# Production Report — RICS Report Genius AI

**Document purpose:** Single reference for production deployment, scale, AI operations, and ownership.  
**Scope:** Current codebase behaviour (FastAPI, SQLite default, FAISS, LangChain, OpenAI), target production on **AWS** with **OpenAI** retained.  
**Audience:** Engineering leads, DevOps, AI engineers, backend engineers, frontend engineers, security/compliance.

---

## Table of contents

1. [Executive summary](#1-executive-summary)  
2. [System architecture (as implemented)](#2-system-architecture-as-implemented)  
3. [Data & state](#3-data--state)  
4. [AI & LLM pipeline](#4-ai--llm-pipeline)  
5. [APIs, concurrency & failure modes](#5-apis-concurrency--failure-modes)  
6. [Security & tenancy](#6-security--tenancy)  
7. [Performance & scale](#7-performance--scale)  
8. [AWS production stack (OpenAI retained)](#8-aws-production-stack-openai-retained)  
9. [Observability & operations](#9-observability--operations)  
10. [Role responsibilities & deliverables](#10-role-responsibilities--deliverables)  
11. [Pre-launch checklist](#11-pre-launch-checklist)  
12. [Post-launch & continuous improvement](#12-post-launch--continuous-improvement)  

---

## 1. Executive summary

The application is a **FastAPI** service serving a **static frontend**, with **per-tenant RAG** (FAISS + LangChain embeddings), **OpenAI** for generation and several post-processing passes, and **async SQLite** by default. It is **fit for pilot or single-tenant–style** deployments out of the box. For **thousands of concurrent users**, production requires:

| Pillar | Gap in default / local shape | Production requirement |
|--------|------------------------------|-------------------------|
| **Data** | SQLite + local files | **PostgreSQL (RDS)** + **S3** for uploads |
| **Coordination** | In-process rate limits, per-pod FAISS/cache | **Redis (ElastiCache)** + **SQS** workers + clear vector strategy |
| **Tenancy** | `X-Tenant-ID` header only | **Authenticated identity** mapped to tenant |
| **AI reliability** | OpenAI rate limits, retries, cost | **Queues, concurrency caps**, timeouts, observability |
| **Ops** | Ephemeral disk on some hosts | **Backups**, alarms, stuck-job handling |

**OpenAI remains** the LLM provider; AWS provides **compute, data, queue, cache, secrets, and logs** — not a replacement for OpenAI’s API limits or model behaviour.

---

## 2. System architecture (as implemented)

### 2.1 Runtime

- **Framework:** FastAPI (`app/main.py`).
- **Entry:** Uvicorn; Docker image targets port **7860** (HF Spaces) or **8000** (local/docker-compose) per `Dockerfile` / env.
- **Startup:** DB init, vector store pre-warm, embedding client pre-warm; optional KB ingest in background; **production guard** requires `OPENAI_API_KEY` when `DEV_MODE=false`.

### 2.2 Major components

| Component | Location / behaviour |
|-----------|----------------------|
| **HTTP API** | `app/api/*` — reports, generate, upload, status, export, photos, etc. |
| **Generation orchestration** | `app/services/generation.py` — RAG, style, LLM, verify, retries, persist. |
| **Vector store** | `app/vectorstore/factory.py` — **FAISS** singleton, LangChain-backed. |
| **Embeddings** | `app/embeddings/factory.py` — default **HuggingFace** local; optional **OpenAI** via config. |
| **Prompts** | `app/generator/prompts.py` — LCEL vars, interference levels, token budgets. |
| **LLM adapter** | `app/generator/adapter.py` — OpenAI / mock. |
| **Post-process** | `app/generator/postprocess.py` — `async_enforce_verify`, etc. |
| **Section cache** | `app/cache/section_cache.py` — **filesystem** JSON keyed by hash of inputs. |
| **Rate limiting** | `app/api/rate_limit.py` — **in-process** sliding window per `tenant_id` from header. |

### 2.3 Generation modes (API)

- **`generate`** — Full pipeline (default product path; `notes_only_generation` forces standard pipeline in current code paths).
- **`proofread`** / **`enhance`** — Separate pipelines reusing parts of generation.

