Think Members

name: analyze-sentiment
type: Think
description: Analyze text sentiment

config:
  provider: openai
  model: gpt-4o-mini
  routing: cloudflare-gateway
  temperature: 0.3
  maxTokens: 100
  systemPrompt: |
    Analyze the sentiment of the given text.
    Respond with: positive, negative, or neutral.

// members/analyze-sentiment/index.ts
// No implementation needed for Think members!
export default null;

config:
  # OpenAI (GPT models)
  provider: openai
  model: gpt-4o

  # Anthropic (Claude models)
  provider: anthropic
  model: claude-3-5-sonnet-20241022

  # Workers AI (Edge models)
  provider: workers-ai
  model: "@cf/meta/llama-3.1-8b-instruct"

  # Groq (Ultra-fast inference)
  provider: groq
  model: llama-3.1-70b-versatile

config:
  # AI Gateway (recommended) - caching + analytics
  routing: cloudflare-gateway

  # Platform-native (Workers AI only) - ultra-fast
  routing: cloudflare

  # Direct API calls - bypass gateway
  routing: direct

config:
  # Creative (varied output)
  temperature: 0.9

  # Balanced
  temperature: 0.7

  # Deterministic (consistent output)
  temperature: 0.1

config:
  # Short responses
  maxTokens: 100

  # Medium responses
  maxTokens: 500

  # Long responses
  maxTokens: 2000

  # Maximum (model-dependent)
  maxTokens: 4000

config:
  systemPrompt: |
    You are a helpful assistant that analyzes text sentiment.
    Always respond in JSON format.

config:
  systemPrompt: |
    Analyze the company and respond with JSON:
    {
      "industry": "string",
      "size": "small" | "medium" | "large",
      "confidence": number (0-1),
      "summary": "string"
    }

config:
  systemPrompt: |
    You are an expert business analyst with 20 years of experience.
    Analyze companies objectively and provide actionable insights.
    Focus on:
    - Financial health
    - Market position
    - Growth potential
    - Competitive advantages

config:
  systemPrompt: |
    Classify customer feedback sentiment.

    Examples:
    Input: "I love this product! Best purchase ever!"
    Output: {"sentiment": "positive", "confidence": 0.95}

    Input: "It's okay, nothing special."
    Output: {"sentiment": "neutral", "confidence": 0.7}

    Input: "Terrible quality, waste of money."
    Output: {"sentiment": "negative", "confidence": 0.9}

    Now classify the following:

schema:
  input:
    type: object
    properties:
      text:
        type: string

// Ensemble passes:
input: {
  text: "I love this product!"
}

// Think member uses it in prompt automatically

schema:
  input:
    type: object
    properties:
      companyName:
        type: string
      website:
        type: string
      industry:
        type: string

config:
  systemPrompt: |
    Analyze ${input.companyName} in the ${input.industry} industry.
    Website: ${input.website}

schema:
  input:
    type: object
    properties:
      messages:
        type: array

// Pass conversation history
input: {
  messages: [
    { role: "user", content: "What's the capital of France?" },
    { role: "assistant", content: "Paris" },
    { role: "user", content: "What's its population?" }
  ]
}

config:
  provider: openai
  model: gpt-4o
  responseFormat:
    type: json_schema
    json_schema:
      name: company_analysis
      strict: true
      schema:
        type: object
        properties:
          industry:
            type: string
          employees:
            type: number
          founded:
            type: number
          summary:
            type: string
        required: [industry, employees, founded, summary]
        additionalProperties: false

config:
  responseFormat:
    type: json_object
  systemPrompt: |
    Respond only with valid JSON.

config:
  provider: openai
  model: gpt-4o

  # Function calling
  functions:
    - name: get_weather
      description: Get weather for a location
      parameters:
        type: object
        properties:
          location:
            type: string

  # Structured output
  responseFormat:
    type: json_schema

  # Reasoning models (o1)
  model: o1-preview
  temperature: 1.0  # Fixed for o1

config:
  provider: anthropic
  model: claude-3-5-sonnet-20241022

  # Extended thinking (new feature)
  thinkingBudget: 5000  # tokens for internal reasoning

  # System prompt (required for Claude)
  systemPrompt: "You are an expert analyst."

config:
  provider: workers-ai
  model: "@cf/meta/llama-3.1-8b-instruct"
  routing: cloudflare  # Platform-native

