Aska
7c02d31194
- llm: ChatStreamWithTools sends OpenAI-format tools; streamed tool_call deltas are reassembled by index into complete ToolCalls on the Done token - model: LLMMessage gains ToolCallID/ToolCalls; StreamToken gains ToolCalls - orchestrator: new ToolExecutor interface and streamWithTools loop — execute requested tools, feed results back, up to 3 rounds, final round forces a plain text answer; normal chats still stream token by token. Also adds Pipeline.FinishRound (assistant reply recording + post-round hook), wired to transports in the next commit. - main: inject the plugin registry via toolExecAdapter Includes an httptest-based test covering delta reassembly, tool execution, and result feedback.
103 lines
3.5 KiB
Go
103 lines
3.5 KiB
Go
// Package model defines shared data types for the AI-Core engine.
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package model
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import "time"
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// Message represents a single chat message in a conversation.
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type Message struct {
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ID string `json:"id"`
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SessionID string `json:"session_id"`
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Role string `json:"role"` // "user" | "assistant" | "action" | "system"
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Content string `json:"content"`
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CreatedAt time.Time `json:"created_at"`
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}
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// Session represents a conversation session.
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type Session struct {
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ID string `json:"id"`
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UserID string `json:"user_id"`
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Title string `json:"title"`
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State string `json:"state"` // "active" | "idle"
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CreatedAt time.Time `json:"created_at"`
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UpdatedAt time.Time `json:"updated_at"`
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}
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// SubSessionResult holds the result from a single subsession provider.
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type SubSessionResult struct {
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Provider string `json:"provider"` // "general" | "memory" | "iot" | "review"
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Content string `json:"content"`
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Error string `json:"error,omitempty"`
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}
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// Intent represents the detected intent of a user message.
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type Intent struct {
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Type string `json:"type"` // "chat" | "iot_control" | "iot_query" | "greeting" | "memory_trigger" | "knowledge" | "task"
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Confidence float64 `json:"confidence"` // 0.0 - 1.0
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Entities []string `json:"entities"` // Detected entities (device names, etc.)
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RawText string `json:"raw_text"`
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}
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// LLMMessage is the standardized format sent to LLM APIs.
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type LLMMessage struct {
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Role string `json:"role"` // "system" | "user" | "assistant" | "tool"
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Content string `json:"content"`
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Name string `json:"name,omitempty"`
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// ToolCallID links a tool result message to the call that produced it (role="tool").
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ToolCallID string `json:"tool_call_id,omitempty"`
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// ToolCalls carries the calls requested by the assistant (role="assistant").
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// Marshaled to the OpenAI wire format by the llm package, hence "-".
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ToolCalls []ToolCall `json:"-"`
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}
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// LLMResponse represents a non-streaming LLM response.
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type LLMResponse struct {
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Content string `json:"content"`
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FinishReason string `json:"finish_reason"` // "stop" | "length" | "tool_calls"
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Model string `json:"model"`
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TokensUsed int `json:"tokens_used"`
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}
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// StreamToken is a single token from a streaming LLM response.
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type StreamToken struct {
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Content string `json:"content"`
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FinishReason string `json:"finish_reason,omitempty"`
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Done bool `json:"done"`
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// ToolCalls is set on the final Done token when the model requested tool calls.
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ToolCalls []ToolCall `json:"tool_calls,omitempty"`
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}
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// ReviewSegment represents a parsed action/chat segment from LLM output.
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type ReviewSegment struct {
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Type string `json:"type"` // "action" | "chat"
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Content string `json:"content"`
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}
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// ToolCall represents a tool call requested by the LLM.
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type ToolCall struct {
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ID string `json:"id"`
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Name string `json:"name"`
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Arguments map[string]any `json:"arguments"`
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}
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// Intent types
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const (
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IntentChat = "chat"
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IntentIoTControl = "iot_control"
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IntentIoTQuery = "iot_query"
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IntentGreeting = "greeting"
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IntentMemoryTrigger = "memory_trigger"
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IntentKnowledge = "knowledge"
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IntentTask = "task"
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)
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// LLM purposes for model routing
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type LLMPurpose string
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const (
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PurposeChat LLMPurpose = "chat"
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PurposeIntentAnalysis LLMPurpose = "intent_analysis"
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PurposeDeepThinking LLMPurpose = "deep_thinking"
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PurposeMemoryExtraction LLMPurpose = "memory_extraction"
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PurposeToolCalling LLMPurpose = "tool_calling"
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)
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