Configuration

Config Model

The extension config is ai.Config and is passed programmatically (ai.NewExtension(...) or ai.NewExtensionWithConfig(...)).

Top-Level Fields (ai.Config)

• EnableLLM (default true)
• EnableAgents (default true)
• EnableTraining (default false)
• EnableInference (default true)
• EnableCoordination (default true)
• MaxConcurrency (default 10)
• RequestTimeout (default 30s)
• CacheSize (default 1000)
• LLM (LLMConfiguration)
• Inference (InferenceConfiguration)
• Agents (AgentConfiguration)
• Middleware (MiddlewareConfiguration)
• StateStore (StateStoreConfig)
• VectorStore (VectorStoreConfig)
• Training (TrainingConfiguration)

LLM Configuration (LLMConfiguration)

Fields:

• DefaultProvider
• Providers
• MaxRetries
• RetryDelay
• Timeout

Important behavior:

• CreateLLMManager uses DefaultProvider, retries, and timeout.
• Providers from Providers are currently not auto-registered.
• Register providers manually before startup for real model calls.

State Store (StateStoreConfig)

Type values:

• memory (default)
• postgres
• redis

Sub-configs:

• Memory.TTL
• Postgres.ConnString, Postgres.TableName
• Redis.Addrs, Redis.Password, Redis.DB

Behavior:

• invalid config or init errors trigger memory fallback in extension registration.

Vector Store (VectorStoreConfig)

Type values:

• memory (supported)
• postgres (declared, currently not implemented)
• pinecone (declared, currently not implemented)
• weaviate (declared, currently not implemented)

Behavior:

• unsupported/non-implemented backends return errors in factory code.
• extension then falls back to in-memory vector store.

Training (TrainingConfiguration)

Important fields:

• Enabled
• CheckpointPath
• ModelPath
• DataPath
• MaxConcurrentJobs
• DefaultResources (cpu, memory, gpu, timeout, priority)
• Storage (local, s3, gcs, azure)

Important behavior:

• DI registration of training services is controlled by Training.Enabled.

Inference, Agents, Middleware Blocks

Inference, Agents, and Middleware config structs are available for package-level modules and future extension wiring.

Current extension behavior does not automatically instantiate inference engine or middleware from these blocks.

Practical Production Config (Go)

cfg := ai.DefaultConfig()

cfg.LLM.DefaultProvider = "openai"
cfg.LLM.MaxRetries = 3
cfg.LLM.Timeout = 30 * time.Second

cfg.StateStore = ai.StateStoreConfig{
    Type: "postgres",
    Postgres: &ai.PostgresStateConfig{
        ConnString: os.Getenv("DATABASE_URL"),
        TableName:  "agent_state",
    },
}

// Keep memory unless you have a custom vector constructor.
cfg.VectorStore = ai.VectorStoreConfig{Type: "memory"}

cfg.Training = ai.TrainingConfiguration{
    Enabled:           true,
    CheckpointPath:    "./checkpoints",
    ModelPath:         "./models",
    DataPath:          "./data",
    MaxConcurrentJobs: 4,
    DefaultResources: ai.ResourcesConfig{
        CPU:      "4",
        Memory:   "8Gi",
        GPU:      0,
        Timeout:  4 * time.Hour,
        Priority: 1,
    },
    Storage: ai.StorageConfig{
        Type: "local",
        Local: &ai.LocalStorageConfig{BasePath: "./training-storage"},
    },
}

ext := ai.NewExtension(ai.WithConfig(cfg))

Validation Tips

• Fail fast on startup by keeping constructors eager.
• Verify your provider can execute a simple prompt after app start.
• Confirm state and vector store effective types from startup logs.