  # No API key needed!
  # Uses Workers AI binding

config:
  provider: groq
  model: llama-3.1-70b-versatile
  routing: cloudflare-gateway

  # Extremely fast inference (< 500ms)
  # Good for real-time applications

name: classify-intent
type: Think

config:
  provider: workers-ai
  model: "@cf/meta/llama-3.1-8b-instruct"
  routing: cloudflare
  temperature: 0.2
  maxTokens: 50
  systemPrompt: |
    Classify user intent into: question, request, complaint, or praise.
    Respond with only one word.

name: summarize-article
type: Think

config:
  provider: openai
  model: gpt-4o-mini
  routing: cloudflare-gateway
  temperature: 0.5
  maxTokens: 200
  systemPrompt: |
    Summarize the article in 2-3 sentences.
    Focus on key points and main ideas.

name: extract-company-info
type: Think

config:
  provider: anthropic
  model: claude-3-5-sonnet-20241022
  routing: cloudflare-gateway
  temperature: 0.1
  maxTokens: 500
  responseFormat:
    type: json_object
  systemPrompt: |
    Extract company information from the text.
    Return JSON with: name, industry, location, employees, founded.

name: generate-blog-post
type: Think

config:
  provider: openai
  model: gpt-4o
  routing: cloudflare-gateway
  temperature: 0.8
  maxTokens: 2000
  systemPrompt: |
    Write an engaging blog post on the given topic.
    Include:
    - Attention-grabbing headline
    - Introduction with hook
    - 3-5 main points with examples
    - Conclusion with call-to-action

name: answer-question
type: Think

config:
  provider: anthropic
  model: claude-3-5-sonnet-20241022
  routing: cloudflare-gateway
  temperature: 0.3
  maxTokens: 500
  systemPrompt: |
    Answer the question based only on the provided context.
    If the answer isn't in the context, say "I don't know."

    Context: ${input.context}

# ✅ Good - use mini for simple tasks
- member: classify-email
  config:
    model: gpt-4o-mini  # $0.15/1M tokens

# ❌ Wasteful - using flagship for simple task
- member: classify-email
  config:
    model: gpt-4o  # $5/1M tokens

- member: analyze-sentiment
  cache:
    ttl: 86400  # 24 hours
  config:
    routing: cloudflare-gateway  # Persistent cache

config:
  temperature: 0.1  # More deterministic = better cache hit rate

config:
  maxTokens: 100  # Only what you need
  systemPrompt: "Be concise. Maximum 50 words."

# Sub-50ms cold start, sub-10ms warm
config:
  provider: workers-ai
  model: "@cf/meta/llama-3.1-8b-instruct"
  routing: cloudflare

# ~200ms response time
config:
  provider: groq
  model: llama-3.1-70b-versatile

# Cached responses in < 5ms
config:
  routing: cloudflare-gateway
cache:
  ttl: 3600

- member: generate-content
  retry:
    maxAttempts: 3
    backoff: exponential

- member: generate-content
  scoring:
    thresholds:
      minimum: 0.7
    onFailure: retry
    retryLimit: 5

flow:
  - member: primary-ai
    continue_on_error: true

  - member: fallback-ai
    condition: ${!primary-ai.success}

import { describe, it, expect } from 'vitest';
import { TestConductor } from '@ensemble-edge/conductor/testing';

describe('analyze-sentiment', () => {
  it('should analyze positive sentiment', async () => {
    const conductor = await TestConductor.create({
      mocks: {
        ai: {
          responses: {
            'analyze-sentiment': {
              sentiment: 'positive',
              confidence: 0.95
            }
          }
        }
      }
    });

    const result = await conductor.executeMember('analyze-sentiment', {
      text: 'I love this product!'
    });

    expect(result).toBeSuccessful();
    expect(result.output.sentiment).toBe('positive');
  });
});