sdk

type Conversation ¶

type Conversation struct {
	// contains filtered or unexported fields
}

Conversation represents an active conversation

func (c *Conversation) AddToolResult(toolCallID, result string) error

func (c *Conversation) Continue(ctx context.Context) (*Response, error)

func (c *Conversation) GetHistory() []types.Message

func (c *Conversation) GetID() string

func (c *Conversation) GetPendingTools() []tools.PendingToolInfo

func (c *Conversation) GetUserID() string

func (c *Conversation) HasPendingTools() bool

func (c *Conversation) Send(ctx context.Context, userMessage string, opts ...SendOptions) (*Response, error)

func (c *Conversation) SendStream(ctx context.Context, userMessage string, opts ...SendOptions) (<-chan StreamEvent, error)

type ConversationConfig ¶

type ConversationConfig struct {
	// Required fields
	UserID     string // User who owns this conversation
	PromptName string // Task type from the pack (e.g., "support", "sales")

	// Optional fields
	ConversationID string                 // If empty, auto-generated
	Variables      map[string]interface{} // Template variables
	SystemPrompt   string                 // Override system prompt
	Metadata       map[string]interface{} // Custom metadata

	// Context policy (token budget management)
	ContextPolicy *middleware.ContextBuilderPolicy
}

ConversationConfig configures a new conversation

type ConversationManager ¶

type ConversationManager struct {
	// contains filtered or unexported fields
}

ConversationManager provides high-level API for managing LLM conversations. It automatically constructs the pipeline with appropriate middleware based on the PromptPack configuration.

Key Features:

• Load PromptPacks and create conversations for specific prompts
• Automatic pipeline construction with middleware stack
• State persistence via StateStore
• Support for streaming and tool execution
• Multi-turn conversation management

func NewConversationManager(opts ...ManagerOption) (*ConversationManager, error)

func (cm *ConversationManager) CreateConversation(ctx context.Context, pack *Pack, config ConversationConfig) (*Conversation, error)

func (cm *ConversationManager) GetConversation(
	ctx context.Context, conversationID string, pack *Pack,
) (*Conversation, error)

func (cm *ConversationManager) LoadPack(packPath string) (*Pack, error)

type CustomContextMiddleware ¶

type CustomContextMiddleware interface {
	pipeline.Middleware
}

CustomContextMiddleware is an example of custom middleware for context building. Users can implement similar middleware for their specific needs.

Example:

type MyContextMiddleware struct {
    ragClient *RAGClient
}

func (m *MyContextMiddleware) Process(execCtx *pipeline.ExecutionContext, next func() error) error {
    // Extract query from last user message
    query := execCtx.Messages[len(execCtx.Messages)-1].Content

    // Fetch relevant documents
    docs, _ := m.ragClient.Search(query, 5)

    // Add to variables for template substitution
    execCtx.Variables["rag_context"] = formatDocs(docs)

    return next()
}

type ManagerConfig ¶

type ManagerConfig struct {
	// MaxConcurrentExecutions limits parallel pipeline executions
	MaxConcurrentExecutions int

	// DefaultTimeout for LLM requests
	DefaultTimeout time.Duration

	// EnableMetrics enables built-in metrics collection
	EnableMetrics bool
}

ManagerConfig configures the ConversationManager

type ManagerOption ¶

type ManagerOption func(*ConversationManager) error

ManagerOption configures ConversationManager

func WithConfig(config ManagerConfig) ManagerOption

func WithProvider(provider providers.Provider) ManagerOption

func WithStateStore(store statestore.Store) ManagerOption

func WithToolRegistry(registry *tools.Registry) ManagerOption

type MiddlewareConfig ¶

type MiddlewareConfig struct {
	Type   string                 `json:"type"`
	Config map[string]interface{} `json:"config,omitempty"`
}

MiddlewareConfig defines a single middleware configuration

type ModelOverride ¶

type ModelOverride struct {
	SystemTemplateSuffix string `json:"system_template_suffix,omitempty"`
}

ModelOverride defines model-specific template overrides

type ObservabilityMiddleware ¶

type ObservabilityMiddleware interface {
	pipeline.Middleware
}

ObservabilityMiddleware is an example of observability middleware. Users can implement similar middleware for LangFuse, DataDog, etc.

