biblical-guide.com/AI_SMART_SUGGESTIONS_PLAN.md

# AI-Powered Smart Suggestions - Implementation Plan

## 📋 Overview

Implement AI-powered features that provide intelligent suggestions, thematic discovery, semantic search, and personalized recommendations to enhance Bible study and deepen Scripture understanding.

**Status:** Planning Phase
**Priority:** 🔵 Future
**Estimated Time:** 4-6 weeks (160-240 hours)
**Target Completion:** TBD

---

## 🎯 Goals & Objectives

### Primary Goals
1. Provide AI-powered verse recommendations
2. Enable semantic (meaning-based) search
3. Generate study questions automatically
4. Discover thematic connections
5. Personalize user experience with ML

### User Value Proposition
- **For students**: Discover related content automatically
- **For scholars**: Find thematic patterns
- **For personal study**: Get personalized recommendations
- **For teachers**: Generate discussion questions
- **For explorers**: Uncover hidden connections

---

## ✨ Feature Specifications

### 1. AI Architecture

```typescript
interface AIConfig {
  // Providers
  provider: 'openai' | 'azure' | 'ollama' | 'anthropic'
  model: string // gpt-4, gpt-3.5-turbo, claude-3, llama2, etc.
  apiKey?: string
  endpoint?: string

  // Features
  enableSuggestions: boolean
  enableSemanticSearch: boolean
  enableQuestionGeneration: boolean
  enableSummarization: boolean
  enableThematicAnalysis: boolean

  // Behavior
  cacheResponses: boolean
  maxTokens: number
  temperature: number // 0-1, creativity
  enableRAG: boolean // Retrieval Augmented Generation
}

interface AIService {
  // Core methods
  generateSuggestions(verse: VerseReference): Promise<Suggestion[]>
  semanticSearch(query: string): Promise<SearchResult[]>
  generateQuestions(passage: string): Promise<Question[]>
  summarizeChapter(book: string, chapter: number): Promise<string>
  analyzeThemes(verses: string[]): Promise<Theme[]>
  explainVerse(verse: string): Promise<Explanation>
}
```

### 2. Smart Verse Suggestions

```typescript
interface Suggestion {
  id: string
  type: 'related' | 'thematic' | 'contextual' | 'application' | 'cross-ref'
  verse: VerseReference
  reason: string // Why this was suggested
  relevanceScore: number // 0-1
  metadata?: {
    theme?: string
    category?: string
    connection?: string
  }
}

const SmartSuggestions: React.FC<{
  currentVerse: VerseReference
}> = ({ currentVerse }) => {
  const [suggestions, setSuggestions] = useState<Suggestion[]>([])
  const [loading, setLoading] = useState(false)

  useEffect(() => {
    loadSuggestions()
  }, [currentVerse])

  const loadSuggestions = async () => {
    setLoading(true)

    try {
      const response = await fetch('/api/ai/suggestions', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          verse: currentVerse,
          limit: 10
        })
      })

      const data = await response.json()
      setSuggestions(data.suggestions)
    } catch (error) {
      console.error('Failed to load suggestions:', error)
    } finally {
      setLoading(false)
    }
  }

  return (
    <Card>
      <CardHeader
        title="AI Suggestions"
        avatar={<AutoAwesomeIcon />}
        action={
          <IconButton onClick={loadSuggestions} disabled={loading}>
            <RefreshIcon />
          </IconButton>
        }
      />
      <CardContent>
        {loading ? (
          <Box display="flex" justifyContent="center" p={3}>
            <CircularProgress />
          </Box>
        ) : suggestions.length === 0 ? (
          <Alert severity="info">
            No suggestions available for this verse.
          </Alert>
        ) : (
          <List>
            {suggestions.map(suggestion => (
              <ListItem key={suggestion.id} divider>
                <ListItemIcon>
                  {getIconForType(suggestion.type)}
                </ListItemIcon>
                <ListItemText
                  primary={formatVerseReference(suggestion.verse)}
                  secondary={suggestion.reason}
                />
                <Chip
                  label={`${Math.round(suggestion.relevanceScore * 100)}%`}
                  size="small"
                  color={suggestion.relevanceScore > 0.7 ? 'success' : 'default'}
                />
              </ListItem>
            ))}
          </List>
        )}
      </CardContent>
    </Card>
  )
}
```

