Files
micro--go-micro/agent/memory.go
T
wehub-resource-sync e071084ebe
govulncheck / govulncheck (push) Waiting to run
Harness (E2E) / Harnesses (mock LLM) (push) Waiting to run
Harness (E2E) / Provider harnesses (live LLM conformance) (push) Waiting to run
Lint / golangci-lint (push) Waiting to run
Run Tests / Unit Tests (push) Waiting to run
Run Tests / Etcd Integration Tests (push) Waiting to run
chore: import upstream snapshot with attribution
2026-07-13 12:40:33 +08:00

373 lines
10 KiB
Go

package agent
import (
"encoding/json"
"fmt"
"sort"
"strings"
"sync"
"go-micro.dev/v6/ai"
"go-micro.dev/v6/store"
)
// Memory is an agent's conversation memory. Like the rest of the
// framework it is pluggable: the default is store-backed and durable
// across restarts, but any implementation can be supplied with
// WithMemory — in-process, a database, or a semantic/vector store.
type Memory interface {
// Add appends a message to the conversation.
Add(role, content string)
// Messages returns the retained conversation, oldest first.
Messages() []ai.Message
// Clear resets the conversation.
Clear()
}
// MemorySummaryFunc turns older conversation messages into a compact
// replacement message for active context. It is called while the default
// memory is locked, so implementations should be deterministic and avoid
// calling back into the same memory instance.
type MemorySummaryFunc func([]ai.Message) ai.Message
// MemoryCompaction configures deterministic, store-backed context compaction
// for the default memory implementation. When the retained conversation grows
// past MaxMessages, older turns are collapsed into a summary message while the
// newest KeepRecent turns stay verbatim for provider-neutral continuity.
type MemoryCompaction struct {
MaxMessages int
KeepRecent int
Summarize MemorySummaryFunc
}
// MemoryRecall is implemented by memory backends that can retrieve durable
// prior context relevant to a new turn without replaying every stored message.
type MemoryRecall interface {
Recall(query string, limit int) []ai.Message
}
// MemorySummary is implemented by memory backends that expose their current
// compacted summary for inspection. It lets long-running agents make memory
// compaction observable without coupling callers to a concrete store.
type MemorySummary interface {
Summary() string
}
// Summary returns the current compacted-memory summary for m, when supported.
// It returns an empty string for memory backends that have not compacted or do
// not expose an inspectable summary.
func Summary(m Memory) string {
if m == nil {
return ""
}
summarizer, ok := m.(MemorySummary)
if !ok {
return ""
}
return summarizer.Summary()
}
// NewMemory returns the default store-backed memory: an in-process
// conversation buffer (truncated to limit) that persists to the store
// under key, so an agent picks up where it left off after a restart.
// A nil store or empty key yields non-persistent memory.
func NewMemory(s store.Store, key string, limit int) Memory {
m := &storeMemory{store: s, key: key, hist: ai.NewHistory(limit)}
m.load()
return m
}
// NewRetrievalMemory returns store-backed memory that keeps a bounded active
// conversation and archives every turn for retrieval. It is useful when callers
// want relevant durable recall without summary compaction in the active context.
// A nil store or empty key keeps only the active in-process buffer.
func NewRetrievalMemory(s store.Store, key string, activeLimit int) Memory {
m := &storeMemory{store: s, key: key, hist: ai.NewHistory(activeLimit), retrieveAll: true}
m.load()
return m
}
// NewCompactingMemory returns store-backed memory with explicit compaction and
// retrieval controls. It keeps all messages in the backing store, compacts older
// turns into a deterministic summary when the conversation exceeds maxMessages,
// and lets callers recall relevant prior turns with Recall.
func NewCompactingMemory(s store.Store, key string, maxMessages, keepRecent int) Memory {
return NewCompactingMemoryWithOptions(s, key, MemoryCompaction{MaxMessages: maxMessages, KeepRecent: keepRecent})
}
// NewCompactingMemoryWithOptions returns store-backed memory configured with
// explicit compaction options, including an optional summarization hook.
func NewCompactingMemoryWithOptions(s store.Store, key string, compaction MemoryCompaction) Memory {
maxMessages := compaction.MaxMessages
keepRecent := compaction.KeepRecent
if keepRecent <= 0 {
keepRecent = maxMessages / 2
}
if keepRecent < 1 {
keepRecent = 1
}
m := &storeMemory{
store: s,
key: key,
// Use an unlimited buffer here; compaction, not truncation, decides
// what remains in active context so a summary can preserve older turns.
hist: ai.NewHistory(0),
compaction: MemoryCompaction{
MaxMessages: maxMessages,
KeepRecent: keepRecent,
Summarize: compaction.Summarize,
},
}
m.load()
m.compact()
return m
}
// NewInMemory returns conversation memory that is not persisted.
func NewInMemory(limit int) Memory {
return &storeMemory{hist: ai.NewHistory(limit)}
}
// storeMemory is the default Memory: an ai.History buffer optionally
// persisted to a store.
type storeMemory struct {
mu sync.Mutex
store store.Store
key string
hist *ai.History
compaction MemoryCompaction
archive []ai.Message
summary string
retrieveAll bool
}
func (m *storeMemory) Add(role, content string) {
m.mu.Lock()
if m.retrieveAll {
m.archive = append(m.archive, ai.Message{Role: role, Content: content})
}
m.hist.Add(role, content)
m.mu.Unlock()
m.compact()
m.save()
}
func (m *storeMemory) Messages() []ai.Message {
m.mu.Lock()
defer m.mu.Unlock()
return m.hist.Messages()
}
func (m *storeMemory) Clear() {
m.mu.Lock()
m.hist.Reset()
m.archive = nil
m.summary = ""
m.mu.Unlock()
m.save()
}
// Summary returns the latest compacted summary text, if this memory has
// compacted older turns. The returned value is safe to show in debug UIs or
// checkpoints because it is exactly the summary retained in active context.
func (m *storeMemory) Summary() string {
m.mu.Lock()
defer m.mu.Unlock()
return m.summary
}
// Recall returns archived messages whose content contains words from query.
