Building a Context-Aware Multi-Agent AI System Using Nomic Embeddings and Gemini LLM

!pip install -qU langchain-nomic langchain-core langchain-community langchain-google-genai faiss-cpu numpy matplotlib


import os
import getpass
import numpy as np
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from langchain_nomic import NomicEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_core.documents import Document
from langchain_google_genai import ChatGoogleGenerativeAI
import json


if not os.getenv("NOMIC_API_KEY"):
   os.environ["NOMIC_API_KEY"] = getpass.getpass("Enter your Nomic API key: ")


if not os.getenv("GOOGLE_API_KEY"):
   os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google API key (for Gemini): ")

@dataclass
class AgentMemory:
   """Agent's episodic and semantic memory"""
   episodic: List[Dict[str, Any]]
   semantic: Dict[str, Any]
   working: Dict[str, Any]


class IntelligentAgent:
   """Advanced AI Agent with Nomic Embeddings for semantic reasoning"""
  
   def __init__(self, agent_name: str = "AIAgent", personality: str = "helpful"):
       self.name = agent_name
       self.personality = personality
      
       self.embeddings = NomicEmbeddings(
           model="nomic-embed-text-v1.5",
           dimensionality=384, 
           inference_mode="remote"
       )
      
       self.llm = ChatGoogleGenerativeAI(
           model="gemini-1.5-flash", 
           temperature=0.7,
           max_tokens=512
       )
      
       self.memory = AgentMemory(
           episodic=[],
           semantic={},
           working={}
       )
      
       self.knowledge_base = None
       self.vector_store = None
      
       self.capabilities = {
           "reasoning": True,
           "memory_retrieval": True,
           "knowledge_search": True,
           "context_awareness": True,
           "learning": True
       }
      
       print(f"🤖 {self.name} initialized with Nomic embeddings + Gemini LLM")
      
   def add_knowledge(self, documents: List[str], metadata: List[Dict] = None):
       """Add knowledge to agent's semantic memory"""
       if metadata is None:
           metadata = [{"source": f"doc_{i}"} for i in range(len(documents))]
          
       docs = [Document(page_content=doc, metadata=meta)
               for doc, meta in zip(documents, metadata)]
      
       if self.vector_store is None:
           self.vector_store = InMemoryVectorStore.from_documents(docs, self.embeddings)
       else:
           self.vector_store.add_documents(docs)
          
       print(f"📚 Added {len(documents)} documents to knowledge base")
      
   def remember_interaction(self, user_input: str, agent_response: str, context: Dict = None):
       """Store interaction in episodic memory"""
       memory_entry = {
           "timestamp": len(self.memory.episodic),
           "user_input": user_input,
           "agent_response": agent_response,
           "context": context or {},
           "embedding": self.embeddings.embed_query(f"{user_input} {agent_response}")
       }
       self.memory.episodic.append(memory_entry)
      
   def retrieve_similar_memories(self, query: str, k: int = 3) -> List[Dict]:
       """Retrieve similar past interactions"""
       if not self.memory.episodic:
           return []
          
       query_embedding = self.embeddings.embed_query(query)
       similarities = []
      
       for memory in self.memory.episodic:
           similarity = np.dot(query_embedding, memory["embedding"])
           similarities.append((similarity, memory))
          
       similarities.sort(reverse=True, key=lambda x: x[0])
       return [mem for _, mem in similarities[:k]]
      
   def search_knowledge(self, query: str, k: int = 3) -> List[Document]:
       """Search knowledge base for relevant information"""
       if self.vector_store is None:
           return []
       return self.vector_store.similarity_search(query, k=k)
      
   def reason_and_respond(self, user_input: str) -> str:
       """Main reasoning pipeline with context integration"""
      
       similar_memories = self.retrieve_similar_memories(user_input, k=2)
      
       relevant_docs = self.search_knowledge(user_input, k=3)
      
       context = {
           "similar_memories": similar_memories,
           "relevant_knowledge": [doc.page_content for doc in relevant_docs],
           "working_memory": self.memory.working
       }
      
       response = self._generate_contextual_response(user_input, context)
      
       self.remember_interaction(user_input, response, context)
      
       self.memory.working["last_query"] = user_input
       self.memory.working["last_response"] = response
      
       return response
  
   def _generate_contextual_response(self, query: str, context: Dict) -> str:
       """Generate response using Gemini LLM with context"""
      
       context_info = ""
      
       if context["relevant_knowledge"]:
           context_info += f"Relevant Knowledge: {' '.join(context['relevant_knowledge'][:2])}\n"
          
       if context["similar_memories"]:
           memory = context["similar_memories"][0]
           context_info += f"Similar Past Interaction: User asked '{memory['user_input']}', I responded '{memory['agent_response'][:100]}...'\n"
          
       prompt = f"""You are {self.name}, an AI agent with personality: {self.personality}.


