lakehouse/docs/PRD.md

# PRD: Lakehouse — Rust-First Substrate for Versioned Knowledge Stores

**Status:** Active — Phases 0-18 shipped; hybrid Parquet+HNSW ⊕ Lance operational; scheduled ingest live; PDF OCR live; entering horizon items
**Created:** 2026-03-27
**Last reframed:** 2026-04-17 — hybrid architecture proven end-to-end on 100K vectors (see §Phase 18 + ADR-019)
**Owner:** J

---

## Problem

### Use case 1 — Staffing analytics (reference implementation)

Legacy data systems silo information across CRMs, databases, spreadsheets, and file shares. Querying across them requires manual ETL, pre-defined schemas, and expensive database licenses. When AI enters the picture, these systems can't handle the dual requirement of fast analytical queries AND semantic retrieval over unstructured text.

A staffing company (our reference case) has candidate records in an ATS, client data in a CRM, timesheets in billing software, call logs from a phone system, and email records from Exchange. Answering "find every Java developer in Chicago who was called 5+ times but never placed" requires querying across all of them — and no single system can do it.

### Use case 2 — Local AI knowledge substrate (the second half)

Local LLM workloads need a substrate for ingesting, indexing, and retrieving large knowledge corpora. Each running model (or agent) has its own context — documents it cares about, a vector index tuned to its domain, a scoped view of the catalog. That infrastructure is architecturally identical to the staffing problem: ingest messy data, index it, query it, hand it to an AI. Building one substrate that serves both prevents fragmentation.

Concretely this means a running Ollama model like `qwen2.5:7b` or `claude-code-local` should be able to:
- Bind to a named set of datasets
- Get a scoped vector index pre-warmed for its domain
- Issue searches that only see its bound data
- Have its trial/tuning history isolated from other models
- Swap between knowledge generations (today's, yesterday's) without rebuild

The same infrastructure that lets a recruiter query 2.47M rows of staffing data also lets a local 7B model answer questions grounded in a 500K-chunk documentation corpus. Same substrate, different tenant.

### Shared requirements

- Any data source (CSV, DB export, PDF, JSON, Postgres table) can be ingested without pre-defined schemas
- Structured data is queryable via SQL at scale (millions of rows, sub-second)
- Unstructured data is searchable via AI embeddings with per-profile indexes
- An LLM can answer natural language questions against scoped data
- Indexes can be hot-swapped between generations without rebuild downtime
- Trials are first-class data — the system remembers how it was tuned
- Everything runs locally — no cloud APIs, total data privacy
- The system is rebuildable from repository + object storage alone

---

## Solution

A modular Rust service mesh over S3-compatible object storage, with a local AI layer for embeddings and generation.

### Locked Stack

| Layer | Technology | Locked |
|---|---|---|
| Frontend | Dioxus | Yes |
| API | Axum + Tokio | Yes |
| Object Storage Interface | Apache Arrow `object_store` | Yes |
| Storage Backend | LocalFileSystem → RustFS → S3 | Yes |
| Query Engine | DataFusion | Yes |
| Data Format | Parquet + Arrow | Yes |
| RPC (internal) | tonic (gRPC) | Yes |
| AI Runtime | Ollama (local models) | Yes |
| AI Boundary | Python FastAPI sidecar → Ollama HTTP API | Yes |
| Vector Index | TBD — evaluate `hora`, `qdrant` crate, or HNSW from scratch | **Open** |

No new frameworks without documented ADR.

---

## Architecture

### Services

| Service | Responsibility |
|---|---|
| **gateway** | HTTP/gRPC ingress, routing, auth, CORS, body limits, `X-Lakehouse-Bucket` header routing |
| **catalogd** | Metadata control plane — dataset registry, schema versions, manifests, per-dataset resync from parquet footers |
| **storaged** | Object I/O — `BucketRegistry` (multi-backend), rescue fallback, error journal, append-log batching pattern |
| **queryd** | SQL execution — DataFusion over Parquet, MemTable hot cache, delta merge-on-read |
| **ingestd** | Ingest pipeline: CSV / JSON / PDF / Postgres-stream → normalize → Parquet → catalog |
| **vectord** | Embedding store + vector indexes + HNSW trial system (EmbeddingCache, trial journal, eval harness) |
| **journald** | Append-only mutation event log (ADR-012) — distinct from storaged error journal |
| **aibridge** | Rust↔Python boundary — HTTP client to FastAPI sidecar |
| **ui** | Dioxus frontend — Ask, Explore, SQL, System tabs |
| **shared** | Types, errors, Arrow helpers, config, protobuf definitions, **secrets provider trait**, **PII detection** |

