { datastoreId, file, customMetadata, configuration } = propsValue;
|
||||
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
let metadataString: string | undefined;
|
||||
if (customMetadata && Object.keys(customMetadata).length > 0) {
|
||||
metadataString = JSON.stringify({
|
||||
custom_metadata: customMetadata,
|
||||
});
|
||||
}
|
||||
|
||||
const uploadableFile = await toFile(file.data, file.filename || 'uploaded-file', {
|
||||
type: file.extension ? `application/${file.extension}` : 'application/octet-stream',
|
||||
});
|
||||
|
||||
const fileData = {
|
||||
file: uploadableFile,
|
||||
custom_metadata: metadataString,
|
||||
configuration: configuration,
|
||||
};
|
||||
|
||||
const response = await client.datastores.documents.ingest(datastoreId, fileData);
|
||||
|
||||
return {
|
||||
document_id: response.id,
|
||||
status: 'Document ingestion started. Use the document ID to check status.',
|
||||
};
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,55 @@
|
||||
import { createAction, Property } from "@activepieces/pieces-framework";
|
||||
import { contextualAiAuth } from "../../index";
|
||||
import { ContextualAI } from 'contextual-client';
|
||||
|
||||
export const inviteUsersAction = createAction({
|
||||
auth: contextualAiAuth,
|
||||
name: 'invite_users',
|
||||
displayName: 'Invite Users',
|
||||
description: 'Invite new users to the Contextual AI workspace',
|
||||
props: {
|
||||
users: Property.Array({
|
||||
displayName: 'Users to Invite',
|
||||
description: 'List of users to invite',
|
||||
required: true,
|
||||
properties: {
|
||||
email: Property.ShortText({
|
||||
displayName: 'Email',
|
||||
description: 'Email address of the user to invite',
|
||||
required: true,
|
||||
}),
|
||||
},
|
||||
}),
|
||||
tenantShortName: Property.ShortText({
|
||||
displayName: 'Tenant Short Name',
|
||||
description: 'The short name of the tenant/workspace',
|
||||
required: true,
|
||||
}),
|
||||
},
|
||||
async run({ auth, propsValue }) {
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const { users, tenantShortName } = propsValue;
|
||||
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const newUsers = users.map((user: any) => ({
|
||||
email: user.email,
|
||||
agent_level_roles: ['AGENT_LEVEL_USER' as const],
|
||||
}));
|
||||
|
||||
const response = await client.users.invite({
|
||||
new_users: newUsers,
|
||||
tenant_short_name: tenantShortName,
|
||||
});
|
||||
|
||||
return {
|
||||
invited_users: response.invited_user_emails,
|
||||
errors: response.error_details,
|
||||
total_invited: response.invited_user_emails.length,
|
||||
total_errors: Object.keys(response.error_details).length,
|
||||
};
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,98 @@
|
||||
import { createAction, Property } from "@activepieces/pieces-framework";
|
||||
import { contextualAiAuth } from "../../index";
|
||||
import { ContextualAI } from 'contextual-client';
|
||||
|
||||
export const parseFileAction = createAction({
|
||||
auth: contextualAiAuth,
|
||||
name: 'parse_file',
|
||||
displayName: 'Parse File',
|
||||
description: 'Parse a document file into structured Markdown and/or JSON format',
|
||||
props: {
|
||||
file: Property.File({
|
||||
displayName: 'Document File',
|
||||
description: 'The document file to parse (PDF, DOC, DOCX, PPT, PPTX)',
|
||||
required: true,
|
||||
}),
|
||||
parseMode: Property.StaticDropdown({
|
||||
displayName: 'Parse Mode',
|
||||
description: 'Parsing mode - basic for simple text, standard for complex documents',
|
||||
required: true,
|
||||
options: {
|
||||
options: [
|
||||
{ label: 'Basic (text-only)', value: 'basic' },
|
||||
{ label: 'Standard (complex documents)', value: 'standard' },
|
||||
],
|
||||
},
|
||||
defaultValue: 'standard',
|
||||
}),
|
||||
pageRange: Property.ShortText({
|
||||
displayName: 'Page Range',
|
||||
