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| --- | ||
| title: "GCP" | ||
| description: "" | ||
| --- | ||
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| This documentation is to deploy activepieces on VM Instance or VM Instance Group, we should first create VM template | ||
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| ## Create VM Template | ||
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| First choose machine type (e.g e2-medium) | ||
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| After configuring the VM Template, you can proceed to click on "Deploy Container" and specify the following container-specific settings: | ||
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| - Image: activepieces/activepieces | ||
| - Run as a privileged container: true | ||
| - Environment Variables: | ||
| - `AP_QUEUE_MODE`: MEMORY | ||
| - `AP_DB_TYPE`: SQLITE3 | ||
| - `AP_FRONTEND_URL`: http://localhost:80 | ||
| - `AP_EXECUTION_MODE`: SANDBOXED | ||
| - Firewall: Allow HTTP traffic (for testing purposes only) | ||
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| Once these details are entered, click on the "Deploy" button and patiently wait for the container deployment process to complete.\ | ||
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| After a successful deployment, you can access the ActivePieces application by visiting the external IP address of the VM on GCP. | ||
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| ## Production Deployment | ||
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| Please visit [ActivePieces](../configuration) for more details on how to customize the application. |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,30 +1,33 @@ | ||
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| import { APChatMessage } from '@activepieces/shared' | ||
| import { ApEmbeddings } from '../embedings' | ||
| import { APLLM } from './llm' | ||
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| type ChatbotAnswerContext = { | ||
| type ChatbotAskContext = { | ||
| settings: { | ||
| prompt: string | ||
| } | ||
| input: string | ||
| llm: APLLM | ||
| embeddings: ApEmbeddings | ||
| history: APChatMessage[] | ||
| } | ||
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| class IChatbot { | ||
| constructor( | ||
| public readonly name: string, | ||
| public readonly run: (ctx: ChatbotAnswerContext) => Promise<string>, | ||
| public readonly run: (ctx: ChatbotAskContext) => Promise<string>, | ||
| ) { } | ||
| } | ||
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| export const createChatbot = (request: { | ||
| name: string | ||
| run: (ctx: ChatbotAnswerContext) => Promise<string> | ||
| run: (ctx: ChatbotAskContext) => Promise<string> | ||
| }) => { | ||
| return new IChatbot( | ||
| request.name, | ||
| request.run, | ||
| ) | ||
| } | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,30 +1,96 @@ | ||
| import { ChatMessageHistory, ConversationSummaryMemory } from 'langchain/memory' | ||
| import { BaseChatMessageHistory, SystemMessage } from 'langchain/schema' | ||
| import { BaseLanguageModel } from 'langchain/dist/base_language' | ||
| import { OpenAI } from 'langchain/llms/openai' | ||
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| import { BufferWindowMemory } from 'langchain/memory' | ||
| import { PromptTemplate } from 'langchain/prompts' | ||
| import { ConversationChain } from 'langchain/chains' | ||
| import { APChatMessage } from '@activepieces/shared' | ||
| export type APLLM = { | ||
| chat: ({ input, temperature, maxTokens }: AskChat) => Promise<string> | ||
| } | ||
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| export const llm = (openAIApiKey: string, modelName: string) => { | ||
| return { | ||
| async chat({ input, temperature, maxTokens }: AskChat) { | ||
| async chat({ input, temperature, maxTokens, history, settingsPrompt }: AskChat) { | ||
| const model = new OpenAI({ | ||
| modelName, | ||
| openAIApiKey, | ||
| temperature: temperature || 0.7, | ||
| maxTokens, | ||
| }) | ||
| const response = await model.call(input) | ||
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| const template = ` | ||
| ${settingsPrompt} | ||
| The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. | ||
| Current conversation: | ||
| System: {chat_summary} | ||
| {recent_chat_history} | ||
| Human: {human_input} | ||
| AI:` | ||
| const prompt = new PromptTemplate({ | ||
| inputVariables: ['chat_summary', 'human_input', 'recent_chat_history'], | ||
| template, | ||
| }) | ||
| const k = 10 | ||
| const summary = history.length > k ? await summarizeMessages(model, history) : '' | ||
| const historyThread = await createChatMessageHistory(history) | ||
| const memory = new BufferWindowMemory({ | ||
| chatHistory: historyThread, | ||
| memoryKey: 'recent_chat_history', | ||
| inputKey: 'human_input', | ||
| k, | ||
| returnMessages: false, | ||
| }) | ||
| const chain = new ConversationChain({ | ||
| memory, | ||
| verbose: true, | ||
| llm: model, | ||
| prompt, | ||
| }) | ||
| const response = await chain.predict({ | ||
| chat_summary: summary, | ||
| human_input: input, | ||
| }) | ||
| return response | ||
| }, | ||
| } | ||
| } | ||
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| type AskChat = { | ||
| export type AskChat = { | ||
| input: string | ||
| history: { | ||
| text: string | ||
| role: 'bot' | 'user' | ||
| }[] | ||
| history: APChatMessage[] | ||
| settingsPrompt: string | ||
| temperature?: number | ||
| maxTokens?: number | ||
| } | ||
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| export const summarizeMessages = async (model: BaseLanguageModel, messages: APChatMessage[]): Promise<string> => { | ||
| const summary_memory = new ConversationSummaryMemory({ | ||
| llm: model, | ||
| chatHistory: await createChatMessageHistory(messages), | ||
| }) | ||
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| const summary = await summary_memory.predictNewSummary(await summary_memory.chatHistory.getMessages(), '') | ||
| return summary | ||
| } | ||
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| export const createChatMessageHistory = async (messages: APChatMessage[]): Promise<BaseChatMessageHistory> => { | ||
| const history = new ChatMessageHistory() | ||
| for (const message of messages) { | ||
| switch (message.role) { | ||
| case 'user': { | ||
| await history.addUserMessage(message.text) | ||
| break | ||
| } | ||
| case 'bot': { | ||
| await history.addAIChatMessage(message.text) | ||
| break | ||
| } | ||
| default: { | ||
| await history.addMessage(new SystemMessage(message.text)) | ||
| break | ||
| } | ||
| } | ||
| } | ||
| return history | ||
| } |
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