1  #!/usr/bin/env python3
  2  """
  3  TinyLlama + Cloud LLM Orchestrator - BLUEPRINT
  4  This shows the architecture. Add your own API keys to make it work.
  5  """
  6  import os
  7  import sys
  8  import subprocess
  9  import json
 10  from typing import List, Dict
 11  import asyncio
 12  import aiohttp  # Would need: pip install aiohttp
 13  
 14  # ==================== TINYLLAMA (LOCAL) ====================
 15  class TinyLlamaLocal:
 16      def __init__(self):
 17          self.llama_path = os.path.expanduser("~/llama.cpp/build/bin/llama-cli")
 18          self.model_path = os.path.expanduser("~/models/tinyllama.gguf")
 19      
 20      def query(self, prompt: str, max_tokens: int = 100) -> str:
 21          """Query local TinyLlama"""
 22          cmd = [
 23              self.llama_path,
 24              "-m", self.model_path,
 25              "-p", prompt,
 26              "-n", str(max_tokens),
 27              "--temp", "0.7",
 28              "-no-cnv"
 29          ]
 30          
 31          try:
 32              result = subprocess.run(cmd, capture_output=True, text=True, timeout=20)
 33              for line in result.stdout.split('\n'):
 34                  if prompt in line:
 35                      response = line[line.find(prompt) + len(prompt):].strip()
 36                      return response.lstrip(':').strip()
 37              return ""
 38          except:
 39              return ""
 40      
 41      def analyze_task(self, user_request: str) -> Dict:
 42          """Analyze and break down task"""
 43          prompt = f"""Analyze this request and suggest how to approach it: {user_request}
 44          
 45          Return simple analysis:"""
 46          
 47          analysis = self.query(prompt, 120)
 48          return {"analysis": analysis, "original_request": user_request}
 49      
 50      def synthesize_responses(self, subtask_results: List[str], original_request: str) -> str:
 51          """Combine multiple responses into one"""
 52          prompt = f"""Combine these pieces into a coherent answer for: {original_request}
 53          
 54          Pieces:
 55          {' | '.join(subtask_results)}
 56          
 57          Final answer:"""
 58          
 59          return self.query(prompt, 150)
 60  
 61  # ==================== CLOUD LLM DELEGATES ====================
 62  class CloudLLMDelegator:
 63      """Handles delegation to various cloud LLMs"""
 64      
 65      # UNCOMMENT AND ADD YOUR API KEYS
 66      """
 67      def __init__(self):
 68          # Example - would need actual API keys
 69          self.openai_key = os.getenv("OPENAI_API_KEY", "")
 70          self.anthropic_key = os.getenv("ANTHROPIC_API_KEY", "")
 71          self.google_key = os.getenv("GOOGLE_AI_KEY", "")
 72      
 73      async def delegate_to_gpt4(self, prompt: str) -> str:
 74          # Implementation for OpenAI GPT-4
 75          async with aiohttp.ClientSession() as session:
 76              async with session.post(
 77                  "https://api.openai.com/v1/chat/completions",
 78                  headers={"Authorization": f"Bearer {self.openai_key}"},
 79                  json={
 80                      "model": "gpt-4",
 81                      "messages": [{"role": "user", "content": prompt}],
 82                      "max_tokens": 500
 83                  }
 84              ) as response:
 85                  data = await response.json()
 86                  return data["choices"][0]["message"]["content"]
 87      
 88      async def delegate_to_claude(self, prompt: str) -> str:
 89          # Implementation for Anthropic Claude
 90          async with aiohttp.ClientSession() as session:
 91              async with session.post(
 92                  "https://api.anthropic.com/v1/messages",
 93                  headers={
 94                      "x-api-key": self.anthropic_key,
 95                      "anthropic-version": "2023-06-01"
 96                  },
 97                  json={
 98                      "model": "claude-3-opus-20240229",
 99                      "max_tokens": 500,
100                      "messages": [{"role": "user", "content": prompt}]
101                  }
102              ) as response:
103                  data = await response.json()
104                  return data["content"][0]["text"]
105      """
106      
107      @staticmethod
108      def get_demo_responses(prompt: str) -> str:
109          """Demo responses since we don't have API keys"""
110          responses = [
111              f"[GPT-4 would analyze: {prompt[:50]}...]",
112              f"[Claude would provide detailed explanation of: {prompt[:50]}...]",
113              f"[Gemini would add insights about: {prompt[:50]}...]"