### 3. Semantic Search with Vector Embeddings

```typescript
// Generate embeddings for Bible verses
const generateEmbedding = async (text: string): Promise<number[]> => {
  const response = await fetch('/api/ai/embed', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ text })
  })

  const data = await response.json()
  return data.embedding
}

// Semantic search implementation
const semanticSearch = async (query: string): Promise<SearchResult[]> => {
  // Generate embedding for query
  const queryEmbedding = await generateEmbedding(query)

  // Find similar verses using vector similarity
  const results = await prisma.$queryRaw`
    SELECT
      v."id",
      v."book",
      v."chapter",
      v."verseNum",
      v."text",
      1 - (v."embedding" <=> ${queryEmbedding}::vector) AS similarity
    FROM "BibleVerse" v
    WHERE v."embedding" IS NOT NULL
    ORDER BY v."embedding" <=> ${queryEmbedding}::vector
    LIMIT 20
  `

  return results
}

const SemanticSearch: React.FC = () => {
  const [query, setQuery] = useState('')
  const [results, setResults] = useState<SearchResult[]>([])
  const [searching, setSearching] = useState(false)

  const handleSearch = async () => {
    if (!query.trim()) return

    setSearching(true)

    try {
      const response = await fetch('/api/ai/search/semantic', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ query })
      })

      const data = await response.json()
      setResults(data.results)
    } catch (error) {
      console.error('Semantic search failed:', error)
    } finally {
      setSearching(false)
    }
  }

  return (
    <Box>
      <Typography variant="h6" gutterBottom>
        Semantic Search
      </Typography>

      <Alert severity="info" sx={{ mb: 2 }}>
        Search by meaning, not just keywords. Ask questions like "verses about hope" or "God's love for humanity"
      </Alert>

      <Box display="flex" gap={1} mb={3}>
        <TextField
          fullWidth
          placeholder="What are you looking for? (e.g., 'overcoming fear')"
          value={query}
          onChange={(e) => setQuery(e.target.value)}
          onKeyPress={(e) => e.key === 'Enter' && handleSearch()}
        />
        <Button
          variant="contained"
          onClick={handleSearch}
          disabled={searching}
          startIcon={searching ? <CircularProgress size={20} /> : <SearchIcon />}
        >
          Search
        </Button>
      </Box>

      {/* Results */}
      {results.map(result => (
        <Card key={result.id} sx={{ mb: 2 }}>
          <CardContent>
            <Box display="flex" justifyContent="space-between" alignItems="start">
              <Box>
                <Typography variant="subtitle2" color="primary" gutterBottom>
                  {result.book} {result.chapter}:{result.verseNum}
                </Typography>
                <Typography variant="body2">
                  {result.text}
                </Typography>
              </Box>
              <Chip
                label={`${Math.round(result.similarity * 100)}% match`}
                size="small"
                color={result.similarity > 0.8 ? 'success' : 'default'}
              />
            </Box>
          </CardContent>
        </Card>
      ))}
    </Box>
  )
}
```

### 4. AI Study Question Generator

```typescript
interface Question {
  id: string
  type: 'comprehension' | 'application' | 'reflection' | 'analysis' | 'discussion'
  question: string
  difficulty: 'easy' | 'medium' | 'hard'
  suggestedAnswer?: string
}

const generateStudyQuestions = async (
  passage: string,
  count: number = 5
): Promise<Question[]> => {
  const prompt = `
Generate ${count} thoughtful study questions for the following Bible passage.
Include a mix of comprehension, application, and reflection questions.

Passage:
${passage}

Return as JSON array with format:
[
  {
    "type": "comprehension|application|reflection|analysis|discussion",
    "question": "the question",
    "difficulty": "easy|medium|hard"
  }
]
`

  const response = await fetch('/api/ai/generate', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      prompt,
      temperature: 0.7,
      maxTokens: 1000
    })
  })

  const data = await response.json()
  return JSON.parse(data.response)
}

const StudyQuestionGenerator: React.FC<{
  passage: string
}> = ({ passage }) => {
  const [questions, setQuestions] = useState<Question[]>([])
  const [generating, setGenerating] = useState(false)

  const handleGenerate = async () => {
    setGenerating(true)

    try {
      const generated = await generateStudyQuestions(passage)
      setQuestions(generated)
    } catch (error) {
      console.error('Failed to generate questions:', error)
    } finally {
      setGenerating(false)
    }
  }

  return (
    <Box>
      <Box display="flex" justifyContent="space-between" alignItems="center" mb={2}>
        <Typography variant="h6">
          Study Questions
        </Typography>
        <Button
          variant="outlined"
          onClick={handleGenerate}
          disabled={generating}
          startIcon={generating ? <CircularProgress size={20} /> : <AutoAwesomeIcon />}
        >
          Generate Questions
        </Button>
      </Box>

      {questions.length > 0 && (
        <List>
          {questions.map((question, index) => (
            <Card key={index} sx={{ mb: 2 }}>
              <CardContent>
                <Box display="flex" gap={1} mb={1}>
                  <Chip label={question.type} size="small" />
                  <Chip
                    label={question.difficulty}
                    size="small"
                    color={
                      question.difficulty === 'easy' ? 'success' :
                      question.difficulty === 'medium' ? 'warning' : 'error'
                    }
                  />
                </Box>
                <Typography variant="body1" fontWeight="500">
                  {index + 1}. {question.question}
                </Typography>
              </CardContent>
            </Card>
          ))}
        </List>
      )}
    </Box>
  )
}
```

