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tinkle-community
286 lines
10 KiB
Go
286 lines
10 KiB
Go
package agent
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import (
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"encoding/json"
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"fmt"
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"nofx/auth"
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"nofx/logger"
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"nofx/mcp"
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"nofx/telegram/session"
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"strings"
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)
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const maxIterations = 10
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// apiRequestTool is the single tool exposed to the LLM.
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// Native function calling means the LLM returns EITHER ToolCalls OR Content — never both.
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// This makes narration structurally impossible: text cannot appear alongside a tool call.
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var apiRequestTool = mcp.Tool{
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Type: "function",
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Function: mcp.FunctionDef{
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Name: "api_request",
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Description: "Call the NOFX trading system REST API",
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Parameters: map[string]any{
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"type": "object",
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"properties": map[string]any{
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"method": map[string]any{
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"type": "string",
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"enum": []string{"GET", "POST", "PUT", "DELETE"},
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"description": "HTTP method",
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},
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"path": map[string]any{
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"type": "string",
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"description": "API path; include query params in path: /api/positions?trader_id=xxx",
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},
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"body": map[string]any{
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"type": "object",
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"description": "Request body; use {} for GET requests",
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},
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},
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"required": []string{"method", "path", "body"},
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},
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},
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}
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// Agent is a stateful AI agent for one Telegram chat.
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// It exposes a single "api_request" tool and runs a loop until the LLM
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// returns a plain-text reply (no tool calls).
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type Agent struct {
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apiTool *apiCallTool
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getLLM func() mcp.AIClient
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memory *session.Memory
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systemPrompt string
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userID string
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}
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// New creates an Agent for one chat session.
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func New(apiPort int, botToken, userID string, getLLM func() mcp.AIClient, systemPrompt string) *Agent {
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return &Agent{
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apiTool: newAPICallTool(apiPort, botToken),
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getLLM: getLLM,
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memory: session.NewMemory(getLLM()),
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systemPrompt: systemPrompt,
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userID: userID,
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}
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}
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// GenerateBotToken creates a long-lived JWT for the bot's internal API calls.
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// userID must match the actual registered user's ID so bot-made changes
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// are visible in the frontend (shared user namespace).
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func GenerateBotToken(userID string) (string, error) {
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return auth.GenerateJWT(userID, "bot@internal")
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}
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// buildAccountContext fetches the live account state (models, exchanges, strategies, traders,
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// and per-trader account summary + statistics) and returns it as a formatted string for
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// injection into the LLM context at the start of each conversation.
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func (a *Agent) buildAccountContext() string {
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var sb strings.Builder
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sb.WriteString(fmt.Sprintf("[Current Account State — User: %s]\n\n", a.userID))
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// ── AI Models ─────────────────────────────────────────────────────────────
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modelsRaw := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/models"})
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sb.WriteString("## AI Models\n")
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sb.WriteString("⚠️ When creating a trader, use the EXACT \"id\" value below for \"ai_model_id\".\n")
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sb.WriteString(" DO NOT use the \"provider\" field — it is NOT a valid ai_model_id.\n\n")
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var models []struct {
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ID string `json:"id"`
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Name string `json:"name"`
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Provider string `json:"provider"`
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Enabled bool `json:"enabled"`
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}
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if err := json.Unmarshal([]byte(modelsRaw), &models); err == nil && len(models) > 0 {
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for _, m := range models {
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status := "disabled"
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if m.Enabled {
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status = "ENABLED"
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}
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sb.WriteString(fmt.Sprintf(" • ai_model_id=\"%s\" provider=%s name=%s [%s]\n", m.ID, m.Provider, m.Name, status))
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}
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} else {
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sb.WriteString(modelsRaw)
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}
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sb.WriteString("\n")
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// ── Exchanges ─────────────────────────────────────────────────────────────
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exchangesRaw := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/exchanges"})
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sb.WriteString("## Exchanges\n")
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sb.WriteString("⚠️ Use the EXACT \"id\" value below for \"exchange_id\" when creating a trader.\n\n")
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var exchanges []struct {
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ID string `json:"id"`
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Name string `json:"name"`
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ExchangeType string `json:"exchange_type"`
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AccountName string `json:"account_name"`
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Enabled bool `json:"enabled"`
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}
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if err := json.Unmarshal([]byte(exchangesRaw), &exchanges); err == nil && len(exchanges) > 0 {
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for _, e := range exchanges {
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status := "disabled"
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if e.Enabled {
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status = "ENABLED"
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}
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sb.WriteString(fmt.Sprintf(" • exchange_id=\"%s\" type=%s account=%s [%s]\n", e.ID, e.ExchangeType, e.AccountName, status))
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}
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} else {
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sb.WriteString(exchangesRaw)
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}
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sb.WriteString("\n")
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// ── Strategies ────────────────────────────────────────────────────────────
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strategiesRaw := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/strategies"})
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sb.WriteString("## Strategies\n")
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var strategies []struct {
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ID string `json:"id"`
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Name string `json:"name"`
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}
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if err := json.Unmarshal([]byte(strategiesRaw), &strategies); err == nil && len(strategies) > 0 {
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for _, s := range strategies {
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sb.WriteString(fmt.Sprintf(" • strategy_id=\"%s\" name=%s\n", s.ID, s.Name))
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}
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} else {
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sb.WriteString(strategiesRaw)
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}
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sb.WriteString("\n")
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// ── Traders ───────────────────────────────────────────────────────────────
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tradersRaw := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/my-traders"})
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sb.WriteString("## Traders\n")
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var traders []struct {
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TraderID string `json:"trader_id"`
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Name string `json:"trader_name"`
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IsRunning bool `json:"is_running"`
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}
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if err := json.Unmarshal([]byte(tradersRaw), &traders); err == nil && len(traders) > 0 {
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for _, t := range traders {
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status := "stopped"
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if t.IsRunning {
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status = "RUNNING"
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}
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sb.WriteString(fmt.Sprintf(" • trader_id=\"%s\" name=%s [%s]\n", t.TraderID, t.Name, status))
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}
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} else {
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sb.WriteString(tradersRaw)
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}
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sb.WriteString("\n")
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// ── Per-trader live data (running traders only) ────────────────────────────
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for _, t := range traders {
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if !t.IsRunning {
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continue
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}
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acct := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/account?trader_id=" + t.TraderID})
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sb.WriteString(fmt.Sprintf("Account [%s]:\n%s\n\n", t.Name, acct))
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stats := a.apiTool.execute(&apiRequest{Method: "GET", Path: "/api/statistics?trader_id=" + t.TraderID})
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sb.WriteString(fmt.Sprintf("Statistics [%s]:\n%s\n\n", t.Name, stats))
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}
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return sb.String()
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}
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// Run processes one user message through the native function-calling agent loop.