**Federation building blocks** (shipped 2026-04-16):
- `shared::secrets::SecretsProvider` trait + `FileSecretsProvider` reading `/etc/lakehouse/secrets.toml` (0600 enforced)
- `storaged::registry::BucketRegistry` — multi-bucket resolution with `rescue_bucket` read fallback
- `storaged::append_log::AppendLog` — write-once batched append pattern (no RMW, no small-file problem)
- `storaged::error_journal::ErrorJournal` — bucket operation failure log at `primary://_errors/bucket_errors/batch_*.jsonl`

### Data Flow

```
Raw data → ingestd (normalize, chunk, detect schema)
    ├→ storaged (Parquet files to object storage)
    ├→ catalogd (register dataset + schema)
    ├→ vectord (embed text chunks, build index)
    └→ queryd  (auto-register as queryable table)

User question → gateway
    ├→ vectord (semantic search for relevant chunks)  ← RAG path
    ├→ queryd  (SQL over structured data)             ← Analytics path
    └→ aibridge → Ollama (generate answer from context)
```

### Query Paths

**Analytical (SQL):** "What's the average bill rate for .NET devs in Chicago?"
→ DataFusion scans Parquet columnar, returns in <200ms

**Semantic (RAG):** "Find candidates who could do data engineering work"
→ Embed question → vector search across resume embeddings → retrieve top chunks → LLM answers

**Hybrid:** "Which clients are we losing money on, and why?"
→ SQL for margin calculations + RAG over client notes/emails for context → LLM synthesizes

### Invariants

1. Object storage = source of truth for all data
2. catalogd = sole metadata authority
3. No raw data in catalog — only pointers
4. vectord stores embeddings AS Parquet (portable, not a proprietary format) — see ADR-018 for the Parquet-vs-Lance trade review
5. ingestd is idempotent — re-ingesting the same file is a no-op
6. Hot cache is a performance layer, not a source of truth — eviction is safe
7. All services modular and independently replaceable
8. **Indexes are hot-swappable.** A new index generation can be built in the background while the current one serves queries. Promotion is atomic (pointer swap). Rollback to a prior generation is always possible. (Phase 16)
9. **Every reader gets its own profile.** Human operators, AI agents, and local models are all clients of the same substrate. Each has a named profile with its own bucket, vector indexes, trial history, and dataset bindings. Profiles are a first-class architectural concept, not a tenancy afterthought. (Phase 17)
10. **Trials are data, not logs.** Every index build is a trial with measurable metrics. The trial journal IS the agent's memory for how to tune itself. Stored as write-once batched JSONL per the ADR-018 append-log pattern.
11. **Operational failures are findable in one HTTP call.** The bucket error journal, trial journal, and audit log all expose `/storage/errors`, `/hnsw/trials`, `/access/audit` with structured filter + aggregation. No `grep` archaeology to answer "what broke?"

---

## Phases

### Phase 0-5: Foundation ✅ COMPLETE

- Rust workspace, Axum gateway, object storage, catalog, DataFusion query engine
- Python sidecar with real Ollama models (embed, generate, rerank)
- Dioxus UI with Ask (NL→SQL), Explore, SQL, System tabs
- gRPC, OpenTelemetry, auth middleware, TOML config
- Validated with 286K row staffing company dataset across 7 tables
- Cross-reference queries (JOINs across candidates, placements, timesheets, calls) in <150ms

### Phase 6: Ingest Pipeline

Build the data on-ramp. Accept messy real-world data, normalize it, make it queryable.

