Extract structured data from unstructured documents in seconds -- not hours.
| If you're evaluating for... | Where to look | Training behind it |
|---|---|---|
| AI / ML Engineer | Agentic RAG ReAct loop (app/services/agentic_rag.py), RAGAS evaluation pipeline (app/services/ragas_evaluator.py), QLoRA fine-tuning pipeline (scripts/train_qlora.py, adapters/), W&B experiment tracking, golden eval CI gate |
IBM GenAI Engineering (144h), IBM RAG & Agentic AI (24h), DeepLearning.AI Deep Learning (120h) |
| Backend / Platform Engineer | Circuit breaker model fallback (app/services/circuit_breaker.py), async ARQ job queue (worker/), K8s/HPA manifests (deploy/k8s/), Terraform IaC (deploy/aws/), sliding-window rate limiter |
Microsoft AI & ML Engineering (75h), Google Cloud GenAI Leader (25h) |
| Full-Stack AI Engineer | 14-page Streamlit dashboard (frontend/), SSE streaming progress, MCP tool server (mcp_server.py), interactive demo sandbox |
IBM BI Analyst (141h), Google Data Analytics (181h), Microsoft Data Viz (87h) |
| MLOps / LLMOps Engineer | Prompt versioning + regression testing (app/services/prompt_registry.py), model A/B testing with z-test significance (app/services/model_ab_test.py), DeepEval CI gates, cost tracking per request |
Duke LLMOps (48h), Google Advanced Data Analytics (200h) |
→ Full cert-to-code mapping: docs/certifications.md (1,208h across 14 certifications)
git clone https://github.com/ChunkyTortoise/docextract.git
cd docextract
cp .env.example .env # Add ANTHROPIC_API_KEY + GEMINI_API_KEY
docker compose up -d
open http://localhost:8501 # Streamlit UIServices: API at :8000 (/docs for Swagger) | Frontend at :8501 | PostgreSQL :5432 | Redis :6379
First visit may take 30 seconds to wake up. Pre-cached results for invoice, contract, and receipt extraction.
Local demo (no API key needed):
DEMO_MODE=true streamlit run frontend/app.pygraph LR
A[Browser / API Client] -->|POST /documents| B[FastAPI]
B -->|enqueue| C[ARQ Worker]
C -->|classify| D{Model Router}
D -->|primary| E[Claude Sonnet]
D -->|fallback| F[Claude Haiku]
E -->|Pass 2: extract + correct| G[pgvector HNSW]
B -->|SSE stream stages| A
G -->|semantic search| B
B -->|/metrics| H[Prometheus]
D --- I[Circuit Breaker]
| Model | Provider | Env Var | Notes |
|---|---|---|---|
claude-sonnet-4-6 |
Anthropic | ANTHROPIC_API_KEY |
Default extraction model |
claude-haiku-4-5-20251001 |
Anthropic | ANTHROPIC_API_KEY |
Default classification + circuit breaker fallback |
glm-4-plus |
Zhipu AI | ZHIPUAI_API_KEY |
Chinese AI model, OpenAI-compatible API |
glm-4-flash |
Zhipu AI | ZHIPUAI_API_KEY |
Fast/cheap GLM variant |
| Gemini (embedding) | GEMINI_API_KEY |
Used for pgvector embeddings only |
GLM-4 models use an OpenAI-compatible endpoint (https://open.bigmodel.cn/api/paas/v4/). Configure via EXTRACTION_MODELS env var.
| Upload & Extraction | Extracted Records & ROI |
|---|---|
 |
 |
Real-time progress: PREPROCESSING > EXTRACTING > CLASSIFYING > VALIDATING > EMBEDDING > COMPLETED
- Extraction: Two-pass Claude pipeline (draft + verify via
tool_use), 6 document types, 94.6% accuracy on 28-fixture eval suite - Search & RAG: pgvector semantic search (768-dim HNSW), hybrid BM25+RRF retrieval, agentic ReAct loop with 5 tools, map-reduce multi-document synthesis, semantic deduplication cache
- Reliability: Circuit breaker (Sonnet to Haiku fallback), dead-letter queue, idempotent retries, HMAC-signed webhooks with 4-attempt retry, SHA-256 upload dedup
- Observability: OpenTelemetry traces (Jaeger/Tempo), Prometheus metrics, Grafana dashboards, per-request cost tracking, structured logging
- Developer Experience: SSE streaming progress, MCP server integration, prompt versioning (semver), model A/B testing (z-test), 12 ADRs, 90%+ test coverage
| Metric | Value |
|---|---|
| Document extraction (p50) | ~8s (two-pass Claude) |
| SSE first token (p50) | <500ms |
| Semantic search (p95) | <100ms |
| Extraction accuracy (golden eval) | 94.6% across 28 fixtures, 6 document types |
| Test suite | ~5s (1,155 tests) |
| Coverage | 90%+ (CI-enforced) |
Measured against 28 hand-crafted golden fixtures (including 12 adversarial cases with 4 prompt injection attacks) covering all 6 document types. Scores are field-level F1. Run in CI on every push.
