pgvector vs DeepEval for RAG: Which Should You Use?

pgvector and DeepEval solve different problems, and that’s the first thing to get straight.

pgvector is a PostgreSQL extension for storing and querying embeddings with vector, halfvec, sparsevec, and indexes like HNSW and IVFFlat. DeepEval is an evaluation framework for testing RAG quality with metrics like AnswerRelevancyMetric, FaithfulnessMetric, and ContextualRecallMetric.

For RAG, use pgvector for retrieval infrastructure and DeepEval for measuring whether your RAG system actually works.

Quick Comparison

When pgvector Wins

• •

  You need retrieval inside your existing Postgres stack.
  If your app already uses PostgreSQL for users, documents, metadata, and access control, pgvector keeps everything in one place. You can filter by tenant, document type, or compliance flags in the same query as vector search.

• •

  You want production-grade retrieval without adding another service.
  pgvector gives you real database operations: transactions, backups, replication, roles, constraints. That matters in banking and insurance where “just run a vector DB” is not an acceptable architecture review answer.

• •

  You need hybrid filtering with SQL.
  A common RAG pattern is “find the nearest chunks for this embedding, but only from approved policy docs updated after a certain date.” With pgvector you can do that directly in SQL instead of stitching together a search service plus a metadata store.

• •

  You want predictable ops and easier governance.
  Security teams already understand Postgres. They do not need a separate mental model for another datastore just to support embeddings.

Example schema:

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  tenant_id uuid NOT NULL,
  content text NOT NULL,
  embedding vector(1536),
  created_at timestamptz DEFAULT now()
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

Example query:

SELECT id, content
FROM documents
WHERE tenant_id = '2f4c2c0a-8d7b-4c9d-b9f1-5f0b7b5a1111'
ORDER BY embedding <=> '[0.12, 0.03, ...]'::vector
LIMIT 5;

When DeepEval Wins

• •

  You need to know whether your RAG answers are actually good.
  Retrieval returning five chunks means nothing if the final answer hallucinates or ignores context. DeepEval is built to score outputs using metrics like FaithfulnessMetric and AnswerRelevancyMetric.

• •

  You’re running regression tests across prompts or model versions.
  This is where DeepEval earns its keep. You define test cases once and compare changes when you tweak chunking strategy, retriever settings, prompts, or switch models.

• •

  You care about measurable quality gates before deployment.
  In regulated environments, “it looked fine in manual testing” is not enough. DeepEval lets you turn subjective RAG behavior into repeatable checks with thresholds.

• •

  You need LLM-as-judge style evaluation at scale.
  For example: did the answer stay grounded in retrieved context? Did it answer the question directly? Did it miss important facts from the context? DeepEval is built around those questions.

Example test case:

from deepeval.test_case import LLMTestCase
from deepeval.metrics import FaithfulnessMetric

test_case = LLMTestCase(
    input="What does our claims policy say about duplicate submissions?",
    actual_output="Duplicate submissions are rejected unless corrected within 24 hours.",
    retrieval_context=[
        "Claims submitted twice within the same business day must be flagged for review.",
        "Corrections may be made within 24 hours if no payout has been issued."
    ]
)

metric = FaithfulnessMetric(threshold=0.8)
metric.measure(test_case)
print(metric.score)

For RAG Specifically

Use pgvector to power retrieval and DeepEval to validate the full pipeline. If you have to pick one first for a RAG project in production, pick pgvector because without solid retrieval infrastructure you do not have a reliable RAG system at all.

But if your retrieval layer already exists and the real problem is answer quality, pick DeepEval immediately. That’s how you catch hallucinations, weak grounding, and prompt regressions before users do.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.