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//
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// Architecture:
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// - LLM receives the api_request tool definition alongside conversation history.
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// - LLM response is EITHER ToolCalls (execute API) OR Content (final reply) — never both.
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// This is enforced by the protocol: narration is structurally impossible.
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// - Loop continues until the LLM returns a plain-text reply (no tool calls).
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//
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// On the first message of a conversation the live account state is fetched and injected.
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// onChunk is optional; when set it is called once with the complete final reply text.
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func (a *Agent) Run(userMessage string, onChunk func(string)) string {
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llm := a.getLLM()
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if llm == nil {
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return "AI assistant unavailable. Please configure an AI model in the Web UI."
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}
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// Build initial user message: prepend account state on first turn, history on subsequent turns.
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histCtx := a.memory.BuildContext()
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var firstUserContent string
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if histCtx == "" {
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accountCtx := a.buildAccountContext()
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firstUserContent = accountCtx + "\n[User Message]\n" + userMessage
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} else {
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firstUserContent = histCtx + "\n---\nUser: " + userMessage
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}
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turnMsgs := []mcp.Message{mcp.NewUserMessage(firstUserContent)}
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for i := 0; i < maxIterations; i++ {
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req, err := mcp.NewRequestBuilder().
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WithSystemPrompt(a.systemPrompt).
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AddConversationHistory(turnMsgs).
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AddTool(apiRequestTool).
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WithToolChoice("auto").
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Build()
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if err != nil {
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logger.Errorf("Agent: failed to build request: %v", err)
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break
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}
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resp, err := llm.CallWithRequestFull(req)
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if err != nil {
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logger.Errorf("Agent: LLM call failed (iteration %d): %v", i+1, err)
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return "AI assistant temporarily unavailable. Please try again."
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}
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// No tool calls → LLM returned a final text reply.
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if len(resp.ToolCalls) == 0 {
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reply := strings.TrimSpace(resp.Content)
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if onChunk != nil {
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onChunk(reply)
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}
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a.memory.Add("user", userMessage)
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a.memory.Add("assistant", reply)
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return reply
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}
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// Tool call iteration — show thinking indicator.
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if onChunk != nil {
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onChunk("⏳")
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}
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// Append assistant message carrying the tool calls (no content field).
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turnMsgs = append(turnMsgs, mcp.Message{
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Role: "assistant",
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ToolCalls: resp.ToolCalls,
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})
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// Execute each tool call and append the results as tool messages.
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for _, tc := range resp.ToolCalls {
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var apiReq apiRequest
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if err := json.Unmarshal([]byte(tc.Function.Arguments), &apiReq); err != nil {
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logger.Errorf("Agent: invalid tool args for call %s: %v", tc.ID, err)
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turnMsgs = append(turnMsgs, mcp.Message{
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Role: "tool",
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ToolCallID: tc.ID,
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Content: fmt.Sprintf(`{"error":"invalid arguments: %s"}`, err.Error()),
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})
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continue
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}
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logger.Infof("Agent: iter=%d tool=%s %s %s", i+1, tc.ID, apiReq.Method, apiReq.Path)
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result := a.apiTool.execute(&apiReq)
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turnMsgs = append(turnMsgs, mcp.Message{
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Role: "tool",
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ToolCallID: tc.ID,
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Content: result,
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})
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}
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}
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// Safety: max iterations reached.
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logger.Warnf("Agent: max iterations (%d) reached for message: %q", maxIterations, userMessage)
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reply := "Operation completed. Please check your account for the latest status. / 操作已完成，请检查您的账户查看最新状态。"
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a.memory.Add("user", userMessage)
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a.memory.Add("assistant", reply)
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return reply
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}
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// ResetMemory clears conversation history (called on /start).
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func (a *Agent) ResetMemory() {
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a.memory.ResetFull()
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}
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