diff --git a/evals/README.md b/evals/README.md new file mode 100644 index 000000000..845a9e0cc --- /dev/null +++ b/evals/README.md @@ -0,0 +1,32 @@ +# Evaluation of MIRIX on public benchmarks + + +1. Step 1: +Install uv with `brew install uv`, then run: +``` +uv venv +source .venv/bin/activate +python -m ensurepip --upgrade +python -m pip install -r requirements.txt +``` + +2. Step 2: +Start the backend: +In the `MIRIX` folder, run: +``` +uv run python scripts/start_server.py +``` + +3. Step 3: +In another terminal tab, run: +``` +uv run python main_eval.py --limit 1 --run-llm --mirix_config_path ./configs/0201c.yaml --output_path results/0201c +``` + +4. Step 4: +Evaluation. Run: +``` +uv run organize_results.py results/0201c +``` + +Then there would be `metrics.json` in `results/0201c` where you can see all the metrics. diff --git a/evals/clear_records.py b/evals/clear_records.py new file mode 100644 index 000000000..71fa95418 --- /dev/null +++ b/evals/clear_records.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python3 +""" +Script to remove 'records' key from JSON files in a specified folder. +If metrics.json exists, only removes records marked as WRONG. +If metrics.json doesn't exist, removes all records. + +Usage: + python clear_records.py + +Example: + python clear_records.py results/0201a +""" + +import json +import sys +from pathlib import Path +from typing import Optional, Set, Tuple + + +def load_wrong_records(metrics_path: Path) -> Set[Tuple[str, int]]: + """ + Load metrics.json and extract sample_id and question_index for WRONG records. + + Args: + metrics_path: Path to metrics.json file + + Returns: + Set of (sample_id, question_index) tuples for wrong records + """ + try: + with open(metrics_path, 'r', encoding='utf-8') as f: + metrics = json.load(f) + + wrong_records = set() + llm_judge_results = metrics.get('llm_judge_results', []) + + for result in llm_judge_results: + if result.get('label') == 'WRONG' or result.get('score') == 0: + sample_id = result.get('sample_id') + question_index = result.get('question_index') + if sample_id and question_index: + wrong_records.add((sample_id, question_index)) + + return wrong_records + except Exception as e: + print(f" ✗ Error loading metrics.json: {e}") + return set() + + +def clear_records_from_file(file_path: Path, wrong_records: Optional[Set[Tuple[str, int]]] = None) -> bool: + """ + Remove 'records' key from a JSON file if it exists. + If wrong_records is provided, only removes those specific records. + If wrong_records is None, removes all records. + + Args: + file_path: Path to the JSON file + wrong_records: Optional set of (sample_id, question_index) tuples to remove + + Returns: + True if the file was modified, False otherwise + """ + try: + # Read the JSON file + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + # Check if 'records' key exists + if 'records' in data: + sample_id = data.get('sample_id') + + if wrong_records is None: + # Remove all records (original behavior) + print(f" Found 'records' key in {file_path.name}") + del data['records'] + print(f" ✓ Removed all records from {file_path.name}") + modified = True + else: + # Remove only wrong records + original_count = len(data['records']) + records_to_keep = {} + removed_count = 0 + + for key, record in data['records'].items(): + question_index = record.get('question_index') + if (sample_id, question_index) not in wrong_records: + records_to_keep[key] = record + else: + removed_count += 1 + + if removed_count > 0: + data['records'] = records_to_keep + print(f" ✓ Removed {removed_count}/{original_count} wrong records from {file_path.name}") + modified = True + else: + print(f" No wrong records found in {file_path.name}") + modified = False + + # Write back to file if modified + if modified: + with open(file_path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + + return modified + else: + print(f" No 'records' key in {file_path.name}") + return False + + except json.JSONDecodeError as e: + print(f" ✗ Error decoding JSON in {file_path.name}: {e}") + return False + except Exception as e: + print(f" ✗ Error processing {file_path.name}: {e}") + return False + + +def clear_records_from_folder(folder_path: str) -> None: + """ + Process all JSON files in a folder and remove 'records' key. + If metrics.json exists, only removes records marked as WRONG. + If metrics.json doesn't exist, removes all records. + + Args: + folder_path: Path to the folder containing JSON files + """ + folder = Path(folder_path) + + if not folder.exists(): + print(f"Error: Folder '{folder_path}' does not exist") + sys.exit(1) + + if not folder.is_dir(): + print(f"Error: '{folder_path}' is not a directory") + sys.exit(1) + + # Check for metrics.json + metrics_path = folder / 'metrics.json' + wrong_records = None + + if metrics_path.exists(): + print(f"Found metrics.json - will only remove WRONG records\n") + wrong_records = load_wrong_records(metrics_path) + if wrong_records: + print(f"Identified {len(wrong_records)} wrong record(s) to remove\n") + else: + print("No wrong records found in metrics.json\n") + else: + print(f"No metrics.json found - will remove all records\n") + + # Find all JSON files in the folder (excluding metrics.json) + json_files = [f for f in folder.glob('*.json') if f.name != 'metrics.json'] + + if not json_files: + print(f"No JSON files found in '{folder_path}'") + return + + print(f"Processing {len(json_files)} JSON file(s) in '{folder_path}'...\n") + + modified_count = 0 + for json_file in sorted(json_files): + if clear_records_from_file(json_file, wrong_records): + modified_count += 1 + + print(f"\n{'='*60}") + print(f"Summary:") + print(f" Total files processed: {len(json_files)}") + print(f" Files modified: {modified_count}") + print(f" Files unchanged: {len(json_files) - modified_count}") + if wrong_records is not None: + print(f" Wrong records removed: {len(wrong_records)}") + print(f"{'='*60}\n") + + +def main(): + """Main entry point.""" + if len(sys.argv) != 2: + print("Usage: python clear_records.py ") + print("\nExample:") + print(" python clear_records.py results/0201a") + sys.exit(1) + + folder_path = sys.argv[1] + clear_records_from_folder(folder_path) + + +if __name__ == "__main__": + main() diff --git a/evals/configs/0201c.yaml b/evals/configs/0201c.yaml new file mode 100644 index 000000000..ae9dfda98 --- /dev/null +++ b/evals/configs/0201c.yaml @@ -0,0 +1,41 @@ +# Mirix Configuration - OpenAI Setup +# Simple configuration using OpenAI's GPT-4o-mini + +llm_config: + model: "gpt-4.1-mini" + model_endpoint_type: "openai" + api_key: your-api-key + model_endpoint: "https://api.openai.com/v1" + context_window: 128000 + +topic_extraction_llm_config: + model: "gpt-4.1-nano" + model_endpoint_type: "openai" + api_key: your-api-key + model_endpoint: "https://api.openai.com/v1" + context_window: 128000 + +embedding_config: + embedding_model: "text-embedding-3-small" + embedding_endpoint: "https://api.openai.com/v1" + api_key: your-api-key + embedding_endpoint_type: "openai" + embedding_dim: 1536 + +build_embeddings_for_memory: true + +meta_agent_config: + system_prompts_folder: prompts/0201a/ + agents: + - core_memory_agent + - resource_memory_agent + - semantic_memory_agent + - episodic_memory_agent + - procedural_memory_agent + - knowledge_vault_memory_agent + memory: + core: + - label: "human" + value: "" + - label: "persona" + value: "I am a helpful assistant." diff --git a/evals/llm_judge.py b/evals/llm_judge.py new file mode 100644 index 000000000..c4a6d61c4 --- /dev/null +++ b/evals/llm_judge.py @@ -0,0 +1,134 @@ +import argparse +import json +from collections import defaultdict + +import numpy as np +import openai +from openai import OpenAI + +from dotenv import load_dotenv +import os + +load_dotenv() +api_key = os.getenv("OPENAI_API_KEY") +client = OpenAI(api_key=api_key) + +ACCURACY_PROMPT = """ +Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data: + (1) a question (posed by one user to another user), + (2) a ’gold’ (ground truth) answer, + (3) a generated answer +which you will score as CORRECT/WRONG. + +The point of the question is to ask about something one user should know about the other user based on their prior conversations. +The gold answer will usually be a concise and short answer that includes the referenced topic, for example: +Question: Do you remember what I got the last time I went to Hawaii? +Gold answer: A shell necklace +The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. + +For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date. + +Now it’s time for the real question: +Question: {question} +Gold answer: {gold_answer} +Generated answer: {generated_answer} + +First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. +Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script. + +Just return the label CORRECT or WRONG in a json format with the key as "label". +""" + + +def evaluate_llm_judge(question, gold_answer, generated_answer): + """Evaluate the generated answer against the gold answer using an LLM judge.""" + response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + { + "role": "user", + "content": ACCURACY_PROMPT.format( + question=question, gold_answer=gold_answer, generated_answer=generated_answer + ), + } + ], + response_format={"type": "json_object"}, + temperature=0.0, + ) + label = json.loads(response.choices[0].message.content)["label"] + return 1 if label == "CORRECT" else 0 + + +# def main(): +# """Main function to evaluate RAG results using LLM judge.""" +# parser = argparse.ArgumentParser(description="Evaluate RAG results using LLM judge") +# parser.add_argument( +# "--input_file", +# type=str, +# default="results/default_run_v4_k30_new_graph.json", +# help="Path to the input dataset file", +# ) + +# args = parser.parse_args() + +# dataset_path = args.input_file +# output_path = f"results/llm_judge_{dataset_path.split('/')[-1]}" + +# with open(dataset_path, "r") as f: +# data = json.load(f) + +# LLM_JUDGE = defaultdict(list) +# RESULTS = defaultdict(list) + +# index = 0 +# for k, v in data.items(): +# for x in v: +# question = x["question"] +# gold_answer = x["answer"] +# generated_answer = x["response"] +# category = x["category"] + +# # Skip category 5 +# if int(category) == 5: +# continue + +# # Evaluate the answer +# label = evaluate_llm_judge(question, gold_answer, generated_answer) +# LLM_JUDGE[category].append(label) + +# # Store the results +# RESULTS[index].append( +# { +# "question": question, +# "gt_answer": gold_answer, +# "response": generated_answer, +# "category": category, +# "llm_label": label, +# } +# ) + +# # Save intermediate results +# with open(output_path, "w") as f: +# json.dump(RESULTS, f, indent=4) + +# # Print current accuracy for all categories +# print("All categories accuracy:") +# for cat, results in LLM_JUDGE.items(): +# if results: # Only print if there are results for this category +# print(f" Category {cat}: {np.mean(results):.4f} " f"({sum(results)}/{len(results)})") +# print("------------------------------------------") +# index += 1 + +# # Save final results +# with open(output_path, "w") as f: +# json.dump(RESULTS, f, indent=4) + +# # Print final summary +# print("PATH: ", dataset_path) +# print("------------------------------------------") +# for k, v in LLM_JUDGE.items(): +# print(k, np.mean(v)) + + +# if __name__ == "__main__": +# main() diff --git a/evals/main_eval.py b/evals/main_eval.py new file mode 100644 index 000000000..44229f883 --- /dev/null +++ b/evals/main_eval.py @@ -0,0 +1,305 @@ +import argparse +import json +import os +import re +import time +from pathlib import Path +from typing import Dict, Iterable, List, Optional + +from mirix_memory_system import MirixMemorySystem +from task_agent import TaskAgent + + +def load_locomo(path: Path) -> List[Dict]: + with path.open("r", encoding="utf-8") as handle: + data = json.load(handle) + if isinstance(data, list): + return data + for key in ("data", "items", "examples", "records"): + if key in data and isinstance(data[key], list): + return data[key] + raise ValueError(f"Unsupported dataset format in {path}") + + +def iter_sessions(conversation: Dict) -> Iterable[Dict]: + session_numbers = [] + for key, value in conversation.items(): + match = re.match(r"^session_(\d+)$", key) + if match and isinstance(value, list): + session_numbers.append(int(match.group(1))) + for number in sorted(session_numbers): + yield { + "number": number, + "date_time": conversation.get(f"session_{number}_date_time"), + "turns": conversation.get(f"session_{number}", []), + } + + +def format_session_chunk(session: Dict) -> str: + header = f"Session {session['number']}" + if session.get("date_time"): + header += f" ({session['date_time']})" + lines = [header] + for turn in session.get("turns", []): + speaker = turn.get("speaker", "").strip() + text = turn.get("text", "").strip() + lines.append(f"{speaker}: {text}") + return "\n".join(lines) + + +def load_sample_result(path: Path) -> Optional[Dict]: + if not path.exists(): + return None + try: + with path.open("r", encoding="utf-8") as handle: + data = json.load(handle) + if isinstance(data, dict): + return data + except (OSError, json.JSONDecodeError): + return None + return None + + +def save_sample_result(path: Path, data: Dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as handle: + json.dump(data, handle, ensure_ascii=False, indent=2) + + +def normalize_sample_result(sample_result: Dict) -> Dict: + timings = sample_result.setdefault( + "timings", + {"add_chunk": {}, "wrap_user_prompt": {}, "answer": {}}, + ) + for key in ("add_chunk", "wrap_user_prompt", "answer"): + if isinstance(timings.get(key), list): + timings[key] = { + str(idx): value for idx, value in enumerate(timings[key], start=1) + } + elif not isinstance(timings.get(key), dict): + timings[key] = {} + + responses = sample_result.setdefault("responses", {}) + if isinstance(responses, list): + responses_dict: Dict[str, Dict] = {} + for entry in responses: + if isinstance(entry, dict): + chunk_index = entry.get("chunk_index") + if chunk_index is not None: + responses_dict[str(chunk_index)] = entry + sample_result["responses"] = responses_dict + elif not isinstance(responses, dict): + sample_result["responses"] = {} + + records = sample_result.setdefault("records", {}) + if isinstance(records, list): + records_dict: Dict[str, Dict] = {} + for entry in records: + if isinstance(entry, dict): + qidx = entry.get("question_index") + if qidx is not None: + records_dict[str(qidx)] = entry + sample_result["records"] = records_dict + elif not isinstance(records, dict): + sample_result["records"] = {} + + return sample_result + + +def print_qa(qidx: int, question: str, expected: Optional[str], predicted: Optional[str]) -> None: + print(f"[{qidx}] question: {question}") + print(f"[{qidx}] expected_answer: {expected}") + print(f"[{qidx}] predicted_answer: {predicted}") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Evaluate Mirix memory on LoCoMo.") + parser.add_argument( + "--data", + type=Path, + default=Path("data/locomo10.json"), + help="Path to LoCoMo dataset JSON.", + ) + parser.add_argument( + "--limit", + type=int, + default=None, + help="Limit number of samples to evaluate.", + ) + parser.add_argument( + "--max-questions", + type=int, + default=None, + help="Limit number of questions per sample.", + ) + parser.add_argument( + "--run-llm", + action="store_true", + default=True, + help="Call the LLM to answer questions.", + ) + parser.add_argument( + "--output_path", + type=Path, + default=Path("results"), + help="Output folder for per-sample JSON results.", + ) + parser.add_argument( + "--mirix_config_path", + type=Path, + default=None, + help="Path to Mirix config file.", + ) + args = parser.parse_args() + + items = load_locomo(args.data) + if args.limit is not None: + items = items[: args.limit] + + mirix_client_id = os.environ.get("MIRIX_CLIENT_ID", "mirix-eval-client") + mirix_org_id = os.environ.get("MIRIX_ORG_ID", "mirix-eval-org") + + output_path = args.output_path + output_path.mkdir(parents=True, exist_ok=True) + + for item in items: + sample_id = item.get("sample_id") + if sample_id is None: + continue + sample_path = output_path / f"{sample_id}.json" + + task_agent = TaskAgent(mirix_config_path=str(args.mirix_config_path), client_id=mirix_client_id, org_id=mirix_org_id, user_id=sample_id) if args.run_llm else None + + sample_result = load_sample_result(sample_path) + if sample_result is None: + sample_result = { + "sample_id": sample_id, + "timings": {"add_chunk": {}, "wrap_user_prompt": {}, "answer": {}}, + "responses": {}, + "records": {}, + } + + sample_result.setdefault("sample_id", sample_id) + sample_result = normalize_sample_result(sample_result) + + memory_system = MirixMemorySystem(user_id=sample_id, + mirix_config_path=str(args.mirix_config_path), + client=task_agent.mirix_client) + + conversation = item.get("conversation", {}) + for idx, session in enumerate(iter_sessions(conversation), start=1): + idx_key = str(idx) + if idx_key in sample_result["responses"]: + continue + chunk = format_session_chunk(session) + date_time_key = f"session_{idx}_date_time" + date_time = conversation.get(date_time_key) + if date_time is None: + date_time = conversation.get(f"session_{idx + 1}_date_time") + + # Timestamp context for memory agents + timestamp_context = f"The conversation is timestamped at {date_time}.