// It is deterministic and provider-neutral: no embeddings or model calls are
// required, but semantic/vector stores can replace Memory for richer retrieval.
// When created with NewRetrievalMemory the archive contains every persisted
// turn; when created with NewCompactingMemory it contains compacted older turns.
func (m *storeMemory) Recall(query string, limit int) []ai.Message {
m.mu.Lock()
defer m.mu.Unlock()
if limit <= 0 {
limit = 5
}
terms := recallTerms(query)
type match struct {
msg ai.Message
score int
index int
}
matches := make([]match, 0, len(m.archive))
for i := len(m.archive) - 1; i >= 0; i-- {
msg := m.archive[i]
if score := recallScore(msg, terms); score > 0 {
matches = append(matches, match{msg: msg, score: score, index: i})
}
}
sort.SliceStable(matches, func(i, j int) bool {
if matches[i].score != matches[j].score {
return matches[i].score > matches[j].score
}
return matches[i].index > matches[j].index
})
if len(matches) > limit {
matches = matches[:limit]
}
out := make([]ai.Message, 0, len(matches))
for _, match := range matches {
out = append(out, match.msg)
}
return out
}
func (m *storeMemory) load() {
if m.store == nil || m.key == "" {
return
}
recs, err := m.store.Read(m.key)
if err != nil || len(recs) == 0 {
return
}
var state memoryState
if err := json.Unmarshal(recs[0].Value, &state); err != nil {
var msgs []ai.Message
if err := json.Unmarshal(recs[0].Value, &msgs); err != nil {
return
}
state.Messages = msgs
}
m.mu.Lock()
m.archive = state.Archive
m.summary = state.Summary
if m.retrieveAll && len(m.archive) == 0 {
m.archive = append(m.archive, state.Messages...)
}
for _, msg := range state.Messages {
m.hist.Add(msg.Role, msg.Content)
}
if m.summary == "" {
m.summary = currentMemorySummary(state.Messages)
}
m.mu.Unlock()
}
func (m *storeMemory) save() {
if m.store == nil || m.key == "" {
return
}
m.mu.Lock()
data, err := json.Marshal(memoryState{
Messages: m.hist.Messages(),
Archive: m.archive,
Summary: m.summary,
})
m.mu.Unlock()
if err != nil {
return
}
_ = m.store.Write(&store.Record{Key: m.key, Value: data})
}
func (m *storeMemory) compact() {
if m.compaction.MaxMessages <= 0 {
return
}
m.mu.Lock()
defer m.mu.Unlock()
msgs := m.hist.Messages()
if len(msgs) <= m.compaction.MaxMessages {
return
}
keep := m.compaction.KeepRecent
if keep <= 0 || keep >= m.compaction.MaxMessages {
keep = m.compaction.MaxMessages - 1
}
if keep < 1 {
keep = 1
}
cut := len(msgs) - keep
older := msgs[:cut]
recent := msgs[cut:]
m.archive = append(m.archive, older...)
summarize := m.compaction.Summarize
if summarize == nil {
summarize = defaultMemorySummary
}
summary := summarize(older)
if summary.Role == "" {
summary.Role = "system"
}
m.summary = fmt.Sprint(summary.Content)
m.hist.Reset()
m.hist.Add(summary.Role, summary.Content)
for _, msg := range recent {
m.hist.Add(msg.Role, msg.Content)
}
}
func currentMemorySummary(msgs []ai.Message) string {
for _, msg := range msgs {
if msg.Role != "system" {
continue
}
text := fmt.Sprint(msg.Content)
if strings.HasPrefix(text, "Conversation memory summary:") {
return text
}
}
return ""
}
func defaultMemorySummary(msgs []ai.Message) ai.Message {
return ai.Message{
Role: "system",
Content: fmt.Sprintf("Conversation memory summary: %s", summarizeMessages(msgs)),
}
}
func summarizeMessages(msgs []ai.Message) string {
var b strings.Builder
for i, msg := range msgs {
if i > 0 {
b.WriteString(" | ")
}
fmt.Fprintf(&b, "%s: %s", msg.Role, compactText(fmt.Sprint(msg.Content), 120))
}
return b.String()
}
func compactText(s string, max int) string {
s = strings.Join(strings.Fields(s), " ")
if max > 0 && len(s) > max {
return s[:max] + "…"
}
return s
}
func recallScore(msg ai.Message, terms []string) int {
text := strings.ToLower(fmt.Sprint(msg.Content))
score := 0
for _, term := range terms {
if strings.Contains(text, term) {
score++
}
}
return score
}
func recallTerms(query string) []string {
seen := map[string]bool{}
var terms []string
for _, term := range strings.Fields(strings.ToLower(query)) {
term = strings.Trim(term, ".,!?;:\"'()[]{}")
if len(term) < 3 || seen[term] {
continue
}
seen[term] = true
terms = append(terms, term)
}
return terms
}
type memoryState struct {
Messages []ai.Message `json:"messages"`
Archive []ai.Message `json:"archive,omitempty"`
Summary string `json:"summary,omitempty"`
}