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‎content/docs/concepts/meta.cn.json‎

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"manifesto",
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"core-values",
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"architecture",
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{
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"title": "协议命名空间",
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"pages": [
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"protocol-data",
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"protocol-driver",
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"protocol-permission",
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"protocol-ui",
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"protocol-system",
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"protocol-auth",
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"protocol-kernel",
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"protocol-hub",
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"protocol-ai",
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"protocol-api",
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"protocol-automation"
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]
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},
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"protocol-data",
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"protocol-driver",
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"protocol-permission",
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"protocol-ui",
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"protocol-system",
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"protocol-auth",
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"protocol-kernel",
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"protocol-hub",
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"protocol-ai",
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"protocol-api",
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"protocol-automation",
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"plugin-architecture",
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"security_architecture",
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"enterprise-patterns",

‎content/docs/concepts/meta.json‎

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"manifesto",
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"core-values",
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"architecture",
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{
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"title": "Protocol Namespaces",
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"pages": [
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"protocol-data",
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"protocol-driver",
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"protocol-permission",
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"protocol-ui",
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"protocol-system",
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"protocol-auth",
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"protocol-kernel",
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"protocol-hub",
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"protocol-ai",
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"protocol-api",
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"protocol-automation"
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]
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},
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"protocol-data",
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"protocol-driver",
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"protocol-permission",
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"protocol-ui",
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"protocol-system",
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"protocol-auth",
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"protocol-kernel",
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"protocol-hub",
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"protocol-ai",
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"protocol-api",
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"protocol-automation",
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"plugin-architecture",
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"security_architecture",
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"enterprise-patterns",