Example:

type LangFuseMiddleware struct {
    client *langfuse.Client
}

func (m *LangFuseMiddleware) Process(execCtx *pipeline.ExecutionContext, next func() error) error {
    traceID := m.client.StartTrace(...)
    spanID := m.client.StartSpan(traceID, ...)

    start := time.Now()
    err := next()
    duration := time.Since(start)

    m.client.EndSpan(spanID, langfuse.SpanResult{
        Duration: duration,
        TokensInput: execCtx.CostInfo.InputTokens,
        TokensOutput: execCtx.CostInfo.OutputTokens,
        Error: err,
    })

    return err
}

type Pack ¶

type Pack struct {
	// Pack identity
	ID          string `json:"id"`
	Name        string `json:"name"`
	Version     string `json:"version"`
	Description string `json:"description"`

	// Shared configuration across all prompts
	TemplateEngine TemplateEngine `json:"template_engine"`

	// Map of task_type -> prompt configuration
	Prompts map[string]*Prompt `json:"prompts"`

	// Shared fragments used by all prompts
	Fragments map[string]string `json:"fragments,omitempty"`

	// Tool definitions (referenced by prompts)
	Tools map[string]*Tool `json:"tools,omitempty"`
	// contains filtered or unexported fields
}

Pack represents a loaded PromptPack containing multiple prompts for related task types. A pack is a portable, JSON-based bundle created by the packc compiler.

DESIGN DECISION: Why separate Pack types in sdk vs runtime?

This SDK Pack is optimized for LOADING & EXECUTION:

• Loaded from .pack.json files for application use
• Includes Tools map for runtime tool access
• Includes filePath to track source file location
• Thread-safe with sync.RWMutex for concurrent access
• Returns validation errors for application error handling
• Rich types (*Variable, *Validator, *Tool) with full functionality
• Has CreateRegistry() to convert to runtime.Registry for pipeline execution
• Has convertToRuntimeConfig() to bridge SDK ↔ runtime formats

The runtime.prompt.Pack is optimized for COMPILATION:

• Created by PackCompiler during prompt compilation
• Includes Compilation and Metadata fields for provenance tracking
• Returns validation warnings ([]string) for compiler feedback
• No thread-safety (single-threaded compilation process)
• Simple types for clean JSON serialization
• No conversion methods (produces, doesn't consume)

Both types serialize to/from the SAME JSON format (.pack.json files), ensuring full interoperability between compilation and execution phases. The duplication is intentional and provides:

1. Clear separation of concerns (compile vs execute)
2. No circular dependencies (sdk imports runtime, not vice versa)
3. Independent evolution of each module
4. Type-specific optimizations (thread-safety, validation behavior)

Design: A pack contains MULTIPLE prompts (task_types) that share common configuration like template engine and fragments, but each prompt has its own template, variables, tools, and validators.

See runtime/prompt/pack.go for the corresponding runtime-side documentation.

func (p *Pack) CreateRegistry() (*prompt.Registry, error)

func (p *Pack) GetPrompt(taskType string) (*Prompt, error)

func (p *Pack) GetTools(taskType string) ([]*Tool, error)

func (p *Pack) ListPrompts() []string

type PackManager ¶

type PackManager struct {
	// contains filtered or unexported fields
}

PackManager manages loading and caching of PromptPacks

func NewPackManager() *PackManager

func (pm *PackManager) GetPack(packPath string) (*Pack, bool)

func (pm *PackManager) LoadPack(packPath string) (*Pack, error)

type Parameters ¶

type Parameters struct {
	Temperature float32 `json:"temperature,omitempty"`
	MaxTokens   int     `json:"max_tokens,omitempty"`
	TopP        float32 `json:"top_p,omitempty"`
	TopK        *int    `json:"top_k,omitempty"`
}

Parameters defines LLM generation parameters

type PipelineBuilder ¶

type PipelineBuilder struct {
	// contains filtered or unexported fields
}

PipelineBuilder provides low-level API for constructing custom pipelines with middleware.

Use this when you need:

• Custom middleware injection
• Custom context builders
• Observability integration (LangFuse, DataDog, etc.)
• Advanced pipeline control

For simple use cases, use ConversationManager instead.

Example:

builder := sdk.NewPipelineBuilder().
    WithProvider(provider).
    WithMiddleware(customMiddleware).
    WithMiddleware(observabilityMiddleware)

pipe := builder.Build()
result, err := pipe.Execute(ctx, "user", "Hello!")

func NewPipelineBuilder() *PipelineBuilder

func (pb *PipelineBuilder) Build() *pipeline.Pipeline

func (pb *PipelineBuilder) WithConfig(config *pipeline.PipelineRuntimeConfig) *PipelineBuilder

func (pb *PipelineBuilder) WithMiddleware(m pipeline.Middleware) *PipelineBuilder

func (pb *PipelineBuilder) WithProvider(provider providers.Provider, toolRegistry *tools.Registry, toolPolicy *pipeline.ToolPolicy) *PipelineBuilder

func (pb *PipelineBuilder) WithSimpleProvider(provider providers.Provider) *PipelineBuilder

func (pb *PipelineBuilder) WithTemplate() *PipelineBuilder

type PipelineConfig ¶

type PipelineConfig struct {
	Stages     []string            `json:"stages"`
	Middleware []*MiddlewareConfig `json:"middleware"`
}