### 5. Thematic Analysis

```typescript
interface Theme {
  name: string
  description: string
  verses: VerseReference[]
  relevance: number // 0-1
  keywords: string[]
}

const analyzeThemes = async (verses: string[]): Promise<Theme[]> => {
  const prompt = `
Analyze the following Bible verses and identify the main themes, topics, and theological concepts.
For each theme, provide:
- Name
- Description
- Keywords
- Relevance score (0-1)

Verses:
${verses.join('\n\n')}

Return as JSON array.
`

  const response = await fetch('/api/ai/analyze/themes', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt, verses })
  })

  const data = await response.json()
  return data.themes
}

const ThematicAnalysis: React.FC<{
  book: string
  chapter: number
}> = ({ book, chapter }) => {
  const [themes, setThemes] = useState<Theme[]>([])
  const [analyzing, setAnalyzing] = useState(false)

  useEffect(() => {
    performAnalysis()
  }, [book, chapter])

  const performAnalysis = async () => {
    setAnalyzing(true)

    try {
      // Fetch chapter verses
      const verses = await fetchChapterVerses(book, chapter)

      // Analyze themes
      const themes = await analyzeThemes(verses.map(v => v.text))
      setThemes(themes)
    } catch (error) {
      console.error('Theme analysis failed:', error)
    } finally {
      setAnalyzing(false)
    }
  }

  return (
    <Box>
      <Typography variant="h6" gutterBottom>
        Thematic Analysis
      </Typography>

      {analyzing ? (
        <Box display="flex" justifyContent="center" p={3}>
          <CircularProgress />
        </Box>
      ) : (
        <Grid container spacing={2}>
          {themes.map((theme, index) => (
            <Grid item xs={12} sm={6} key={index}>
              <Card>
                <CardContent>
                  <Typography variant="h6" gutterBottom>
                    {theme.name}
                  </Typography>
                  <Typography variant="body2" color="text.secondary" paragraph>
                    {theme.description}
                  </Typography>
                  <Box display="flex" gap={0.5} flexWrap="wrap" mb={1}>
                    {theme.keywords.map(keyword => (
                      <Chip key={keyword} label={keyword} size="small" />
                    ))}
                  </Box>
                  <LinearProgress
                    variant="determinate"
                    value={theme.relevance * 100}
                    sx={{ mt: 1 }}
                  />
                  <Typography variant="caption" color="text.secondary">
                    Relevance: {Math.round(theme.relevance * 100)}%
                  </Typography>
                </CardContent>
              </Card>
            </Grid>
          ))}
        </Grid>
      )}
    </Box>
  )
}
```

