Fast, Guarded Retrieval with Provenance
A Vector Database stores embeddings (numeric representations of text/code/images/audio) so you can retrieve semantically similar content—not just exact keyword matches.
SolveForce designs vector stacks for RAG (Retrieval-Augmented Generation) that are fast, guarded, and auditable: labeled indices, hard access filters, ontology-aware reranking, and “cite-or-refuse” generation—wired to security and governance.
Where this fits in the SolveForce system:
🧠 AI layer → SolveForce AI • 📚 Standardization → AI Knowledge Standardization
🏛️ Truth source → Data Warehouse / Lakes • 🔄 Pipelines → ETL / ELT
🔒 Controls → Cybersecurity • IAM / SSO / MFA • DLP • SIEM / SOAR
🎯 Outcomes (Why Vector DB + Guarded RAG)
- Precision ↑ — semantic + keyword/hybrid search retrieves the right chunks.
- Hallucinations ↓ — label filters + ontology rerank + “cite or refuse” enforcement.
- Latency ↓ — tuned ANN (approximate nearest neighbor) indexes stay sub-second at scale.
- Trust ↑ — every answer carries citations and provenance; unknowns trigger honest refusal.
- Cost ↓ — sharded, domain-scoped indices reduce context length and model calls.
🧭 Scope (What we index)
- Text & code — docs, policies, tickets, runbooks, schemas, wikis, repos, APIs.
- Structured → text — curated warehouse tables (dim/fact) summarized into embeddings. → Data Warehouse / Lakes
- Multimodal — images/charts (captions + vectors), audio transcripts, PDFs with layout-aware chunking.
- Event logs — normalized security/ops events for semantic incident recall. → SIEM / SOAR
🧱 Building Blocks (Spelled out)
- Embeddings — domain-specific models; stable dimensions (e.g., 384–1536+); versioned.
- Chunking — semantic segments (headings/sections/code blocks), ≤ 200–600 tokens per chunk; overlap where needed.
- Metadata — labels (domain, sensitivity, jurisdiction, product, lifecycle), timestamps, authors, lineage.
- ANN Indexes — HNSW / IVF / PQ/OPQ hybrids; M/ef (HNSW) and nlist/nprobe (IVF) tuned per SLO.
- Hybrid search — dense (vector) + sparse (BM25/keyword) reranked with ontology signals.
- Filters — hard pre-filters on labels/ACLs before ANN search; soft rerank after.
Definitions & terms come from the Codex and ontology to keep queries consistent. → SolveForce Codex • Language of Code Ontology
🏗️ Reference Architecture (Ingest → Normalize → Embed → Index → Retrieve → Generate → Cite)
1) Ingest
Connectors pull docs/code/tickets/emails; OCR for scans; attach provenance (source path, commit, timestamps). → ETL / ELT
2) Normalize & Chunk
Clean HTML/markdown; split semantically; add labels (domain/sensitivity/region/owner). → AI Knowledge Standardization
3) Embed & Index
Generate embeddings (versioned); write to vector store with metadata; build HNSW/IVF-PQ depending on dataset size & SLO.
4) Guarded Retrieval
Query → pre-filter by labels/ACLs/jurisdiction → ANN search (k) → hybrid rerank (dense+sparse+ontology).
5) Generate & Cite
LLM composes grounded answer with inline citations; if insufficient evidence → refuse with reason.
6) Observe & Tune
Store Q/A with votes; track precision@k, latency, refusal correctness, and drift; refresh embeddings on content change.
🔒 Security & Governance (Zero-Trust Retrieval)
- Access-first — enforce role/region/sensitivity filters before vector search. → IAM / SSO / MFA
- DLP-aware — redact/mask Restricted fields on retrieval; some labels return read-only snippets or deny. → DLP
- Provenance-required — no source → no claim; block generation without citations.
- Jurisdictional split — separate indices by region (EU/US/etc.); cross-region queries by policy only.
- Audit trails — every query/retrieval/generation → SIEM with user/labels/citations/latency. → SIEM / SOAR
⚙️ Performance & Capacity (What we tune)
- Recall vs. latency — HNSW ef search, IVF nprobe; target p95 < 200–600 ms retrieval.
- Memory vs. cost — PQ/OPQ to compress vectors; cache hot shards in RAM/NVMe.
- Shard by domain/label — small, focused indices beat one giant index for precision & speed.
- Batch vs. streaming updates — micro-batch embeddings (e.g., 1–5 min); eventual consistency OK with provenance.