Context Information:
{context_info}


User Query: {query}


Please provide a helpful response based on the context. Keep it concise (under 150 words) and maintain your personality."""


       try:
           response = self.llm.invoke(prompt)
           return response.content.strip()
       except Exception as e:
           if context["relevant_knowledge"]:
               knowledge_summary = " ".join(context["relevant_knowledge"][:2])
               return f"Based on my knowledge: {knowledge_summary[:200]}..."
           elif context["similar_memories"]:
               last_memory = context["similar_memories"][0]
               return f"I recall a similar question. Previously: {last_memory['agent_response'][:150]}..."
           else:
               return "I need more information to provide a comprehensive answer."

class ResearchAgent(IntelligentAgent):
   """Specialized agent for research and analysis tasks"""
  
   def __init__(self):
       super().__init__("ResearchBot", "analytical and thorough")
       self.research_domains = []
      
   def analyze_topic(self, topic: str) -> Dict[str, Any]:
       """Analyze a topic using semantic similarity and Gemini reasoning"""
      
       related_docs = self.search_knowledge(topic, k=5)
      
       if not related_docs:
           return {"analysis": "No relevant information found", "confidence": 0.0}
          
       topic_embedding = self.embeddings.embed_query(topic)
       doc_embeddings = [self.embeddings.embed_query(doc.page_content)
                        for doc in related_docs]
      
       similarities = [np.dot(topic_embedding, doc_emb)
                      for doc_emb in doc_embeddings]
      
       context = " ".join([doc.page_content for doc in related_docs[:3]])
       analysis_prompt = f"""As a research analyst, analyze the topic: {topic}


Available information:
{context}


Provide a structured analysis including:
1. Key insights (2-3 points)
2. Confidence level assessment
3. Research gaps or limitations
4. Practical implications


Keep response under 200 words."""


       try:
           gemini_analysis = self.llm.invoke(analysis_prompt)
           detailed_analysis = gemini_analysis.content.strip()
       except:
           detailed_analysis = f"Analysis of {topic} based on available documents with {len(related_docs)} relevant sources."
      
       analysis = {
           "topic": topic,
           "related_documents": len(related_docs),
           "max_similarity": max(similarities),
           "avg_similarity": np.mean(similarities),
           "key_insights": [doc.page_content[:100] + "..." for doc in related_docs[:3]],
           "confidence": max(similarities),
           "detailed_analysis": detailed_analysis
       }
      
       return analysis


class ConversationalAgent(IntelligentAgent):
   """Agent optimized for natural conversations"""
  
   def __init__(self):
       super().__init__("ChatBot", "friendly and engaging")
       self.conversation_history = []
      
   def maintain_conversation_context(self, user_input: str) -> str:
       """Maintain conversation flow with context awareness"""
      
       self.conversation_history.append({"role": "user", "content": user_input})
      
       recent_context = " ".join([msg["content"] for msg in self.conversation_history[-3:]])
      
       response = self.reason_and_respond(recent_context)
      
       self.conversation_history.append({"role": "assistant", "content": response})
      
       return response

def demonstrate_agent_capabilities():
   """Comprehensive demonstration of agent capabilities"""
  
   print("🎯 Creating and testing AI agents...")
  
   research_agent = ResearchAgent()
   chat_agent = ConversationalAgent()
  
   knowledge_documents = [
       "Artificial intelligence is transforming industries through automation and intelligent decision-making systems.",
       "Machine learning algorithms require large datasets to identify patterns and make predictions.",
       "Natural language processing enables computers to understand and generate human language.",
       "Computer vision allows machines to interpret and analyze visual information from images and videos.",
       "Robotics combines AI with physical systems to create autonomous machines.",
       "Deep learning uses neural networks with multiple layers to solve complex problems.",
       "Reinforcement learning teaches agents to make decisions through trial and error.",
       "Quantum computing promises to solve certain problems exponentially faster than classical computers."
   ]
  
   research_agent.add_knowledge(knowledge_documents)
   chat_agent.add_knowledge(knowledge_documents)
  
   print("\n🔬 Testing Research Agent...")
  
   topics = ["machine learning", "robotics", "quantum computing"]
  
   for topic in topics:
       analysis = research_agent.analyze_topic(topic)
       print(f"\n📊 Analysis of '{topic}':")
       print(f"   Confidence: {analysis['confidence']:.3f}")
       print(f"   Related docs: {analysis['related_documents']}")
       print(f"   Detailed Analysis: {analysis.get('detailed_analysis', 'N/A')[:200]}...")
       print(f"   Key insight: {analysis['key_insights'][0] if analysis['key_insights'] else 'None'}")
  
   print("\n💬 Testing Conversational Agent...")
  