114          ]
115          import random
116          return random.choice(responses)
117  
118  # ==================== MAIN ORCHESTRATOR ====================
119  class AIOrchestrator:
120      def __init__(self):
121          self.tinyllama = TinyLlamaLocal()
122          self.cloud_delegator = CloudLLMDelegator()
123      
124      def orchestrate(self, user_request: str) -> Dict:
125          """Main orchestration flow"""
126          print(f"\n{'='*60}")
127          print(f"ORCHESTRATING: {user_request}")
128          print(f"{'='*60}")
129          
130          # Step 1: Local analysis with TinyLlama
131          print("\n🔍 STEP 1: Local Analysis (TinyLlama)")
132          print("   Understanding request and planning approach...")
133          analysis = self.tinyllama.analyze_task(user_request)
134          print(f"   Analysis: {analysis['analysis'][:100]}...")
135          
136          # Step 2: Decompose into subtasks
137          print("\n📋 STEP 2: Task Decomposition")
138          print("   Breaking into parallelizable subtasks...")
139          subtasks = self._create_subtasks(user_request)
140          
141          # Step 3: Delegate to cloud LLMs (concept)
142          print("\n☁️  STEP 3: Cloud Delegation")
143          print("   Sending subtasks to appropriate cloud LLMs...")
144          cloud_responses = []
145          for i, subtask in enumerate(subtasks, 1):
146              print(f"   Subtask {i}: {subtask[:50]}...")
147              # In real system: await cloud_delegator.delegate_to_gpt4(subtask)
148              cloud_response = self.cloud_delegator.get_demo_responses(subtask)
149              cloud_responses.append(cloud_response)
150              print(f"     ↳ Response received")
151          
152          # Step 4: Local synthesis with TinyLlama
153          print("\n🧩 STEP 4: Local Synthesis (TinyLlama)")
154          print("   Combining cloud responses into final answer...")
155          final_answer = self.tinyllama.synthesize_responses(cloud_responses, user_request)
156          
157          return {
158              "analysis": analysis,
159              "subtasks": subtasks,
160              "cloud_responses": cloud_responses,
161              "final_answer": final_answer
162          }
163      
164      def _create_subtasks(self, request: str) -> List[str]:
165          """Create subtasks from request"""
166          # Simple decomposition - in real system, use TinyLlama for this
167          base_topics = [
168              "historical context and development",
169              "core principles and theory", 
170              "practical applications and examples",
171              "current research and future directions"
172          ]
173          
174          return [f"{topic} of {request}" for topic in base_topics]
175  
176  # ==================== DEMO ====================
177  def main():
178      orchestrator = AIOrchestrator()
179      
180      print("🤖 AI ORCHESTRATOR SYSTEM - ARCHITECTURE DEMO")
181      print("=" * 60)
182      print("This demonstrates how a small local LLM (TinyLlama)")
183      print("can coordinate with large cloud LLMs (GPT-4, Claude, etc.)")
184      print("=" * 60)
185      
186      if len(sys.argv) > 1:
187          request = " ".join(sys.argv[1:])
188      else:
189          print("\nEnter a complex topic (e.g., 'quantum computing'):")
190          request = input("> ").strip()
191          if not request:
192              request = "artificial intelligence"
193      
194      result = orchestrator.orchestrate(request)
195      
196      print(f"\n{'='*60}")
197      print("✅ ORCHESTRATION COMPLETE")
198      print(f"{'='*60}")
199      
200      print(f"\n📊 ANALYSIS:")
201      print(f"   {result['analysis']['analysis']}")
202      
203      print(f"\n🔧 SUBTASKS CREATED:")
204      for i, subtask in enumerate(result['subtasks'], 1):
205          print(f"   {i}. {subtask}")
206      
207      print(f"\n☁️  CLOUD RESPONSES (Demo):")
208      for i, response in enumerate(result['cloud_responses'], 1):
209          print(f"   {i}. {response}")
210      
211      print(f"\n🧩 FINAL SYNTHESIZED ANSWER:")
212      print(f"   {result['final_answer']}")
213      
214      print(f"\n{'='*60}")
215      print("💡 TO MAKE THIS WORK:")
216      print("=" * 60)
217      print("1. Get API keys from: OpenAI, Anthropic, Google AI")
218      print("2. Install: pip install openai anthropic google-generativeai")
219      print("3. Uncomment the CloudLLMDelegator implementation")
220      print("4. Set environment variables with your API keys")
221      print("=" * 60)
222      
223      # Save demo
224      import time
225      timestamp = time.strftime("%Y%m%d_%H%M%S")
226      with open(f"orchestrator_demo_{timestamp}.txt", "w") as f:
227          f.write(json.dumps(result, indent=2))
228      
229      print(f"\n📁 Demo saved to: orchestrator_demo_{timestamp}.txt")
230  
231  if __name__ == "__main__":
232      main()