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Business Implementation Playbook

For decision-makers

A practical playbook for turning Orazaka capabilities into outcomes for a business — what to deploy, in what order, and what it delivers. Written for decision-makers, not just engineers.

Developer playbook demonstrating how to build a product (CinePulse) on Orazaka.

1. CinePulse Product Case Study

CinePulse is a multi-modal entertainment discovery platform:

  • Text Recommendations: Moody/contextual query recommendations (e.g. "What to watch tonight?").
  • Audio Recognition: Identifies movie soundtracks using audio fingerprints.
  • Visual Recognition: Identifies movie screenshots, posters, or scenes.
ScreenPurposeOrazaka Pipeline
DashboardRecommendation feedUserContextResolver + RagInterceptor
RecognitionVisual/Audio media matchingVision + Audio pre-processor pipeline
WatchlistSaved watchlist trackingJPA persistence (Postgres)
StudioGenerates teaser video trailersVideoEngine (AnimateDiff-Lightning)
SettingsStreaming preferences and filter flagskrizaka-users-core preference profile

2. Architecture Mapping

3. Step-by-Step Implementation

Step 1: Database Model (orazaka-persistence/app)

@Entity
@Table(name = "cinepulse_movies")
public class MovieEntity {
    @Id private Long tmdbId;
    private String title;
    private String originalTitle;
    private String overview;
    private String genres; // JSON list
    private String posterPath;
    private Double imdbRating;
    private LocalDate releaseDate;
    
    @Convert(converter = JsonMapConverter.class)
    private Map<String, Boolean> streamingAvailability;
    
    @Column(columnDefinition = "vector(1536)")
    private float[] embedding; // PGVector embedding
}
  • Migration (V12__cinepulse_movies.sql): 
    CREATE TABLE cinepulse_movies (
    tmdb_id BIGINT PRIMARY KEY,
    title VARCHAR(500) NOT NULL,
    overview TEXT,
    genres JSONB NOT NULL DEFAULT '[]',
    poster_path VARCHAR(255),
    imdb_rating DECIMAL(3,1),
    streaming_availability JSONB NOT NULL DEFAULT '{}',
    embedding vector(1536)
);
CREATE INDEX idx_movies_genres ON cinepulse_movies USING GIN (genres);
CREATE INDEX idx_movies_embedding ON cinepulse_movies USING ivfflat (embedding vector_cosine_ops);

Step 2: Register Tools (orazaka-tools)

@Component
public class TmdbLookupTool {
    @Tool(description = "Search TMDb movies metadata")
    public MovieResult searchMovie(@ToolParam("Title query") String query) { ... }

    @Tool(description = "Check streaming availability across platforms")
    public StreamingResult checkAvailability(@ToolParam("TMDb ID") Long tmdbId, @ToolParam("Country code") String country) { ... }
}

Step 3: Embed Movies Catalog (RAG)

@Service
public class MovieEmbeddingService {
    private final EmbeddingModel embeddingModel;
    private final MovieRepository movieRepository;

    @Transactional
    public void embedMovieCatalog(List<MovieEntity> movies) {
        movies.forEach(movie -> {
            String text = String.format("%s. %s.", movie.getTitle(), movie.getOverview());
            movie.setEmbedding(embeddingModel.embed(text));
        });
        movieRepository.saveAll(movies);
    }
}

Step 4: Controller Mapping (orazaka-conversation-service)

@RestController
@RequestMapping("/api/v1/cinepulse")
public class CinePulseController {
    private final AiClient aiClient;

    @PostMapping("/recommend")
    public Flux<ChatResponse> recommend(@RequestBody RecommendRequest req, @RequestHeader("Authorization") String token) {
        return aiClient.stream(AiRequest.builder()
            .prompt(req.query())
            .context(Context.fromToken(token))
            .tools("tmdb-lookup")
            .build());
    }
}

4. Operational Playbook

Build Sequence

# 1. Build identity logic
mvn clean install -pl krizaka/krizaka-users/krizaka-users-core

# 2. Compile gateway API with dependencies
mvn clean compile -pl orazaka-apps/services/orazaka-conversation-service -am

# 3. Build CLI executable
npm run build --prefix orazaka-apps/ui/orazaka-cli

# 4. Install UI workspace dependencies
npm install --prefix orazaka-apps/ui

Start Services

# Docker databases, Redis & RabbitMQ
docker compose -f infra/docker-compose.yml up -d

# Spin up Ollama
ollama serve &
ollama pull llama3.2:latest

CLI Multi-Modal Usage

# Authenticate
npx orazaka login user@example.com password123

# Prompt Recommendations
npx orazaka chat "Romantic movie recommendation for tonight"

# Identify Soundtrack Audio
npx orazaka chat --audio "var/soundtrack.mp3" "What movie is this song from?"

# Analyze Movie Poster Image
npx orazaka chat --image "var/poster.png" "Who is the director of this movie?"

# Generate Teaser Video
npx orazaka video "An animated camera flythrough of a futuristic neon city" --duration 4 --output "scratch/teaser.mp4"

5. Environment Reference

KeyDefaultPurpose
SPRING_DATASOURCE_URLjdbc:postgresql://localhost:5432/orazaka_dbMain PostgreSQL database
REDIS_URLredis://localhost:6379Cache rate limits & tokens
SPRING_RABBITMQ_HOSTlocalhostRabbitMQ event broker
VIDEO_WORKER_PORT8188Local Python SVD XT worker probe port
TMDB_API_KEY(required)TMDb metadata client
ACOUSTID_API_KEY(required)Audio fingerprinting service
NEXTAUTH_SECRET(required)BFF cookie encryption

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On this page

1. CinePulse Product Case Study2. Architecture Mapping3. Step-by-Step ImplementationStep 1: Database Model (orazaka-persistence/app)Step 2: Register Tools (orazaka-tools)Step 3: Embed Movies Catalog (RAG)Step 4: Controller Mapping (orazaka-conversation-service)4. Operational PlaybookBuild SequenceStart ServicesCLI Multi-Modal Usage5. Environment ReferenceRelated Documentation