### 6. RAG (Retrieval Augmented Generation)

```typescript
// RAG implementation for contextual AI responses
const ragQuery = async (question: string, context: string[]): Promise<string> => {
  // Step 1: Find relevant verses using semantic search
  const relevantVerses = await semanticSearch(question)

  // Step 2: Build context from retrieved verses
  const contextText = relevantVerses
    .slice(0, 5)
    .map(v => `${v.book} ${v.chapter}:${v.verseNum} - ${v.text}`)
    .join('\n\n')

  // Step 3: Generate response with context
  const prompt = `
You are a Bible study assistant. Answer the following question using ONLY the provided Scripture context.
Be accurate and cite specific verses.

Context:
${contextText}

Question: ${question}

Answer:
`

  const response = await fetch('/api/ai/generate', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      prompt,
      temperature: 0.3, // Lower temperature for accuracy
      maxTokens: 500
    })
  })

  const data = await response.json()
  return data.response
}

const RAGChatbot: React.FC = () => {
  const [messages, setMessages] = useState<Array<{ role: 'user' | 'assistant', content: string }>>([])
  const [input, setInput] = useState('')
  const [thinking, setThinking] = useState(false)

  const handleSend = async () => {
    if (!input.trim()) return

    const userMessage = { role: 'user' as const, content: input }
    setMessages(prev => [...prev, userMessage])
    setInput('')
    setThinking(true)

    try {
      const answer = await ragQuery(input, [])
      setMessages(prev => [...prev, { role: 'assistant', content: answer }])
    } catch (error) {
      console.error('RAG query failed:', error)
    } finally {
      setThinking(false)
    }
  }

  return (
    <Box>
      <Typography variant="h6" gutterBottom>
        Ask the Bible
      </Typography>

      <Paper sx={{ height: 400, overflow: 'auto', p: 2, mb: 2 }}>
        {messages.map((msg, index) => (
          <Box
            key={index}
            sx={{
              mb: 2,
              display: 'flex',
              justifyContent: msg.role === 'user' ? 'flex-end' : 'flex-start'
            }}
          >
            <Paper
              sx={{
                p: 2,
                maxWidth: '70%',
                bgcolor: msg.role === 'user' ? 'primary.main' : 'grey.200',
                color: msg.role === 'user' ? 'white' : 'text.primary'
              }}
            >
              <Typography variant="body2">{msg.content}</Typography>
            </Paper>
          </Box>
        ))}

        {thinking && (
          <Box display="flex" gap={1} alignItems="center">
            <CircularProgress size={20} />
            <Typography variant="caption">Thinking...</Typography>
          </Box>
        )}
      </Paper>

      <Box display="flex" gap={1}>
        <TextField
          fullWidth
          placeholder="Ask a question about the Bible..."
          value={input}
          onChange={(e) => setInput(e.target.value)}
          onKeyPress={(e) => e.key === 'Enter' && handleSend()}
        />
        <Button variant="contained" onClick={handleSend} disabled={thinking}>
          Send
        </Button>
      </Box>
    </Box>
  )
}
```

---

## 🗄️ Database Schema

```prisma
model BibleVerse {
  // ... existing fields
  embedding   Float[]? @db.Vector(1536) // For semantic search
  embeddedAt  DateTime?
}

model AISuggestion {
  id            String   @id @default(cuid())
  userId        String
  user          User     @relation(fields: [userId], references: [id])

  sourceVerse   String   // book:chapter:verse
  targetVerse   String
  type          String   // related, thematic, contextual, etc.
  reason        String
  relevance     Float

  clicked       Boolean  @default(false)
  helpful       Boolean?

  createdAt     DateTime @default(now())

  @@index([userId, sourceVerse])
}

model AICache {
  id        String   @id @default(cuid())
  query     String   @unique
  response  Json
  provider  String
  model     String
  tokens    Int
  createdAt DateTime @default(now())
  expiresAt DateTime

  @@index([query])
  @@index([expiresAt])
}
```

---

## 📅 Implementation Timeline

### Phase 1: Foundation (Week 1-2)
- [ ] Set up AI provider integration
- [ ] Implement vector embeddings
- [ ] Build semantic search
- [ ] Create caching layer

### Phase 2: Features (Week 3-4)
- [ ] Smart suggestions engine
- [ ] Question generator
- [ ] Thematic analysis
- [ ] RAG chatbot

### Phase 3: Optimization (Week 5-6)
- [ ] Performance tuning
- [ ] Cost optimization
- [ ] A/B testing
- [ ] User feedback loop

---

## 💰 Cost Considerations

### OpenAI Pricing (estimated)
- GPT-4: $0.03/1K input tokens, $0.06/1K output
- GPT-3.5-turbo: $0.0005/1K tokens
- Embeddings: $0.0001/1K tokens

### Monthly estimates for 10,000 active users:
- Embeddings (one-time): ~$50
- Suggestions (10/user/month): ~$150
- Semantic search (50/user/month): ~$25
- Questions (5/user/month): ~$200
- **Total**: ~$425/month

### Cost Optimization:
- Cache all responses (reduce by 60%)
- Use GPT-3.5 where possible
- Rate limiting per user
- Consider self-hosted Ollama for basic tasks

---

**Document Version:** 1.0
**Last Updated:** 2025-10-13
**Status:** Ready for Implementation