| Step | Deliverable | Gate |
|---|---|---|
| 6.1 | `ingestd` crate with CSV parser → Arrow RecordBatch → Parquet | CSV file → queryable dataset |
| 6.2 | JSON ingest (newline-delimited JSON, nested objects) | JSON file → flat Parquet |
| 6.3 | Schema detection — infer column types from data | No manual schema definition needed |
| 6.4 | Deduplication — detect and skip already-ingested files (content hash) | Re-ingest same file = no-op |
| 6.5 | Text chunking — split large text fields for embedding | Long text → overlapping chunks |
| 6.6 | Auto-registration — ingest writes to storage AND registers in catalog | Single API call: file in → queryable |
| 6.7 | Gateway endpoint: `POST /ingest` with file upload | Upload CSV from browser → query in seconds |

**Gate:** Upload a raw CSV or JSON file → auto-detected schema → stored as Parquet → registered → immediately queryable via SQL. No manual steps.

**Risk:** Schema detection on messy data (mixed types, nulls, inconsistent formatting). Mitigation: conservative type inference (default to string), let user override.

### Phase 7: Vector Index + RAG Pipeline

Make unstructured data searchable by meaning, not just keywords.

| Step | Deliverable | Gate |
|---|---|---|
| 7.1 | `vectord` crate with embedding storage as Parquet (doc_id, chunk_text, vector) | Embeddings stored as portable Parquet |
| 7.2 | Chunking strategy — configurable chunk size + overlap for text columns | Large text fields split into embeddable chunks |
| 7.3 | Brute-force vector search via DataFusion (cosine similarity SQL) | Semantic search works, correctness verified |
| 7.4 | HNSW index for fast approximate nearest neighbor | Search over 100K+ vectors in <50ms |
| 7.5 | RAG endpoint: `POST /rag` — question → embed → search → retrieve → generate | Natural language question → grounded answer |
| 7.6 | Auto-embed on ingest — text columns automatically embedded during ingest | No separate embedding step needed |
| 7.7 | Hybrid search — combine SQL filters with vector similarity | "Java devs in Chicago" (SQL) + "who could do data engineering" (semantic) |

**Gate:** Ingest 15K candidate resumes → auto-embed → ask "find someone who could handle our Kubernetes migration" → system returns relevant candidates ranked by semantic match, with LLM explanation.

**Risk: HNSW in Rust at scale.** This is the hardest technical problem. Options:
- `hora` crate — Rust-native ANN, but less mature than FAISS
- Store HNSW index as a serialized file alongside Parquet data
- Fallback: brute-force scan is fine up to ~100K vectors; optimize later
- Nuclear option: use Qdrant as an external vector store (breaks "no new services" rule)

**Decision needed:** Evaluate `hora` vs external Qdrant vs brute-force at J's data scale.

### Phase 8: Hot Cache + Incremental Updates

Make frequently-accessed data fast, and handle real-time updates without full rewrite.

| Step | Deliverable | Gate |
|---|---|---|
| 8.1 | MemTable hot cache — pin active datasets in memory | Queries on hot data: <10ms |
| 8.2 | Cache policy — LRU eviction based on access patterns | Memory-bounded, auto-manages |
| 8.3 | Incremental writes — append new rows without rewriting entire Parquet file | Update one candidate's phone → no full table rewrite |
| 8.4 | Merge-on-read — query combines base Parquet + delta files | Correct results from base + updates |
| 8.5 | Compaction — periodic merge of delta files into base Parquet | Prevent delta file proliferation |
| 8.6 | Upsert semantics — insert or update by primary key | Same candidate ID → update in place |

**Gate:** Update a single row in a 15K-row dataset. Query reflects the change immediately. No full Parquet rewrite. Memory cache serves hot data in <10ms.

**Risk: This is the Delta Lake problem.** Full ACID transactions over Parquet files is what Databricks spent years building. We're NOT building Delta Lake — we're building a pragmatic version:
- Append-only delta files (easy)
- Merge-on-read (moderate)
- Compaction (moderate)
- Full ACID isolation (NOT attempting — single-writer model instead)

### Phase 8.5: Agent Workspaces ✅ COMPLETE

Per-contract overlays with daily/weekly/monthly tiers and instant handoff.

