Add long term memory

agent/actions.go

@@ -68,6 +68,7 @@ func (a *Agent) decision(
 		Tools:      tools,
 		ToolChoice: toolchoice,
 	}
+
 	resp, err := a.client.CreateChatCompletion(ctx, decision)
 	if err != nil {
 		return nil, err
@@ -96,6 +97,14 @@ func (m Messages) ToOpenAI() []openai.ChatCompletionMessage {
 	return []openai.ChatCompletionMessage(m)
 }
 
+func (m Messages) String() string {
+	s := ""
+	for _, cc := range m {
+		s += cc.Role + ": " + cc.Content + "\n"
+	}
+	return s
+}
+
 func (m Messages) Exist(content string) bool {
 	for _, cc := range m {
 		if cc.Content == content {

agent/agent.go

@@ -151,21 +151,59 @@ func (a *Agent) SetConversation(conv []openai.ChatCompletionMessage) {
 	a.currentConversation = conv
 }
 
+func (a *Agent) askLLM(ctx context.Context, conversation []openai.ChatCompletionMessage) (openai.ChatCompletionMessage, error) {
+	resp, err := a.client.CreateChatCompletion(ctx,
+		openai.ChatCompletionRequest{
+			Model:    a.options.LLMAPI.Model,
+			Messages: conversation,
+		},
+	)
+	if err != nil {
+		return openai.ChatCompletionMessage{}, err
+	}
+
+	if len(resp.Choices) != 1 {
+		return openai.ChatCompletionMessage{}, fmt.Errorf("no enough choices: %w", err)
+	}
+
+	return resp.Choices[0].Message, nil
+}
+
 func (a *Agent) ResetConversation() {
 	a.Lock()
 	defer a.Unlock()
 
+	xlog.Info("Resetting conversation", "agent", a.Character.Name)
+
 	// store into memory the conversation before pruning it
 	// TODO: Shall we summarize the conversation into a bullet list of highlights
 	// using the LLM instead?
-	if a.options.enableKB {
-		for _, message := range a.currentConversation {
-			if message.Role == "user" {
-				if err := a.options.ragdb.Store(message.Content); err != nil {
-					xlog.Error("Error storing into memory", "error", err)
+	if a.options.enableLongTermMemory {
+		xlog.Info("Saving conversation", "agent", a.Character.Name, "conversation size", len(a.currentConversation))
+
+		if a.options.enableSummaryMemory {
+
+			msg, err := a.askLLM(a.context.Context, []openai.ChatCompletionMessage{{
+				Role:    "user",
+				Content: "Summarize the conversation below, keep the highlights as a bullet list:\n" + Messages(a.currentConversation).String(),
+			}})
+			if err != nil {
+				xlog.Error("Error summarizing conversation", "error", err)
+			}
+
+			if err := a.options.ragdb.Store(msg.Content); err != nil {
+				xlog.Error("Error storing into memory", "error", err)
+			}
+		} else {
+			for _, message := range a.currentConversation {
+				if message.Role == "user" {
+					if err := a.options.ragdb.Store(message.Content); err != nil {
+						xlog.Error("Error storing into memory", "error", err)
+					}
 				}
 			}
 		}
+
 	}
 
 	a.currentConversation = []openai.ChatCompletionMessage{}
@@ -612,34 +650,15 @@ func (a *Agent) consumeJob(job *Job, role string) {
 		return
 	}
 
-	resp, err := a.client.CreateChatCompletion(ctx,
-		openai.ChatCompletionRequest{
-			Model: a.options.LLMAPI.Model,
-			// Force the AI to response without using any tool
-			// Why: some models might be silly enough to attempt to call tools even if evaluated
-			// that a reply was not necessary anymore
-			Messages: append(a.currentConversation, openai.ChatCompletionMessage{
-				Role:    "system",
-				Content: "The assistant needs to reply without using any tool.",
-				// + replyResponse.Message,
-			},
-			),
-			//Messages: a.currentConversation,
-		},
-	)
+	msg, err := a.askLLM(ctx, append(a.currentConversation, openai.ChatCompletionMessage{
+		Role:    "system",
+		Content: "The assistant needs to reply without using any tool.",
+	}))
 	if err != nil {
 		job.Result.Finish(err)
 		return
 	}
 
-	if len(resp.Choices) != 1 {
-		job.Result.Finish(fmt.Errorf("no enough choices: %w", err))
-		return
-	}
-
-	// display OpenAI's response to the original question utilizing our function
-	msg := resp.Choices[0].Message
-
 	// If we didn't got any message, we can use the response from the action
 	if chosenAction.Definition().Name.Is(action.ReplyActionName) && msg.Content == "" ||
 		strings.Contains(msg.Content, "<tool_call>") {
@@ -661,9 +680,6 @@ func (a *Agent) periodicallyRun(timer *time.Timer) {
 	// Remember always to reset the timer - if we don't the agent will stop..
 	defer timer.Reset(a.options.periodicRuns)
 
-	if !a.options.standaloneJob {
-		return
-	}
 	a.StopAction()
 	xlog.Debug("Agent is running periodically", "agent", a.Character.Name)
 