description: 'Optional page range to parse (e.g., "0,1,2" or "0-2,5,6")',
|
||||
required: false,
|
||||
}),
|
||||
enableDocumentHierarchy: Property.Checkbox({
|
||||
displayName: 'Enable Document Hierarchy',
|
||||
description: 'Add table of contents with document structure (beta feature)',
|
||||
required: false,
|
||||
defaultValue: false,
|
||||
}),
|
||||
enableSplitTables: Property.Checkbox({
|
||||
displayName: 'Enable Split Tables',
|
||||
description: 'Split large tables into multiple tables with headers',
|
||||
required: false,
|
||||
defaultValue: false,
|
||||
}),
|
||||
maxSplitTableCells: Property.Number({
|
||||
displayName: 'Max Split Table Cells',
|
||||
description: 'Threshold for splitting large tables (only used when split tables is enabled)',
|
||||
required: false,
|
||||
}),
|
||||
figureCaptionMode: Property.StaticDropdown({
|
||||
displayName: 'Figure Caption Mode',
|
||||
description: 'How thorough figure captions should be',
|
||||
required: false,
|
||||
options: {
|
||||
options: [
|
||||
{ label: 'Concise', value: 'concise' },
|
||||
{ label: 'Detailed (beta)', value: 'detailed' },
|
||||
],
|
||||
},
|
||||
defaultValue: 'concise',
|
||||
}),
|
||||
},
|
||||
async run({ auth, propsValue }) {
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const {
|
||||
file,
|
||||
parseMode,
|
||||
pageRange,
|
||||
enableDocumentHierarchy,
|
||||
enableSplitTables,
|
||||
maxSplitTableCells,
|
||||
figureCaptionMode,
|
||||
} = propsValue;
|
||||
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const parseParams: any = {
|
||||
raw_file: file.data,
|
||||
parse_mode: parseMode,
|
||||
};
|
||||
|
||||
if (pageRange) parseParams.page_range = pageRange;
|
||||
if (enableDocumentHierarchy !== undefined) parseParams.enable_document_hierarchy = enableDocumentHierarchy;
|
||||
if (enableSplitTables !== undefined) parseParams.enable_split_tables = enableSplitTables;
|
||||
if (maxSplitTableCells !== undefined) parseParams.max_split_table_cells = maxSplitTableCells;
|
||||
if (figureCaptionMode) parseParams.figure_caption_mode = figureCaptionMode;
|
||||
|
||||
const response = await client.parse.create(parseParams);
|
||||
|
||||
return {
|
||||
job_id: response.job_id,
|
||||
status: 'Parse job started. Use the job ID to check status and get results.',
|
||||
};
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,96 @@
|
||||
import { createAction, Property } from "@activepieces/pieces-framework";
|
||||
import { contextualAiAuth } from "../../index";
|
||||
import { ContextualAI } from 'contextual-client';
|
||||
import type { Agent } from 'contextual-client/resources/agents';
|
||||
|
||||
export const queryAgentAction = createAction({
|
||||
auth: contextualAiAuth,
|
||||
name: 'query_agent',
|
||||
displayName: 'Query Agent',
|
||||
description: 'Send a message to a Contextual AI agent and get a response',
|
||||
props: {
|
||||
agentId: Property.Dropdown({
|
||||
auth: contextualAiAuth,
|
||||
displayName: 'Agent',
|
||||
description: 'Select the agent to query',
|
||||
required: true,
|
||||
refreshers: [],
|
||||
options: async ({ auth }) => {
|
||||
try {
|
||||
if (!auth) {
|
||||
return {
|
||||
disabled: true,
|
||||
options: [],
|
||||
placeholder: 'Please connect your account first',
|
||||
};
|
||||
}
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const allAgents: Agent[] = [];
|
||||
for await (const agent of client.agents.list()) {
|
||||
allAgents.push(agent);
|
||||
}
|
||||
|
||||
return {
|
||||
options: allAgents.map((agent: Agent) => ({
|
||||
label: agent.name,
|
||||
value: agent.id,
|
||||
})),
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
options: [],
|
||||
error: 'Failed to fetch agents. Please check your API key.',
|
||||
};
|
||||
}
|
||||
},
|
||||
}),
|
||||
message: Property.LongText({
|
||||
displayName: 'Message',
|
||||
description: 'The message to send to the agent',
|
||||
required: true,