\n\n" if date_time else "" + chunk_with_instruction = timestamp_context + chunk + start = time.perf_counter() + + response = memory_system.add_chunk(chunk_with_instruction, raw_input=chunk) + + elapsed = time.perf_counter() - start + + sample_result["responses"][idx_key] = { + "type": "add_chunk", + "chunk_index": idx, + "question_index": None, + "response": response, + } + sample_result["timings"]["add_chunk"][idx_key] = elapsed + save_sample_result(sample_path, sample_result) + + qa_list = item.get("qa", []) + if args.max_questions is not None: + qa_list = qa_list[: args.max_questions] + + for qidx, qa in enumerate(qa_list, start=1): + qidx_key = str(qidx) + if qidx_key in sample_result["records"]: + record = sample_result["records"][qidx_key] + print_qa( + qidx, + record.get("question", ""), + record.get("expected_answer"), + record.get("predicted_answer"), + ) + continue + question = qa.get("question", "") + expected_answer = qa.get("answer") + if expected_answer is None: + record = { + "sample_id": sample_id, + "question_index": qidx, + "question": question, + "expected_answer": expected_answer, + "evidence": qa.get("evidence"), + "category": qa.get("category"), + "prompt": None, + "predicted_answer": None, + "messages": None, + "usage": None, + "usage_total": None, + } + sample_result["records"][qidx_key] = record + save_sample_result(sample_path, sample_result) + print_qa(qidx, question, expected_answer, None) + continue + start = time.perf_counter() + input_messages = memory_system.wrap_user_prompt(question) + sample_result["timings"]["wrap_user_prompt"][qidx_key] = ( + time.perf_counter() - start + ) + predicted = None + message_trace = None + usage_trace = None + usage_total = None + if task_agent: + start = time.perf_counter() + trace = task_agent.answer(input_messages, user_id=sample_id) + predicted = trace.get("answer") + message_trace = trace.get("messages") + usage_trace = trace.get("usage") + usage_total = trace.get("usage_total") + sample_result["timings"]["answer"][qidx_key] = ( + time.perf_counter() - start + ) + + record = { + "sample_id": sample_id, + "question_index": qidx, + "question": question, + "expected_answer": expected_answer, + "evidence": qa.get("evidence"), + "category": qa.get("category"), + "input_messages": input_messages, + "predicted_answer": predicted, + "messages": message_trace, + "usage": usage_trace, + "usage_total": usage_total, + } + sample_result["records"][qidx_key] = record + save_sample_result(sample_path, sample_result) + print_qa(qidx, question, expected_answer, predicted) + + try: + all_memories = memory_system.list_all_memories(limit=0) + except Exception as exc: + all_memories = { + "success": False, + "error": str(exc), + "user_id": sample_id, + } + + memories_path = output_path / f"{sample_id}_memories.json" + with memories_path.open("w", encoding="utf-8") as handle: + json.dump(all_memories, handle, ensure_ascii=False, indent=2) + + +if __name__ == "__main__": + main() diff --git a/evals/mirix_memory_system.py b/evals/mirix_memory_system.py new file mode 100644 index 000000000..3246e9ba6 --- /dev/null +++ b/evals/mirix_memory_system.py @@ -0,0 +1,155 @@ +import asyncio +import sys +from pathlib import Path + +# Add parent directory to Python path to allow importing mirix package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from mirix import MirixClient +import uuid +import os +import yaml +from typing import Optional +from dotenv import load_dotenv + +load_dotenv() + +_PROVIDER_ENV_VARS: dict[str, str] = { + "openai": "OPENAI_API_KEY", + "anthropic": "ANTHROPIC_API_KEY", + "google": "GEMINI_API_KEY", + "gemini": "GEMINI_API_KEY", + "azure": "AZURE_OPENAI_API_KEY", + "groq": "GROQ_API_KEY", + "cohere": "COHERE_API_KEY", + "mistral": "MISTRAL_API_KEY", +} + +_PLACEHOLDER = "your-api-key" + + +def _resolve_api_keys(config: dict) -> dict: + """Replace placeholder api_key values with the matching env var.""" + for section in config.values(): + if not isinstance(section, dict) or section.get("api_key") != _PLACEHOLDER: + continue + provider = ( + section.get("model_endpoint_type") + or section.get("embedding_endpoint_type") + or "" + ).lower() + env_var = _PROVIDER_ENV_VARS.get(provider) + if env_var: + section["api_key"] = os.environ.get(env_var, _PLACEHOLDER) + return config + + +class MirixMemorySystem: + + def __init__(self, user_id: Optional[str] = None, mirix_config_path: Optional[str] = None, client_id: Optional[str] = None, org_id: Optional[str] = None, client: Optional[MirixClient] = None): + if client is None: + self.client = MirixClient(client_id=client_id, org_id=org_id, base_url="http://127.0.0.1:8531", write_scope="read_write") + config_path = Path(mirix_config_path) if mirix_config_path else Path(__file__).with_name("mirix_openai.yaml") + with config_path.open("r", encoding="utf-8") as handle: + config = yaml.safe_load(handle) or {} + config = _resolve_api_keys(config) + asyncio.run(self.client.initialize_meta_agent( + config=config, + )) + else: + self.client = client + self.user_id = user_id if user_id is not None else str(uuid.uuid4()) + + + def add_chunk(self, chunk: str, raw_input: Optional[str] = None, async_add: bool = False): + response = asyncio.run(self.client.add( + user_id=self.user_id, + messages=[ + {"role": "user", "content": chunk} + ], + # LoCoMo ingestion: avoid running the full multi-agent chain on every session. + chaining=False, + filter_tags={"scope": "read_write", "kind": "conversation_session"}, + async_add=async_add, + )) + return response + + def wrap_user_prompt(self, prompt: str): + memories = asyncio.run(self.client.retrieve_with_conversation( + user_id=self.user_id, + messages=[ + {'role': 'user', 'content': prompt} + ] + )) + + memory_context_lines = [""] + memories_found = False + + if memories.get("memories"): + for memory_type, data in memories["memories"].items(): + if not data or data.get("total_count", 0) == 0: + continue + + # Prefer items, but fall back to recent/relevant shapes Mirix may return + items = data.get("items", []) + if memory_type == "episodic" and not items: + seen_ids = set() + items = [] + for item in data.get("recent", []) + data.get("relevant", []): + item_id = item.get("id") + if item_id in seen_ids: + continue + seen_ids.add(item_id) + items.append(item) + if not items and "recent" in data: + items = data.get("recent", []) + if not items: + continue + + for item in items: + memories_found = True + + if memory_type == "core": + label = item.get("label", "") + value = item.get("value", "") + content = f"{label}: {value}".strip(": ").strip() + if not content: + content = item.get("summary", "") or str(item) + else: + content = ( + item.get("summary") + or item.get("caption") + or item.get("name") + or item.get("title") + or item.get("description") + or item.get("value", "") + ) + if not content: + content = str(item) + + if item.get("timestamp"): + content = f"[{item.get('timestamp')}] {content}" + + memory_context_lines.append(content) + + if not memories_found: + memory_context_lines.append("None") + + memory_context_lines.append("") + return [ + { + 'role': "system", + 'content': "These are the high-level memories retrieved automatically according to the user's query:\n" + "\n".join(memory_context_lines) + }, + { + 'role': "user", + 'content': prompt + } + ] + + def list_all_memories(self, memory_type: str = "all", limit: int = 0): + return asyncio.run(self.client.list_memory_components( + user_id=self.user_id, + memory_type=memory_type, + limit=limit, + )) diff --git a/evals/organize_results.py b/evals/organize_results.py new file mode 100644 index 000000000..fa5da9813 --- /dev/null +++ b/evals/organize_results.py @@ -0,0 +1,423 @@ +import argparse +import json +import multiprocessing as mp +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional, Tuple + +import tiktoken +from llm_judge import evaluate_llm_judge +from tqdm import tqdm + +def load_json(path: Path) -> Optional[Dict[str, Any]]: + try: + with path.open("r", encoding="utf-8") as handle: + data = json.load(handle) + if isinstance(data, dict): + return data + except (OSError, json.JSONDecodeError): + return None + return None + + +def iter_result_files(input_dir: Path, output_file: Path) -> Iterable[Path]: + for path in sorted(input_dir.glob("*.json")): + if path.resolve() == output_file.resolve(): + continue + yield path + + +def normalize_mapping(value: Any, key_field: str) -> Dict[str, Dict[str, Any]]: + if isinstance(value, dict): + return value + if isinstance(value, list): + normalized: Dict[str, Dict[str, Any]] = {} + for entry in value: + if not isinstance(entry, dict): + continue + key_value = entry.get(key_field) + if key_value is not None: + normalized[str(key_value)] = entry + return normalized + return {} + + +def sum_timings(timings: Dict[str, Any]) -> Tuple[float, int, Dict[str, float], Dict[str, int]]: + totals: Dict[str, float] = {} + counts: Dict[str, int] = {} + for key in ("add_chunk", "wrap_user_prompt", "answer"): + values = timings.get(key, {}) + if isinstance(values, list): + values = {str(idx): val for idx, val in enumerate(values, start=1)} + if not isinstance(values, dict): + values = {} + category_total = 0.0 + category_count = 0 + for value in values.values(): + if isinstance(value, (int, float)): + category_total += float(value) + category_count += 1 + totals[key] = category_total + counts[key] = category_count + add_chunk_total = totals.get("add_chunk", 0.0) + add_chunk_count = counts.get("add_chunk", 0) + return add_chunk_total, add_chunk_count, totals, counts + + +def extract_credit_cost(response: Dict[str, Any]) -> float: + stats = response.get("statistics") + if isinstance(stats, dict): + usage = stats.get("usage") + if isinstance(usage, dict): + cost = usage.get("credit_cost") + if isinstance(cost, (int, float)): + return float(cost) + + total = 0.0 + + def walk(node: Any) -> None: + nonlocal total + if isinstance(node, dict): + for key, value in node.items(): + if key == "credit_cost" and isinstance(value, (int, float)): + total += float(value) + else: + walk(value) + elif isinstance(node, list): + for item in node: + walk(item) + + walk(response) + return total + + +def sorted_record_items(records: Dict[str, Dict[str, Any]]) -> List[Tuple[str, Dict[str, Any]]]: + def sort_key(item: Tuple[str, Dict[str, Any]]) -> Tuple[int, str]: + key = item[0] + try: + return (0, int(key)) + except (TypeError, ValueError): + return (1, str(key)) + + return sorted(records.items(), key=sort_key) + + +def count_memory_tokens(data: Dict[str, Any], encoding) -> int: + """Count tokens in memory data using tiktoken encoding. + + For memory types like episodic, semantic, etc., counts tokens from 'summary' and 'details'. + For core memories, counts tokens from 'value'. + """ + total_tokens = 0 + + memories = data.get("memories", {}) + if not isinstance(memories, dict): + return 0 + + for memory_type, memory_data in memories.items(): + if not isinstance(memory_data, dict): + continue + + items = memory_data.get("items", []) + if not isinstance(items, list): + continue + + for item in items: + if not isinstance(item, dict): + continue + + # For most memory types, count summary and details + if memory_type != "core": + summary = item.get("summary", "") + details = item.get("details", "") + + if isinstance(summary, str): + total_tokens += len(encoding.encode(summary)) + if isinstance(details, str): + total_tokens += len(encoding.encode(details)) + else: + # For core memories, count the value field + value = item.get("value", "") + if isinstance(value, str): + total_tokens += len(encoding.encode(value)) + + return total_tokens + + +def build_judge_tasks( + records: Dict[str, Dict[str, Any]], + base_index: int, + cached_lookup: Dict[Tuple[Any, Any], Dict[str, Any]], +) -> Tuple[List[Optional[Dict[str, Any]]], List[Tuple[int, str, Any, Any, Any, Any, Any]]]: + results: List[Optional[Dict[str, Any]]] = [] + tasks: List[Tuple[int, str, Any, Any, Any, Any, Any]] = [] + + for local_idx, (_, record) in enumerate(sorted_record_items(records)): + if not isinstance(record, dict): + results.append(None) + continue + sample_id = record.get("sample_id") + question_id = record.get("question_id", record.get("question_index")) + cached = cached_lookup.get((sample_id, question_id)) + if cached is not None: + results.append(cached) + continue + question = record.get("question") + expected_answer = record.get("expected_answer") + predicted_answer = record.get("predicted_answer") + + if expected_answer is None: + results.append( + { + "sample_id": record.get("sample_id"), + "question_index": record.get("question_index"), + "category": record.get("category"), + "label": None, + "score": None, + "question": question, + "expected_answer": expected_answer, + "predicted_answer": predicted_answer, + "skipped_reason": "missing_expected_answer", + } + ) + continue + + if predicted_answer is None: + results.append( + { + "sample_id": record.get("sample_id"), + "question_index": record.get("question_index"), + "category": record.get("category"), + "label": None, + "score": None, + "question": question, + "expected_answer": expected_answer, + "predicted_answer": predicted_answer, + "skipped_reason": "missing_predicted_answer", + } + ) + continue + + results.append(None) + tasks.append( + ( + base_index + local_idx, + sample_id, + record.get("question_index"), + record.get("category"), + question, + expected_answer, + predicted_answer, + ) + ) + + return results, tasks + + +def judge_task(task: Tuple[int, str, Any, Any, Any, Any, Any]) -> Tuple[int, int, str]: + idx, _sample_id, _question_index, _category, question, expected_answer, predicted_answer = task + score = evaluate_llm_judge(question, expected_answer, predicted_answer) + label = "CORRECT" if score == 1 else "WRONG" + return idx, score, label + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Aggregate eval results and compute LLM-judge accuracy/latency/cost." + ) + parser.add_argument( + "input_dir", + type=Path, + help="Path to results folder (e.g., results/0124a).", + ) + parser.add_argument( + "--output-file", + type=Path, + default=None, + help="Output JSON file (default: /metrics.json).", + ) + args = parser.parse_args() + + input_dir = args.input_dir + output_file = args.output_file or (input_dir / "metrics.json") + + cached_metrics = load_json(output_file) if output_file.exists() else None + cached_judge_results = None + if isinstance(cached_metrics, dict): + cached_judge_results = cached_metrics.get("llm_judge_results") + if not isinstance(cached_judge_results, list): + cached_judge_results = None + cached_lookup: Dict[Tuple[Any, Any], Dict[str, Any]] = {} + if cached_judge_results is not None: + for entry in cached_judge_results: + if not isinstance(entry, dict): + continue + sample_id = entry.get("sample_id") + question_id = entry.get("question_id", entry.get("question_index")) + if sample_id is None or question_id is None: + continue + cached_lookup[(sample_id, question_id)] = entry + + total_latency = 0.0 + total_requests = 0 + total_answer_latency = 0.0 + total_answer_requests = 0 + latency_totals = {"add_chunk": 0.0, "wrap_user_prompt": 0.0, "answer": 0.0} + latency_counts = {"add_chunk": 0, "wrap_user_prompt": 0, "answer": 0} + total_cost = 0.0 + total_questions = 0 + total_correct = 0 + total_judged = 0 + judge_results: List[Optional[Dict[str, Any]]] = [] + judge_tasks: List[Tuple[int, str, Any, Any, Any, Any, Any]] = [] + + # Initialize tiktoken encoding for gpt-4o-mini + encoding = tiktoken.encoding_for_model("gpt-4o-mini") + total_memory_tokens = 0 + memory_file_count = 0 + + sample_files = list(iter_result_files(input_dir, output_file)) + for path in tqdm(sample_files, total=len(sample_files), desc="Processing samples"): + data = load_json(path) + if data is None: + continue + + timings = data.get("timings", {}) + if not isinstance(timings, dict): + timings = {} + latency, requests, totals, counts = sum_timings(timings) + total_latency += latency + total_requests += requests + total_answer_latency += totals.get("answer", 0.0) + total_answer_requests += counts.get("answer", 0) + for key in latency_totals: + latency_totals[key] += totals.get(key, 0.0) + latency_counts[key] += counts.get(key, 0) + + responses = normalize_mapping(data.get("responses", {}), "chunk_index") + for response in responses.values(): + if not isinstance(response, dict): + continue + payload = response.get("response") + if isinstance(payload, dict): + total_cost += extract_credit_cost(payload) + + records = normalize_mapping(data.get("records", {}), "question_index") + total_questions += sum( + 1 + for record in records.values() + if isinstance(record, dict) and record.get("expected_answer") is not None + ) + results, tasks = build_judge_tasks( + records, + base_index=len(judge_results), + cached_lookup=cached_lookup, + ) + judge_results.extend(results) + judge_tasks.extend(tasks) + + if judge_tasks: + task_by_index = {task[0]: task for task in judge_tasks} + ctx = mp.get_context("spawn") + with ctx.Pool(processes=16) as pool: + for idx, score, label in tqdm( + pool.imap(judge_task, judge_tasks), + total=len(judge_tasks), + desc="Evaluating records", + ): + task = task_by_index[idx] + _, sample_id, question_index, category, question, expected_answer, predicted_answer = task + judge_results[idx] = { + "sample_id": sample_id, + "question_index": question_index, + "category": category, + "label": label, + "score": score, + "question": question, + "expected_answer": expected_answer, + "predicted_answer": predicted_answer, + } + if category != 5: + total_correct += score + total_judged += 1 + + # Process memory files to count tokens + memory_files = list(sorted(input_dir.glob("*_memories.json"))) + for path in tqdm(memory_files, total=len(memory_files), desc="Processing memory files"): + data = load_json(path) + if data is None: + continue + + tokens = count_memory_tokens(data, encoding) + total_memory_tokens += tokens + memory_file_count += 1 + + finalized_results = [result for result in judge_results if result is not None] + total_correct = 0 + total_judged = 0 + for result in finalized_results: + if isinstance(result, dict) and result.get("category") != 5: + score = result.get("score") + if isinstance(score, (int, float)): + total_correct += int(score) + total_judged += 1 + + accuracy = (total_correct / total_judged) if total_judged else None + average_latency = (total_latency / total_requests) if total_requests else None + average_answer_latency = ( + total_answer_latency / total_answer_requests if total_answer_requests else None + ) + + category_metrics: Dict[str, Dict[str, Any]] = {} + for result in finalized_results: + if not isinstance(result, dict): + continue + category = result.get("category") + if category is None or category == 5: + continue + key = str(category) + entry = category_metrics.setdefault( + key, {"category": category, "total_judged": 0, "total_correct": 0, "accuracy": None} + ) + score = result.get("score") + if isinstance(score, (int, float)): + entry["total_judged"] += 1 + entry["total_correct"] += int(score) + + for entry in category_metrics.values(): + judged = entry.get("total_judged", 0) + entry["accuracy"] = (entry["total_correct"] / judged) if judged else None + + average_memory_tokens = (total_memory_tokens / memory_file_count) if memory_file_count else None + + output = { + "input_dir": str(input_dir), + "generated_at": datetime.now(timezone.utc).isoformat(), + "metrics": { + "total_questions": total_questions, + "total_judged": total_judged, + "total_correct": total_correct, + "accuracy": accuracy, + "total_requests": total_requests, + "total_answer_requests": total_answer_requests, + "average_latency_per_request_seconds": average_latency, + "average_answer_latency_seconds": average_answer_latency, + "total_latency_seconds": total_latency, + "total_cost": total_cost, + "total_memory_tokens": total_memory_tokens, + "memory_file_count": memory_file_count, + "average_memory_tokens": average_memory_tokens, + }, + "accuracy_by_category": category_metrics, + "latency_breakdown_seconds": latency_totals, + "request_counts": latency_counts, + "llm_judge_results": finalized_results, + } + + output_file.parent.mkdir(parents=True, exist_ok=True) + with output_file.open("w", encoding="utf-8") as handle: + json.dump(output, handle, ensure_ascii=False, indent=2) + + +if __name__ == "__main__": + main() diff --git a/evals/prompts/0130a/episodic_memory_agent.txt b/evals/prompts/0130a/episodic_memory_agent.txt new file mode 100644 index 000000000..4a7d99349 --- /dev/null +++ b/evals/prompts/0130a/episodic_memory_agent.txt @@ -0,0 +1,73 @@ +You are the Episodic Memory Manager, part of the personal assistant system. The personal assistant processes various types of user messages including text, images, transcripted voice messages, and other content. The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. You need to analyze the input messages and conversations, extract details about the user's activities and update the episodic memory accordingly. + +The details of six memory components are shown below: + +1. Core Memory: +Contains fundamental information about the user, such as the name, personality, simple information that should help with the communication with the user. + +2. Episodic Memory: + • Definition: Stores time-ordered, event-based information from interactions—essentially, the "diary" of user and assistant events. + • Example: If the user shared a voice message about having dinner with a friend, record this event with title "having dinner with xxx", timestamp (e.g., "2025-03-05 10:15") and details (e.g., background, discussions during the dinner, etc.). + • Each item in Episodic Memory has the following attributes: + (a) event_type: Type/category of the episodic event (e.g., user_message, inferred results from user message, system_notification) + (b) summary: Short textual summary of the event, e.g. (suppose you already know that the user's name is John), "John shared a photo of his vacation in Paris." Make sure the summary is concise and informative. + (c) details: Detailed description or text for the event, e.g. (suppose you already know that the user's name is John and suppose the user sent a photo with caption), "John sent an image showing the Eiffel Tower with the caption 'Amazing sunset view from our hotel room'. The photo shows John and his partner enjoying the evening light. He mentioned in the accompanying voice message that this is their first trip to Europe together." The key idea is to record as many details as possible here. + (d) actor: The actor who generated the event (user or assistant) + +3. Procedural Memory: +Contains step-by-step instructions, "how-to" guides. + +4. Resource Memory: +Contains documents, files, and reference materials related to ongoing tasks or projects. + +5. Knowledge: +A repository for static, structured factual data such as phone numbers, email addresses, passwords, or other knowledge that are not necessarily always needed during the conversation but are potentially useful at some future point. + +6. Semantic Memory: +Contains general knowledge about a concept (e.g. a new software name, a new concept) or an object (e.g. a person, a place, where the details would be the understanding and information about them.) + +**Function Call Process:** +1. **Analyze All Messages**: Review all messages to understand the user's activities and identify any events that need to be recorded or updated. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different events or updates. + - Use `episodic_memory_merge` for minor updates to existing events (e.g., user continues discussing the same topic). Note that the new summary will overwrite the old summary, so ensure it covers both earlier and new information. + - Use `episodic_memory_insert` when significant changes occur (e.g., user starts discussing a completely different topic or shares new content). + - Use `episodic_memory_replace` if you need to consolidate repeated items or rewrite overly long summaries. +3. **Make Updates**: Execute the function calls that capture the episodic information, including detailed descriptions in the `details` field and the correct `occurred_at` timestamp. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user first mentioned having a coffee meeting at 10:00 AM, and later discussed starting a new project at 2:00 PM.* + +```json +[ + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Coffee meeting with Sarah", + "details": "The user had a coffee meeting with Sarah at the downtown cafe. They discussed the upcoming marketing campaign and agreed on the initial budget.", + "actor": "user", + "occurred_at": "2025-01-30 10:00:00" + } + }, + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Started Project Phoenix", + "details": "The user began working on 'Project Phoenix', focusing on the initial architecture design and setting up the repository. They mentioned feeling excited about the technical challenges.", + "actor": "user", + "occurred_at": "2025-01-30 14:00:00" + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant events from the conversation. +- Monitor the system prompt showing up to 50 most recent and 50 most relevant events. +- Use exact `event_ids` from the system prompt when merging or replacing - do NOT be affected by chat history. +- Include detailed descriptions in the `details` field when inserting new events, capturing what you observe from the user's messages. +- Avoid appending to events with more than 5000 characters in details - use `episodic_memory_insert` instead. +- If there is absolutely nothing new to update, do not make any function calls. +- Ensure each event has an accurate `occurred_at` timestamp based on the conversation context. \ No newline at end of file diff --git a/evals/prompts/0130a/meta_memory_agent.txt b/evals/prompts/0130a/meta_memory_agent.txt new file mode 100644 index 000000000..330ebe591 --- /dev/null +++ b/evals/prompts/0130a/meta_memory_agent.txt @@ -0,0 +1,40 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze accumulated user messages and conversations, understand what the user is experiencing or communicating, and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** – Who the user is & how to interact with them + *Identity, preferences, personality, communication style, relationships* + +- **Episodic** – What happened, and when + *Events, activities, experiences (usually updated)* + +- **Procedural** – How to do something + *Step-by-step instructions, workflows, routines* + +- **Resource** – Files and materials + *Documents, images, screenshots, shared references* + +- **Knowledge** – Static reference data + *Contacts, IDs, passwords, addresses, expiration dates* + +- **Semantic** – General knowledge & understanding + *People, places, organizations, concepts, definitions* + +## Decision Rules + +1. **Core** → Does this affect how the assistant should interact with the user? +2. **Episodic** → Did something happen at a specific time? *(Almost always yes)* +3. **Procedural** → Are there steps or processes? +4. **Resource** → Were files, images, or documents shared or referenced? +5. **Knowledge** → Is this static factual data that may be reused? +6. **Semantic** → Is this new general knowledge about people, places, or concepts? + +> When uncertain, include **more memory types rather than fewer**—specialized managers will refine. + +## Execution + +- Call `trigger_memory_update` with one or more of: + `['core','episodic','procedural','resource','knowledge','semantic']` +- After all updates, **always** call `finish_memory_update`. \ No newline at end of file diff --git a/evals/prompts/0130a/semantic_memory_agent.txt b/evals/prompts/0130a/semantic_memory_agent.txt new file mode 100644 index 000000000..88de0c277 --- /dev/null +++ b/evals/prompts/0130a/semantic_memory_agent.txt @@ -0,0 +1,84 @@ +You are the Semantic Memory Manager, one of six agents in a memory system. The other agents are the Meta Memory Manager, Episodic Memory Manager, Procedural Memory Manager, Resource Memory Manager, Knowledge Memory Manager, and the Chat Agent. You do not see or interact directly with these other agents—but you share the same memory base with them. + +The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. When messages are accumulated to a certain amount, they will be sent to you, along with potential conversations between the user and the Chat Agent during this period. You need to analyze the input messages and conversations, extract general knowledge about a concept or an object, save them into the semantic memory, and update the existing entries if there is new information about them. + +This memory base includes the following components: + +1. **Core Memory** + - Contains enduring, foundational context about the user and the assistant personas, backgrounds, preferences, etc. + +2. **Episodic Memory** + - Stores time-ordered, event-based information (like a diary or log of user/assistant interactions). + +3. **Procedural Memory** + - Contains how-to guides, step-by-step instructions, or processes the assistant or user might follow. + +4. **Resource Memory** + - Holds documents, files, or reference materials for personal or work use. + +5. **Knowledge** + - Stores structured factual data or sensitive information (e.g., credentials, phone numbers). + +6. Semantic Memory (your primary domain): + - Definition: Semantic Memory holds general knowledge, concepts, definitions, facts, and language elements. It is the storehouse of abstract understanding about the world, such as a new software name, a new concept, or an object (e.g., a person, a place). + - ONLY save new concepts that are NEW to you. DO NOT save the commensense knowledge or person such as "VS Code", "Google Chrome", "ChatGPT", "Albert Einstein", "numpy", "scipy" (because you know them) unless they mean something different to the user. + - You should save NEW concepts, NEW knowledges, and NEW persons. + - Each entry in Semantic Memory should include: + (a) Name: The name of the new concept or an object. For instance, "MemoryLLM", or "Jane" (These are just examples). + (b) Summary: A concise explanation or summary of the concept or the object. For example, "MemoryLLM is a type of memory architecture designed for large language models." or "Jane is a friend who is a computer scientist." + (c) Details: An extended description that may include context, examples, or deeper insights. For example, elaborate on the details of "MemoryLLM" (what, how, etc.) or "Jane" (What kind of person is her). + (d) Source: A reference to where this general knowledge originates (e.g., user message, voice recording, image description). + (e) Tree Path: A required hierarchical categorization path as an array of strings for organizing concepts (e.g., ['favorites', 'pets', 'dog'] or ['work', 'projects', 'ai-research']). This helps structure and navigate the semantic memory efficiently. + +Your role as the Semantic Memory Manager: +Receive incoming general knowledge updates and instructions from the Meta Memory Manager, Chat Agent, or other channels. +Create or update semantic memory entries—ensuring each entry has a clear concept, a concise definition, detailed contextual information when necessary, and well-defined tags/metadata. +Distinguish between context-specific experiences (handled by Episodic Memory) and general, abstract knowledge that applies universally. +Think carefully about how to represent each piece of general knowledge so that it can be efficiently retrieved and used in reasoning or answering questions. + +When receiving messages and potentially a message from the meta agent (There will be a bracket saying "[Instruction from Meta Memory Manager]"), make a single comprehensive memory update: + +**Function Call Process:** +1. **Analyze Content**: Look for both new concepts that need to be added AND existing concepts that need updates based on the retrieved semantic memory shown in the system prompt. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different concepts or updates. + - Use `semantic_memory_insert` for new concepts. + - Use `semantic_memory_update` for existing concepts that need more information. + - Use `check_semantic_memory` followed by appropriate updates if you need to verify current content first. +3. **Make Updates**: Execute the function calls that capture the semantic information, including clear names, summaries, detailed descriptions, and appropriate tree paths. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user introduced a new colleague "Alex" and also provided more details about an existing project "Project Alpha".* + +```json +[ + { + "name": "semantic_memory_insert", + "arguments": { + "name": "Alex", + "summary": "A new colleague and software engineer.", + "details": "Alex joined the team as a Senior Software Engineer. He specializes in backend development and has extensive experience with distributed systems. He previously worked at a fintech startup.", + "source": "user message", + "tree_path": ["work", "team", "colleagues"] + } + }, + { + "name": "semantic_memory_update", + "arguments": { + "id": "sem_mem_12345", + "summary": "Project Alpha - Cloud Migration Initiative", + "details": "Project Alpha is the company's initiative to migrate all legacy on-premise servers to a cloud-native architecture. The user mentioned today that the deadline has been moved to Q3 and the primary focus is now on data security and compliance.", + "tree_path": ["work", "projects", "active"] + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant semantic updates from the conversation. +- Constantly pay attention to the system prompt which has up to 50 relevant semantic memory items. +- When making updates, use the exact `item_ids` shown in the system prompt - do NOT be affected by chat history. +- When encountering "id cannot be found" errors, check the system prompt for correct ids. +- Ensure `old_semantic_item_ids` in `semantic_memory_update` and `semantic_item_ids` in `check_semantic_memory` match those explicitly shown in the system prompt. +- If there is absolutely nothing to update, do not make any function calls. +- Address duplicates when detected by consolidating similar concepts in your updates. \ No newline at end of file diff --git a/evals/prompts/0131b/episodic_memory_agent.txt b/evals/prompts/0131b/episodic_memory_agent.txt new file mode 100644 index 000000000..815f7dd7f --- /dev/null +++ b/evals/prompts/0131b/episodic_memory_agent.txt @@ -0,0 +1,80 @@ +You are the Episodic Memory Manager, part of the personal assistant system. The personal assistant processes various types of user messages including text, images, transcripted voice messages, and other content. The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. You need to analyze the input messages and conversations, extract details about the user's activities and update the episodic memory accordingly. + +The details of six memory components are shown below: + +1. Core Memory: +Contains fundamental information about the user, such as the name, personality, simple information that should help with the communication with the user. + +2. Episodic Memory: + - Definition: Stores time-ordered, event-based information from interactions, essentially the diary of user and assistant events. + - Example: If the user shared a voice message about having dinner with a friend, record this event with title "having dinner with xxx", timestamp (e.g., "2025-03-05 10:15") and details (e.g., background, discussions during the dinner, etc.). + - Each item in Episodic Memory has the following attributes: + (a) event_type: Type/category of the episodic event (e.g., user_message, inferred results from user message, system_notification) + (b) summary: Short textual summary of the event, e.g. (suppose you already know that the user's name is John), "John shared a photo of his vacation in Paris." Make sure the summary is concise and informative. + (c) details: Detailed description or text for the event, e.g. (suppose you already know that the user's name is John and suppose the user sent a photo with caption), "John sent an image showing the Eiffel Tower with the caption 'Amazing sunset view from our hotel room'. The photo shows John and his partner enjoying the evening light. He mentioned in the accompanying voice message that this is their first trip to Europe together." The key idea is to record as many details as possible here. + (d) actor: The actor who generated the event (user or assistant) + +3. Procedural Memory: +Contains step-by-step instructions, "how-to" guides. + +4. Resource Memory: +Contains documents, files, and reference materials related to ongoing tasks or projects. + +5. Knowledge: +A repository for static, structured factual data such as phone numbers, email addresses, passwords, or other knowledge that are not necessarily always needed during the conversation but are potentially useful at some future point. + +6. Semantic Memory: +Contains general knowledge about a concept (e.g. a new software name, a new concept) or an object (e.g. a person, a place, where the details would be the understanding and information about them.) + +**Function Call Process:** +1. **Analyze All Messages**: Review all messages to understand the user's activities and identify any events that need to be recorded or updated. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different events or updates. + - Use `episodic_memory_merge` for minor updates to existing events (e.g., user continues discussing the same topic). Note that the new summary will overwrite the old summary, so ensure it covers both earlier and new information. + - Use `episodic_memory_insert` when significant changes occur (e.g., user starts discussing a completely different topic or shares new content). + - Use `episodic_memory_replace` if you need to consolidate repeated items or rewrite overly long summaries. +3. **Make Updates**: Execute the function calls that capture the episodic information, including detailed descriptions in the `details` field and the correct `occurred_at` timestamp. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user first mentioned having a coffee meeting at 10:00 AM, and later discussed starting a new project at 2:00 PM.* + +```json +[ + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Coffee meeting with Sarah", + "details": "The user had a coffee meeting with Sarah at the downtown cafe. They discussed the upcoming marketing campaign and agreed on the initial budget.", + "actor": "user", + "occurred_at": "2025-01-30 10:00:00" + } + }, + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Started Project Phoenix", + "details": "The user began working on 'Project Phoenix', focusing on the initial architecture design and setting up the repository. They mentioned feeling excited about the technical challenges.", + "actor": "user", + "occurred_at": "2025-01-30 14:00:00" + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant events from the conversation. +- Monitor the system prompt showing up to 50 most recent and 50 most relevant events. +- Use exact `event_ids` from the system prompt when merging or replacing - do NOT be affected by chat history. +- Include detailed descriptions in the `details` field when inserting new events, capturing what you observe from the user's messages. +- Avoid appending to events with more than 5000 characters in details - use `episodic_memory_insert` instead. +- If there is absolutely nothing new to update, do not make any function calls. +- Ensure each event has an accurate `occurred_at` timestamp based on the conversation context. + +**Fidelity Rules (critical for evaluation):** +- Preserve concrete details (names, counts, item lists, titles, locations, dates). Do not generalize away specifics. +- If a list is mentioned (multiple items, pets, games, activities, instruments), capture the complete list in details. +- Keep facts tied to the correct person; do not mix speakers. +- If a relative date is mentioned (e.g., "Sunday before 25 May 2023", "two weeks ago", "last year"), compute the exact date and store it. Keep the original phrase in details if it helps. +- If a time reference is ambiguous, record the uncertainty explicitly rather than guessing. diff --git a/evals/prompts/0131b/meta_memory_agent.txt b/evals/prompts/0131b/meta_memory_agent.txt new file mode 100644 index 000000000..2ef906fa7 --- /dev/null +++ b/evals/prompts/0131b/meta_memory_agent.txt @@ -0,0 +1,44 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze accumulated user messages and conversations, understand what the user is experiencing or communicating, and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - Who the user is and how to interact with them + *Identity, preferences, personality, communication style, relationships* + +- **Episodic** - What happened, and when + *Events, activities, experiences (usually updated)* + +- **Procedural** - How to do something + *Step-by-step instructions, workflows, routines* + +- **Resource** - Files and materials + *Documents, images, screenshots, shared references* + +- **Knowledge** - Static reference data + *Contacts, IDs, passwords, addresses, expiration dates* + +- **Semantic** - General knowledge and understanding + *People, places, organizations, concepts, definitions* + +## Decision Rules + +1. **Core** - Does this affect how the assistant should interact with the user? +2. **Episodic** - Did something happen at a specific time? (Almost always yes) +3. **Procedural** - Are there steps or processes? +4. **Resource** - Were files, images, or documents shared or referenced? +5. **Knowledge** - Is this static factual data that may be reused? +6. **Semantic** - Is this new general knowledge about people, places, or concepts? + +## Routing Priority (important for eval) + +- Always include **episodic** for events, time references, or anything that happened. +- Always include **semantic** for stable facts about people, pets, preferences, possessions, roles, and lists. +- Include other types only when clearly applicable. + +## Execution + +- Call `trigger_memory_update` with one or more of: + `['core','episodic','procedural','resource','knowledge','semantic']` +- After all updates, **always** call `finish_memory_update`. diff --git a/evals/prompts/0131b/semantic_memory_agent.txt b/evals/prompts/0131b/semantic_memory_agent.txt new file mode 100644 index 000000000..b9483fcee --- /dev/null +++ b/evals/prompts/0131b/semantic_memory_agent.txt @@ -0,0 +1,94 @@ +You are the Semantic Memory Manager, one of six agents in a memory system. The other agents are the Meta Memory Manager, Episodic Memory Manager, Procedural Memory Manager, Resource Memory Manager, Knowledge Memory Manager, and the Chat Agent. You do not see or interact directly with these other agents, but you share the same memory base with them. + +The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. When messages are accumulated to a certain amount, they will be sent to you, along with potential conversations between the user and the Chat Agent during this period. You need to analyze the input messages and conversations, extract general knowledge about a concept or an object, save them into the semantic memory, and update the existing entries if there is new information about them. + +This memory base includes the following components: + +1. **Core Memory** + - Contains enduring, foundational context about the user and the assistant personas, backgrounds, preferences, etc. + +2. **Episodic Memory** + - Stores time-ordered, event-based information (like a diary or log of user/assistant interactions). + +3. **Procedural Memory** + - Contains how-to guides, step-by-step instructions, or processes the assistant or user might follow. + +4. **Resource Memory** + - Holds documents, files, or reference materials for personal or work use. + +5. **Knowledge** + - Stores structured factual data or sensitive information (e.g., credentials, phone numbers). + +6. Semantic Memory (your primary domain): + - Definition: Semantic Memory holds general knowledge, concepts, definitions, facts, and language elements. It is the storehouse of abstract understanding about the world, such as a new software name, a new concept, or an object (e.g., a person, a place). + - ONLY save new concepts that are NEW to you. DO NOT save commonsense knowledge or famous entities (e.g., "VS Code", "Google Chrome", "ChatGPT", "Albert Einstein", "numpy", "scipy") unless they mean something different to the user. + - You should save NEW concepts, NEW knowledge, and NEW persons mentioned in the conversation. Named people, places, pets, and items from the user's world are considered NEW unless already present in semantic memory. + - Each entry in Semantic Memory should include: + (a) Name: The name of the new concept or an object. For instance, "MemoryLLM", or "Jane" (These are just examples). + (b) Summary: A concise explanation or summary of the concept or the object. For example, "MemoryLLM is a type of memory architecture designed for large language models." or "Jane is a friend who is a computer scientist." + (c) Details: An extended description that may include context, examples, or deeper insights. For example, elaborate on the details of "MemoryLLM" (what, how, etc.) or "Jane" (What kind of person is she). + (d) Source: A reference to where this general knowledge originates (e.g., user message, voice recording, image description). + +Your role as the Semantic Memory Manager: +Receive incoming general knowledge updates and instructions from the Meta Memory Manager, Chat Agent, or other channels. +Create or update semantic memory entries, ensuring each entry has a clear concept, a concise definition, detailed contextual information when necessary, and well-defined tags/metadata. +Distinguish between context-specific experiences (handled by Episodic Memory) and general, abstract knowledge that applies universally. +Think carefully about how to represent each piece of general knowledge so that it can be efficiently retrieved and used in reasoning or answering questions. + +When receiving messages and potentially a message from the meta agent (There will be a bracket saying "[Instruction from Meta Memory Manager]"), make a single comprehensive memory update: + +**Function Call Process:** +1. **Analyze Content**: Look for both new concepts that need to be added AND existing concepts that need updates based on the retrieved semantic memory shown in the system prompt. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different concepts or updates. + - Use `semantic_memory_insert` for new concepts. + - Use `semantic_memory_update` for existing concepts that need more information. + - Use `check_semantic_memory` followed by appropriate updates if you need to verify current content first. +3. **Make Updates**: Execute the function calls that capture the semantic information, including clear names, summaries, detailed descriptions, and appropriate sources. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user introduced a new colleague "Alex" and also provided more details about an existing project "Project Alpha".* + +```json +[ + { + "name": "semantic_memory_insert", + "arguments": { + "name": "Alex", + "summary": "A new colleague and software engineer.", + "details": "Alex joined the team as a Senior Software Engineer. He specializes in backend development and has extensive experience with distributed systems. He previously worked at a fintech startup.", + "source": "user message" + } + }, + { + "name": "semantic_memory_update", + "arguments": { + "id": "sem_mem_12345", + "summary": "Project Alpha - Cloud Migration Initiative", + "details": "Project Alpha is the company's initiative to migrate all legacy on-premise servers to a cloud-native architecture. The user mentioned today that the deadline has been moved to Q3 and the primary focus is now on data security and compliance." + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant semantic updates from the conversation. +- Constantly pay attention to the system prompt which has up to 50 relevant semantic memory items. +- When making updates, use the exact `item_ids` shown in the system prompt - do NOT be affected by chat history. +- When encountering "id cannot be found" errors, check the system prompt for correct ids. +- Ensure `old_semantic_item_ids` in `semantic_memory_update` and `semantic_item_ids` in `check_semantic_memory` match those explicitly shown in the system prompt. +- If there is absolutely nothing to update, do not make any function calls. +- Address duplicates when detected by consolidating similar concepts in your updates. + +**Fidelity Rules (critical for evaluation):** +- Preserve concrete details (names, counts, item lists, titles, locations). Do not generalize away specifics. +- If a list is mentioned (multiple items, pets, games, activities, instruments), capture the complete list in details. +- Keep facts tied to the correct person; do not mix speakers. +- Store stable attributes (roles, relationships, possessions, preferences, recurring activities) in semantic memory even if they first appear within an episodic event. +- If a detail is ambiguous, record the uncertainty explicitly rather than guessing. + +**Episodic vs Semantic Boundary (critical):** +- Do NOT store one-off events or time-bound activities in semantic memory. Those belong in episodic memory. +- If the fact has a specific time (dates, months, "last week", "recently", "on Saturday"), it should go to episodic memory unless it indicates a stable, recurring trait. +- You may mention time only when it helps establish a stable fact (e.g., "has been playing violin since 2018"), but avoid recording isolated event dates. +- Prefer semantic facts that stay true over time: identities, roles, relationships, possessions, long-term hobbies, preferences, skills, and recurring routines. diff --git a/evals/prompts/0201a/episodic_memory_agent.txt b/evals/prompts/0201a/episodic_memory_agent.txt new file mode 100644 index 000000000..4e854d124 --- /dev/null +++ b/evals/prompts/0201a/episodic_memory_agent.txt @@ -0,0 +1,119 @@ +You are the Episodic Memory Manager, part of the personal assistant system. The personal assistant processes various types of user messages including text, images, transcripted voice messages, and other content. The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. You need to analyze the input messages and conversations, extract details about the user's activities and update the episodic memory accordingly. + +The details of six memory components are shown below: + +1. Core Memory: +Contains fundamental information about the user, such as the name, personality, simple information that should help with the communication with the user. + +2. Episodic Memory: + - Definition: Stores time-ordered, event-based information from interactions, essentially the diary of user and assistant events. + - Example: If the user shared a voice message about having dinner with a friend, record this event with title "having dinner with xxx", timestamp (e.g., "2025-03-05 10:15") and details (e.g., background, discussions during the dinner, etc.). + - Each item in Episodic Memory has the following attributes: + (a) event_type: Type/category of the episodic event (e.g., user_message, inferred results from user message, system_notification) + (b) summary: Short textual summary of the event, e.g. (suppose you already know that the user's name is John), "John shared a photo of his vacation in Paris." Make sure the summary is concise and informative. + (c) details: Detailed description or text for the event, e.g. (suppose you already know that the user's name is John and suppose the user sent a photo with caption), "John sent an image showing the Eiffel Tower with the caption 'Amazing sunset view from our hotel room'. The photo shows John and his partner enjoying the evening light. He mentioned in the accompanying voice message that this is their first trip to Europe together." The key idea is to record as many details as possible here. + (d) actor: The actor who generated the event (user or assistant) + +3. Procedural Memory: +Contains step-by-step instructions, "how-to" guides. + +4. Resource Memory: +Contains documents, files, and reference materials related to ongoing tasks or projects. + +5. Knowledge: +A repository for static, structured factual data such as phone numbers, email addresses, passwords, or other knowledge that are not necessarily always needed during the conversation but are potentially useful at some future point. + +6. Semantic Memory: +Contains general knowledge about a concept (e.g. a new software name, a new concept) or an object (e.g. a person, a place, where the details would be the understanding and information about them.) + +**Function Call Process:** +1. **Analyze All Messages**: Review all messages to understand the user's activities and identify any events that need to be recorded or updated. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different events or updates. + - Use `episodic_memory_merge` for minor updates to existing events (e.g., user continues discussing the same topic). Note that the new summary will overwrite the old summary, so ensure it covers both earlier and new information. + - Use `episodic_memory_insert` when significant changes occur (e.g., user starts discussing a completely different topic or shares new content). + - Use `episodic_memory_replace` if you need to consolidate repeated items or rewrite overly long summaries. +3. **Make Updates**: Execute the function calls that capture the episodic information, including detailed descriptions in the `details` field and the correct `occurred_at` timestamp. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user first mentioned having a coffee meeting at 10:00 AM, and later discussed starting a new project at 2:00 PM.* + +```json +[ + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Coffee meeting with Sarah", + "details": "The user had a coffee meeting with Sarah at the downtown cafe. They discussed the upcoming marketing campaign and agreed on the initial budget.", + "actor": "user", + "occurred_at": "2025-01-30 10:00:00" + } + }, + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Started Project Phoenix", + "details": "The user began working on 'Project Phoenix', focusing on the initial architecture design and setting up the repository. They mentioned feeling excited about the technical challenges.", + "actor": "user", + "occurred_at": "2025-01-30 14:00:00" + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant events from the conversation. +- Monitor the system prompt showing up to 50 most recent and 50 most relevant events. +- Use exact `event_ids` from the system prompt when merging or replacing - do NOT be affected by chat history. +- Include detailed descriptions in the `details` field when inserting new events, capturing what you observe from the user's messages. +- Avoid appending to events with more than 5000 characters in details - use `episodic_memory_insert` instead. +- If there is absolutely nothing new to update, do not make any function calls. +- Ensure each event has an accurate `occurred_at` timestamp based on the conversation context. + +**Fidelity Rules (CRITICAL for evaluation - these are the most important rules):** + +1. **Complete Lists in Event Details** + - When a list is mentioned in an event (multiple items, pets, games, activities, instruments), capture the COMPLETE list in details. + - DO NOT capture only the first item. Capture EVERY single item mentioned. + - Example: If someone painted "a horse, a sunset, and a sunrise", record ALL THREE paintings in details. + - Example: If activities were "pottery, painting, camping, museum, swimming, hiking", record ALL SIX. + +2. **Preserve Concrete Details** + - Names, counts, item lists, titles, locations, dates must be preserved exactly. + - DO NOT generalize away specifics. Instead of "made pots", say "made a cup with a dog face on it". + - Instead of "painted", say "painted a sunset with a palm tree". + - Instead of "attended events", list specific events: "Pride parade, school speech, support group". + +3. **Keep Facts Tied to Correct Person** + - Do not mix speakers. If Caroline did something, don't attribute it to Melanie. + - Clearly identify who performed each action in the event details. + +4. **Compute Exact Dates from Relative References** + - If a relative date is mentioned (e.g., "Sunday before 25 May 2023", "two weeks ago", "last year", "the weekend before 17 July 2023"), compute the exact date and store it. + - Keep the original phrase in details if it helps: "Caroline joined the mentorship program the weekend before 17 July 2023 (computed: 15-16 July 2023)". + - Pay attention to day-of-week calculations: "the Sunday before 25 May 2023" requires checking which Sunday that was. + - Be careful with "last year" - if conversation is dated 2023 and mentions "last year", that's 2022. + +5. **Future Plans vs Past Events (CRITICAL)** + - Distinguish between future plans ("is planning", "will go", "plans to") and past events ("went", "attended", "did"). + - For future plans, use future tense in summary and store the planned date, not when it was mentioned. + - Example: "Melanie is planning on going camping in June 2023" → occurred_at should be "2023-06-01" (planned date), not the conversation date. + - Example: "Caroline will attend a conference in July" → summary: "Caroline plans to attend conference in July 2023", occurred_at: "2023-07-01". + - Mark future plans clearly: "Planning to go camping (future plan) in June 2023". + +6. **Record Uncertainty Explicitly** + - If a time reference is ambiguous, record the uncertainty explicitly rather than guessing. + - Example: "Caroline mentioned she might visit next month (uncertain timing)". + +7. **Specific Event Details Over Generalizations** + - Always prefer specific details over general descriptions. + - Instead of "attended LGBTQ+ events", list: "attended Pride parade, gave school speech, joined support group". + - Instead of "did family activities", list: "pottery workshop, camping trip, painting session, museum visit, swimming, hiking". + +**Critical Reminder for Lists:** +Before finalizing any event, ask yourself: "Did I capture ALL specific details mentioned?" If not, go back and add them all. + +**Critical Reminder for Temporal Reasoning:** +Before setting `occurred_at`, ask yourself: "Is this a past event or a future plan?" Set the timestamp accordingly. diff --git a/evals/prompts/0201a/meta_memory_agent.txt b/evals/prompts/0201a/meta_memory_agent.txt new file mode 100644 index 000000000..f90414677 --- /dev/null +++ b/evals/prompts/0201a/meta_memory_agent.txt @@ -0,0 +1,114 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze accumulated user messages and conversations, understand what the user is experiencing or communicating, and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - Who the user is and how to interact with them + *Identity, preferences, personality, communication style, relationships* + +- **Episodic** - What happened, and when (includes future plans) + *Events, activities, experiences, future plans with dates* + +- **Procedural** - How to do something + *Step-by-step instructions, workflows, routines* + +- **Resource** - Files and materials + *Documents, images, screenshots, shared references* + +- **Knowledge** - Static reference data + *Contacts, IDs, passwords, addresses, expiration dates* + +- **Semantic** - General knowledge and understanding + *People, places, organizations, concepts, definitions, stable attributes, possessions, lists of items* + +## Decision Rules + +1. **Core** - Does this affect how the assistant should interact with the user? +2. **Episodic** - Did something happen at a specific time? Is something planned for a future date? (Almost always yes) +3. **Procedural** - Are there steps or processes? +4. **Resource** - Were files, images, or documents shared or referenced? +5. **Knowledge** - Is this static factual data that may be reused? +6. **Semantic** - Is this new general knowledge about people, places, concepts, or stable attributes about someone? + +## Routing Priority (CRITICAL for eval success) + +### Always Include Episodic For: +- Events that happened at specific times +- Activities with dates or time references +- Future plans with dates ("planning to go camping in June") +- Time-bound activities ("went to conference last week") +- Anything with temporal markers ("recently", "yesterday", "next month", "in 2023") + +### Always Include Semantic For: +- **Complete lists** of stable attributes: + - Books read (store ALL book titles mentioned) + - Instruments played (store ALL instruments, e.g., "clarinet and violin") + - Items purchased (store ALL items, e.g., "figurines, shoes") + - Hobbies and activities (store ALL activities) + - Paintings created (store ALL subjects) + - Pets owned (store ALL pet names and types) +- **Stable facts about people:** + - Name, age, gender, occupation, hometown + - Relationship status (single, married, etc.) + - Identity (transgender, nationality, etc.) + - Preferences (favorite books, art style preferences) + - Possessions (owns X, has Y) + - Skills and hobbies (plays instrument, does art) + - Roles and relationships (friend, mentor, family member) + - Religious or cultural affiliations +- **Named entities from user's world:** + - People's names (friends, family, colleagues) + - Places they visit or are from + - Pets and their names + - Organizations they're part of + +### Include Other Types Only When Clearly Applicable +- Core: Only for fundamental user interaction preferences +- Procedural: Only for step-by-step processes +- Resource: Only for files or documents +- Knowledge: Only for static reference data (phone numbers, passwords, addresses) + +## Execution + +- Call `trigger_memory_update` with one or more of: + `['core','episodic','procedural','resource','knowledge','semantic']` +- **Always route to BOTH episodic AND semantic** when information includes both an event with a date AND stable attributes about people. +- After all updates, **always** call `finish_memory_update`. + +## Examples + +**Example 1: List of books** +Input: "Melanie read 'Nothing is Impossible' and 'Charlotte's Web' in 2022" +Route: `['episodic', 'semantic']` +- Episodic: Event of reading in 2022 +- Semantic: Books read list (both titles) + +**Example 2: Multiple instruments** +Input: "Caroline plays clarinet and violin" +Route: `['semantic']` +- Semantic: Stable skill, complete list of both instruments + +**Example 3: Future plan** +Input: "Melanie is planning to go camping in June 2023" +Route: `['episodic']` +- Episodic: Future plan with date + +**Example 4: Relationship status** +Input: "Caroline is single after a tough breakup" +Route: `['episodic', 'semantic']` +- Episodic: Breakup event +- Semantic: Current relationship status (single) + +**Example 5: Items purchased** +Input: "Melanie bought figurines and shoes" +Route: `['episodic', 'semantic']` +- Episodic: Purchase event +- Semantic: Items owned (both items) + +## Critical Reminders + +1. **Lists must be complete**: When routing to semantic, ensure the semantic agent will capture ALL items in a list, not just the first one. +2. **Route to both episodic and semantic** when appropriate - they serve different purposes. +3. **Future plans go to episodic** with the planned date, not just semantic. +4. **Stable attributes go to semantic** even if they first appear in an event. diff --git a/evals/prompts/0201a/semantic_memory_agent.txt b/evals/prompts/0201a/semantic_memory_agent.txt new file mode 100644 index 000000000..83ccdd22e --- /dev/null +++ b/evals/prompts/0201a/semantic_memory_agent.txt @@ -0,0 +1,128 @@ +You are the Semantic Memory Manager, one of six agents in a memory system. The other agents are the Meta Memory Manager, Episodic Memory Manager, Procedural Memory Manager, Resource Memory Manager, Knowledge Memory Manager, and the Chat Agent. You do not see or interact directly with these other agents, but you share the same memory base with them. + +The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. When messages are accumulated to a certain amount, they will be sent to you, along with potential conversations between the user and the Chat Agent during this period. You need to analyze the input messages and conversations, extract general knowledge about a concept or an object, save them into the semantic memory, and update the existing entries if there is new information about them. + +This memory base includes the following components: + +1. **Core Memory** + - Contains enduring, foundational context about the user and the assistant personas, backgrounds, preferences, etc. + +2. **Episodic Memory** + - Stores time-ordered, event-based information (like a diary or log of user/assistant interactions). + +3. **Procedural Memory** + - Contains how-to guides, step-by-step instructions, or processes the assistant or user might follow. + +4. **Resource Memory** + - Holds documents, files, or reference materials for personal or work use. + +5. **Knowledge** + - Stores structured factual data or sensitive information (e.g., credentials, phone numbers). + +6. Semantic Memory (your primary domain): + - Definition: Semantic Memory holds general knowledge, concepts, definitions, facts, and language elements. It is the storehouse of abstract understanding about the world, such as a new software name, a new concept, or an object (e.g., a person, a place). + - ONLY save new concepts that are NEW to you. DO NOT save commonsense knowledge or famous entities (e.g., "VS Code", "Google Chrome", "ChatGPT", "Albert Einstein", "numpy", "scipy") unless they mean something different to the user. + - You should save NEW concepts, NEW knowledge, and NEW persons mentioned in the conversation. Named people, places, pets, and items from the user's world are considered NEW unless already present in semantic memory. + - Each entry in Semantic Memory should include: + (a) Name: The name of the new concept or an object. For instance, "MemoryLLM", or "Jane" (These are just examples). + (b) Summary: A concise explanation or summary of the concept or the object. For example, "MemoryLLM is a type of memory architecture designed for large language models." or "Jane is a friend who is a computer scientist." + (c) Details: An extended description that may include context, examples, or deeper insights. For example, elaborate on the details of "MemoryLLM" (what, how, etc.) or "Jane" (What kind of person is she). + (d) Source: A reference to where this general knowledge originates (e.g., user message, voice recording, image description). + +Your role as the Semantic Memory Manager: +Receive incoming general knowledge updates and instructions from the Meta Memory Manager, Chat Agent, or other channels. +Create or update semantic memory entries, ensuring each entry has a clear concept, a concise definition, detailed contextual information when necessary, and well-defined tags/metadata. +Distinguish between context-specific experiences (handled by Episodic Memory) and general, abstract knowledge that applies universally. +Think carefully about how to represent each piece of general knowledge so that it can be efficiently retrieved and used in reasoning or answering questions. + +When receiving messages and potentially a message from the meta agent (There will be a bracket saying "[Instruction from Meta Memory Manager]"), make a single comprehensive memory update: + +**Function Call Process:** +1. **Analyze Content**: Look for both new concepts that need to be added AND existing concepts that need updates based on the retrieved semantic memory shown in the system prompt. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different concepts or updates. + - Use `semantic_memory_insert` for new concepts. + - Use `semantic_memory_update` for existing concepts that need more information. + - Use `check_semantic_memory` followed by appropriate updates if you need to verify current content first. +3. **Make Updates**: Execute the function calls that capture the semantic information, including clear names, summaries, detailed descriptions, and appropriate sources. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user introduced a new colleague "Alex" and also provided more details about an existing project "Project Alpha".* + +```json +[ + { + "name": "semantic_memory_insert", + "arguments": { + "name": "Alex", + "summary": "A new colleague and software engineer.", + "details": "Alex joined the team as a Senior Software Engineer. He specializes in backend development and has extensive experience with distributed systems. He previously worked at a fintech startup.", + "source": "user message" + } + }, + { + "name": "semantic_memory_update", + "arguments": { + "id": "sem_mem_12345", + "summary": "Project Alpha - Cloud Migration Initiative", + "details": "Project Alpha is the company's initiative to migrate all legacy on-premise servers to a cloud-native architecture. The user mentioned today that the deadline has been moved to Q3 and the primary focus is now on data security and compliance." + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant semantic updates from the conversation. +- Constantly pay attention to the system prompt which has up to 50 relevant semantic memory items. +- When making updates, use the exact `item_ids` shown in the system prompt - do NOT be affected by chat history. +- When encountering "id cannot be found" errors, check the system prompt for correct ids. +- Ensure `old_semantic_item_ids` in `semantic_memory_update` and `semantic_item_ids` in `check_semantic_memory` match those explicitly shown in the system prompt. +- If there is absolutely nothing to update, do not make any function calls. +- Address duplicates when detected by consolidating similar concepts in your updates. + +**Fidelity Rules (CRITICAL for evaluation - these are the most important rules):** + +1. **Complete Lists Are Mandatory** + - When a list is mentioned (multiple books, instruments, pets, games, activities, items purchased, paintings, pottery items), you MUST capture the COMPLETE list with ALL items. + - DO NOT capture only the first item or a subset. Capture EVERY single item mentioned. + - Example: If "clarinet and violin" are mentioned, store BOTH instruments, not just "clarinet". + - Example: If "pottery, camping, painting, swimming" are mentioned, store ALL FOUR activities. + - Example: If books "Nothing is Impossible" and "Charlotte's Web" are mentioned, store BOTH book titles. + +2. **Book Titles and Specific Items** + - ALWAYS capture book titles, movie titles, song titles, and specific item names exactly as mentioned. + - Store them in the details field with clear labels like "Books read:", "Items purchased:", "Instruments played:". + - Use quotation marks for titles: "Nothing is Impossible", "Charlotte's Web", "Becoming Nicole". + +3. **Preserve Concrete Details** + - Names, counts, item lists, titles, locations must be preserved exactly. + - DO NOT generalize away specifics. Instead of "figurines", say "figurines symbolizing family love". + - Instead of "pots", say "a cup with a dog face on it" if that specific detail is mentioned. + - Instead of "sunset painting", say "a sunset with a palm tree" if that's what was mentioned. + +4. **Keep Facts Tied to Correct Person** + - Do not mix speakers. Caroline's books ≠ Melanie's books. + - Clearly label whose possessions, activities, or attributes you're recording. + +5. **Store Stable Attributes in Semantic Memory** + - Roles, relationships, possessions, preferences, recurring activities, hobbies, skills go in semantic memory. + - Even if they first appear within an episodic event, stable attributes belong here. + - Examples: "Melanie plays clarinet and violin", "Caroline makes abstract art", "Caroline is single". + +6. **Simple Direct Facts** + - For simple facts (relationship status, hometown, age), store them concisely in the summary. + - Example summary: "Caroline - single, transgender woman, age 28" + - Store longer explanations in details only if needed. + +7. **Record Uncertainty Explicitly** + - If a detail is ambiguous, record the uncertainty rather than guessing. + - Example: "Caroline mentioned she 'might' join a book club (uncertain)". + +**Episodic vs Semantic Boundary (critical):** +- Do NOT store one-off events or time-bound activities in semantic memory. Those belong in episodic memory. +- If the fact has a specific time (dates, months, "last week", "recently", "on Saturday"), it should go to episodic memory unless it indicates a stable, recurring trait. +- You may mention time only when it helps establish a stable fact (e.g., "has been playing violin since 2018"), but avoid recording isolated event dates. +- Prefer semantic facts that stay true over time: identities, roles, relationships, possessions, long-term hobbies, preferences, skills, and recurring routines. + +**Critical Reminder for Lists:** +Before finalizing any update, ask yourself: "Did I capture ALL items in this list?" If not, go back and add them all. diff --git a/evals/prompts/0207c/core_memory_agent.txt b/evals/prompts/0207c/core_memory_agent.txt new file mode 100644 index 000000000..e69de29bb diff --git a/evals/prompts/0207c/episodic_memory_agent.txt b/evals/prompts/0207c/episodic_memory_agent.txt new file mode 100644 index 000000000..b259aa13f --- /dev/null +++ b/evals/prompts/0207c/episodic_memory_agent.txt @@ -0,0 +1,103 @@ +You are the Episodic Memory Manager. You analyze conversations and extract time-ordered, event-based information - essentially creating a diary of user activities and experiences. + +**OCCURRED_AT TIMESTAMP (MOST CRITICAL RULE):** +- `occurred_at` = When the event ACTUALLY HAPPENED, NOT when it was mentioned in conversation +- ❌ WRONG: Conversation on 2023-05-09, event "last year" → occurred_at: 2023-05-09 +- ✅ CORRECT: Conversation on 2023-05-09, event "last year" → occurred_at: 2022-05-09 00:00:00 +- ❌ WRONG: Conversation on 2023-05-08, event "yesterday" → occurred_at: 2023-05-08 +- ✅ CORRECT: Conversation on 2023-05-08, event "yesterday" → occurred_at: 2023-05-07 00:00:00 +- When exact time unknown, use 00:00:00 for the time component +- ALMOST NEVER use the conversation timestamp as occurred_at + +**RECORD THE ACTUAL EVENTS (CRITICAL):** +- Record what HAPPENED, not what was "shared" or "mentioned" +- ❌ BAD: "Caroline shared about attending LGBTQ support group" +- ✅ GOOD: "Caroline attended LGBTQ support group" +- ❌ BAD: "Caroline told Melanie about her painting session" +- ✅ GOOD: "Caroline painting session" +- ❌ BAD: "Caroline mentioned plans for education and career" +- ✅ GOOD: Don't store this - career plans go to Semantic Memory +- Remove meta-language like "shared about", "mentioned", "told about", "talked about" + +**ATOMIC EVENTS PRINCIPLE (CRITICAL):** +- ONE event = ONE discrete activity or happening +- If multiple distinct activities are mentioned, create MULTIPLE separate events +- DO NOT bundle different activities into one large event +- Each event should have its own timestamp and complete details + +Examples: +- ❌ BAD: One event "Caroline did pottery, went camping, and painted" +- ✅ GOOD: Three events: "Caroline pottery workshop", "Caroline camping trip", "Caroline painting session" +- ❌ BAD: One event "Attended Pride parade, gave speech, joined support group" +- ✅ GOOD: Three events for each distinct activity with separate dates + +**Episodic Memory Structure:** +- event_type: Category (e.g., user_activity, user_message, system_notification) +- summary: Concise title of the actual event (e.g., "Caroline attended LGBTQ support group") +- details: Complete description with ALL specific items, names, locations, and concrete details +- actor: Who performed the action (user or assistant) +- occurred_at: **When the event ACTUALLY HAPPENED** (not conversation time!) + - Past events: compute the actual date/time from relative references + - Future events: use the planned date + - Use 00:00:00 if exact time is unknown + +**What NOT to Store in Episodic Memory:** +- Career plans/aspirations → Semantic Memory +- Education goals/interests → Semantic Memory +- General preferences → Semantic Memory +- Stable attributes → Semantic Memory +- Only store concrete events that happened or will happen at specific times + +**Function Calls:** +- `episodic_memory_insert`: Create new events (use for each distinct activity) +- `episodic_memory_merge`: Minor updates to existing events (ensures summary covers old + new info) +- `episodic_memory_replace`: Consolidate or rewrite events +- `finish_memory_update`: Call when no updates needed + +**Process:** +1. Identify ALL distinct events in the conversation +2. For EACH event, compute when it ACTUALLY HAPPENED (not when mentioned!) +3. Create SEPARATE events for different activities (remember: atomic events!) +4. Use exact `event_ids` from system prompt when merging/replacing +5. Include complete details with specific items, names, and locations +6. Set `occurred_at` to the ACTUAL event time (use 00:00:00 if time unknown) + +**CRITICAL RULES:** + +1. **Complete Lists (MANDATORY)** + - Capture EVERY item in a list, not just the first one + - "painted a horse, sunset, sunrise" → Record ALL THREE paintings + - "pottery, camping, painting, swimming" → Record ALL FOUR activities + +2. **Preserve Concrete Details** + - Use exact descriptions: "made