PipelineConfig defines pipeline middleware configuration

type Prompt ¶

type Prompt struct {
	ID          string `json:"id"`
	Name        string `json:"name"`
	Description string `json:"description"`
	Version     string `json:"version"`

	// Template
	SystemTemplate string `json:"system_template"`

	// Variables for this prompt
	Variables []*Variable `json:"variables"`

	// Tool references (names that map to pack.Tools)
	ToolNames []string `json:"tools,omitempty"`

	// Tool policy
	ToolPolicy *ToolPolicy `json:"tool_policy,omitempty"`

	// Multimodal media configuration
	MediaConfig *prompt.MediaConfig `json:"media,omitempty"`

	// Pipeline configuration
	Pipeline *PipelineConfig `json:"pipeline,omitempty"`

	// LLM parameters
	Parameters *Parameters `json:"parameters,omitempty"`

	// Validators
	Validators []*Validator `json:"validators,omitempty"`

	// Model testing results
	TestedModels []*TestedModel `json:"tested_models,omitempty"`

	// Model-specific overrides
	ModelOverrides map[string]*ModelOverride `json:"model_overrides,omitempty"`
}

Prompt represents a single prompt configuration within a pack

type Response ¶

type Response struct {
	Content      string
	ToolCalls    []types.MessageToolCall
	TokensUsed   int
	Cost         float64
	LatencyMs    int64
	Validations  []types.ValidationResult
	Truncated    bool                    // True if context was truncated
	PendingTools []tools.PendingToolInfo // Tools awaiting external approval/input
}

Response represents a conversation turn response

type SendOptions ¶

type SendOptions struct {
	Stream       bool                   // Enable streaming
	MaxToolCalls int                    // Max tool calls per turn (0 = use prompt default)
	Metadata     map[string]interface{} // Turn-specific metadata
}

SendOptions configures message sending behavior

type StreamEvent ¶

type StreamEvent struct {
	Type     string // "content", "tool_call", "done", "error"
	Content  string
	ToolCall *types.MessageToolCall
	Error    error
	Final    *Response // Set when Type="done"
}

StreamEvent represents a streaming response event

type TemplateEngine ¶

type TemplateEngine struct {
	Version  string   `json:"version"`
	Syntax   string   `json:"syntax"`
	Features []string `json:"features"`
}

TemplateEngine describes the template engine configuration shared across prompts

type TestedModel ¶

type TestedModel struct {
	Provider     string  `json:"provider"`
	Model        string  `json:"model"`
	Date         string  `json:"date"`
	SuccessRate  float64 `json:"success_rate"`
	AvgTokens    int     `json:"avg_tokens"`
	AvgCost      float64 `json:"avg_cost"`
	AvgLatencyMs int     `json:"avg_latency_ms"`
}

TestedModel contains testing results for a specific model

type Tool ¶

type Tool struct {
	Name        string                 `json:"name"`
	Description string                 `json:"description"`
	Parameters  map[string]interface{} `json:"parameters"`
}

Tool defines a tool that can be called by the LLM

type ToolPolicy ¶

type ToolPolicy struct {
	ToolChoice          string   `json:"tool_choice"` // "auto", "required", "none"
	MaxRounds           int      `json:"max_rounds,omitempty"`
	MaxToolCallsPerTurn int      `json:"max_tool_calls_per_turn,omitempty"`
	Blocklist           []string `json:"blocklist,omitempty"`
}

ToolPolicy defines tool usage constraints

type Validator ¶

type Validator struct {
	Type            string                 `json:"type"`
	Enabled         bool                   `json:"enabled"`
	FailOnViolation bool                   `json:"fail_on_violation"`
	Params          map[string]interface{} `json:"params"`
}

Validator defines a validation rule

type Variable ¶

type Variable struct {
	Name        string                 `json:"name"`
	Type        string                 `json:"type"` // "string", "number", "boolean", "object", "array"
	Required    bool                   `json:"required"`
	Default     interface{}            `json:"default,omitempty"`
	Description string                 `json:"description"`
	Example     interface{}            `json:"example,omitempty"`
	Validation  map[string]interface{} `json:"validation,omitempty"`
}

Variable defines a template variable with validation rules