|
||||
}),
|
||||
conversationId: Property.ShortText({
|
||||
displayName: 'Conversation ID',
|
||||
description: 'Optional conversation ID to continue an existing conversation (leave empty for new conversation)',
|
||||
required: false,
|
||||
}),
|
||||
includeRetrievalContent: Property.Checkbox({
|
||||
displayName: 'Include Retrieval Content',
|
||||
description: 'Include the text of retrieved contents in the response',
|
||||
required: false,
|
||||
defaultValue: false,
|
||||
}),
|
||||
},
|
||||
async run({ auth, propsValue }) {
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const { agentId, message, conversationId, includeRetrievalContent } = propsValue;
|
||||
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const messages: Array<{ role: 'user' | 'system' | 'assistant' | 'knowledge'; content: string }> =
|
||||
conversationId ? [] : [{ role: 'user' as const, content: message }];
|
||||
|
||||
const response = await client.agents.query.create(agentId, {
|
||||
messages: messages,
|
||||
conversation_id: conversationId,
|
||||
include_retrieval_content_text: includeRetrievalContent,
|
||||
});
|
||||
|
||||
return {
|
||||
conversation_id: response.conversation_id,
|
||||
message: response.message,
|
||||
retrieval_contents: response.retrieval_contents,
|
||||
attributions: response.attributions,
|
||||
groundedness_scores: response.groundedness_scores,
|
||||
message_id: response.message_id,
|
||||
};
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,72 @@
|
||||
import { createTrigger, TriggerStrategy, AppConnectionValueForAuthProperty } from '@activepieces/pieces-framework';
|
||||
import { DedupeStrategy, Polling, pollingHelper } from '@activepieces/pieces-common';
|
||||
import { contextualAiAuth } from '../../index';
|
||||
import { ContextualAI } from 'contextual-client';
|
||||
import type { Agent } from 'contextual-client/resources/agents';
|
||||
|
||||
const polling: Polling<AppConnectionValueForAuthProperty<typeof contextualAiAuth>, Record<string, never>> = {
|
||||
strategy: DedupeStrategy.TIMEBASED,
|
||||
items: async ({ auth, lastFetchEpochMS }) => {
|
||||
const { apiKey, baseUrl } = auth.props;
|
||||
const client = new ContextualAI({
|
||||
apiKey: apiKey,
|
||||
baseURL: baseUrl || 'https://api.contextual.ai/v1',
|
||||
});
|
||||
|
||||
const allAgents: Agent[] = [];
|
||||
for await (const agent of client.agents.list()) {
|
||||
allAgents.push(agent);
|
||||
}
|
||||
|
||||
const newAgents = lastFetchEpochMS
|
||||
? allAgents.filter(agent => {
|
||||
return true;
|
||||
})
|
||||
: allAgents;
|
||||
|
||||
const items = newAgents.map((agent: Agent) => ({
|
||||
epochMilliSeconds: Date.now(),
|
||||
data: {
|
||||
id: agent.id,
|
||||
name: agent.name,
|
||||
description: agent.description,
|
||||
},
|
||||
}));
|
||||
|
||||
return items;
|
||||
},
|
||||
};
|
||||
|
||||
export const newAgentTrigger = createTrigger({
|
||||
auth: contextualAiAuth,
|
||||
name: 'new_agent',
|
||||
displayName: 'New Agent',
|
||||
description: 'Triggers when a new Contextual AI agent is created',
|
||||
props: {},
|
||||
type: TriggerStrategy.POLLING,
|
||||
sampleData: {
|
||||
id: 'agent_123',
|
||||
name: 'Sample Agent',
|
||||
description: 'A sample agent for testing',
|
||||
},
|
||||
async onEnable(context) {
|
||||
await pollingHelper.onEnable(polling, {
|
||||
auth: context.auth,
|
||||
store: context.store,
|
||||
propsValue: context.propsValue,
|
||||
});
|
||||
},
|
||||
async onDisable(context) {
|
||||
await pollingHelper.onDisable(polling, {
|
||||
auth: context.auth,
|
||||
store: context.store,
|
||||
propsValue: context.propsValue,
|
||||
});
|
||||
},
|
||||
async test(context) {
|
||||
return await pollingHelper.test(polling, context);
|
||||
},
|
||||
async run(context) {
|
||||
return await pollingHelper.poll(polling, context);
|
||||
},
|
||||
});
|
||||
Reference in New Issue
Block a user