Split View: PostgreSQL + pgvector 벡터 검색 실전 가이드: RAG부터 하이브리드 검색까지

# Ubuntu/Debian
sudo apt install postgresql-17-pgvector

# macOS (Homebrew)
brew install pgvector

# Docker
docker run -d --name pgvector \
  -e POSTGRES_PASSWORD=secret \
  -p 5432:5432 \
  pgvector/pgvector:pg17

-- Install pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Check version
SELECT extversion FROM pg_extension WHERE extname = 'vector';
-- 0.8.0

-- Documents table (1536 dimensions = OpenAI text-embedding-3-small)
CREATE TABLE documents (
    id BIGSERIAL PRIMARY KEY,
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    embedding vector(1536),  -- Vector column!
    metadata JSONB DEFAULT '{}',
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- 384 dimensions (sentence-transformers/all-MiniLM-L6-v2)
CREATE TABLE chunks (
    id BIGSERIAL PRIMARY KEY,
    doc_id BIGINT REFERENCES documents(id),
    chunk_text TEXT NOT NULL,
    embedding vector(384),
    chunk_index INT
);

-- Single insert
INSERT INTO documents (title, content, embedding)
VALUES (
    'Kubernetes RBAC Guide',
    'RBAC is a method of regulating access...',
    '[0.1, 0.2, 0.3, ...]'::vector  -- 1536-dimension vector
);

-- Batch insert from Python

import psycopg2
from pgvector.psycopg2 import register_vector
import numpy as np

conn = psycopg2.connect("dbname=mydb user=postgres password=secret")
register_vector(conn)

cur = conn.cursor()

# Generate OpenAI embeddings
from openai import OpenAI
client = OpenAI()

texts = ["Kubernetes RBAC guide", "Docker networking basics", ...]
response = client.embeddings.create(
    model="text-embedding-3-small",
    input=texts
)

# Batch insert
for text, emb_data in zip(texts, response.data):
    embedding = np.array(emb_data.embedding)
    cur.execute(
        "INSERT INTO documents (title, content, embedding) VALUES (%s, %s, %s)",
        (text, text, embedding)
    )

conn.commit()

-- Cosine similarity (most common)
SELECT id, title,
       1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 10;

-- L2 distance
SELECT id, title,
       embedding <-> '[0.1, 0.2, ...]'::vector AS distance
FROM documents
ORDER BY embedding <-> '[0.1, 0.2, ...]'::vector
LIMIT 10;

-- Inner Product — equivalent to cosine for normalized vectors
SELECT id, title,
       (embedding <#> '[0.1, 0.2, ...]'::vector) * -1 AS similarity
FROM documents
ORDER BY embedding <#> '[0.1, 0.2, ...]'::vector
LIMIT 10;

-- Create IVFFlat index
CREATE INDEX ON documents
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

-- Set probe count for search (accuracy vs speed trade-off)
SET ivfflat.probes = 10;

-- Create HNSW index (longer build time but faster search)
CREATE INDEX ON documents
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 200);

-- Set ef_search for search
SET hnsw.ef_search = 100;

-- Add tsvector column
ALTER TABLE documents ADD COLUMN tsv tsvector
  GENERATED ALWAYS AS (to_tsvector('english', title || ' ' || content)) STORED;

CREATE INDEX ON documents USING gin(tsv);

-- Hybrid search function
CREATE OR REPLACE FUNCTION hybrid_search(
    query_text TEXT,
    query_embedding vector(1536),
    match_count INT DEFAULT 10,
    vector_weight FLOAT DEFAULT 0.7,
    text_weight FLOAT DEFAULT 0.3
)
RETURNS TABLE (id BIGINT, title TEXT, score FLOAT) AS $$
BEGIN
    RETURN QUERY
    WITH vector_results AS (
        SELECT d.id, d.title,
               1 - (d.embedding <=> query_embedding) AS vector_score
        FROM documents d
        ORDER BY d.embedding <=> query_embedding
        LIMIT match_count * 3
    ),
    text_results AS (
        SELECT d.id, d.title,
               ts_rank(d.tsv, plainto_tsquery('english', query_text)) AS text_score
        FROM documents d
        WHERE d.tsv @@ plainto_tsquery('english', query_text)
        LIMIT match_count * 3
    ),
    combined AS (
        SELECT
            COALESCE(v.id, t.id) AS id,
            COALESCE(v.title, t.title) AS title,
            COALESCE(v.vector_score, 0) * vector_weight +
            COALESCE(t.text_score, 0) * text_weight AS score
        FROM vector_results v
        FULL OUTER JOIN text_results t ON v.id = t.id
    )
    SELECT c.id, c.title, c.score
    FROM combined c
    ORDER BY c.score DESC
    LIMIT match_count;
END;
$$ LANGUAGE plpgsql;

