Class: Llmemory::Memory
- Inherits:
-
Object
- Object
- Llmemory::Memory
- Defined in:
- lib/llmemory/memory.rb
Constant Summary collapse
- DEFAULT_SESSION_ID =
"default"- STATE_KEY_MESSAGES =
:messages
Instance Method Summary collapse
- #add_message(role:, content:) ⇒ Object
- #check_context_window! ⇒ Object
- #clear_session! ⇒ Object
- #compact!(max_bytes: nil) ⇒ Object
- #consolidate! ⇒ Object
- #context_tokens ⇒ Object
-
#episodic ⇒ Object
Episodic long-term memory (CoALA): records and retrieves agent trajectories.
-
#initialize(user_id:, session_id: DEFAULT_SESSION_ID, checkpoint: nil, long_term: nil, long_term_type: nil, retrieval_engine: nil, working_memory: nil, episodic: nil, procedural: nil, api_key: nil, encryption_key: :inherit) ⇒ Memory
constructor
A new instance of Memory.
- #last_user_message ⇒ Object
- #llm_usage ⇒ Object
-
#maintain!(**opts) ⇒ Object
Cognitive maintenance pass: consolidate -> reflect -> mine skills -> expire, in one step, closing the CoALA learning loop.
- #maybe_flush_memory! ⇒ Object
- #messages ⇒ Object
-
#mine_skills!(window: SkillMining::Miner::DEFAULT_WINDOW, outcomes: nil, auto_register: false) ⇒ Object
Mines recent episodes for reusable skills (Voyager-style).
-
#procedural ⇒ Object
Procedural long-term memory (Voyager-style skill library).
- #prune!(mode: nil) ⇒ Object
-
#reason(template:, into: Actions::Reason::DEFAULT_SLOT, parse: nil) ⇒ Object
Reasoning action: render a prompt from working memory, call the LLM, write the result back.
- #recall_for(query: nil, max_tokens: nil) ⇒ Object
-
#reflect!(window: 10, category: "insights") ⇒ Object
Reflects over recent episodes and writes distilled insights to the semantic store (file/graph) with provenance back to source episodes.
- #retrieve(query, max_tokens: nil) ⇒ Object
- #should_auto_consolidate? ⇒ Boolean
- #should_compact? ⇒ Boolean
- #user_id ⇒ Object
- #with_overflow_recovery(max_retries: 2, &block) ⇒ Object
-
#working_memory ⇒ Object
Structured working memory for this session (CoALA working memory), parallel to the message checkpoint.
Constructor Details
#initialize(user_id:, session_id: DEFAULT_SESSION_ID, checkpoint: nil, long_term: nil, long_term_type: nil, retrieval_engine: nil, working_memory: nil, episodic: nil, procedural: nil, api_key: nil, encryption_key: :inherit) ⇒ Memory
Returns a new instance of Memory.
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# File 'lib/llmemory/memory.rb', line 13 def initialize(user_id:, session_id: DEFAULT_SESSION_ID, checkpoint: nil, long_term: nil, long_term_type: nil, retrieval_engine: nil, working_memory: nil, episodic: nil, procedural: nil, api_key: nil, encryption_key: :inherit) @user_id = user_id @session_id = session_id resolved_key = encryption_key == :inherit ? nil : encryption_key @cipher = Llmemory.build_cipher(resolved_key) if checkpoint @checkpoint = checkpoint @short_term_store = checkpoint.store else @short_term_store = build_short_term_store(@cipher) @checkpoint = ShortTerm::Checkpoint.new( user_id: user_id, session_id: session_id, store: @short_term_store, cipher: @cipher ) end @working_memory = working_memory @episodic = episodic @procedural = procedural @api_key = api_key unless api_key.to_s.empty? type = long_term_type || Llmemory.configuration.long_term_type || :file_based @long_term = long_term || build_long_term(type) @retrieval_engine = retrieval_engine || Retrieval::Engine.new( @long_term, llm: tracked_llm_client, feedback: Retrieval::FeedbackStore.new(store: @short_term_store) ) end |
Instance Method Details
#add_message(role:, content:) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 105 def (role:, content:) msgs = msgs << { role: role.to_sym, content: content.to_s } save_state(messages: msgs, **preserved_flush_state) true end |
#check_context_window! ⇒ Object
