2nd/10_Wiki/Topics/Architecture/NoSQL-Databases-in-AI.md

---
id: wiki-2026-0508-nosql-databases-in-ai
title: NoSQL Databases in AI
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [NoSQL for AI, Vector DB, Document Store AI]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [nosql, ai, vector-db, mongodb, redis, rag]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mongodb-redis-pinecone
---

# NoSQL Databases in AI

## 매 한 줄
> **"매 AI workload = embedding + metadata + cache; 매 NoSQL 의 each layer 의 fit"**. 2026 RAG / agent stack 의 매 standard: vector DB (Pinecone/Qdrant/pgvector) + document store (MongoDB) + KV cache (Redis). 매 schema flexibility + horizontal scale 의 LLM-era natural fit.

## 매 핵심

### 매 NoSQL family + AI role
- **Vector**: embedding similarity (RAG, recommendation). Pinecone, Qdrant, Weaviate, Milvus.
- **Document**: chat history, agent state, structured output. MongoDB, CouchDB.
- **KV / Cache**: prompt cache, semantic cache, session. Redis, DragonflyDB.
- **Graph**: knowledge graph, entity link. Neo4j, ArangoDB.
- **Wide-column**: time-series telemetry, traces. Cassandra, ScyllaDB.

### 매 access pattern
- **Embed + ANN search**: HNSW / IVF index, top-k cosine.
- **Metadata filter + vector**: hybrid search.
- **TTL cache**: prompt → response 의 24h cache.
- **Append-only chat log**: doc store + per-user shard.

### 매 응용
1. RAG: vector + document hybrid.
2. Agent memory: document (short) + vector (long-term).
3. Personalization: KV (recent) + graph (relations).

## 💻 패턴

### Pinecone (managed vector)
```python
from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
pc.create_index(
    name="docs",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
idx = pc.Index("docs")

idx.upsert(vectors=[
    {"id": "doc1", "values": embed("Hello world"),
     "metadata": {"source": "intro.md", "section": "overview"}},
])

res = idx.query(
    vector=embed("greeting example"),
    top_k=5,
    filter={"source": {"$eq": "intro.md"}},
    include_metadata=True,
)
```

### Qdrant (self-host)
```python
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct

client = QdrantClient("localhost", port=6333)
client.recreate_collection(
    "docs",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
client.upsert("docs", points=[
    PointStruct(id=1, vector=embed(text), payload={"text": text, "source": "x.md"}),
])

hits = client.search("docs", query_vector=embed(q), limit=5,
                    query_filter={"must": [{"key": "source", "match": {"value": "x.md"}}]})
```

### pgvector (Postgres + vector)
```sql
CREATE EXTENSION vector;
CREATE TABLE docs (
  id BIGSERIAL PRIMARY KEY,
  text TEXT,
  source TEXT,
  embedding vector(1536)
);
CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops);

-- hybrid query
SELECT id, text FROM docs
WHERE source = 'intro.md'
ORDER BY embedding <=> $1::vector
LIMIT 5;
```

### MongoDB Atlas Vector Search
```javascript
import { MongoClient } from "mongodb";
const col = new MongoClient(uri).db("ai").collection("docs");

await col.aggregate([
  { $vectorSearch: {
      index: "doc_embedding",
      path: "embedding",
      queryVector: await embed(q),
      numCandidates: 100,
      limit: 5,
      filter: { source: "intro.md" },
  }},
  { $project: { text: 1, score: { $meta: "vectorSearchScore" } } },
]).toArray();
```

### Redis semantic cache
```python
import redis, hashlib, json
r = redis.Redis()

def cached_completion(prompt: str, ttl=3600):
    key = "ai:" + hashlib.sha256(prompt.encode()).hexdigest()
    if v := r.get(key): return json.loads(v)
    out = anthropic.messages.create(
        model="claude-opus-4-7",
        messages=[{"role":"user","content":prompt}],
        max_tokens=1024,
    )
    r.setex(key, ttl, json.dumps({"text": out.content[0].text}))
    return {"text": out.content[0].text}
```

### Redis Vector (semantic cache, near-match)
```python
# RediSearch + HNSW
from redis.commands.search.field import VectorField, TextField
from redis.commands.search.indexDefinition import IndexDefinition

r.ft("ai_cache").create_index(
    [TextField("prompt"),
     VectorField("v", "HNSW", {"TYPE":"FLOAT32","DIM":1536,"DISTANCE_METRIC":"COSINE"})],
    definition=IndexDefinition(prefix=["cache:"]),
)
```

### Agent state (MongoDB)
```python
from pymongo import MongoClient
c = MongoClient(uri).agents.runs

run_id = c.insert_one({
    "user_id": "u1",
    "messages": [{"role":"system","content":"..."}],
    "tool_calls": [],
    "status": "running",
    "created_at": datetime.utcnow(),
}).inserted_id

c.update_one({"_id": run_id}, {"$push": {"messages": new_msg}})
```

### Knowledge graph (Neo4j)
```cypher
MERGE (a:Person {name: 'Alice'})
MERGE (c:Company {name: 'Acme'})
MERGE (a)-[:WORKS_AT {since: 2020}]->(c)
```

```python
# answer "who at Acme works with Alice's manager?"
session.run("""
MATCH (a:Person {name: $n})-[:WORKS_AT]->(c)<-[:WORKS_AT]-(p)
RETURN p.name LIMIT 10
""", n="Alice")
```

### Hybrid retrieval (BM25 + vector)
```python
keyword_hits = es.search(index="docs", query={"match": {"text": q}})
vector_hits = idx.query(vector=embed(q), top_k=10).matches
fused = reciprocal_rank_fusion([keyword_hits, vector_hits], k=60)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Small RAG (<1M docs) | pgvector (single Postgres) |
| Medium RAG (1M-100M) | Qdrant / Weaviate self-host |
| Large / managed | Pinecone / MongoDB Atlas |
| Agent state + chat | MongoDB document store |
| Prompt cache | Redis (exact + semantic) |
| Entity reasoning | Neo4j |

**기본값**: pgvector (start) → Qdrant (scale) + Redis cache + MongoDB for agent state.

## 🔗 Graph
- 응용: [[RAG]] · [[Semantic Search|Semantic-Search]] · [[Agent-Memory]]
- Adjacent: [[Embeddings]] · [[Hybrid Search]] · [[pgvector]]

## 🤖 LLM 활용
**언제**: 매 schema design 의 propose, query construction 의 boilerplate, hybrid-search blend 의 tune.
**언제 X**: 매 capacity / cost projection, ANN index parameter (M, efConstruction) tuning — measure on real workload.

## ❌ 안티패턴
- **Vector DB only**: 매 metadata filter 의 ignore = 매 irrelevant top-k.
- **No re-ranker**: top-50 vector hits 의 직접 LLM 의 feed = noise. Cohere Rerank or cross-encoder.
- **Cache prompt verbatim**: 매 1-char diff = 매 cache miss. Use semantic cache.
- **Mixing OLTP + vector**: 매 single Postgres 의 both = 매 index bloat. Separate.

## 🧪 검증 / 중복
- Verified (Pinecone docs, Qdrant docs, MongoDB Atlas Vector Search 2025, "Designing Data-Intensive Applications", LangChain RAG cookbook).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — NoSQL families mapped to AI/RAG/agent workloads |