### 2.4 AI interference (product)

- **Qualitative levels:** `minimum` | `medium` | `maximum` (`interference_level` on `GenerateRequest`; maps to internal `ai_percent` bands in `app/models/schemas.py`).
- **Prompts** branch on interference level in `app/generator/prompts.py`.

---

## 3. Data & state

### 3.1 Database

- **Default:** `sqlite+aiosqlite:///./dev.db` (`app/config.py`).
- **Production risk:** SQLite **single-writer** semantics, file locking, no horizontal write scale — **unsuitable** for high concurrent write load.
- **Target:** **Amazon RDS PostgreSQL** (or Aurora PostgreSQL) with connection pooling (app uses `pool_size` / `max_overflow` for non-SQLite in `app/db/database.py`).

### 3.2 Uploads & artifacts

- **Default:** Local `upload_dir` on instance filesystem.
- **Production risk:** **Ephemeral** disks lose data on redeploy/restart; multi-replica **inconsistent** views of files.
- **Target:** **Amazon S3** + DB rows storing object keys; presigned URLs for client download where needed.

### 3.3 Vector index (FAISS)

- **Current:** In-process singleton; persisted under app-configured paths (e.g. user home / cache dirs).
- **Production risk:** **Not shared** across replicas; each pod has its own index unless you mount shared storage (generally discouraged for FAISS at scale).
- **Target options:**  
  - **RDS `pgvector`** — vectors in Postgres; one operational system.  
  - **Amazon OpenSearch** kNN — separate service; strong scale/filter story.  
  - **Single replica** only — acceptable only for early internal launch, not “thousands concurrent.”

### 3.4 Section result cache

- **Current:** File-based cache in `settings.cache_dir`; atomic write via temp + rename.
- **Production risk:** **Per-node** only; no cross-instance deduplication.
- **Target:** **Redis** for hot cache keys (optional) or accept cache miss on each pod.

---

## 4. AI & LLM pipeline

### 4.1 OpenAI usage (representative chain per section)

Depending on settings and branch, a **generate** path may include:

- **Notes expansion** — LLM when OpenAI key present and expansion not skipped (`app/generator/notes_expander.py`).
- **Main section generation** — LangChain LCEL + ChatOpenAI (`app/generator/adapter.py`).
- **`async_enforce_verify`** — Additional LLM pass when key present (`app/generator/postprocess.py`).
- **Retries** — Verbatim ratio, identity consistency, survey-tier hints — can trigger **extra** `generate_section` calls.
- **Optional LLM section validator** — `llm_section_validator_enabled` in `app/config.py` (default **false**); when true, extra validate + possible regenerate.
- **Photo / vision** — When photos exist and policy triggers (`app/services/photo_vision.py`).

**Implication:** Wall-clock and **cost scale with number of LLM round-trips**, not only “one GPT call per section.”

### 4.2 Embeddings

- **Default:** `prefer_local_embeddings=True` → **HuggingFace** sentence-transformers on **CPU** per process (`app/embeddings/factory.py`).
- **Production trade-off:** Each replica loads model into **RAM**; cold start and memory multiply with replica count.
- **Mitigation:** Set **`prefer_local_embeddings=false`** to use **OpenAI embeddings** API (cost vs RAM); or keep HF on **GPU** workers dedicated to ingest (heavier ops design).

### 4.3 Quality & guardrails (behavioural, not infra)

- **Notes-only mode** (`notes_only_generation`, default **true**): bullets are factual authority; uploads are **reference-only** in that design; provenance suppressed accordingly.
- **Interference levels** encode how much drafting vs mapping is allowed (see `app/generator/prompts.py` and schemas).
- **Production AI engineering:** golden-set evaluation, pinned models, documented limits of each guard (what verify does / does not guarantee).