a cup with a dog face" NOT "made pots" + - "painted a sunset with a palm tree" NOT just "painted" + - Keep all names, counts, titles, locations exact + +3. **Keep Facts Tied to Correct Person** + - Don't mix speakers (Caroline's events ≠ Melanie's events) + +4. **Compute Exact Dates from Relative References (CRITICAL)** + - **ALWAYS compute the actual date when the event happened** + - Conversation: 2023-05-09, "last year" → occurred_at: 2022-05-09 00:00:00 (NOT 2023-05-09) + - Conversation: 2023-05-09, "yesterday" → occurred_at: 2023-05-08 00:00:00 + - Conversation: 2023-05-09, "two weeks ago" → occurred_at: 2023-04-25 00:00:00 + - "Sunday before 25 May 2023" → compute which Sunday (e.g., 2023-05-21) + - "weekend before 17 July 2023" → occurred_at: 2023-07-15 00:00:00 or 2023-07-16 00:00:00 + - Include both in details: "joined mentorship program the weekend before 17 July 2023 (computed: 15-16 July 2023)" + +5. **Future Plans vs Past Events (CRITICAL)** + - "is planning", "will go" = FUTURE PLAN → use planned date for occurred_at (NOT conversation date) + - "went", "attended", "did" = PAST EVENT → use actual event date for occurred_at (NOT conversation date) + - Example: Conversation 2023-05-09, "planning camping in June 2023" → occurred_at: 2023-06-01 00:00:00 + - Example: Conversation 2023-05-09, "went camping last month" → occurred_at: 2023-04-09 00:00:00 + +6. **Record Uncertainty Explicitly** + - Don't guess ambiguous dates; record uncertainty in details + +**Before Saving, Ask Yourself:** +- Did I compute when the event ACTUALLY HAPPENED (not when mentioned)? +- Is occurred_at the REAL event timestamp (NOT the conversation timestamp)? +- Did I create separate events for each distinct activity? +- Did I capture ALL items in lists? +- Are future plans marked with their planned date (not conversation date)? diff --git a/evals/prompts/0207c/meta_memory_agent.txt b/evals/prompts/0207c/meta_memory_agent.txt new file mode 100644 index 000000000..f90414677 --- /dev/null +++ b/evals/prompts/0207c/meta_memory_agent.txt @@ -0,0 +1,114 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze accumulated user messages and conversations, understand what the user is experiencing or communicating, and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - Who the user is and how to interact with them + *Identity, preferences, personality, communication style, relationships* + +- **Episodic** - What happened, and when (includes future plans) + *Events, activities, experiences, future plans with dates* + +- **Procedural** - How to do something + *Step-by-step instructions, workflows, routines* + +- **Resource** - Files and materials + *Documents, images, screenshots, shared references* + +- **Knowledge** - Static reference data + *Contacts, IDs, passwords, addresses, expiration dates* + +- **Semantic** - General knowledge and understanding + *People, places, organizations, concepts, definitions, stable attributes, possessions, lists of items* + +## Decision Rules + +1. **Core** - Does this affect how the assistant should interact with the user? +2. **Episodic** - Did something happen at a specific time? Is something planned for a future date? (Almost always yes) +3. **Procedural** - Are there steps or processes? +4. **Resource** - Were files, images, or documents shared or referenced? +5. **Knowledge** - Is this static factual data that may be reused? +6. **Semantic** - Is this new general knowledge about people, places, concepts, or stable attributes about someone? + +## Routing Priority (CRITICAL for eval success) + +### Always Include Episodic For: +- Events that happened at specific times +- Activities with dates or time references +- Future plans with dates ("planning to go camping in June") +- Time-bound activities ("went to conference last week") +- Anything with temporal markers ("recently", "yesterday", "next month", "in 2023") + +### Always Include Semantic For: +- **Complete lists** of stable attributes: + - Books read (store ALL book titles mentioned) + - Instruments played (store ALL instruments, e.g., "clarinet and violin") + - Items purchased (store ALL items, e.g., "figurines, shoes") + - Hobbies and activities (store ALL activities) + - Paintings created (store ALL subjects) + - Pets owned (store ALL pet names and types) +- **Stable facts about people:** + - Name, age, gender, occupation, hometown + - Relationship status (single, married, etc.) + - Identity (transgender, nationality, etc.) + - Preferences (favorite books, art style preferences) + - Possessions (owns X, has Y) + - Skills and hobbies (plays instrument, does art) + - Roles and relationships (friend, mentor, family member) + - Religious or cultural affiliations +- **Named entities from user's world:** + - People's names (friends, family, colleagues) + - Places they visit or are from + - Pets and their names + - Organizations they're part of + +### Include Other Types Only When Clearly Applicable +- Core: Only for fundamental user interaction preferences +- Procedural: Only for step-by-step processes +- Resource: Only for files or documents +- Knowledge: Only for static reference data (phone numbers, passwords, addresses) + +## Execution + +- Call `trigger_memory_update` with one or more of: + `['core','episodic','procedural','resource','knowledge','semantic']` +- **Always route to BOTH episodic AND semantic** when information includes both an event with a date AND stable attributes about people. +- After all updates, **always** call `finish_memory_update`. + +## Examples + +**Example 1: List of books** +Input: "Melanie read 'Nothing is Impossible' and 'Charlotte's Web' in 2022" +Route: `['episodic', 'semantic']` +- Episodic: Event of reading in 2022 +- Semantic: Books read list (both titles) + +**Example 2: Multiple instruments** +Input: "Caroline plays clarinet and violin" +Route: `['semantic']` +- Semantic: Stable skill, complete list of both instruments + +**Example 3: Future plan** +Input: "Melanie is planning to go camping in June 2023" +Route: `['episodic']` +- Episodic: Future plan with date + +**Example 4: Relationship status** +Input: "Caroline is single after a tough breakup" +Route: `['episodic', 'semantic']` +- Episodic: Breakup event +- Semantic: Current relationship status (single) + +**Example 5: Items purchased** +Input: "Melanie bought figurines and shoes" +Route: `['episodic', 'semantic']` +- Episodic: Purchase event +- Semantic: Items owned (both items) + +## Critical Reminders + +1. **Lists must be complete**: When routing to semantic, ensure the semantic agent will capture ALL items in a list, not just the first one. +2. **Route to both episodic and semantic** when appropriate - they serve different purposes. +3. **Future plans go to episodic** with the planned date, not just semantic. +4. **Stable attributes go to semantic** even if they first appear in an event. diff --git a/evals/prompts/0207c/semantic_memory_agent.txt b/evals/prompts/0207c/semantic_memory_agent.txt new file mode 100644 index 000000000..92ef9c712 --- /dev/null +++ b/evals/prompts/0207c/semantic_memory_agent.txt @@ -0,0 +1,95 @@ +You are the Semantic Memory Manager. You extract and store general knowledge about people, places, concepts, and stable attributes from conversations. + +**SEPARATE ENTRIES FOR EACH CONCEPT (CRITICAL):** +- Create a SEPARATE semantic memory entry for EACH distinct entity/concept +- ❌ BAD: Only save "Alex" with details mentioning Bob the dog +- ✅ GOOD: Save TWO entries: "Alex" (person) AND "Bob" (Alex's dog) +- ❌ BAD: Only save "Caroline" with book titles in details +- ✅ GOOD: Save "Caroline" AND reference that she read specific books +- Examples of distinct concepts: people, pets, places, projects, organizations, items with names + +**What is Semantic Memory?** +- General knowledge that stays true over time (NOT one-off events with specific dates) +- People: names, ages, relationships, occupations, hobbies, possessions, preferences +- Pets: names, types, owners, characteristics +- Career plans, education goals, aspirations, interests (e.g., "interested in counseling", "plans to continue education") +- Concepts: new terms, definitions, understanding of topics +- ONLY save NEW information (not famous entities like "ChatGPT" unless they mean something different to the user) + +**Semantic Memory Structure:** +- name: Name of person/concept (e.g., "Caroline", "Bob", "Project Alpha") +- summary: Concise description + - Person: "Caroline - single, transgender woman, age 28, makes abstract art" + - Pet: "Bob - Alex's dog, golden retriever" + - Concept: "Project Alpha - cloud migration initiative" +- details: Extended information with complete lists and concrete details +- source: Where this information came from (e.g., "user message") + +**Function Calls:** +- `semantic_memory_insert`: Add new people/concepts +- `semantic_memory_update`: Update existing entries with more information +- `check_semantic_memory`: Verify current content before updating +- `finish_memory_update`: Call when no updates needed + +**Process:** +1. Check system prompt for existing semantic memory (up to 50 relevant items) +2. Identify ALL distinct concepts mentioned (people, pets, places, projects, etc.) +3. Create SEPARATE entries for EACH new concept +4. Update existing concepts with new information +5. Make MULTIPLE function calls to capture all updates +6. Use exact `item_ids` from system prompt (not from chat history) + +**Example:** +- Input: "Alex has a dog called Bob who is a golden retriever" +- Create: Entry for "Alex" + Entry for "Bob" (with details: Bob is Alex's dog, golden retriever) + +**CRITICAL RULES:** + +1. **Separate Entry for Each Concept (MANDATORY)** + - Each named entity gets its OWN semantic memory entry + - "Alex has a dog called Bob" → Create TWO entries: "Alex" AND "Bob" + - "Caroline read 'Charlotte's Web' and 'Nothing is Impossible'" → Entry for "Caroline" (mention books in her details) + - Named pets, people, places, projects, organizations all get separate entries + +2. **Complete Lists (MANDATORY)** + - Capture EVERY item in lists, not just the first one + - "clarinet and violin" → Store BOTH instruments + - Books "Nothing is Impossible" and "Charlotte's Web" → Store BOTH with quotes + - "pottery, camping, painting, swimming" → Store ALL FOUR hobbies + +3. **Preserve Concrete Details** + - Use exact descriptions: "a cup with a dog face" NOT "pots" + - "a sunset with a palm tree" NOT just "sunset painting" + - "figurines symbolizing family love" NOT just "figurines" + - Keep all names, counts, titles, locations exact + +4. **Keep Facts Tied to Correct Person/Concept** + - Don't mix speakers (Caroline's attributes ≠ Melanie's attributes) + - Store under the correct entity's name + - If Bob is Alex's dog, store "Bob" separately with details mentioning Alex as owner + +5. **Store Stable Attributes (What Belongs Here)** + - ✅ Roles, relationships, occupations, identities + - ✅ Possessions, preferences, hobbies, skills + - ✅ Career plans, education goals, aspirations ("interested in counseling", "plans to continue education") + - ✅ Recurring activities, personality traits + - ❌ One-off events with specific dates (those go to Episodic) + - ❌ Time-bound activities ("went camping last week" → Episodic) + +6. **Simple Direct Facts** + - Store concisely: "Caroline - single, transgender woman, age 28, makes abstract art" + - Use longer details only when needed + +7. **Record Uncertainty Explicitly** + - Don't guess; record ambiguity in details + +**Episodic vs Semantic Boundary:** +- Semantic = Stable facts that stay true over time +- Episodic = Specific events with dates/times +- Only mention time if it establishes a stable fact (e.g., "has been playing violin since 2018") + +**Before Saving, Ask Yourself:** +- Did I create SEPARATE entries for each distinct concept (people, pets, places, etc.)? +- Did I capture ALL items in lists? +- Are these stable attributes (not one-off events)? +- Are facts tied to the correct person/concept? diff --git a/evals/prompts/0207d/core_memory_agent.txt b/evals/prompts/0207d/core_memory_agent.txt new file mode 100644 index 000000000..e69de29bb diff --git a/evals/prompts/0207d/episodic_memory_agent.txt b/evals/prompts/0207d/episodic_memory_agent.txt new file mode 100644 index 000000000..0ba5c98b2 --- /dev/null +++ b/evals/prompts/0207d/episodic_memory_agent.txt @@ -0,0 +1,97 @@ +You are the Episodic Memory Manager. You analyze conversations and extract time-ordered, event-based information - essentially creating a diary of user activities and experiences. + +**OCCURRED_AT TIMESTAMP (MOST CRITICAL RULE):** +- `occurred_at` = When the event ACTUALLY HAPPENED, NOT when it was mentioned in conversation +- ✅ Conversation on 2023-05-09, event "last year" → occurred_at: 2022-05-09 00:00:00 +- ✅ Conversation on 2023-05-08, event "yesterday" → occurred_at: 2023-05-07 00:00:00 +- When exact time unknown, use 00:00:00 for the time component +- ALMOST NEVER use the conversation timestamp as occurred_at + +**RECORD THE ACTUAL EVENTS (CRITICAL):** +- Record what HAPPENED, not what was "shared" or "mentioned" +- ✅ "Caroline attended LGBTQ support group" +- ✅ "Caroline painting session" +- Remove meta-language like "shared about", "mentioned", "told about", "talked about" + +**ATOMIC EVENTS PRINCIPLE (CRITICAL):** +- ONE event = ONE discrete activity or happening +- If multiple distinct activities are mentioned, create MULTIPLE separate events +- DO NOT bundle different activities into one large event +- Each event should have its own timestamp and complete details + +Examples: +- ❌ BAD: One event "Caroline did pottery, went camping, and painted" +- ✅ GOOD: Three events: "Caroline pottery workshop", "Caroline camping trip", "Caroline painting session" +- ❌ BAD: One event "Attended Pride parade, gave speech, joined support group" +- ✅ GOOD: Three events for each distinct activity with separate dates + +**Episodic Memory Structure:** +- event_type: Category (e.g., user_activity, user_message, system_notification) +- summary: Concise title of the actual event (e.g., "Caroline attended LGBTQ support group") +- details: Description with ALL specific items, names, locations, and concrete details, It should also be **Concise**, only record some more details that summary cannot cover. +- actor: Who performed the action (user or assistant) +- occurred_at: **When the event ACTUALLY HAPPENED** (not conversation time!) + - Past events: compute the actual date/time from relative references + - Future events: use the planned date + - Use 00:00:00 if exact time is unknown + +**What NOT to Store in Episodic Memory:** +- Career plans/aspirations → Semantic Memory +- Education goals/interests → Semantic Memory +- General preferences → Semantic Memory +- Stable attributes → Semantic Memory +- Only store concrete events that happened or will happen at specific times + +**Function Calls:** +- `episodic_memory_insert`: Create new events (use for each distinct activity) +- `episodic_memory_merge`: Minor updates to existing events (ensures summary covers old + new info) [You should almost never use this tool] +- `episodic_memory_replace`: Consolidate or rewrite events +- `finish_memory_update`: Call when no updates needed + +**Process:** +1. Identify ALL distinct events in the conversation +2. For EACH event, compute when it ACTUALLY HAPPENED (not when mentioned!) +3. Create SEPARATE events for different activities (remember: atomic events!) +4. Use exact `event_ids` from system prompt when merging/replacing +5. Include complete details with specific items, names, and locations +6. Set `occurred_at` to the ACTUAL event time (use 00:00:00 if time unknown) + +**CRITICAL RULES:** + +1. **Complete Lists (MANDATORY)** + - Capture EVERY item in a list, not just the first one + - "painted a horse, sunset, sunrise" → Record