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
🔄 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
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Cybersecurity
The practices and controls used to protect identities, devices, networks, applications, and data from unauthorized access, disruption, or manipulation.
Zero Trust
A security model that does not automatically trust a user or device because of its location. Access is continuously verified and limited to what is necessary.
Identity and Access Management (IAM)
The systems and policies that determine who a user is, what resources they may access, and how that access is authenticated and reviewed.
Multi-Factor Authentication (MFA)
A login control requiring more than one form of verification, such as a password plus an authenticator app, security key, or biometric factor.
API
An application programming interface is a defined way for software systems to exchange data or request functions from one another.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.