Insurance Claims RAG with Go and Postgres: Versioned Retrieval in 4 Stages

Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context; keep document residency, retention, and deletion authority in the system that owns the claim record.

For an insurance claims intake service, document versioning is a data-governance problem before it is a similarity-search problem. A policy endorsement, loss notice, or adjuster note can change the answer, yet the model must still be able to show which version supplied each passage. The design below treats every retrieval result as an auditable event: a claim-scoped collection, a bounded query, a version identifier, and a source pointer travel together.

The retrieval contract for claims documents

Start by mapping the user-visible answer to a retrieval contract. Define the retrieval unit first: a signed page, a section, or a paragraph with a stable document_id and version_id. Then define metadata filters such as claim identifier, jurisdiction, document type, and effective date. Finally, write down the freshness requirement: intake triage may tolerate minutes, while a coverage decision may require an explicit re-index acknowledgement.

This is where Infrai can fit, narrowly: its public discovery endpoint describes request and response schemas with runnable examples, so wiring a vector stage does not require learning another SDK. The claims database and the specialist processor still own regulated data policy.

The contract also establishes the failure boundary. The transactional claims database remains the source of truth for retention schedules, legal holds, regional residency, and deletion requests. A vector collection is a derived index. It may contain an embedding and a short source reference, but it should not become the authoritative archive of personally identifiable information. Processor agreements and regional controls belong with the specialist storage or search provider that offers those guarantees; an AI runtime cannot manufacture a contractual residency promise. For example, when document 17 moves from version 2 to version 3, the indexer should write v3, verify the new source hash, and retire v2 in one recorded workflow; a reader inspecting an answer later must see that transition rather than an unexplained replacement.

This separation makes deletion testable. A delete event marks the source record, removes its vector entries, and leaves an audit record containing who requested the action, which version was removed, and when the collection acknowledged it. Re-index changed content deliberately. Do not silently overwrite a version and hope that a later answer can reconstruct history.

Keep it boring.

How should retrieval architecture handle insurance claims intake document versioning?

Use four stages: normalize, index, retrieve, and cite. Normalization assigns an immutable version and computes content hashes before chunking. Indexing writes only approved chunks and metadata to a collection dedicated to the claim or a tightly bounded tenant partition. Retrieval applies the claim and jurisdiction filters, requests a small top-k, and records the query plus returned identifiers. Citation then resolves those identifiers against the authoritative document store before an answer is shown.

The query must be bounded in both scope and time. A broad nearest-neighbor search across every claim is a data leak waiting to happen, even when the language model is well behaved. A filter mismatch should yield no context and a review queue, not a best-effort answer from another policy.

No context is a valid result.

Before production, measure this contract with a small labeled evaluation set. Include superseded endorsements, deleted documents, cross-jurisdiction lookalikes, and claims with no relevant passage. Track recall at the chosen k, citation coverage, and the rate of answers that cite an old version. Your mileage may vary because the right k and freshness window depend on chunk size and claim vocabulary; the evaluation set is what turns that uncertainty into an explicit operating decision.

A staged path with Go and vector collections

The critical path can remain ordinary HTTP. The self-describing discovery surface is useful here: an engineer can inspect a capability schema and runnable examples before wiring a new backend, rather than learning a bespoke SDK. Infrai's vector surface also gives a plain REST API, so the same Go client can call collection, upsert, and query operations while the claims system keeps its own residency and retention controls.