### 4.4 Cost & limits (OpenAI)

- **Account-level rate limits** and **token pricing** apply regardless of AWS scale.
- **Mitigation:** **SQS-backed workers** with **global + per-tenant concurrency caps**, exponential backoff on **429**, timeouts, circuit breaker patterns.

---

## 5. APIs, concurrency & failure modes

### 5.1 Report generation locking

- `POST /reports/{report_id}/generate` uses a **compare-and-swap** so `Report.status` is only flipped to **`generating`** if not already `generating` (`app/api/generate.py`).
- **Second concurrent** generate for same report → **409** until the first completes or fails.
- **Worker crash** after CAS but before completion can leave status **`generating`** until the **stale-generation sweeper** marks the report failed (`generation_stale_sweep_seconds`, `generation_started_at` on `reports`). Runs on the API process and on the Temporal worker when enabled.

### 5.2 Background execution

- Generation runs as **`asyncio.create_task`** from the API process; errors in `run_generation` set report **`failed`** and `error_message` (`app/services/generation.py`).
- **Risk:** Process kill loses in-flight task; see stuck state above.

### 5.3 Rate limiting

- **Generate:** default **20 RPM per tenant id** (header); **Read:** **120 RPM** (`app/api/rate_limit.py`, env `RATE_LIMIT_GENERATE_RPM`, `RATE_LIMIT_READ_RPM`).
- **Limitation:** **In-process** — incorrect under **multiple workers/pods** (each has its own counter). Production should use **Redis** or **API Gateway** throttling.

### 5.4 HTTP status summary (client expectations)

| Code | Typical cause |
|------|----------------|
| **401** | Missing / empty `X-Tenant-ID` on non-exempt paths (`app/api/middleware.py`). |
| **404** | Report or document not found / wrong tenant. |
| **409** | Report already `generating`; or business rule conflicts elsewhere. |
| **413** | Payload too large (e.g. section text PATCH limit). |
| **422** | Validation (e.g. invalid `interference_level`). |
| **429** | Rate limit exceeded. |

---

## 6. Security & tenancy

### 6.1 Current tenant model

- **`TenantAuthMiddleware`:** requires non-empty **`X-Tenant-ID`** header; no cryptographic proof of ownership (`app/api/middleware.py`).

### 6.2 Production requirement

- **Authenticate** users (Cognito, Auth0, custom JWT, API keys behind API Gateway, etc.) and **derive** `tenant_id` from trusted claims — do not treat the raw header as the security boundary.

### 6.3 Secrets

- **`OPENAI_API_KEY`:** required in production when `DEV_MODE=false` (`app/main.py`).
- **`TENANT_SECRET_KEY`:** defaults trigger **warnings**; should be non-sentinel before any future signing use (`app/main.py`).

### 6.4 CORS

- `allowed_origins` default `["*"]` with credentials false in `app/main.py` — tighten for production web origins.

---

## 7. Performance & scale

### 7.1 Dominant bottlenecks

1. **Sequential LLM calls** per section (expand → generate → verify → retries).  
2. **OpenAI latency + rate limits.**  
3. **Database** under concurrent writes if SQLite remains.  
4. **Per-replica FAISS + HF embeddings** — memory and duplicate ingest work.

### 7.2 Application-level levers (documented in config)

- Retrieval pool sizes (`hierarchical_k_*`, `retrieval_top_k`, `rerank_top_n` in `app/config.py`).
- Optional **LLM section validator** (off by default).
- **Interference level** — Maximum mode increases output tokens and time.

### 7.3 Architecture-level levers

- **Queue + workers** (SQS) for generate jobs.  
- **Bounded concurrency** per tenant and globally toward OpenAI.  
- **Shared vector store** (pgvector or OpenSearch) + **Postgres** OLTP.  
- **Redis** for rate limits and optional response cache.

