Tutorials

Interactive Jupyter notebooks covering Director-AI from first principles to production deployment. Every notebook runs in Google Colab with zero local setup.

Learning Path

graph TD
    subgraph "Foundations (30 min)"
        A["00 Quickstart<br/>5 min"] --> B["01 Coherence Engine<br/>15 min"]
    end

    subgraph "Core Tracks"
        B --> C["09 Production Guardrails<br/>guard() + SDK wrapping"]
        B --> D["02 Streaming Oversight<br/>token-level halt"]
        B --> V["16 Verification Gems<br/>8 standalone modules"]
    end

    subgraph "Deep Dives"
        C --> E["10 Vector RAG Pipeline<br/>ChromaDB + reranking"]
        C --> F["12 Domain Presets & Config<br/>8 profiles"]
        D --> G["11 Streaming Halt Deep Dive<br/>hard/soft/trend"]
        E --> H["06 Medical RAG Chatbot<br/>domain-specific"]
    end

    subgraph "Production (enterprise)"
        F --> I["13 Batch Processing<br/>evaluation pipelines"]
        I --> J["14 Enterprise Multi-Tenant<br/>Docker + K8s"]
        J --> K["15 Custom Fine-Tuning<br/>domain NLI + ONNX export"]
    end

    style A fill:#7c4dff,color:#fff
    style B fill:#7c4dff,color:#fff
    style C fill:#2e7d32,color:#fff
    style D fill:#1565c0,color:#fff
    style V fill:#ff8f00,color:#fff
    style K fill:#c62828,color:#fff

---

Getting Started

Start here. These two notebooks teach the core concepts in under 30 minutes.

#	Notebook	What You Learn	Time	Colab
00	Quickstart	Install, score, guard, stream, presets	5 min
01	Coherence Engine	CoherenceScorer, SafetyKernel, CoherenceAgent, dual-entropy formula	15 min

---

Core Features

Deep dives into the four pillars of Director-AI.

#	Notebook	What You Learn	Time	Colab
09	Production Guardrails	guard() for OpenAI / Anthropic / Bedrock / Gemini / Cohere, failure modes, streaming guards	20 min
10	Vector RAG Pipeline	Semantic retrieval, ChromaDB, pluggable backends, reranking, multi-tenant KB	25 min
11	Streaming Halt Deep Dive	Hard limit, sliding window, trend detection, async, per-token visualization	20 min
12	Domain Presets & Config	8 profiles, env vars, YAML, backends, strict mode, multi-GPU, LLM-as-judge	15 min

---

Domain Applications

Real-world integrations and domain-specific patterns.

#	Notebook	What You Learn	Time	Colab
02	Streaming Oversight	StreamingKernel basics, token-by-token monitoring	10 min
03	Vector Store	VectorGroundTruthStore, InMemoryBackend, fact ingestion	10 min
05	SSGF Geometry	Self-similar geometry foundation concepts	10 min
06	Medical RAG Chatbot	Healthcare-specific guardrails, high thresholds, evidence citations	20 min
07	LangChain Integration	CoherenceCallbackHandler, chain integration, output parsing	15 min
08	Provider Adapters	OpenAI, Anthropic, Bedrock, Gemini, Cohere adapter patterns	10 min

---

Verification & Analysis

Standalone analysis modules — no NLI model required. All stdlib-only.

#	Resource	What You Learn	Time	Colab
16	Verification Gems	All 8 gems: numeric, reasoning, temporal, consensus, conformal, feedback loops, agentic, REST API	15 min
—	Guide: Verification Gems	Full parameter reference for all 8 gems	15 min	—
—	Example: verification_gems_demo.py	Runnable demo of all 7 standalone verification modules	5 min	—

CLI quick start:

director-ai verify-numeric "Revenue grew 50% from \$100 to \$120"
director-ai verify-reasoning "Step 1: A is true. Step 2: Therefore B."
director-ai temporal-freshness "The CEO of Apple is Tim Cook"
director-ai check-step "Find revenue data" "search" "revenue Q3"

REST quick start (with server running):

curl -X POST http://localhost:8080/v1/verify/numeric \
  -H "Content-Type: application/json" \
  -d '{"text": "Revenue grew 50% from $100 to $120."}'

---

Enterprise & Production

Scale, evaluate, fine-tune, and deploy.

#	Notebook	What You Learn	Time	Colab
04	End-to-End Benchmark	Full benchmark suite, latency profiling, accuracy metrics	15 min
13	Batch Processing & Evaluation	BatchProcessor, evaluation pipelines, claim attribution, regression gates	20 min
14	Enterprise Multi-Tenant	Tenant isolation, REST/gRPC servers, Docker, Kubernetes, monitoring	25 min
15	Custom Fine-Tuning	JSONL data prep, validation, training, anti-forgetting, ONNX export, REST API	30 min

---

Prerequisites

All notebooks run on Python 3.11+ with pip install director-ai.

Notebooks requiring optional extras note this in their first cell:

Extra	Install	Notebooks
NLI scoring	pip install director-ai[nli]	01, 04, 06, 09–15
Vector store	pip install director-ai[vector]	10, 14
Fine-tuning	pip install director-ai[finetune]	15
Server	pip install director-ai[server]	14
gRPC	pip install director-ai[grpc]	14

Running Locally

git clone https://github.com/anulum/director-ai.git
cd director-ai
pip install -e ".[dev,nli]"
jupyter lab notebooks/