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# File 'lib/llmemory/memory.rb', line 233 def check_context_window! return false if .empty? flushed = false if should_auto_consolidate? && Llmemory.configuration.memory_flush_enabled consolidate! flushed = true end compacted = false if should_compact? compacted = compact! end flushed || compacted end |
#clear_session! ⇒ Object
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# File 'lib/llmemory/memory.rb', line 165 def clear_session! @checkpoint.clear_state true end |
#compact!(max_bytes: nil) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 170 def compact!(max_bytes: nil) max = max_bytes || Llmemory.configuration.compact_max_bytes msgs = current_bytes = (msgs) return false if current_bytes <= max flushed = flush_memory_before_compaction!(msgs) old_msgs, recent_msgs = (msgs, max) return false if old_msgs.empty? summary = (old_msgs) compacted = [{ role: :system, content: summary }] + recent_msgs state = restore_state_for_save flush_ts = flushed ? Time.now : (state[:last_flush_at] || state["last_flush_at"]) save_state(messages: compacted, last_compact_at: Time.now, last_flush_at: flush_ts) true end |
#consolidate! ⇒ Object
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# File 'lib/llmemory/memory.rb', line 157 def consolidate! msgs = return true if msgs.empty? conversation_text = msgs.map { |m| (m) }.join("\n") @long_term.memorize(conversation_text) true end |
#context_tokens ⇒ Object
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# File 'lib/llmemory/memory.rb', line 198 def context_tokens estimated_tokens() end |
#episodic ⇒ Object
Episodic long-term memory (CoALA): records and retrieves agent trajectories. Additive — coexists with the semantic store (file/graph). Lazily built.
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# File 'lib/llmemory/memory.rb', line 55 def episodic @episodic ||= LongTerm::Episodic::Memory.new( user_id: @user_id, storage: LongTerm::Episodic::Storages.build(cipher: @cipher), cipher: @cipher ) end |
#last_user_message ⇒ Object
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# File 'lib/llmemory/memory.rb', line 136 def msgs = idx = msgs.rindex { |m| (m[:role] || m["role"]).to_s == "user" } idx ? (msgs[idx][:content] || msgs[idx]["content"]).to_s : "" end |
#llm_usage ⇒ Object
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# File 'lib/llmemory/memory.rb', line 254 def llm_usage Llmemory::LLM::UsageLedger.new(store: @short_term_store).totals(@user_id) end |
#maintain!(**opts) ⇒ Object
Cognitive maintenance pass: consolidate -> reflect -> mine skills -> expire, in one step, closing the CoALA learning loop. Each step is isolated; a failure in one is captured in the report and never aborts the others.
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# File 'lib/llmemory/memory.rb', line 97 def maintain!(**opts) Maintenance::CognitivePass.run!( @user_id, memory: self, episodic: episodic, procedural: procedural, semantic: @long_term, llm: tracked_llm_client, **opts ) end |
#maybe_flush_memory! ⇒ Object
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# File 'lib/llmemory/memory.rb', line 189 def maybe_flush_memory! return false unless Llmemory.configuration.memory_flush_enabled msgs = return false if msgs.empty? return false if estimated_tokens(msgs) < Llmemory.configuration.memory_flush_threshold_tokens consolidate! end |
#messages ⇒ Object
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# File 'lib/llmemory/memory.rb', line 112 def state = @checkpoint.restore_state return [] unless state.is_a?(Hash) list = state[STATE_KEY_MESSAGES] || state[STATE_KEY_MESSAGES.to_s] list = list.is_a?(Array) ? list.dup : [] (list) end |
#mine_skills!(window: SkillMining::Miner::DEFAULT_WINDOW, outcomes: nil, auto_register: false) ⇒ Object
Mines recent episodes for reusable skills (Voyager-style). Human-in-the-loop
by default: returns skill proposals and writes nothing. With
auto_register: true, registers them in procedural memory (with provenance
back to the source episodes) and returns the new skill ids.