---

## 8. AWS production stack (OpenAI retained)

**Minimal recommended set** (OpenAI unchanged):

| Service | Role |
|---------|------|
| **Amazon RDS PostgreSQL** | Primary OLTP; optional **`pgvector`** for embeddings index. |
| **Amazon S3** | Uploads, exports, large artifacts. |
| **Amazon ElastiCache Redis** | Distributed rate limits, locks, optional cache. |
| **Amazon SQS** (+ DLQ) | Async generation jobs, backoff, worker scaling. |
| **Amazon ECS on Fargate** (or EKS) | API service + worker service. |
| **Application Load Balancer** | Routing, health checks. |
| **AWS Secrets Manager** | OpenAI key, DB credentials. |
| **Amazon CloudWatch** | Logs, metrics, alarms. |

**Defer until needed:** WAF, Step Functions, X-Ray, OpenSearch (if pgvector suffices), EventBridge (can add for stuck-report sweeper).

---

## 9. Observability & operations

### 9.1 Minimum viable

- **Structured logs** (JSON): `request_id`, `tenant_id`, `report_id`, `section_code`, latency, OpenAI error codes.  
- **Alarms:** 5xx rate, p95 latency, queue depth, DB connections, **OpenAI 429** spikes, **stuck `generating` %**.

### 9.2 Backups & DR

- **RDS** automated backups + restore drill.  
- **S3** versioning / lifecycle as per compliance.  
- **RPO/RTO** targets documented.

### 9.3 Runbooks

- OpenAI key rotation.  
- Queue poison message (DLQ replay).  
- Stuck report reset procedure until automated sweeper exists.

---

## 10. Role responsibilities & deliverables

| Role | Owns | Key deliverables |
|------|------|------------------|
| **DevOps / SRE** | AWS accounts, networking, secrets, CI/CD, scaling, backups | RDS, S3, Redis, SQS, ECS/Fargate, ALB, Secrets Manager, CloudWatch alarms, TLS |
| **Backend** | Application integration with AWS; job lifecycle; vector adapter | Postgres schema, S3 ingest, SQS consumer, Redis limiter, pgvector/OpenSearch `VectorStore`, stuck-job handling, real auth → tenant |
| **AI engineering** | Models, prompts, evals, cost/latency policy | Golden sets, pinned models, retry policy, document guardrail limits, Bedrock **not** required if staying on OpenAI |
| **Frontend** | UX for auth, long jobs, errors | Bearer/session auth, 429/409 handling, progress against real job status |

---

## 11. Pre-launch checklist

- [ ] **RDS Postgres** live; app `DATABASE_URL` pointed; migrations applied.  
- [ ] **S3** for uploads; no critical path depends on local disk alone.  
- [ ] **Redis** rate limiter (or gateway limits) — verified under **multi-task** load.  
- [ ] **SQS** (or equivalent) — worker processes tested; DLQ monitored.  
- [ ] **Vector strategy** chosen — pgvector **or** OpenSearch **or** explicit single-replica limitation documented.  
- [ ] **Auth** — tenant cannot be spoofed via header alone.  
- [ ] **Secrets** — OpenAI + DB in Secrets Manager; no keys in images.  
- [ ] **Stuck `generating`** — sweeper or manual runbook tested once.  
- [ ] **Load test** — representative: upload → ingest → N sections generate; measure p95 and error rate.  
- [ ] **AI eval** — regression set run on candidate prod config.  
- [ ] **CORS** — restricted to real frontends.  

---

## 12. Post-launch & continuous improvement

- Review **OpenAI spend** and **429** dashboards weekly after launch.  
- Tune **worker concurrency** vs **OpenAI tier**.  
- Expand **golden sets** with production-redacted examples.  
- Revisit **retrieval k** and **interference defaults** based on measured quality vs latency.

---

## Document control

| Field | Value |
|-------|--------|
| **Repository path** | `docs/PRODUCTION_REPORT.md` |
| **Intent** | Living reference — update when architecture or AWS choices change. |

*End of report.*