-- Usage
SELECT * FROM hybrid_search(
    'Kubernetes RBAC security',
    '[0.1, 0.2, ...]'::vector(1536)
);

CREATE OR REPLACE FUNCTION rrf_search(
    query_text TEXT,
    query_embedding vector(1536),
    match_count INT DEFAULT 10,
    rrf_k INT DEFAULT 60
)
RETURNS TABLE (id BIGINT, title TEXT, rrf_score FLOAT) AS $$
BEGIN
    RETURN QUERY
    WITH vector_ranked AS (
        SELECT d.id, d.title,
               ROW_NUMBER() OVER (ORDER BY d.embedding <=> query_embedding) AS rank
        FROM documents d
        LIMIT match_count * 5
    ),
    text_ranked AS (
        SELECT d.id, d.title,
               ROW_NUMBER() OVER (
                   ORDER BY ts_rank(d.tsv, plainto_tsquery('english', query_text)) DESC
               ) AS rank
        FROM documents d
        WHERE d.tsv @@ plainto_tsquery('english', query_text)
        LIMIT match_count * 5
    ),
    fused AS (
        SELECT
            COALESCE(v.id, t.id) AS id,
            COALESCE(v.title, t.title) AS title,
            COALESCE(1.0 / (rrf_k + v.rank), 0) +
            COALESCE(1.0 / (rrf_k + t.rank), 0) AS rrf_score
        FROM vector_ranked v
        FULL OUTER JOIN text_ranked t ON v.id = t.id
    )
    SELECT f.id, f.title, f.rrf_score
    FROM fused f
    ORDER BY f.rrf_score DESC
    LIMIT match_count;
END;
$$ LANGUAGE plpgsql;

import psycopg2
from pgvector.psycopg2 import register_vector
from openai import OpenAI
import numpy as np

client = OpenAI()
conn = psycopg2.connect("dbname=ragdb user=postgres password=secret")
register_vector(conn)

def embed(text: str) -> list[float]:
    resp = client.embeddings.create(
        model="text-embedding-3-small", input=text
    )
    return resp.data[0].embedding

def rag_query(question: str, top_k: int = 5) -> str:
    query_vec = embed(question)

    cur = conn.cursor()
    cur.execute("""
        SELECT title, content,
               1 - (embedding <=> %s::vector) AS similarity
        FROM documents
        WHERE 1 - (embedding <=> %s::vector) > 0.7
        ORDER BY embedding <=> %s::vector
        LIMIT %s
    """, (query_vec, query_vec, query_vec, top_k))

    results = cur.fetchall()
    context = "\n\n".join([
        f"[{r[0]}] (similarity: {r[2]:.3f})\n{r[1]}"
        for r in results
    ])

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": f"Answer based on context:\n{context}"},
            {"role": "user", "content": question}
        ]
    )
    return response.choices[0].message.content

# Usage
answer = rag_query("What is the difference between ClusterRole and Role in Kubernetes RBAC?")
print(answer)

-- Work memory (for index build/search)
SET maintenance_work_mem = '2GB';  -- During HNSW build
SET work_mem = '256MB';             -- During search

-- Parallel processing
SET max_parallel_workers_per_gather = 4;
SET max_parallel_maintenance_workers = 4;

-- HNSW build optimization
SET maintenance_work_mem = '4GB';
-- Revert after build