📐 SLO Guardrails (Experience & safety you can measure)
| SLO / KPI | Target (Recommended) | Notes |
|---|---|---|
| Retrieval latency (p95) | ≤ 200–600 ms | Vector + filters + rerank |
| Answer end-to-end (p95) | ≤ 1.5–3.0 s | Retrieval → LLM → cite |
| Precision@K (gold Q/A) | ≥ 92–95% | After ontology + hybrid tuning |
| Citation coverage | = 100% | “Cite or refuse” policy |
| Refusal correctness | ≥ 98% | Honest “don’t know” |
| Ingest→index freshness (p95) | ≤ 5–15 min | From doc change to searchable |
| Access violations (blocked by filter) | = 0 | Hard filters pre-ANN |
SLO breaches trigger SOAR actions (fallback to keyword, relax rerank, open incident, retrain embeddings). → SIEM / SOAR
🧰 Patterns (By Outcome)
A) Guarded RAG for Enterprise Docs
- Domain-sharded indices; label filters (department/sensitivity/jurisdiction); ontology terms boost; answers always cite; refuse when unknown.
B) Code & API Assistant
- Chunk by function/class/spec; hybrid search (symbol/keyword + vectors); enforce license filters; link to repo commit hashes.
C) Incident Recall (SecOps/ITOps)
- Embed normalized alerts/cases/runbooks; time-window filters; link to evidence; suggest playbooks. → SIEM / SOAR
D) Product/Support Search
- Multi-lingual embeddings; region filters; deflection KPIs; escalation when recall < threshold.
E) Recommendations / Similarity
- User/content vectors with labels for cold-start; guard with DLP for private segments.
🧪 Quality & Safety Loop
1) Gold Q/A benchmarks per domain; measure precision@k and refusal rates.
2) Query rewrite rules from ontology (synonyms/acronyms) to reduce mismatch.
3) Negative sampling & hard examples to improve rerankers.
4) Drift alerts when content/metrics change beyond thresholds; re-embed shards.
🔗 Integrations (Make it a system, not a silo)
- Pipelines & truth — publish from curated marts and docs with provenance. → Data Warehouse / Lakes • ETL / ELT
- Standardization — glossary/ontology links for terms and disambiguation. → AI Knowledge Standardization
- Access & privacy — role/label filters, DLP, tokenization. → IAM / SSO / MFA • DLP
- Runtime — caching, prompt macros, answer templates with inline citations. → SolveForce AI
- Evidence — query logs, citations, refusals, model versions to SIEM. → SIEM / SOAR
📜 Compliance Mapping (Examples)
- PCI DSS / HIPAA / ISO 27001 / NIST / CMMC — access control (ABAC/RBAC), data minimization, encryption, logging/retention, and evidence (queries/citations/refusals).
- Residency — region-bound indices; lawful processing and export controls.
🛠️ Implementation Blueprint (No-Surprise Rollout)
1) Inventory domains & sources; choose labels (domain/sensitivity/jurisdiction/owner).
2) Glossary & ontology sprint (synonyms/acronyms/definitions). → AI Knowledge Standardization
3) Pipelines to normalize, chunk, embed (version), and index; attach provenance. → ETL / ELT
4) Security — pre-filters (role/label/region), DLP redaction, encryption at rest/in transit. → IAM / SSO / MFA • DLP • Encryption
5) Hybrid retrieval — dense + sparse with ontology rerank; set K and thresholds by domain.
6) Guarded generation — “cite or refuse” + templates; refusal ledger.
7) SLO dashboards — latency, precision@k, refusal correctness, freshness; logs → SIEM.
8) Drills — index rebuild, model version swap, content surge; publish RCAs.
✅ Pre-Engagement Checklist
- 📚 Source list, label taxonomy, glossary readiness.
- 🧠 Embedding model choice & dimension; versioning plan.
- 🗂️ Chunking strategy; metadata fields; provenance format.
- 🔐 Filter rules (role/label/region); DLP posture; encryption keys.
- 📈 SLO targets (latency, precision@k, refusal/citation); dashboards.
- 🧪 Benchmarks & gold Q/A per domain; acceptance thresholds.
- 🔄 Refresh cadence (re-embed/reindex); drift alerts & retraining plan.
🔄 Where Vector DBs & RAG Fit (Recursive View)
1) Grammar — content flows over Connectivity & Networks & Data Centers.
2) Syntax — curated truth in Data Warehouse / Lakes feeds embeddings.
3) Semantics — Cybersecurity enforces access, privacy, and logging.
4) Pragmatics — SolveForce AI retrieves with guardrails and cites or refuses.
5) Foundation — Primacy of Language + ontology keep terms coherent.
6) Map — indexed in the SolveForce Codex & Knowledge Hub.
📞 Build Vector Search That’s Fast, Safe & Auditable
Related pages:
SolveForce AI • AI Knowledge Standardization • Data Warehouse / Lakes • ETL / ELT • IAM / SSO / MFA • DLP • Encryption • SIEM / SOAR • Knowledge Hub