---
id: ai-rag-advanced
title: RAG Advanced — Hybrid / Rerank / Multi-modal / Graph
category: Coding
status: draft
source_trust_level: B
verification_status: conceptual
created_at: 2026-05-09
updated_at: 2026-05-09
tags: [ai, rag, advanced, vibe-coding]
tech_stack: { language: "TS / Python", applicable_to: ["Backend"] }
applied_in: []
aliases: [hybrid search, reranking, GraphRAG, multi-vector, contextual retrieval, query expansion]
---

# RAG Advanced

> 단순 vector 만 = 한계. **Hybrid (vector + BM25), reranker, query rewrite, contextual chunking, GraphRAG**. Anthropic Contextual Retrieval = 49% 정확도 향상.

## 📖 핵심 개념
- Hybrid: vector + keyword 가중치.
- Reranker: top-K 다시 정렬 (cross-encoder).
- Query expansion: 짧은 query 풍부화.
- Contextual chunking: chunk 에 문서 context 첨부.
- GraphRAG: entity / relationship 그래프.

## 💻 코드 패턴

### Hybrid search
```ts
async function hybridSearch(query: string, k = 20): Promise<Chunk[]> {
  const [vectorHits, keywordHits] = await Promise.all([
    vectorSearch(query, k * 2),
    bm25Search(query, k * 2),
  ]);

  // RRF (Reciprocal Rank Fusion)
  const scores = new Map<string, number>();
  vectorHits.forEach((c, i) => scores.set(c.id, (scores.get(c.id) ?? 0) + 1 / (60 + i)));
  keywordHits.forEach((c, i) => scores.set(c.id, (scores.get(c.id) ?? 0) + 1 / (60 + i)));

  const merged = [...new Set([...vectorHits, ...keywordHits].map(c => c.id))];
  return merged
    .map(id => ({ ...findChunk(id), score: scores.get(id)! }))
    .sort((a, b) => b.score - a.score)
    .slice(0, k);
}
```

### Reranker (Cohere / Voyage / Jina)
```ts
import { CohereClient } from 'cohere-ai';
const cohere = new CohereClient({ token });

async function rerank(query: string, chunks: Chunk[], topK = 5): Promise<Chunk[]> {
  const r = await cohere.rerank({
    model: 'rerank-multilingual-v3.0',
    query,
    documents: chunks.map(c => c.content),
    topN: topK,
  });
  return r.results.map(res => chunks[res.index]);
}

// Pipeline
const candidates = await hybridSearch(query, 50);  // 50 후보
const top = await rerank(query, candidates, 5);    // 5 정밀
```

### Contextual Retrieval (Anthropic)
```ts
// 각 chunk 에 문서 context 추가
async function buildContextualChunks(doc: Document) {
  const chunks = chunkText(doc.content, 1000);
  return await Promise.all(chunks.map(async (chunk, i) => {
    const context = await llm.complete({
      system: 'Provide 2-3 sentences of context for this chunk in the document.',
      user: `Document: ${doc.title}\n\nChunk: ${chunk}`,
    });
    return {
      content: context + '\n\n' + chunk,
      embedding: await embed(context + '\n\n' + chunk),
      docId: doc.id,
      chunkIdx: i,
    };
  }));
}
```

→ 답이 chunk 안 있는데도 retrieval 정확도 ↑.

### Query rewrite / expansion
```ts
async function expandQuery(query: string): Promise<string[]> {
  const r = await llm.complete({
    system: 'Generate 3 alternative phrasings of the query for search. Output JSON array.',
    user: query,
    response_format: { type: 'json_object' },
  });
  return [query, ...JSON.parse(r).queries];
}

// 각 query 검색 → 결과 합치기 (RRF)
const queries = await expandQuery(userQuery);
const allHits = await Promise.all(queries.map(q => hybridSearch(q)));
const merged = mergeRRF(allHits);
```

### HyDE (Hypothetical Document Embeddings)
```ts
// 가상 답을 먼저 생성 → 그 답의 embedding 으로 검색
async function hyde(query: string): Promise<Chunk[]> {
  const hypoAnswer = await llm.complete({
    system: 'Write a concise hypothetical paragraph that would answer the question.',
    user: query,
  });
  const queryEmb = await embed(hypoAnswer);
  return await vectorSearch(queryEmb, 10);
}
```

→ Query 가 짧을 때 효과.