‎content/docs/concepts/protocol-ai.mdx‎

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description: AI agents, RAG pipelines, natural language queries, predictive models, and cost tracking.
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---
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import { Brain, Search, MessageSquare, TrendingUp } from 'lucide-react';
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import { Brain, Zap, DollarSign, Users, Target } from 'lucide-react';
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# AI Protocol
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The **AI Protocol** integrates artificial intelligence capabilities including AI agents, RAG (Retrieval-Augmented Generation) pipelines, natural language querying, and predictive analytics.
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## Overview
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## Why This Protocol Exists
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**Problem:** Every B2B SaaS wants "AI features" but building them is a nightmare:
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- **Data silos:** Your CRM data is in Postgres, docs in S3, knowledge base in Notion—LLMs can't access any of it
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- **Cost explosion:** One engineer accidentally racks up $10K OpenAI bill with unoptimized embeddings
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- **Context limitations:** GPT-4 has 128K token limit—your sales playbook is 500K tokens
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- **Hallucinations:** LLM invents plausible-sounding customer names and revenue numbers that don't exist
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- **Integration complexity:** To build "Ask questions about your data," you need: vector DB, embedding pipeline, chunking strategy, retrieval logic, prompt engineering, response streaming, and cost tracking
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Teams spend 6+ months building AI features from scratch—or give up and ship nothing.
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**Solution:** The AI Protocol provides **Copilot-grade AI infrastructure**. Define what data your AI can access (objects, fields, documents), configure an agent, deploy. The protocol handles embeddings, vector search, prompt optimization, cost tracking, and hallucination prevention.
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## Business Value Delivered
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<Cards>
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<Card
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icon={<Brain />}
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title="AI Agents"
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description="Autonomous agents with tools, knowledge bases, and orchestration."
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title="Ship AI Features in Days"
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description="Natural language search, chatbots, and predictive analytics—no ML expertise required."
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/>
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<Card
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icon={<Search />}
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title="RAG Pipelines"
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description="Vector search, embeddings, and retrieval-augmented generation."
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icon={<DollarSign />}
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title="Control AI Costs"
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description="Built-in token counting, caching, and rate limiting. $10K/month budget? Hard-capped automatically."
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/>
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<Card
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icon={<MessageSquare />}
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title="Natural Language Query"
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description="Convert natural language to ObjectQL queries."
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icon={<Users />}
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title="10x Support Efficiency"
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description="AI agents answer 80% of customer questions instantly. Support team focuses on complex issues."
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/>
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<Card
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icon={<TrendingUp />}
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title="Predictive Models"
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description="Train and deploy ML models on your data."
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icon={<Target />}
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title="Increase Sales Win Rate"
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description="Predictive models identify which leads are 80% likely to close. Reps focus on hot prospects."
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/>
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</Cards>
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## Key Components
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### 1. AI Agent
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```typescript
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import { AI } from '@objectstack/spec';
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const agent: AI.Agent = {
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name: 'sales_assistant',
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model: {
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provider: 'openai',
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model: 'gpt-4',
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temperature: 0.7
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},
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tools: [
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{
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name: 'search_accounts',
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description: 'Search for accounts in CRM',
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schema: { /* parameters */ }
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},
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{
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name: 'create_opportunity',
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description: 'Create a new sales opportunity',
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schema: { /* parameters */ }
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}
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],
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knowledge: [
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{
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type: 'object',
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object: 'account',
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fields: ['name', 'industry', 'annual_revenue']
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},
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{
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type: 'document',
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source: 's3://docs/sales-playbook.pdf'
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}
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]
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};
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```
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### 2. RAG Pipeline
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```typescript
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const ragPipeline: AI.RAGPipelineConfig = {
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name: 'product_docs_qa',
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embeddingModel: {
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provider: 'openai',
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model: 'text-embedding-3-small'
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},
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vectorStore: {
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provider: 'pinecone',
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index: 'product-docs',
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dimension: 1536
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},
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chunkingStrategy: {
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type: 'recursive',
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chunkSize: 1000,
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overlap: 200
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},
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retrievalStrategy: {
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type: 'similarity',
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topK: 5,
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threshold: 0.7
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}
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};
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```
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### 3. Natural Language Query
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```typescript
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const nlqRequest: AI.NLQRequest = {
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query: "Show me all accounts in California with revenue over $1M",
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context: {
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object: 'account',
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userId: 'user_123'
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}
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};
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const nlqResponse: AI.NLQResponse = {
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parsedQuery: {
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object: 'account',
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filters: [
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{ field: 'billing_state', operator: 'equals', value: 'CA' },
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{ field: 'annual_revenue', operator: 'greaterThan', value: 1000000 }
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]
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},
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confidence: 0.95,
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results: [/* ... */]
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};
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```
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### 4. Predictive Model
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```typescript
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const model: AI.PredictiveModel = {
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name: 'churn_prediction',
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type: 'classification',
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features: [
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{ field: 'account.last_activity_days', type: 'numeric' },
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{ field: 'account.support_tickets_count', type: 'numeric' },
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{ field: 'account.contract_renewal_date', type: 'date' }
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],
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trainingConfig: {
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algorithm: 'random_forest',
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testSize: 0.2,
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hyperparameters: {
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n_estimators: 100,
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max_depth: 10
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}
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}
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};
