pgvector vs Ragas for insurance: Which Should You Use?

pgvector and Ragas solve different problems. pgvector is a PostgreSQL extension for storing and querying embeddings inside your database; Ragas is an evaluation framework for measuring how well your LLM/RAG system behaves. For insurance, start with pgvector if you are building retrieval over policy, claims, or underwriting data; add Ragas once you need to prove answer quality, faithfulness, and retrieval performance.

Quick Comparison

When pgvector Wins

• •

  You need one datastore for both structured insurance data and embeddings.

  Claims systems are full of filters: line of business, state, effective date, policy status, loss type. pgvector lets you combine vector search with SQL predicates in the same query instead of stitching together a separate vector database.

• •

  You are building retrieval into an existing Postgres-backed insurance platform.

  If your core system already runs on PostgreSQL, adding pgvector is operationally simple. You keep backups, access control, replication, audit tooling, and row-level security in one place.

• •

  You care about transactional consistency.

  Insurance workflows often require writes to be visible immediately: new claim note ingested, adjuster comment indexed, endorsement uploaded. With pgvector in Postgres, the embedding record can live alongside the transaction that created it.

• •

  Your use case is metadata-heavy semantic search.

  Example: find similar claims only within a specific carrier program and jurisdiction.

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE claim_chunks (
  id bigserial PRIMARY KEY,
  claim_id bigint NOT NULL,
  state text NOT NULL,
  line_of_business text NOT NULL,
  content text NOT NULL,
  embedding vector(1536)
);

CREATE INDEX claim_chunks_embedding_hnsw
ON claim_chunks
USING hnsw (embedding vector_cosine_ops);

SELECT id, claim_id
FROM claim_chunks
WHERE state = 'TX'
  AND line_of_business = 'auto'
ORDER BY embedding <=> $1
LIMIT 10;

That pattern is exactly why pgvector wins in insurance: filter first by business rules, rank by similarity second.

When Ragas Wins

• •

  You need to prove your insurance assistant is actually good.

  Retrieval working is not the same as answer quality. Ragas gives you metrics like faithfulness, answer_relevancy, context_precision, and context_recall so you can measure whether the assistant cites the right policy language or hallucinates coverage details.

• •

  You are iterating on prompts or retrievers and need regression tests.

  Insurance teams change prompts constantly: claims triage flows, coverage explanations, FNOL assistants. Ragas lets you compare runs against a fixed dataset and catch quality drops before they hit production.

• •

  You have compliance pressure around explainability.

  For insurers, “the model sounded right” is useless. You need evidence that answers are grounded in retrieved context. Ragas is built for this kind of evaluation discipline.

• •

  You want to benchmark multiple RAG architectures.

  Maybe you are comparing chunking strategies for policy documents or testing whether hybrid retrieval beats dense-only search. Ragas gives you a standard way to score those experiments instead of relying on anecdotal review.

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

result = evaluate(
    dataset=test_dataset,
    metrics=[faithfulness, answer_relevancy]
)

print(result)

That is the value: not storage, not retrieval infrastructure — measurement.

For insurance Specifically

Use pgvector first if your problem is getting the right documents back from policy wording, claims history, underwriting notes, or broker communications. Use Ragas once that pipeline exists and you need to validate that answers are grounded enough for regulated workflows.

My recommendation is blunt: pgvector is the foundation; Ragas is the gatekeeper. In insurance systems where incorrect answers create legal and financial risk, build retrieval on pgvector inside Postgres and put Ragas in your CI/evaluation loop before any assistant reaches adjusters or underwriters.

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.