# Agentic AI & Vector Search Implementation Guide

## 🤖 Part 1: Agentic AI Architecture for TraceRTM

### Core Concept
Multi-agent system where AI agents collaborate to analyze requirements, suggest improvements, and maintain traceability.

### Agent Types for TraceRTM

**1. Requirement Analyzer Agent**
```python
# cli/tracertm/agents/requirement_analyzer.py
from langchain.agents import Tool, initialize_agent, AgentType
from langchain.llms import ChatAnthropic

class RequirementAnalyzerAgent:
    def __init__(self):
        self.llm = ChatAnthropic(model="claude-3-5-sonnet")
        self.tools = [
            Tool(
                name="parse_requirement",
                func=self.parse_requirement,
                description="Parse and analyze a requirement"
            ),
            Tool(
                name="check_completeness",
                func=self.check_completeness,
                description="Check if requirement is complete"
            ),
            Tool(
                name="find_conflicts",
                func=self.find_conflicts,
                description="Find conflicts with other requirements"
            )
        ]
    
    def analyze(self, requirement: str) -> dict:
        agent = initialize_agent(
            self.tools,
            self.llm,
            agent=AgentType.OPENAI_FUNCTIONS,
            verbose=True
        )
        return agent.run(f"Analyze this requirement: {requirement}")
```

**2. Link Suggestion Agent**
```python
# Suggests relationships between requirements
class LinkSuggestionAgent:
    def suggest_links(self, item_id: str, all_items: list) -> list:
        # Use semantic similarity to suggest links
        # Return list of (target_id, link_type, confidence)
        pass
```

**3. Quality Assurance Agent**
```python
# Validates requirements against standards
class QAAgent:
    def validate(self, requirement: str) -> dict:
        # Check for clarity, completeness, testability
        # Return validation report
        pass
```

### Agent Orchestration

```python
# cli/tracertm/agents/orchestrator.py
from crew import Crew, Agent, Task

class RequirementOrchestrator:
    def __init__(self):
        self.analyzer = Agent(
            role="Requirement Analyst",
            goal="Analyze and improve requirements",
            backstory="Expert in requirement engineering"
        )
        
        self.linker = Agent(
            role="Link Suggester",
            goal="Find relationships between requirements",
            backstory="Expert in traceability"
        )
        
        self.qa = Agent(
            role="QA Engineer",
            goal="Ensure requirement quality",
            backstory="Expert in quality assurance"
        )
    
    def process_requirement(self, requirement: str) -> dict:
        crew = Crew(
            agents=[self.analyzer, self.linker, self.qa],
            tasks=[
                Task(description=f"Analyze: {requirement}"),
                Task(description=f"Find links for: {requirement}"),
                Task(description=f"QA check: {requirement}")
            ]
        )
        return crew.kickoff()
```

## 🔍 Part 2: Vector Search Implementation

### Setup pgvector in PostgreSQL

```sql
-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Add vector column to items table
ALTER TABLE items ADD COLUMN embedding vector(1536);

-- Create index for fast similarity search
CREATE INDEX ON items USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);
```

### Generate Embeddings

```python
# cli/tracertm/embeddings/generator.py
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

class EmbeddingGenerator:
    def __init__(self):
        self.embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
        self.splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
    
    def generate_for_item(self, item_id: str, content: str) -> list:
        """Generate embeddings for requirement content"""
        chunks = self.splitter.split_text(content)
        embeddings = self.embeddings.embed_documents(chunks)
        return embeddings
    
    def generate_for_query(self, query: str) -> list:
        """Generate embedding for search query"""
        return self.embeddings.embed_query(query)
```

### Semantic Search

```python
# cli/tracertm/search/semantic_search.py
import psycopg2
from pgvector.psycopg2 import register_vector

class SemanticSearch:
    def __init__(self, db_url: str):
        self.conn = psycopg2.connect(db_url)
        register_vector(self.conn)
    
    def search_similar_requirements(
        self,
        query: str,
        limit: int = 5,
        threshold: float = 0.7
    ) -> list:
        """Find similar requirements using vector similarity"""
        query_embedding = self.embeddings.embed_query(query)
        
        cursor = self.conn.cursor()
        cursor.execute("""
            SELECT id, title, content, 1 - (embedding <=> %s) as similarity
            FROM items
            WHERE 1 - (embedding <=> %s) > %s
            ORDER BY embedding <=> %s
            LIMIT %s
        """, (query_embedding, query_embedding, threshold, query_embedding, limit))
        
        return cursor.fetchall()
    
    def find_duplicate_requirements(self, item_id: str) -> list:
        """Find potentially duplicate requirements"""
        cursor = self.conn.cursor()
        cursor.execute("""
            SELECT id, title, 1 - (embedding <=> (
                SELECT embedding FROM items WHERE id = %s
            )) as similarity
            FROM items
            WHERE id != %s
            AND 1 - (embedding <=> (
                SELECT embedding FROM items WHERE id = %s
            )) > 0.85
            ORDER BY similarity DESC
        """, (item_id, item_id, item_id))
        
        return cursor.fetchall()
```