@@ -675,18 +691,24 @@ func (a *Agent) periodicallyRun(timer *time.Timer) {
 
 	xlog.Info("START -- Periodically run is starting")
 
-	if len(a.CurrentConversation()) != 0 {
-		// Here the LLM could decide to store some part of the conversation too in the memory
-		evaluateMemory := NewJob(
-			WithText(
-				`Evaluate the current conversation and decide if we need to store some relevant informations from it`,
-			),
-			WithReasoningCallback(a.options.reasoningCallback),
-			WithResultCallback(a.options.resultCallback),
-		)
-		a.consumeJob(evaluateMemory, SystemRole)
+	// if len(a.CurrentConversation()) != 0 {
+	// 	// Here the LLM could decide to store some part of the conversation too in the memory
+	// 	evaluateMemory := NewJob(
+	// 		WithText(
+	// 			`Evaluate the current conversation and decide if we need to store some relevant informations from it`,
+	// 		),
+	// 		WithReasoningCallback(a.options.reasoningCallback),
+	// 		WithResultCallback(a.options.resultCallback),
+	// 	)
+	// 	a.consumeJob(evaluateMemory, SystemRole)
 
+	// 	a.ResetConversation()
+	// }
+
+	if !a.options.standaloneJob {
 		a.ResetConversation()
+
+		return
 	}
 
 	// Here we go in a loop of

agent/options.go

@@ -14,12 +14,12 @@ type llmOptions struct {
 }
 
 type options struct {
-	LLMAPI                                            llmOptions
-	character                                         Character
-	randomIdentityGuidance                            string
-	randomIdentity                                    bool
-	userActions                                       Actions
-	enableHUD, standaloneJob, showCharacter, enableKB bool
+	LLMAPI                                                                                       llmOptions
+	character                                                                                    Character
+	randomIdentityGuidance                                                                       string
+	randomIdentity                                                                               bool
+	userActions                                                                                  Actions
+	enableHUD, standaloneJob, showCharacter, enableKB, enableSummaryMemory, enableLongTermMemory bool
 
 	canStopItself         bool
 	initiateConversations bool
@@ -127,6 +127,16 @@ var EnablePersonality = func(o *options) error {
 	return nil
 }
 
+var EnableSummaryMemory = func(o *options) error {
+	o.enableSummaryMemory = true
+	return nil
+}
+
+var EnableLongTermMemory = func(o *options) error {
+	o.enableLongTermMemory = true
+	return nil
+}
+
 func WithRAGDB(db RAGDB) Option {
 	return func(o *options) error {
 		o.ragdb = db

example/webui/agentconfig.go

@@ -28,4 +28,6 @@ type AgentConfig struct {
 	KnowledgeBaseResults  int    `json:"kb_results" form:"kb_results"`
 	CanStopItself         bool   `json:"can_stop_itself" form:"can_stop_itself"`
 	SystemPrompt          string `json:"system_prompt" form:"system_prompt"`
+	LongTermMemory        bool   `json:"long_term_memory" form:"long_term_memory"`
+	SummaryLongTermMemory bool   `json:"summary_long_term_memory" form:"summary_long_term_memory"`
 }

example/webui/agentpool.go

@@ -248,6 +248,15 @@ func (a *AgentPool) startAgentWithConfig(name string, config *AgentConfig) error
 	if config.StandaloneJob {
 		opts = append(opts, EnableStandaloneJob)
 	}
+
+	if config.LongTermMemory {
+		opts = append(opts, EnableLongTermMemory)
+	}
+
+	if config.SummaryLongTermMemory {
+		opts = append(opts, EnableSummaryMemory)
+	}
+
 	if config.CanStopItself {
 		opts = append(opts, CanStopItself)
 	}

example/webui/views/create.html

@@ -104,6 +104,11 @@
 
             <label for="random_identity" class="block text-lg font-medium text-gray-400">Random Identity</label>
             <input type="checkbox" name="random_identity" id="random_identity" class="mt-1 focus:ring-indigo-500 focus:border-indigo-500 block w-full shadow-sm sm:text-lg border-gray-300 rounded-md bg-gray-700 text-white">
+
+            <label for="long_term_memory" class="block text-lg font-medium text-gray-400">Long term memory</label>
+            <input type="checkbox" name="long_term_memory" id="long_term_memory" class="mt-1 focus:ring-indigo-500 focus:border-indigo-500 block w-full shadow-sm sm:text-lg border-gray-300 rounded-md bg-gray-700 text-white">
+            <label for="summary_long_term_memory" class="block text-lg font-medium text-gray-400">Long term memory (summarize!)</label>
+            <input type="checkbox" name="summary_long_term_memory" id="summary_long_term_memory" class="mt-1 focus:ring-indigo-500 focus:border-indigo-500 block w-full shadow-sm sm:text-lg border-gray-300 rounded-md bg-gray-700 text-white">
         </div>
         <div class="mb-4">
             <label for="identity_guidance" class="block text-lg font-medium text-gray-400">Identity Guidance</label>