pgvector vs DeepEval for insurance: Which Should You Use?

pgvector and DeepEval solve different problems.

pgvector is a PostgreSQL extension for storing and querying embeddings with SQL. DeepEval is an evaluation framework for testing LLM outputs, RAG pipelines, and agent behavior. For insurance, use pgvector for retrieval and DeepEval for validation; if you must pick one first, start with pgvector because insurance systems need controlled data access before they need benchmark scores.

Quick Comparison

When pgvector Wins

• •

  You need retrieval inside an existing insurance database.

  • •If claims metadata, policy text chunks, or underwriting notes already live in PostgreSQL, pgvector keeps the stack simple.
  • •You can store embeddings in a vector(1536) column and query with plain SQL.
• •

  You care about access control and auditability.

  • •Insurance teams usually need row-level security, tenant isolation, and traceable queries.
  • •pgvector inherits PostgreSQL controls: roles, policies, transactions, backups, replication.
• •

  You want one system of record for structured and unstructured data.

  • •A claims workflow often needs joins between customer records, policy terms, adjuster notes, and retrieved passages.
  • •With pgvector you can run a single query that mixes filters like policy_type = 'auto' with nearest-neighbor search.
• •

  Your team is already running Postgres in production.

  • •Adding pgvector is operationally cheap compared to introducing a separate vector database.
  • •The common pattern is:

    CREATE EXTENSION IF NOT EXISTS vector;
    
    CREATE TABLE policy_chunks (
      id bigserial PRIMARY KEY,
      policy_id text NOT NULL,
      chunk ტექxt NOT NULL,
      embedding vector(1536)
    );
    
    CREATE INDEX ON policy_chunks USING hnsw (embedding vector_cosine_ops);
    

  • •Then retrieve with:

    SELECT chunk
    FROM policy_chunks
    WHERE policy_id = 'POL-123'
    ORDER BY embedding <=> $1
    LIMIT 5;
    

When DeepEval Wins

• •

  You are testing an insurance chatbot or claims assistant.

  • •DeepEval is built to tell you whether the model answered correctly, stayed grounded in context, and avoided hallucinations.
  • •That matters when the output affects coverage explanations or claim status responses.
• •

  You need repeatable regression tests for prompts and RAG flows.

  • •Insurance products change constantly: endorsements, exclusions, underwriting rules.
  • •DeepEval lets you codify expected behavior with test cases instead of manually checking responses after every prompt change.
• •

  You want metrics that map to LLM quality.

  • •DeepEval includes evaluation primitives like GEval, AnswerRelevancyMetric, FaithfulnessMetric, ContextualPrecisionMetric, and ContextualRecallMetric.
  • •That’s useful when the real question is not “did retrieval work?” but “did the model use the retrieved evidence correctly?”
• •

  You are building CI checks around AI behavior.

  • •In regulated workflows you need to catch bad outputs before they reach production.
  • •DeepEval fits into automated test runs where a change to the prompt template or retriever should fail fast if quality drops.

A typical pattern looks like this:

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

test_case = LLMTestCase(
    input="Does this auto policy cover rental cars?",
    actual_output="Yes, rental cars are always covered.",
    retrieval_context=["Rental reimbursement applies only if purchased as an endorsement."]
)

metric = FaithfulnessMetric()
metric.measure(test_case)

print(metric.score)

That kind of check is exactly what you want before shipping a claims copilot.

For insurance Specifically

Use pgvector as the retrieval layer and DeepEval as the quality gate. Insurance workloads are full of controlled documents—policy forms, endorsements, claim guidelines—and those belong close to PostgreSQL where you can enforce security and audit trails.

DeepEval comes in after that to prove your assistant is not making up coverage language or misreading retrieved context. If your team has to choose where to start this quarter: ship pgvector first for document search in production; add DeepEval immediately after so every prompt or RAG change gets tested against insurance-specific failure modes.

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.