### Multi-vector (different aspects)
```ts
// Document 한 개 = 여러 embedding (제목, 요약, 본문)
const chunks = [
  { type: 'title', content: doc.title, embedding: await embed(doc.title) },
  { type: 'summary', content: doc.summary, embedding: await embed(doc.summary) },
  ...sectionChunks,
];

// Query 에 적합한 type 가중치
```

### Late-interaction (ColBERT)
```
Query 와 document 를 token 단위 embedding.
각 query token 의 max score with document tokens.
→ 정확하지만 비싸.
```

→ Vespa / 자체 ColBERT.

### GraphRAG (Microsoft)
```
1. 문서 → entity / relationship 추출 (LLM)
2. 그래프 빌드
3. Community detection (entities cluster)
4. 각 community 의 summary 미리 생성
5. Query → community summary + 관련 entity 직접
```

```ts
async function extractEntities(chunk: string) {
  const r = await llm.complete({
    system: 'Extract entities and relationships as JSON.',
    user: chunk,
    response_format: { type: 'json_object' },
  });
  return JSON.parse(r); // { entities: [...], relationships: [...] }
}
```

→ 큰 문서 모음 + multi-hop reasoning 에 강.

### Multi-modal RAG
```ts
// 이미지 + 텍스트 → 같은 vector space (CLIP / Voyage Multimodal)
const queryEmb = await embedMultimodal({ text: query });
const r = await vectorSearch(queryEmb);
// 결과 = 텍스트 또는 이미지 chunks

// LLM 에 이미지 + 텍스트 같이
const answer = await llm.chat({
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'Based on the context...' },
      ...imageChunks.map(c => ({ type: 'image', source: c.url })),
      ...textChunks.map(c => ({ type: 'text', text: c.content })),
    ],
  }],
});
```

### Self-RAG (model 가 retrieve 결정)
```
LLM 이 답하면서 "내가 더 정보 필요" 결정 → 검색 → 다시 답.
Function calling 으로 구현.
```

```ts
const tools = [{
  name: 'search_kb',
  description: 'Search internal knowledge base',
  input_schema: { ... },
}];

// Loop — LLM 이 tool 사용 결정
```

### Citation + verification
```ts
// 답 + 출처 + 인용 검증
const answer = await llm.complete({
  system: `Answer using context. After each claim, cite [chunk_N]. End with "VERIFIED" or "UNVERIFIED".`,
  user: `Context:\n${contextWithIds}\n\nQ: ${query}`,
});

// Optional: 두 번째 LLM 가 답 ↔ context 검증
const ok = await verifyClaim(answer, retrievedChunks);
```

### Eval — Ragas
```python
from ragas import evaluate
from ragas.metrics import context_recall, context_precision, faithfulness, answer_relevancy

result = evaluate(
    dataset,
    metrics=[context_recall, context_precision, faithfulness, answer_relevancy],
)
```

### Pipeline 정리
```
1. Query → expand / HyDE / rewrite
2. Hybrid search (vector + BM25) → top-50
3. Rerank → top-5
4. Build context (with citations)
5. LLM answer (force citations)
6. Optional: verify
```

## 🤔 의사결정 기준
| 상황 | 기법 |
|---|---|
| 짧은 query | HyDE / expand |
| 대형 corpus + multi-hop | GraphRAG |
| 정확도 strict | Hybrid + rerank |
| 멀티 lang | Cohere multilingual |
| 멀티 modal (image/text) | CLIP / Voyage |
| Out-of-corpus 답 | Self-RAG (tool 결정) |

## ❌ 안티패턴
- **Vector only**: keyword 정확 match 약함.
- **Top-K 가 큼 (50) + LLM 에 모두**: noise. Rerank 후 5-10.
- **Citation 없음**: hallucination 검증 불가.
- **Static chunking 만**: contextual 가 더 강.
- **Eval 없음**: 어떤 변경이 향상 인지 모름.
- **Reranker 모든 query 사용**: latency. cache.
- **GraphRAG 작은 corpus**: overkill. simple RAG 충분.

## 🤖 LLM 활용 힌트
- Hybrid + reranker = sweet spot.
- Contextual chunks = 큰 향상 (49%).
- Citation 강제 system prompt.
- Ragas 로 eval.

## 🔗 관련 문서
- [[AI_RAG_Pattern_Basics]]
- [[AI_Embeddings_Comparison]]
- [[AI_LLM_Eval_Patterns]]
- [[DB_pgvector_Production]]