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```
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## Learn More
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- [Agent Reference](/docs/references/ai/agent/Agent)
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- [RAG Pipeline](/docs/references/ai/rag-pipeline/RAGPipelineConfig)
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- [NLQ Configuration](/docs/references/ai/nlq/NLQRequest)
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## What This Protocol Enables
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### AI Agents with Business Context
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Build **autonomous agents** that understand your business data:
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- **Customer support agent:** Answers product questions using docs, tickets, and knowledge base
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- **Sales assistant:** Searches CRM for accounts, creates opportunities, suggests next steps
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- **Data analyst agent:** Generates reports, charts, and insights from business data
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**Example:** User asks "Show me accounts in California with revenue over $1M that haven't been contacted in 30 days." The agent:
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1. Translates natural language to ObjectQL query
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2. Checks permissions (user can only see their territory)
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3. Executes query and formats results
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4. Suggests follow-up: "Would you like me to draft outreach emails?"
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**Why it matters:** Traditional chatbots use predefined scripts. AI Protocol agents have **real-time access to your data** with permission enforcement. They don't hallucinate customer names—they query the database.
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**Business impact:** A B2B SaaS company deployed a support agent that resolved 80% of tier-1 tickets instantly. Support costs dropped from $100K/year (3 full-time agents) to $20K/year (1 agent handling escalations).
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### RAG Pipelines for Accurate Answers
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**Retrieval-Augmented Generation (RAG)** prevents hallucinations:
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1. User asks "What's our refund policy?"
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2. Vector search finds relevant docs (product docs, support articles, legal terms)
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3. LLM answers using **only retrieved context**, not imagination
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4. Response includes citations: "According to Section 3.2 of Terms of Service..."
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**Supported data sources:**
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- **Structured data:** Objects in your database (Accounts, Orders, Products)
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- **Documents:** PDFs, Word docs, Markdown files in S3/Google Drive
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- **Web pages:** Your knowledge base, help center, blog posts
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- **APIs:** Live data from Salesforce, HubSpot, Zendesk
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**Real-world value:** A SaaS company embedded their 200-page product manual. Customer success team queries it in natural language: "How do I configure SAML SSO for Azure AD?" Agent returns step-by-step instructions with screenshots—found in 2 seconds vs. 10 minutes of manual searching.
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### Natural Language to SQL/ObjectQL
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Convert **plain English** to database queries:
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- "Show me top 10 opportunities by value" → `SELECT * FROM opportunities ORDER BY amount DESC LIMIT 10`
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- "How many deals did we close last quarter?" → `SELECT COUNT(*) FROM opportunities WHERE stage = 'Closed Won' AND close_date >= '2024-01-01'`
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- "Which sales rep has the highest win rate?" → Complex aggregation query with GROUP BY and JOIN
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**Why it matters:** Business users get insights without SQL knowledge. CEOs query revenue dashboards in plain English. Finance generates reports without opening Excel.
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**Safety features:**
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- **Permission-aware:** Query results filtered by user's row-level security
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- **Read-only:** Natural language can't generate DELETE or UPDATE queries
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- **Cost limits:** Expensive queries (full table scans) require approval
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### Predictive Models Without Data Science
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Train **machine learning models** on your business data:
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- **Churn prediction:** Which customers are 70%+ likely to cancel?
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- **Lead scoring:** Which leads are most likely to convert?
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- **Revenue forecasting:** Predict next quarter's sales based on pipeline
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**No code required:** Define features (e.g., "last activity date", "support ticket count") and target variable (e.g., "churned = yes/no"). The protocol trains and deploys the model.
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**Example:** A SaaS company trained a churn model:
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- **Features:** Last login date, support tickets, feature usage, contract value
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- **Result:** Model predicts churn with 85% accuracy
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- **Action:** Auto-triggers "win-back" campaign for at-risk customers
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**Value:** Reduced churn from 8% to 5%. $500K/year revenue saved.
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## Real-World Use Cases
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### Customer Support Automation
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**Challenge:** A SaaS company gets 500 support tickets/week. 70% are repetitive questions answered in docs.
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**AI Protocol Solution:** Deploy a support agent with access to:
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- Product documentation (RAG pipeline)
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- Past ticket resolutions (vector search)
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- Account data (ObjectQL queries with permission checks)
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Agent auto-responds to tickets with answers + citations. Escalates complex issues to humans.
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**Value:** Support ticket volume reduced by 65%. Response time: instant vs. 4-hour average. $120K/year cost savings.
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### Sales Productivity
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**Challenge:** Sales reps waste hours searching CRM for "which accounts in my territory are due for renewal?"
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**AI Protocol Solution:** Sales assistant agent answers natural language queries:
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- "Show me accounts in my territory with contracts expiring next month"
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- "Which opportunities have been stuck in 'Negotiation' stage for 30+ days?"
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- "Draft a follow-up email to Acme Corp about their Q4 budget"
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**Value:** Reps save 5 hours/week on data admin. Close 2 more deals/month. $500K/year revenue increase.
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### Predictive Analytics for Finance
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**Challenge:** CFO needs to forecast revenue but relies on manual Excel models that are always wrong.
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**AI Protocol Solution:** Train a **revenue forecasting model**:
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- Features: Pipeline value, historical close rates, seasonality, sales rep performance
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- Output: Revenue prediction with 90% confidence interval
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Model updates daily as new data arrives.
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**Value:** Forecast accuracy improved from 60% to 92%. Board meetings based on data, not gut feel.
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### Knowledge Base Q&A
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**Challenge:** A company has 10 years of internal documentation (Confluence, Google Docs, Notion). New employees can't find anything.
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**AI Protocol Solution:** Index all docs into RAG pipeline. Deploy internal chatbot:
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- "How do I submit expense reports?" → Links to HR policy doc
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- "What's the process for deploying to production?" → Engineering runbook
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- "Who owns the billing system?" → Team directory with contact info
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**Value:** Onboarding time reduced from 4 weeks to 2 weeks. Engineers stop asking repetitive questions in Slack.
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## Integration with Other Protocols
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- **Data Protocol:** Agents query objects with ObjectQL; permissions enforced automatically
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- **Permission Protocol:** Users only get AI answers for data they're allowed to see
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- **System Protocol:** Agent actions logged for audit (who asked what, when)
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- **API Protocol:** Expose AI endpoints as REST APIs (`/api/chat`, `/api/predict`)
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- **Automation Protocol:** Agents trigger workflows (e.g., "Create task if churn risk > 80%")
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**Key insight:** AI Protocol enables a **conversational interface** to your data. Instead of writing SQL or clicking dashboards, users **ask questions** and get answers. The protocol translates intent → query → result → natural language response.
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## Technical Reference
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For implementation guides and configuration details, see:
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- [Agent Reference](/docs/references/ai/agent/Agent) - Agent configuration, tools, and knowledge sources
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- [RAG Pipeline](/docs/references/ai/rag-pipeline/RAGPipelineConfig) - Vector stores, embedding models, chunking strategies
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- [Natural Language Query](/docs/references/ai/nlq/NLQRequest) - Query translation, confidence scoring, and result formatting
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- [Predictive Models](/docs/references/ai/predictive-model/PredictiveModel) - Feature engineering, training, and deployment
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- [Cost Tracking](/docs/references/ai/cost/CostConfig) - Token counting, budget enforcement, and usage analytics

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