- WorkspaceManager with saved searches, shortlists, activity logs
- Zero-copy handoff between agents (pointer swap, not data copy)
- Persisted to object storage, rebuilt on startup

### Phase 9: Event Journal — Never Destroy Evidence

**Principle:** Every mutation is appended, never overwritten. This is the one decision that's impossible to retrofit — once history is lost, it's gone forever.

| Step | Deliverable | Gate |
|---|---|---|
| 9.1 | `journald` crate: append-only event log as Parquet | Every write/update/delete logged with who, when, what, old value, new value |
| 9.2 | Event schema: entity, field, old_value, new_value, actor, timestamp, source, workspace_id | Standardized across all mutations |
| 9.3 | Journal query: `SELECT * FROM journal WHERE entity = 'CAND-001' ORDER BY timestamp` | Full history of any record |
| 9.4 | Replay capability: rebuild any dataset's state at any point in time | Time-travel queries |
| 9.5 | Journal compaction: roll old events into monthly summary Parquet files | Prevent unbounded growth |

**Gate:** Change a candidate's phone number. Query shows the change. Journal shows old value, new value, who changed it, when, and why. Replay to yesterday's state.

**Why now:** In 3 years, compliance, AI auditability, and "why did the agent recommend this candidate" all require mutation history. Adding it later means you only have history from that day forward.

### Phase 10: Rich Catalog v2 — Metadata as Product

**Principle:** Every dataset should be self-describing. A new team member (or AI agent) should understand what data exists, who owns it, how fresh it is, and what's sensitive — without asking anyone.

| Step | Deliverable | Gate |
|---|---|---|
| 10.1 | Catalog schema upgrade: add owner, sensitivity, freshness_sla, description, tags, lineage | `GET /catalog/datasets` returns rich metadata |
| 10.2 | Sensitivity classification: PII, PHI, financial, public, internal | Sensitive fields tagged at ingest |
| 10.3 | Lineage tracking: source_system → ingest_job → dataset → derived_dataset | "Where did this data come from?" answerable |
| 10.4 | Freshness contracts: expected_update_frequency, last_updated, stale_after | Alert when data goes stale |
| 10.5 | Dataset contracts: required columns, type expectations, validation rules | Ingest rejects data that breaks the contract |
| 10.6 | Auto-documentation: AI generates dataset description from schema + sample data | New datasets self-describe on ingest |

**Gate:** Ingest a CSV. System auto-detects PII columns (email, phone, SSN patterns), tags them, generates a description, sets owner, and tracks lineage back to the source file.

**Why now:** Every dataset you ingest without metadata becomes a "mystery file" in 6 months. The metadata layer makes the difference between a searchable knowledge platform and a data graveyard.

### Phase 11: Embedding Versioning — Model-Proof Vector Layer

**Principle:** Embedding models will change. If you don't track which model created which vectors, upgrading means re-embedding everything from scratch.

| Step | Deliverable | Gate |
|---|---|---|
| 11.1 | Vector index metadata: model_name, model_version, dimensions, created_at | Every index knows its embedding model |
| 11.2 | Multi-version indexes: same data, different models, coexist | Search specifies which model version |
| 11.3 | Incremental re-embed: only new/changed docs get re-embedded on model upgrade | Model swap doesn't require full re-embed |
| 11.4 | A/B search: query both old and new model, compare results | Validate model upgrade before committing |

**Gate:** Upgrade from nomic-embed-text to a new model. Old index still works. New index builds incrementally. Compare search quality. Switch when ready.

