mirror of
https://github.com/NoFxAiOS/nofx.git
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3168a18c0d
- Add Telegram bot with long-polling and AI agent loop (api_call tool)
- SSE streaming with real-time message editing and ⏳ placeholder
- Account state injection at conversation start (models, exchanges,
strategies, traders, per-trader PnL and statistics)
- Lane semaphore per chat serializes concurrent messages (60s timeout)
- Idle timeout watchdog (60s) prevents hung streaming connections
- Look-ahead buffer prevents partial <api_call> tag leaking to user
- Fix PUT /strategies/:id to merge config (read-then-merge pattern)
- Add route registry with full API schema for LLM documentation
- Add TelegramConfig store and Web UI config modal
- Add GetAnyEnabled to AIModel store for bot LLM client selection
106 lines
2.8 KiB
Go
106 lines
2.8 KiB
Go
package session
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import (
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"fmt"
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"nofx/mcp"
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"strings"
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)
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const (
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compactionThresholdTokens = 3000
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charsPerToken = 3 // rough estimate for token counting
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)
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type Message struct {
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Role string // "user" or "assistant"
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Content string
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}
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// Memory manages conversation history with automatic compaction.
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// Inspired by openclaw's compaction pattern:
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// when ShortTerm exceeds threshold, LLM silently summarizes it into LongTerm.
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type Memory struct {
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LongTerm string // Durable summary (survives compaction, user never sees this happen)
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ShortTerm []Message // Recent conversation (cleared on compaction)
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llm mcp.AIClient
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}
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func NewMemory(llm mcp.AIClient) *Memory {
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return &Memory{llm: llm}
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}
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// Add appends a message and triggers compaction if threshold exceeded
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func (m *Memory) Add(role, content string) {
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m.ShortTerm = append(m.ShortTerm, Message{Role: role, Content: content})
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if m.estimateTokens() > compactionThresholdTokens {
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m.compact()
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}
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}
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// BuildContext returns context string for the agent's conversation history.
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func (m *Memory) BuildContext() string {
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var sb strings.Builder
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if m.LongTerm != "" {
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sb.WriteString("[Summary of earlier conversation]\n")
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sb.WriteString(m.LongTerm)
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sb.WriteString("\n\n")
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}
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if len(m.ShortTerm) > 0 {
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sb.WriteString("[Recent conversation]\n")
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for _, msg := range m.ShortTerm {
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sb.WriteString(fmt.Sprintf("%s: %s\n", msg.Role, msg.Content))
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}
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}
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return sb.String()
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}
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// Reset clears short-term history (LongTerm preserved intentionally)
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func (m *Memory) Reset() {
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m.ShortTerm = []Message{}
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}
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// ResetFull clears everything including long-term memory
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func (m *Memory) ResetFull() {
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m.ShortTerm = []Message{}
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m.LongTerm = ""
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}
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func (m *Memory) estimateTokens() int {
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total := len(m.LongTerm)
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for _, msg := range m.ShortTerm {
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total += len(msg.Content)
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}
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return total / charsPerToken
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}
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// compact summarizes short-term history into long-term memory.
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// This runs silently - the user never sees it happen.
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// If LLM call fails, short-term is preserved as-is (no data loss).
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func (m *Memory) compact() {
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if m.llm == nil || len(m.ShortTerm) == 0 {
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return
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}
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history := m.BuildContext()
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systemPrompt := `You are a conversation summarizer. Compress the following trading assistant conversation into a concise summary.
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Must preserve:
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- What the user is configuring (strategy/exchange/model/trader)
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- Confirmed parameters (trading pairs, leverage, stop loss, indicators, etc.)
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- Pending or missing parameters
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- User preferences and requirements
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Output: plain text summary, under 200 words.`
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summary, err := m.llm.CallWithMessages(systemPrompt, history)
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if err != nil {
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// Compaction failed: keep short-term as-is, never lose user data
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return
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}
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if m.LongTerm != "" {
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m.LongTerm = m.LongTerm + "\n" + summary
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} else {
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m.LongTerm = summary
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}
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m.ShortTerm = []Message{}
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}
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