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# File 'lib/llmemory/memory.rb', line 89 def mine_skills!(window: SkillMining::Miner::DEFAULT_WINDOW, outcomes: nil, auto_register: false) SkillMining::Miner.new(episodic: episodic, procedural: procedural, llm: tracked_llm_client) .mine(window: window, outcomes: outcomes, auto_register: auto_register) end |
#procedural ⇒ Object
Procedural long-term memory (Voyager-style skill library). Lazily built.
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# File 'lib/llmemory/memory.rb', line 64 def procedural @procedural ||= LongTerm::Procedural::Memory.new( user_id: @user_id, storage: LongTerm::Procedural::Storages.build(cipher: @cipher), cipher: @cipher ) end |
#prune!(mode: nil) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 142 def prune!(mode: nil) return false unless Llmemory.configuration.prune_tool_results_enabled msgs = return false if msgs.empty? mode ||= Llmemory.configuration.prune_tool_results_mode pruner = ShortTerm::Pruner.new( soft_trim_max_bytes: Llmemory.configuration.prune_tool_results_max_bytes ) pruned = pruner.prune!(msgs, mode: mode) save_state(messages: pruned, **preserved_flush_state) true end |
#reason(template:, into: Actions::Reason::DEFAULT_SLOT, parse: nil) ⇒ Object
Reasoning action: render a prompt from working memory, call the LLM, write the result back. Composable; does not touch long-term memory.
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# File 'lib/llmemory/memory.rb', line 81 def reason(template:, into: Actions::Reason::DEFAULT_SLOT, parse: nil) Actions::Reason.call(working_memory: working_memory, template: template, into: into, parse: parse, llm: tracked_llm_client) end |
#recall_for(query: nil, max_tokens: nil) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 127 def recall_for(query: nil, max_tokens: nil) return "" unless Llmemory.configuration.auto_recall_enabled effective_query = query || return "" if effective_query.to_s.strip.empty? retrieve(effective_query, max_tokens: max_tokens) end |
#reflect!(window: 10, category: "insights") ⇒ Object
Reflects over recent episodes and writes distilled insights to the semantic store (file/graph) with provenance back to source episodes.
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# File 'lib/llmemory/memory.rb', line 74 def reflect!(window: 10, category: "insights") Reflection::Reflector.new(episodic: episodic, semantic: @long_term, llm: tracked_llm_client) .reflect(window: window, category: category) end |
#retrieve(query, max_tokens: nil) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 120 def retrieve(query, max_tokens: nil) msgs = short_context = format_short_term_context(msgs) long_context = @retrieval_engine.retrieve_for_inference(query, user_id: @user_id, max_tokens: max_tokens) combine_contexts(short_context, long_context) end |
#should_auto_consolidate? ⇒ Boolean
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# File 'lib/llmemory/memory.rb', line 202 def should_auto_consolidate? ctx = context_tokens threshold = Llmemory.configuration.context_window_tokens - Llmemory.configuration.reserve_tokens ctx >= threshold end |
#should_compact? ⇒ Boolean
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# File 'lib/llmemory/memory.rb', line 208 def should_compact? ctx = context_tokens threshold = Llmemory.configuration.context_window_tokens - Llmemory.configuration.reserve_tokens ctx >= threshold end |
#user_id ⇒ Object
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# File 'lib/llmemory/memory.rb', line 250 def user_id @user_id end |
#with_overflow_recovery(max_retries: 2, &block) ⇒ Object
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# File 'lib/llmemory/memory.rb', line 214 def with_overflow_recovery(max_retries: 2, &block) return yield unless Llmemory.configuration.overflow_recovery_enabled return yield unless block_given? retries = 0 begin yield rescue Llmemory::LLMError => e msg = e..to_s.downcase overflow = msg.include?("context") || msg.include?("token") || msg.include?("overflow") || msg.include?("limit") raise unless overflow && retries < max_retries prune! if Llmemory.configuration.prune_tool_results_enabled compact! retries += 1 retry end end |
#working_memory ⇒ Object
Structured working memory for this session (CoALA working memory), parallel to the message checkpoint. Lazily built.
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# File 'lib/llmemory/memory.rb', line 45 def working_memory @working_memory ||= WorkingMemory.new( user_id: @user_id, session_id: @session_id, store: build_short_term_store(@cipher) ) end |