### Phase 12: Tool Registry — Agent-Safe Business Actions

**Principle:** In 3 years, AI agents won't just query — they'll act. Instead of every agent getting raw SQL access, expose named, governed, audited business actions.

| Step | Deliverable | Gate |
|---|---|---|
| 12.1 | Tool definition: name, description, parameters, permissions, audit_level | `search_candidates(skills, city, min_years)` as a registered tool |
| 12.2 | Tool execution: validates params, checks permissions, logs usage, runs query | Agent calls tool, gets results, action is logged |
| 12.3 | Read vs write tools: read tools are permissive, write tools require confirmation | `get_candidate` = auto-approved, `update_phone` = requires review |
| 12.4 | MCP-compatible interface: expose tools via Model Context Protocol | Any MCP-compatible agent (Claude, GPT, local) can use them |
| 12.5 | Rate limiting + quotas per agent/tool | Prevent runaway agent from overwhelming the system |

**Gate:** An AI agent calls `search_candidates(skills="Python,AWS", city="Chicago", available=true)` → gets results → calls `shortlist_candidate(workspace_id, candidate_id, reason)` → action is logged, auditable, reversible.

**Why now:** The tool interface is cheap to build (it's just named endpoints with validation). But retrofitting audit logging and permission checks onto raw SQL access is a nightmare. Build the governed interface first.

### Phase 13: Security & Access Control

| Step | Deliverable | Gate |
|---|---|---|
| 13.1 | Field-level sensitivity tags (PII, PHI, financial) in catalog | Sensitive fields identified |
| 13.2 | Row-level access policies (agent A sees their candidates only) | Policy evaluated at query time |
| 13.3 | Column masking (show last 4 of SSN, redact salary for non-managers) | Masked results based on role |
| 13.4 | Query audit log (who queried what, when, which fields) | Every data access recorded |
| 13.5 | Policy-as-code (TOML/YAML rules, not hardcoded) | Non-engineer can update access rules |

### Phase 14: Schema Evolution + AI Migration

| Step | Deliverable | Gate |
|---|---|---|
| 14.1 | Schema diff detection: old schema vs new ingest → list changes | "Column renamed: first_name → full_name" |
| 14.2 | AI-generated migration rules: LLM suggests column mappings | "full_name = concat(first_name, ' ', last_name)" |
| 14.3 | Migration preview: show how old data maps to new schema before applying | Human approves before data transforms |
| 14.4 | Versioned schemas in catalog: v1, v2, v3 coexist | Queries specify version or use latest |

### Phase 15: Infrastructure horizon items

- [x] HNSW vector index with trial system (shipped 2026-04-16)
- [x] Federation foundation — ADR-017 (shipped 2026-04-16)
- [x] Database connector ingest — Postgres batch with streaming (shipped 2026-04-16)
- [x] Federation layer 2 — runtime bucket lifecycle, per-index bucket scoping, profile bucket auto-provisioning (shipped 2026-04-17)
- [x] MySQL streaming connector — mirrors Postgres path, verified on live MariaDB (shipped 2026-04-17)
- [x] PDF OCR for scanned documents — Tesseract 5.5 fallback when lopdf yields no text (shipped 2026-04-17)
- [x] Scheduled ingest — interval-based per-source schedules with CRUD + run-now + auto-trigger agent (shipped 2026-04-17)
- [ ] Multi-node query distribution (DataFusion supports this architecturally)

### Phase 16: Hot-Swap Index Generations

Make indexes upgrade-in-place without dropping queries.

| Step | Deliverable | Gate |
|---|---|---|
| 16.1 | "Active generation" pointer per logical index name | `/vectors/search` routes to current champion automatically |
| 16.2 | Background trial runner: watches trial journal, proposes configs (random search / Bayesian), fires `/hnsw/trial` | Agent autonomously tunes without human POSTing each config |
| 16.3 | Promotion endpoint: `POST /hnsw/promote/{index}/{trial_id}` atomically swaps active pointer | Next search hits new config, zero downtime |
| 16.4 | Rollback: `POST /hnsw/rollback/{index}` reverts to previous generation | Bad promotion recoverable in milliseconds |
| 16.5 | Dataset-append triggers: when `POST /ingest/file` writes to a dataset with attached vector indexes, schedule automatic re-trial (not full rebuild) | New docs get embedded + indexed without manual intervention |

**Gate:** Run the trial agent for 10 minutes against `resumes_100k_v2` with a fresh eval set. It explores the `ef_construction × ef_search` space, promotes the Pareto winner, continues running. Zero human clicks. All trials and promotions appear in `/hnsw/trials/resumes_100k_v2`.