### Backend Integration

```go
// backend/internal/handlers/search_handler.go
package handlers

import (
    "github.com/labstack/echo/v4"
    "tracertm/backend/internal/service"
)

type SearchHandler struct {
    searchService *service.SearchService
}

func (h *SearchHandler) SemanticSearch(c echo.Context) error {
    query := c.QueryParam("q")
    limit := c.QueryParamDefault("limit", "5")
    
    results, err := h.searchService.SemanticSearch(c.Request().Context(), query, limit)
    if err != nil {
        return c.JSON(500, map[string]string{"error": err.Error()})
    }
    
    return c.JSON(200, results)
}

func (h *SearchHandler) FindDuplicates(c echo.Context) error {
    itemID := c.Param("id")
    
    duplicates, err := h.searchService.FindDuplicates(c.Request().Context(), itemID)
    if err != nil {
        return c.JSON(500, map[string]string{"error": err.Error()})
    }
    
    return c.JSON(200, duplicates)
}
```

### Frontend Integration

```typescript
// frontend/src/hooks/useSemanticSearch.ts
import { useQuery } from '@tanstack/react-query'

export function useSemanticSearch(query: string, limit = 5) {
  return useQuery({
    queryKey: ['semantic-search', query],
    queryFn: async () => {
      const response = await fetch(
        `/api/search/semantic?q=${encodeURIComponent(query)}&limit=${limit}`
      )
      return response.json()
    },
    enabled: query.length > 3,
  })
}

export function useFindDuplicates(itemId: string) {
  return useQuery({
    queryKey: ['duplicates', itemId],
    queryFn: async () => {
      const response = await fetch(`/api/search/duplicates/${itemId}`)
      return response.json()
    },
  })
}
```

## 🔗 Part 3: Agent + Vector Search Integration

### Combined Workflow

```python
# cli/tracertm/workflows/intelligent_analysis.py
class IntelligentAnalysisWorkflow:
    def __init__(self):
        self.orchestrator = RequirementOrchestrator()
        self.semantic_search = SemanticSearch()
        self.embeddings = EmbeddingGenerator()
    
    def analyze_new_requirement(self, requirement: str) -> dict:
        # 1. Generate embedding
        embedding = self.embeddings.generate_for_query(requirement)
        
        # 2. Find similar requirements
        similar = self.semantic_search.search_similar_requirements(
            requirement,
            limit=5
        )
        
        # 3. Run agent analysis
        analysis = self.orchestrator.process_requirement(requirement)
        
        # 4. Suggest links based on similarity
        suggested_links = [
            {
                "target_id": item[0],
                "type": "related",
                "confidence": item[2],
                "reason": "Semantic similarity"
            }
            for item in similar
        ]
        
        return {
            "analysis": analysis,
            "similar_requirements": similar,
            "suggested_links": suggested_links,
            "embedding": embedding
        }
```

## 📊 Implementation Checklist

- [ ] Setup pgvector extension
- [ ] Add embedding column to items table
- [ ] Create vector indexes
- [ ] Implement embedding generation
- [ ] Implement semantic search
- [ ] Setup agent framework (LangChain/Crew)
- [ ] Create requirement analyzer agent
- [ ] Create link suggestion agent
- [ ] Create QA agent
- [ ] Integrate agents with backend
- [ ] Add semantic search API endpoints
- [ ] Add frontend search UI
- [ ] Add duplicate detection
- [ ] Setup embedding caching
- [ ] Add monitoring and logging

## 🚀 Deployment Considerations

1. **Embedding Generation:** Batch process existing requirements
2. **Vector Index:** Create indexes for performance
3. **Caching:** Cache embeddings to reduce API calls
4. **Monitoring:** Track embedding quality and search performance
5. **Cost:** Monitor OpenAI API usage for embeddings

## 📈 Expected Benefits

- ✅ Automatic duplicate detection
- ✅ Intelligent link suggestions
- ✅ Semantic requirement search
- ✅ AI-powered requirement analysis
- ✅ Quality assurance automation
- ✅ Better requirement organization

## ⏱️ Effort Estimate

- Setup pgvector: 1 hour
- Embedding generation: 2 hours
- Semantic search: 2 hours
- Agent setup: 3 hours
- Integration: 2 hours
- Testing: 2 hours

**Total: 12 hours**

---

**Status:** Ready for implementation ✅