| Document Type | Accuracy |
|---|---|
| Invoice | 95.0% |
| Purchase Order | 96.4% |
| Bank Statement | 91.6% |
| Medical Record | 98.9% |
| Receipt | 82.1% |
| Identity Document | 81.4% |
| Overall | 94.6% |
# Reproduce locally (no API calls):
python scripts/run_eval_ci.py --ciapp/
api/ -- FastAPI route modules (10 routers)
auth/ -- API key auth + rate limiting middleware
models/ -- SQLAlchemy models (8 tables)
schemas/ -- Pydantic request/response schemas
services/ -- Extraction, classification, embedding, validation
storage/ -- Pluggable storage backends (local, R2)
utils/ -- Hashing, MIME detection, token counting
worker/ -- ARQ async job processor
frontend/ -- Streamlit 14-page dashboard
alembic/ -- Database migrations (001-010)
scripts/ -- Seed scripts (API keys, sample docs, cleanup)
tests/ -- Unit + integration tests
12 Architecture Decision Records (ADRs) document the key design choices: docs/adr/
| ADR | Decision |
|---|---|
| ADR-0001 | ARQ over Celery for async job queue |
| ADR-0002 | pgvector over Pinecone/Weaviate |
| ADR-0003 | Two-pass Claude extraction with confidence gating |
| ADR-0006 | Circuit breaker model fallback chain |
| ADR-0011 | API key auth over OAuth/JWT |
| ADR-0012 | Pluggable storage backend (Local/R2) |
Deployed with: Docker Compose / AWS Terraform (RDS + ElastiCache + ECR) / Kubernetes (Kustomize + HPA). Grafana observability. 80% coverage gate. 94.6% eval gate in CI. HIPAA/SOC2 alignment documented.
Cloud infrastructure (deploy/aws/main.tf, deploy/k8s/): Full Terraform IaC for AWS — RDS PostgreSQL, ElastiCache Redis, ECR registry, EC2 with auto-scaling. Kubernetes manifests with Kustomize overlays, Horizontal Pod Autoscaler, and Ingress. Applied from Google Cloud GenAI Leader (25h) coursework.
| Document | Purpose |
|---|---|
| SLO Targets | Latency, availability, quality, cost targets |
| Common Failure Runbook | Circuit breaker, Redis, DB, queue, vector index recovery |
| Security Guide | API keys, webhooks, CORS, data handling |
| Compliance & Privacy | HIPAA/PHI handling, PII detection, SOC 2 alignment |
| Architecture | Full system architecture overview |
| Case Study | Engineering journey from prototype to production |
| MCP Integration | Claude Desktop / agent framework setup |
| Cost Model | Token costs, per-document pricing, volume estimates |
| Certifications Applied | 1,208h across 14 certifications mapped to features |
Render (one-click):
Kubernetes: kubectl apply -k deploy/k8s/ (HPA auto-scaling, nginx ingress, SSE buffering disabled)
AWS Terraform: cd deploy/aws && terraform apply (EC2 + RDS PostgreSQL 16 + ElastiCache Redis 7, free-tier eligible)
See deploy/ for full manifests and configuration.
pytest tests/ -v # Full suite (1,155 tests, ~5s)
pytest tests/ -v --run-eval # Include golden eval (requires API key)
python scripts/run_eval_ci.py --ci # Deterministic eval (no API key)- Tesseract degradation on handwriting: OCR accuracy drops significantly on handwritten documents. Set
OCR_ENGINE=visionto route through Claude's vision API instead. - English-only extraction prompts: Non-English documents may extract with lower accuracy.
See CONTRIBUTING.md for development setup, testing, and PR guidelines.
MIT