**Risk:** Agent loops into a bad region (e.g. always proposes ef_construction=1). Mitigation: a hardcoded config space constraint + minimum-quality gate (don't promote anything with recall <0.9).

### Phase 17: Model Profiles + Dataset Bindings

Make "different models see different data" real instead of a config string.

| Step | Deliverable | Gate |
|---|---|---|
| 17.1 | `ModelProfile` manifest: id, ollama_name, bucket, bound_datasets[], hnsw_config, embed_model | `GET /models` lists profiles; `POST /models` creates one |
| 17.2 | Profile activation endpoint: `POST /profile/{id}/activate` — warms EmbeddingCache for bound indexes, builds HNSW with profile's config | Next search against bound indexes is <1ms cold |
| 17.3 | Model-scoped search: `POST /search?model=X` filters to bound datasets only | Model A can't see Model B's datasets unless explicitly shared |
| 17.4 | VRAM-aware activation: only one (or small N) model loaded at a time on 16GB A4000 | Activating model B unloads model A via Ollama's keep_alive=0 |
| 17.5 | Audit: every tool invocation by a model is logged with model identity | `GET /models/{id}/audit` shows exactly what each model touched |

**Gate:** Two model profiles defined: `staffing-recruiter` (bound to candidates/placements/timesheets) and `docs-assistant` (bound to a documentation corpus). Activate staffing-recruiter, search for candidates — works. Switch to docs-assistant, same search — returns zero from staffing (not bound) but finds docs. VRAM shows only one embedding model loaded at a time.

**VRAM reality:** 16GB A4000 realistically holds 1-2 loaded models concurrently. "Multi-model" in practice means sequential swap between profiles, not parallel serving. The profile abstraction makes this swap clean.

### Phase 18: Storage format decision (Lance evaluation)

The question raised 2026-04-16 after J's LLMS3 knowledge base identified Lance as `alternative_to` Parquet for vector workloads. Current stack: Parquet with binary-blob vector columns + in-RAM HNSW sidecar. Evaluated against: Lance native vector format with disk-resident indexes.

| Step | Deliverable | Decision criteria |
|---|---|---|
| 18.1 | ✅ Parallel Lance-backed vector index for `resumes_100k_v2` in standalone `crates/lance-bench` | Built 2026-04-16 |
| 18.2 | ✅ Head-to-head benchmark across 8 dimensions (cold-load, search latency, disk, index build, random access, append) | Complete |
| 18.3 | ✅ ADR-019 committed with measured data and decision | See `docs/ADR-019-vector-storage.md` |

**Outcome:** Hybrid architecture. Parquet+HNSW stays primary (2.55× faster search at 100K in-RAM). Lance joins as a second backend for Phase 16 hot-swap (14× faster index builds), Phase C/append workloads (0.08s vs full rewrite), RAG random-access retrieval (112× faster), and indexes past the ~5M RAM ceiling.

Per-profile `vector_backend: Parquet | Lance` becomes part of Phase 17 (model profiles). See ADR-019 for the full scorecard and caveats.

### Phase 19+: Further horizon

- PDF OCR for scanned documents (Tesseract integration)
- Specialized fine-tuned models per domain (staffing matcher, resume parser)
- Video/audio transcript ingest + multimodal embeddings
- True distributed query (DataFusion multi-node) — only if single-machine ceilings bite

---

## Known ceilings (honest)

The current stack has measurable limits. Documenting them so future decisions aren't based on wishful thinking.