   conversation_inputs = [
       "Tell me about artificial intelligence",
       "How does machine learning work?",
       "What's the difference between AI and machine learning?",
       "Can you explain neural networks?"
   ]
  
   for user_input in conversation_inputs:
       response = chat_agent.maintain_conversation_context(user_input)
       print(f"\n👤 User: {user_input}")
       print(f"🤖 Agent: {response}")
  
   print("\n🧠 Memory Analysis...")
   print(f"Research Agent memories: {len(research_agent.memory.episodic)}")
   print(f"Chat Agent memories: {len(chat_agent.memory.episodic)}")
  
   similar_memories = chat_agent.retrieve_similar_memories("artificial intelligence", k=2)
   if similar_memories:
       print(f"\n🔍 Similar memory found:")
       print(f"   Query: {similar_memories[0]['user_input']}")
       print(f"   Response: {similar_memories[0]['agent_response'][:100]}...")

class MultiAgentSystem:
   """Orchestrate multiple specialized agents"""
  
   def __init__(self):
       self.agents = {
           "research": ResearchAgent(),
           "chat": ConversationalAgent()
       }
       self.coordinator_embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5", dimensionality=256)
      
   def route_query(self, query: str) -> str:
       """Route query to most appropriate agent"""
      
       agent_descriptions = {
           "research": "analysis, research, data, statistics, technical information",
           "chat": "conversation, questions, general discussion, casual talk"
       }
      
       query_embedding = self.coordinator_embeddings.embed_query(query)
       best_agent = "chat" 
       best_similarity = 0
      
       for agent_name, description in agent_descriptions.items():
           desc_embedding = self.coordinator_embeddings.embed_query(description)
           similarity = np.dot(query_embedding, desc_embedding)
          
           if similarity > best_similarity:
               best_similarity = similarity
               best_agent = agent_name
              
       return best_agent
  
   def process_query(self, query: str) -> Dict[str, Any]:
       """Process query through appropriate agent"""
      
       selected_agent, confidence = self.route_query_with_confidence(query)
       agent = self.agents[selected_agent]
      
       if selected_agent == "research":
           if "analyze" in query.lower() or "research" in query.lower():
               topic = query.replace("analyze", "").replace("research", "").strip()
               result = agent.analyze_topic(topic)
               response = f"Research Analysis: {result.get('detailed_analysis', str(result))}"
           else:
               response = agent.reason_and_respond(query)
       else:
           response = agent.maintain_conversation_context(query)
          
       return {
           "query": query,
           "selected_agent": selected_agent,
           "response": response,
           "confidence": confidence
       }
  
   def route_query_with_confidence(self, query: str) -> tuple[str, float]:
       """Route query to most appropriate agent and return confidence"""
      
       agent_descriptions = {
           "research": "analysis, research, data, statistics, technical information",
           "chat": "conversation, questions, general discussion, casual talk"
       }
      
       query_embedding = self.coordinator_embeddings.embed_query(query)
       best_agent = "chat" 
       best_similarity = 0.0
      
       for agent_name, description in agent_descriptions.items():
           desc_embedding = self.coordinator_embeddings.embed_query(description)
           similarity = np.dot(query_embedding, desc_embedding)
          
           if similarity > best_similarity:
               best_similarity = similarity
               best_agent = agent_name
              
       return best_agent, best_similarity

if __name__ == "__main__":
   print("\n🚀 Advanced AI Agent System with Nomic Embeddings + Gemini LLM")
   print("=" * 70)
   print("💡 Note: This uses Google's Gemini 1.5 Flash (free tier) for reasoning")
   print("📚 Get your free Google API key at: https://makersuite.google.com/app/apikey")
   print("🎯 Get your Nomic API key at: https://atlas.nomic.ai/")
   print("=" * 70)
  
   demonstrate_agent_capabilities()
  
   print("\n🎛️ Testing Multi-Agent System...")
   multi_system = MultiAgentSystem()
  
   knowledge_docs = [
       "Python is a versatile programming language used in AI development.",
       "TensorFlow and PyTorch are popular machine learning frameworks.",
       "Data preprocessing is crucial for successful machine learning projects."
   ]
  
   for agent in multi_system.agents.values():
       agent.add_knowledge(knowledge_docs)
  
   test_queries = [
       "Analyze the impact of AI on society",
       "How are you doing today?",
       "Research machine learning trends",
       "What's your favorite color?"
   ]
  
   for query in test_queries:
       result = multi_system.process_query(query)
       print(f"\n📝 Query: {query}")
       print(f"🎯 Routed to: {result['selected_agent']} agent")
       print(f"💬 Response: {result['response'][:150]}...")
  
   print("\n✅ Advanced AI Agent demonstration complete!")