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Antigravity Agent
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---
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id: wiki-2026-0508-nosql-databases-in-ai
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title: NoSQL Databases in AI
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [NoSQL for AI, Vector DB, Document Store AI]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [nosql, ai, vector-db, mongodb, redis, rag]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: mongodb-redis-pinecone
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---
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# NoSQL Databases in AI
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 NoSQL family + AI role
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- **Vector**: embedding similarity (RAG, recommendation). Pinecone, Qdrant, Weaviate, Milvus.
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- **Document**: chat history, agent state, structured output. MongoDB, CouchDB.
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- **KV / Cache**: prompt cache, semantic cache, session. Redis, DragonflyDB.
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- **Graph**: knowledge graph, entity link. Neo4j, ArangoDB.
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- **Wide-column**: time-series telemetry, traces. Cassandra, ScyllaDB.
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### 매 access pattern
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- **Embed + ANN search**: HNSW / IVF index, top-k cosine.
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- **Metadata filter + vector**: hybrid search.
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- **TTL cache**: prompt → response 의 24h cache.
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- **Append-only chat log**: doc store + per-user shard.
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### 매 응용
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1. RAG: vector + document hybrid.
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2. Agent memory: document (short) + vector (long-term).
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3. Personalization: KV (recent) + graph (relations).
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## 💻 패턴
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### Pinecone (managed vector)
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```python
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from pinecone import Pinecone, ServerlessSpec
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pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
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pc.create_index(
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name="docs",
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dimension=1536,
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metric="cosine",
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spec=ServerlessSpec(cloud="aws", region="us-east-1"),
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)
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idx = pc.Index("docs")
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idx.upsert(vectors=[
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{"id": "doc1", "values": embed("Hello world"),
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"metadata": {"source": "intro.md", "section": "overview"}},
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])
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res = idx.query(
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vector=embed("greeting example"),
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top_k=5,
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filter={"source": {"$eq": "intro.md"}},
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include_metadata=True,
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)
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```
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### Qdrant (self-host)
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```python
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from qdrant_client import QdrantClient
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from qdrant_client.models import VectorParams, Distance, PointStruct
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client = QdrantClient("localhost", port=6333)
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client.recreate_collection(
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"docs",
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vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
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)
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client.upsert("docs", points=[
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PointStruct(id=1, vector=embed(text), payload={"text": text, "source": "x.md"}),
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])
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hits = client.search("docs", query_vector=embed(q), limit=5,
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query_filter={"must": [{"key": "source", "match": {"value": "x.md"}}]})
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```
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### pgvector (Postgres + vector)
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```sql
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CREATE EXTENSION vector;
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CREATE TABLE docs (
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id BIGSERIAL PRIMARY KEY,
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text TEXT,
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source TEXT,
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embedding vector(1536)
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);
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CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops);
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-- hybrid query
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SELECT id, text FROM docs
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WHERE source = 'intro.md'
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ORDER BY embedding <=> $1::vector
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LIMIT 5;
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```
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### MongoDB Atlas Vector Search
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```javascript
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import { MongoClient } from "mongodb";
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const col = new MongoClient(uri).db("ai").collection("docs");
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await col.aggregate([
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{ $vectorSearch: {
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index: "doc_embedding",
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path: "embedding",
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queryVector: await embed(q),
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numCandidates: 100,
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limit: 5,
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filter: { source: "intro.md" },
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}},
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{ $project: { text: 1, score: { $meta: "vectorSearchScore" } } },
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]).toArray();
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```
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### Redis semantic cache
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```python
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import redis, hashlib, json
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r = redis.Redis()
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def cached_completion(prompt: str, ttl=3600):
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key = "ai:" + hashlib.sha256(prompt.encode()).hexdigest()
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if v := r.get(key): return json.loads(v)
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out = anthropic.messages.create(
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model="claude-opus-4-7",
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messages=[{"role":"user","content":prompt}],
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max_tokens=1024,
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)
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r.setex(key, ttl, json.dumps({"text": out.content[0].text}))
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return {"text": out.content[0].text}
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```
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### Redis Vector (semantic cache, near-match)
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```python
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# RediSearch + HNSW
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from redis.commands.search.field import VectorField, TextField
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from redis.commands.search.indexDefinition import IndexDefinition
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r.ft("ai_cache").create_index(
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[TextField("prompt"),
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VectorField("v", "HNSW", {"TYPE":"FLOAT32","DIM":1536,"DISTANCE_METRIC":"COSINE"})],
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definition=IndexDefinition(prefix=["cache:"]),
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)
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```
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### Agent state (MongoDB)
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```python
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from pymongo import MongoClient
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c = MongoClient(uri).agents.runs
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run_id = c.insert_one({
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"user_id": "u1",
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"messages": [{"role":"system","content":"..."}],
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"tool_calls": [],
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"status": "running",
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"created_at": datetime.utcnow(),
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}).inserted_id
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c.update_one({"_id": run_id}, {"$push": {"messages": new_msg}})
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```
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### Knowledge graph (Neo4j)
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```cypher
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MERGE (a:Person {name: 'Alice'})
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MERGE (c:Company {name: 'Acme'})
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MERGE (a)-[:WORKS_AT {since: 2020}]->(c)
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```
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```python
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# answer "who at Acme works with Alice's manager?"
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session.run("""
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MATCH (a:Person {name: $n})-[:WORKS_AT]->(c)<-[:WORKS_AT]-(p)
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RETURN p.name LIMIT 10
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""", n="Alice")
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```
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### Hybrid retrieval (BM25 + vector)
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```python
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keyword_hits = es.search(index="docs", query={"match": {"text": q}})
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vector_hits = idx.query(vector=embed(q), top_k=10).matches
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fused = reciprocal_rank_fusion([keyword_hits, vector_hits], k=60)
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Small RAG (<1M docs) | pgvector (single Postgres) |
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| Medium RAG (1M-100M) | Qdrant / Weaviate self-host |
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| Large / managed | Pinecone / MongoDB Atlas |
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| Agent state + chat | MongoDB document store |
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| Prompt cache | Redis (exact + semantic) |
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| Entity reasoning | Neo4j |
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**기본값**: pgvector (start) → Qdrant (scale) + Redis cache + MongoDB for agent state.
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## 🔗 Graph
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- 응용: [[RAG]] · [[Semantic Search|Semantic-Search]] · [[Agent-Memory]]
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- Adjacent: [[Embeddings]] · [[Hybrid Search]] · [[pgvector]]
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## 🤖 LLM 활용
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**언제**: 매 schema design 의 propose, query construction 의 boilerplate, hybrid-search blend 의 tune.
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**언제 X**: 매 capacity / cost projection, ANN index parameter (M, efConstruction) tuning — measure on real workload.
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## ❌ 안티패턴
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- **Vector DB only**: 매 metadata filter 의 ignore = 매 irrelevant top-k.
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- **No re-ranker**: top-50 vector hits 의 직접 LLM 의 feed = noise. Cohere Rerank or cross-encoder.
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- **Cache prompt verbatim**: 매 1-char diff = 매 cache miss. Use semantic cache.
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- **Mixing OLTP + vector**: 매 single Postgres 의 both = 매 index bloat. Separate.
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## 🧪 검증 / 중복
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- Verified (Pinecone docs, Qdrant docs, MongoDB Atlas Vector Search 2025, "Designing Data-Intensive Applications", LangChain RAG cookbook).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — NoSQL families mapped to AI/RAG/agent workloads |
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