| Dimension | Current ceiling | Breaks at | Escape hatch |
|---|---|---|---|
| Vector count per index (Parquet+HNSW in-RAM) | ~5M on 128GB | Past 5M | Switch that profile's `vector_backend` to Lance per ADR-019 — IVF_PQ stays on disk-resident quantized codes |
| Concurrent active indexes | ~50-100 at 100K vectors each | 10M×50 configurations | Lance disk-resident + per-profile activation |
| Rows per dataset | 2.47M proven, probably 100M+ fine | Approaches DataFusion memory limits | DataFusion predicate pushdown + partition pruning (existing) |
| Concurrent loaded models | 1-2 on 16GB VRAM (A4000) | 3+ models simultaneous | Not our problem — architectural, driven by Ollama |
| Trial journal growth per index | Thousands of trials, batched JSONL | High-frequency auto-tuning agent | Compaction via `/hnsw/trials/{idx}/compact` |
| Error journal growth | Bounded by ring buffer (2000 events in-memory) + batched JSONL on disk | Continuous failure scenarios | Compaction + retention policy (TODO) |

---

## Reference Workloads

### Workload 1: Staffing Company

Scale-tested on 128GB RAM server:

| Table | Rows | Size | Description |
|---|---|---|---|
| candidates | 100,000 | 10.1 MB | Names, phones, emails, zip, skills, resume text |
| clients | 2,000 | 33 KB | Companies, contacts, verticals |
| job_orders | 15,000 | 0.9 MB | Positions with descriptions, requirements, rates |
| placements | 50,000 | 1.2 MB | Candidate↔job matches with rates, recruiters |
| timesheets | 1,000,000 | 16.7 MB | Weekly hours, bill/pay totals, approvals |
| call_log | 800,000 | 34.3 MB | Phone CDR — who called whom, duration, disposition |
| email_log | 500,000 | 16.0 MB | Email tracking — subject, opened, direction |
| **Total** | **2,467,000** | **79 MB** | **7 tables, cross-referenced** |

### Benchmarks (2.47M rows)

| Query | Cold (Parquet) | Hot (MemCache) | Speedup |
|---|---|---|---|
| 100K candidate filter (skills+city+status) | 257ms | 21ms | 12x |
| 1M timesheet aggregation + JOIN | 942ms | 96ms | 9.8x |
| 800K call log cross-reference (cold leads) | 642ms | — | — |
| Triple JOIN recruiter performance | 487ms | — | — |
| 500K email open rate aggregation | 259ms | — | — |
| COUNT all 2.47M rows | 84ms | — | — |
| 10K vector semantic search (cosine) | 450ms | — | — |
| Natural language → AI SQL → execute | ~3s | — | (model inference) |

### Vector Search

- 10K candidate resumes embedded in 204s (49 chunks/sec via Ollama)
- Semantic search over 10K vectors: ~450ms (brute-force cosine)
- RAG pipeline: question → embed → search → retrieve → LLM answer with citations
- AI correctly refuses to hallucinate when context doesn't support an answer

### Agent Workspaces

- Create per-contract workspace with saved searches + shortlists
- Instant handoff between agents — zero data copy
- Full activity timeline preserved across handoffs

### Workload 2: Local LLM Knowledge Base

The second use case this substrate is built for. Reference corpus: the running `knowledge_base` Postgres database (586 team runs, response cache history, pipeline runs, threat intel) + LLMS3.com published corpus (~243 enriched documents).

Target scale on same 128GB server:
- Documents: 10K-100K per model profile
- Chunks after chunking: 500K-5M per profile
- Embedding dimensions: 768 (nomic-embed-text)
- Query latency: <100ms semantic search, <3s end-to-end RAG including LLM generation
- Concurrent model profiles: 2-5 configured, 1-2 active at a time (VRAM-bound)

Measured to date (Phase 7 + Phase 16 prep):
- 100K candidate-resume chunks embedded in 10 min via Ollama nomic-embed-text
- HNSW search at 100% recall, ~1ms p50 on 100K vectors (ec=80 es=30 locked as default)
- Trial journal instrumented and working for parameter tuning

Gaps still to close for this workload:
- Model profiles (Phase 17) — today, "model" is a string, not a first-class entity
- Hot-swap generations (Phase 16) — today, rebuild = downtime
- Scale past 5M vectors — needs Phase 18 Lance evaluation to decide path

---

## Available Local Models

| Model | Use |
|---|---|
| `nomic-embed-text` | Embeddings (768d) — semantic search, RAG retrieval |
| `qwen2.5` | SQL generation, structured output, summarization |
| `mistral` | General generation, longer context |
| `gemma2` | General generation |
| `llama3.2` | General generation, lightweight |