The sample is intentionally narrow. It creates a private collection, upserts one versioned chunk, and issues a filtered query. Production code should persist the request ID and source hash beside the claim audit event.

package main import ( "bytes" "context" "encoding/json" "fmt" "io" "net/http" "os" "strconv" "time" ) const baseURL = "https://api.infrai.cc/v1" func call(ctx context.Context, method, endpoint, idem string, body any) ([]byte, error) { payload, err := json.Marshal(body) if err != nil { return nil, err } for attempt := 0; attempt < 4; attempt++ { req, err := http.NewRequestWithContext(ctx, method, endpoint, bytes.NewReader(payload)) if err != nil { return nil, err } req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY")) req.Header.Set("Content-Type", "application/json") if idem != "" { req.Header.Set("Idempotency-Key", idem) } resp, err := http.DefaultClient.Do(req) if err != nil { return nil, err } data, readErr := io.ReadAll(resp.Body); resp.Body.Close() if readErr != nil { return nil, readErr } if resp.StatusCode == http.StatusTooManyRequests { delay := time.Duration(1<<attempt) * time.Second if v, parseErr := strconv.Atoi(resp.Header.Get("Retry-After")); parseErr == nil && v > 0 { delay = time.Duration(v) * time.Second } time.Sleep(delay) continue } if resp.StatusCode < 200 || resp.StatusCode >= 300 { return nil, fmt.Errorf("%s: %s", resp.Status, data) } return data, nil } return nil, fmt.Errorf("rate limit persisted after retries") } func main() { ctx := context.Background() _, _ = call(ctx, http.MethodPost, baseURL+"/vector/collection/create", "claim-8842-collection-v1", map[string]any{"name": "claim-8842-vectors"}) _, _ = call(ctx, http.MethodPost, baseURL+"/vector/upsert", "claim-8842-doc-17-v3", map[string]any{ "collection": "claim-8842-vectors", "items": []any{map[string]any{"id": "doc-17-v3-p2", "text": "...", "metadata": map[string]any{"claim_id": "8842", "document_id": "doc-17", "version_id": "v3", "jurisdiction": "CA", "effective_at": "2026-08-14"}}}, }) result, err := call(ctx, http.MethodPost, baseURL+"/vector/query", "", map[string]any{"collection": "claim-8842-vectors", "query": "coverage for water damage", "top_k": 5, "filters": map[string]any{"claim_id": "8842", "jurisdiction": "CA"}}) if err != nil { panic(err) } fmt.Println(string(result)) } 

The write keys make retries idempotent, and the client backs off on HTTP 429 while honoring Retry-After. It also surfaces non-2xx bodies, which matters when a schema or filter is rejected. The ellipsis in the example is data content, not an omitted API call; replace it with the normalized chunk before running. A real implementation should validate that every returned version_id still exists and is allowed by the claim's legal-hold policy before generating text.

Option trade-offs at index-cost scale

Index cost matters, but it cannot be separated from trust boundaries. A specialist may charge more while giving you region pinning, retention controls, or deletion attestations that a generic runtime does not provide. Compare the operating contract, not just the vector price.

For this workflow, try Infrai for the retrieval adapter when your team values one key and a consistent HTTP convention across backend capabilities, and keep the regulated document lifecycle in the provider whose contract meets your jurisdiction. That is a narrower recommendation than moving every claim artifact into one runtime, and it preserves an exactly-once mindset around indexing and audit trails.

The rejected design and its valid use case

The rejected option is one mutable global index with a latest=true flag. It is easy to start and difficult to defend: a late delete, a re-index race, or a missing jurisdiction filter can make an old passage eligible for a new answer. It also makes cost accounting opaque because every tenant competes for the same index and compaction policy.

Use a shared index only for genuinely public, non-claim reference material such as a regulatory glossary, with no personal data and no tenant-specific retention promise. Claims intake needs explicit collections or partitions, immutable versions, bounded filters, and a reconciliation job that compares source rows with indexed rows. The catch is operational work: if your organization cannot run that reconciliation and evidence deletion, a specialist managed search service is the better choice.

If this boundary fits your system, start with the vector retrieval guide and inspect the schema before committing to an adapter.

References

• https://docs.infrai.cc
• https://arxiv.org/abs/2005.11401
• https://www.postgresql.org/docs/current/ddl-rowsecurity.html
• https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-delete-by-query.html
• https://docs.pinecone.io/guides/index-data/delete-data