---

## Non-Goals

- Cloud deployment (local-first, always)
- Full ACID transactions (single-writer model is sufficient)
- Real-time streaming / CDC (batch ingest is the model; scheduled refresh, not transactional replication)
- Replacing the CRM (this is the analytical + AI layer BEHIND the CRM)
- Custom file formats — Parquet for datasets + sidecar indexes for vectors (see ADR-018 for why we stayed Parquet instead of migrating to Lance, and the ceilings that choice implies)
- Hard multi-tenant isolation (profiles and federation provide soft isolation; this is not a SaaS platform with adversarial tenants — operator is single-trust)

Removed from prior non-goals (2026-04-16):
- ~~Multi-tenancy (single-owner system)~~ — federation + profile buckets are now first-class; soft multi-tenancy is a design goal. Hard adversarial multi-tenancy (adversarial tenants on shared infrastructure) remains out of scope.

---

## Risks

### Technical Risks

| Risk | Severity | Mitigation |
|---|---|---|
| Vector search in Rust at scale | **High** | Start brute-force, evaluate `hora` crate, Qdrant as fallback |
| Incremental updates on Parquet | **High** | Delta files + merge-on-read, NOT full Delta Lake |
| Legacy data messiness | **High** | Conservative schema detection, default to string, user overrides |
| 100K+ embedding timeout | **High** | Async background job with progress, not single HTTP request |
| Schema evolution across ingests | **Medium** | Schema fingerprinting + versioned manifests (Phase 14) |
| Memory pressure from hot cache | **Medium** | LRU eviction, configurable memory limit (tested: 408MB for 1.1M rows) |
| HNSW index persistence | **Medium** | Serialize alongside Parquet, rebuild on startup |
| Python sidecar as bottleneck | **Low** | Can replace with direct Ollama HTTP from Rust later |

### Strategic Risks (Future-Proofing)

| Risk | Impact | Phase |
|---|---|---|
| No mutation history → can't audit AI decisions | **Critical** — compliance, trust | Phase 9 (event journal) |
| No metadata → datasets become mystery files | **High** — onboarding, discovery | Phase 10 (rich catalog) |
| Embeddings locked to one model | **High** — can't upgrade models | Phase 11 (versioning) |
| Raw SQL as only interface → ungoverned agent access | **High** — security, auditability | Phase 12 (tool registry) |
| No sensitivity classification → compliance exposure | **Medium** — grows with data volume | Phase 13 (access control) |
| No schema evolution handling → ingest breaks on format change | **Medium** — grows with source count | Phase 14 (AI migration) |

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## Design Principles (Future-Proofing)

These are the decisions that still look smart after the stack changes:

1. **Store the truth openly.** Parquet on object storage. No proprietary formats. Any engine can read it.
2. **Describe it richly.** Every dataset has an owner, lineage, sensitivity tags, freshness contract.
3. **Never destroy evidence.** Every mutation is journaled. Rebuild any state at any point in time.
4. **Secure it centrally.** Permissions live in the data layer, not application code.
5. **Expose it through reusable interfaces.** Named tools with contracts, not raw SQL for every consumer.
6. **Version everything.** Schemas, embeddings, models — all versioned, all coexist during migration.
7. **Make unstructured data first-class.** Every document gets: storage, text extraction, entity tags, chunks, embeddings, linkage.
8. **Separate storage from compute from intelligence.** Scale each independently. Replace any layer without touching the others.

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## Operating Rules

1. PRD > architecture > phases > status > git
2. Git is memory, not chat
3. No undocumented changes
4. No silent architecture drift
5. Always work in smallest valid step
6. Always verify before moving on
7. Flag when something is genuinely hard vs just engineering work
8. If a phase reveals the approach is wrong, update the PRD before continuing
9. **Cheap-now, expensive-later decisions get built first** (event journal, metadata, versioning)
10. **Build the governed interface before the raw interface** (tools before SQL for agents)