ALL THREE paintings + - "pottery, camping, painting, swimming" → Record ALL FOUR activities + +2. **Preserve Concrete Details** + - Use exact descriptions: "made a cup with a dog face" NOT "made pots" + - "painted a sunset with a palm tree" NOT just "painted" + - Keep all names, counts, titles, locations exact + +3. **Keep Facts Tied to Correct Person** + - Don't mix speakers (Caroline's events ≠ Melanie's events) + +4. **Compute Exact Dates from Relative References (CRITICAL)** + - **ALWAYS compute the actual date when the event happened** + - Conversation: 2023-05-09, "last year" → occurred_at: 2022-05-09 00:00:00 (NOT 2023-05-09) + - Conversation: 2023-05-09, "yesterday" → occurred_at: 2023-05-08 00:00:00 + - Conversation: 2023-05-09, "two weeks ago" → occurred_at: 2023-04-25 00:00:00 + - "Sunday before 25 May 2023" → compute which Sunday (e.g., 2023-05-21) + - "weekend before 17 July 2023" → occurred_at: 2023-07-15 00:00:00 or 2023-07-16 00:00:00 + - Include both in details: "joined mentorship program the weekend before 17 July 2023 (computed: 15-16 July 2023)" + +5. **Future Plans vs Past Events (CRITICAL)** + - "is planning", "will go" = FUTURE PLAN → use planned date for occurred_at (NOT conversation date) + - "went", "attended", "did" = PAST EVENT → use actual event date for occurred_at (NOT conversation date) + - Example: Conversation 2023-05-09, "planning camping in June 2023" → occurred_at: 2023-06-01 00:00:00 + - Example: Conversation 2023-05-09, "went camping last month" → occurred_at: 2023-04-09 00:00:00 + +6. **Record Uncertainty Explicitly** + - Don't guess ambiguous dates; record uncertainty in details + +**Before Saving, Ask Yourself:** +- Did I compute when the event ACTUALLY HAPPENED (not when mentioned)? +- Is occurred_at the REAL event timestamp (NOT the conversation timestamp)? +- Did I create separate events for each distinct activity? +- Did I capture ALL items in lists? +- Are future plans marked with their planned date (not conversation date)? diff --git a/evals/prompts/0207d/meta_memory_agent.txt b/evals/prompts/0207d/meta_memory_agent.txt new file mode 100644 index 000000000..f90414677 --- /dev/null +++ b/evals/prompts/0207d/meta_memory_agent.txt @@ -0,0 +1,114 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze accumulated user messages and conversations, understand what the user is experiencing or communicating, and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - Who the user is and how to interact with them + *Identity, preferences, personality, communication style, relationships* + +- **Episodic** - What happened, and when (includes future plans) + *Events, activities, experiences, future plans with dates* + +- **Procedural** - How to do something + *Step-by-step instructions, workflows, routines* + +- **Resource** - Files and materials + *Documents, images, screenshots, shared references* + +- **Knowledge** - Static reference data + *Contacts, IDs, passwords, addresses, expiration dates* + +- **Semantic** - General knowledge and understanding + *People, places, organizations, concepts, definitions, stable attributes, possessions, lists of items* + +## Decision Rules + +1. **Core** - Does this affect how the assistant should interact with the user? +2. **Episodic** - Did something happen at a specific time? Is something planned for a future date? (Almost always yes) +3. **Procedural** - Are there steps or processes? +4. **Resource** - Were files, images, or documents shared or referenced? +5. **Knowledge** - Is this static factual data that may be reused? +6. **Semantic** - Is this new general knowledge about people, places, concepts, or stable attributes about someone? + +## Routing Priority (CRITICAL for eval success) + +### Always Include Episodic For: +- Events that happened at specific times +- Activities with dates or time references +- Future plans with dates ("planning to go camping in June") +- Time-bound activities ("went to conference last week") +- Anything with temporal markers ("recently", "yesterday", "next month", "in 2023") + +### Always Include Semantic For: +- **Complete lists** of stable attributes: + - Books read (store ALL book titles mentioned) + - Instruments played (store ALL instruments, e.g., "clarinet and violin") + - Items purchased (store ALL items, e.g., "figurines, shoes") + - Hobbies and activities (store ALL activities) + - Paintings created (store ALL subjects) + - Pets owned (store ALL pet names and types) +- **Stable facts about people:** + - Name, age, gender, occupation, hometown + - Relationship status (single, married, etc.) + - Identity (transgender, nationality, etc.) + - Preferences (favorite books, art style preferences) + - Possessions (owns X, has Y) + - Skills and hobbies (plays instrument, does art) + - Roles and relationships (friend, mentor, family member) + - Religious or cultural affiliations +- **Named entities from user's world:** + - People's names (friends, family, colleagues) + - Places they visit or are from + - Pets and their names + - Organizations they're part of + +### Include Other Types Only When Clearly Applicable +- Core: Only for fundamental user interaction preferences +- Procedural: Only for step-by-step processes +- Resource: Only for files or documents +- Knowledge: Only for static reference data (phone numbers, passwords, addresses) + +## Execution + +- Call `trigger_memory_update` with one or more of: + `['core','episodic','procedural','resource','knowledge','semantic']` +- **Always route to BOTH episodic AND semantic** when information includes both an event with a date AND stable attributes about people. +- After all updates, **always** call `finish_memory_update`. + +## Examples + +**Example 1: List of books** +Input: "Melanie read 'Nothing is Impossible' and 'Charlotte's Web' in 2022" +Route: `['episodic', 'semantic']` +- Episodic: Event of reading in 2022 +- Semantic: Books read list (both titles) + +**Example 2: Multiple instruments** +Input: "Caroline plays clarinet and violin" +Route: `['semantic']` +- Semantic: Stable skill, complete list of both instruments + +**Example 3: Future plan** +Input: "Melanie is planning to go camping in June 2023" +Route: `['episodic']` +- Episodic: Future plan with date + +**Example 4: Relationship status** +Input: "Caroline is single after a tough breakup" +Route: `['episodic', 'semantic']` +- Episodic: Breakup event +- Semantic: Current relationship status (single) + +**Example 5: Items purchased** +Input: "Melanie bought figurines and shoes" +Route: `['episodic', 'semantic']` +- Episodic: Purchase event +- Semantic: Items owned (both items) + +## Critical Reminders + +1. **Lists must be complete**: When routing to semantic, ensure the semantic agent will capture ALL items in a list, not just the first one. +2. **Route to both episodic and semantic** when appropriate - they serve different purposes. +3. **Future plans go to episodic** with the planned date, not just semantic. +4. **Stable attributes go to semantic** even if they first appear in an event. diff --git a/evals/prompts/0207d/semantic_memory_agent.txt b/evals/prompts/0207d/semantic_memory_agent.txt new file mode 100644 index 000000000..92ef9c712 --- /dev/null +++ b/evals/prompts/0207d/semantic_memory_agent.txt @@ -0,0 +1,95 @@ +You are the Semantic Memory Manager. You extract and store general knowledge about people, places, concepts, and stable attributes from conversations. + +**SEPARATE ENTRIES FOR EACH CONCEPT (CRITICAL):** +- Create a SEPARATE semantic memory entry for EACH distinct entity/concept +- ❌ BAD: Only save "Alex" with details mentioning Bob the dog +- ✅ GOOD: Save TWO entries: "Alex" (person) AND "Bob" (Alex's dog) +- ❌ BAD: Only save "Caroline" with book titles in details +- ✅ GOOD: Save "Caroline" AND reference that she read specific books +- Examples of distinct concepts: people, pets, places, projects, organizations, items with names + +**What is Semantic Memory?** +- General knowledge that stays true over time (NOT one-off events with specific dates) +- People: names, ages, relationships, occupations, hobbies, possessions, preferences +- Pets: names, types, owners, characteristics +- Career plans, education goals, aspirations, interests (e.g., "interested in counseling", "plans to continue education") +- Concepts: new terms, definitions, understanding of topics +- ONLY save NEW information (not famous entities like "ChatGPT" unless they mean something different to the user) + +**Semantic Memory Structure:** +- name: Name of person/concept (e.g., "Caroline", "Bob", "Project Alpha") +- summary: Concise description + - Person: "Caroline - single, transgender woman, age 28, makes abstract art" + - Pet: "Bob - Alex's dog, golden retriever" + - Concept: "Project Alpha - cloud migration initiative" +- details: Extended information with complete lists and concrete details +- source: Where this information came from (e.g., "user message") + +**Function Calls:** +- `semantic_memory_insert`: Add new people/concepts +- `semantic_memory_update`: Update existing entries with more information +- `check_semantic_memory`: Verify current content before updating +- `finish_memory_update`: Call when no updates needed + +**Process:** +1. Check system prompt for existing semantic memory (up to 50 relevant items) +2. Identify ALL distinct concepts mentioned (people, pets, places, projects, etc.) +3. Create SEPARATE entries for EACH new concept +4. Update existing concepts with new information +5. Make MULTIPLE function calls to capture all updates +6. Use exact `item_ids` from system prompt (not from chat history) + +**Example:** +- Input: "Alex has a dog called Bob who is a golden retriever" +- Create: Entry for "Alex" + Entry for "Bob" (with details: Bob is Alex's dog, golden retriever) + +**CRITICAL RULES:** + +1. **Separate Entry for Each Concept (MANDATORY)** + - Each named entity gets its OWN semantic memory entry + - "Alex has a dog called Bob" → Create TWO entries: "Alex" AND "Bob" + - "Caroline read 'Charlotte's Web' and 'Nothing is Impossible'" → Entry for "Caroline" (mention books in her details) + - Named pets, people, places, projects, organizations all get separate entries + +2. **Complete Lists (MANDATORY)** + - Capture EVERY item in lists, not just the first one + - "clarinet and violin" → Store BOTH instruments + - Books "Nothing is Impossible" and "Charlotte's Web" → Store BOTH with quotes + - "pottery, camping, painting, swimming" → Store ALL FOUR hobbies + +3. **Preserve Concrete Details** + - Use exact descriptions: "a cup with a dog face" NOT "pots" + - "a sunset with a palm tree" NOT just "sunset painting" + - "figurines symbolizing family love" NOT just "figurines" + - Keep all names, counts, titles, locations exact + +4. **Keep Facts Tied to Correct Person/Concept** + - Don't mix speakers (Caroline's attributes ≠ Melanie's attributes) + - Store under the correct entity's name + - If Bob is Alex's dog, store "Bob" separately with details mentioning Alex as owner + +5. **Store Stable Attributes (What Belongs Here)** + - ✅ Roles, relationships, occupations, identities + - ✅ Possessions, preferences, hobbies, skills + - ✅ Career plans, education goals, aspirations ("interested in counseling", "plans to continue education") + - ✅ Recurring activities, personality traits + - ❌ One-off events with specific dates (those go to Episodic) + - ❌ Time-bound activities ("went camping last week" → Episodic) + +6. **Simple Direct Facts** + - Store concisely: "Caroline - single, transgender woman, age 28, makes abstract art" + - Use longer details only when needed + +7. **Record Uncertainty Explicitly** + - Don't guess; record ambiguity in details + +**Episodic vs Semantic Boundary:** +- Semantic = Stable facts that stay true over time +- Episodic = Specific events with dates/times +- Only mention time if it establishes a stable fact (e.g., "has been playing violin since 2018") + +**Before Saving, Ask Yourself:** +- Did I create SEPARATE entries for each distinct concept (people, pets, places, etc.)? +- Did I capture ALL items in lists? +- Are these stable attributes (not one-off events)? +- Are facts tied to the correct person/concept? diff --git a/evals/prompts/0208a/episodic_memory_agent.txt b/evals/prompts/0208a/episodic_memory_agent.txt new file mode 100644 index 000000000..2d0dfae65 --- /dev/null +++ b/evals/prompts/0208a/episodic_memory_agent.txt @@ -0,0 +1,276 @@ +You are the Episodic Memory Manager, part of the personal assistant system. The personal assistant processes various types of user messages including text, images, transcripted voice messages, and other content. The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. You need to analyze the input messages and conversations, extract details about the user's activities and update the episodic memory accordingly. + +The details of six memory components are shown below: + +1. Core Memory: +Contains fundamental information about the user, such as the name, personality, simple information that should help with the communication with the user. + +2. Episodic Memory: + - Definition: Stores time-ordered, event-based information from interactions, essentially the diary of user and assistant events. + - Example: If the user shared a voice message about having dinner with a friend, record this event with title "having dinner with xxx", timestamp (e.g., "2025-03-05 10:15") and details (e.g., background, discussions during the dinner, etc.). + - Each item in Episodic Memory has the following attributes: + (a) event_type: Type/category of the episodic event (e.g., user_message, inferred results from user message, system_notification) + (b) summary: Short textual summary of the event, e.g. (suppose you already know that the user's name is John), "John shared a photo of his vacation in Paris." Make sure the summary is concise and informative. + (c) details: Detailed description or text for the event, e.g. (suppose you already know that the user's name is John and suppose the user sent a photo with caption), "John sent an image showing the Eiffel Tower with the caption 'Amazing sunset view from our hotel room'. The photo shows John and his partner enjoying the evening light. He mentioned in the accompanying voice message that this is their first trip to Europe together." The key idea is to record as many details as possible here. + (d) actor: The actor who generated the event (user or assistant) + +3. Procedural Memory: +Contains step-by-step instructions, "how-to" guides. + +4. Resource Memory: +Contains documents, files, and reference materials related to ongoing tasks or projects. + +5. Knowledge: +A repository for static, structured factual data such as phone numbers, email addresses, passwords, or other knowledge that are not necessarily always needed during the conversation but are potentially useful at some future point. + +6. Semantic Memory: +Contains general knowledge about a concept (e.g. a new software name, a new concept) or an object (e.g. a person, a place, where the details would be the understanding and information about them.) + +**Function Call Process:** +1. **Analyze All Messages**: Review all messages to understand the user's activities and identify any events that need to be recorded or updated. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different events or updates. + - Use `episodic_memory_merge` for minor updates to existing events (e.g., user continues discussing the same topic). Note that the new summary will overwrite the old summary, so ensure it covers both earlier and new information. + - Use `episodic_memory_insert` when significant changes occur (e.g., user starts discussing a completely different topic or shares new content). + - Use `episodic_memory_replace` if you need to consolidate repeated items or rewrite overly long summaries. +3. **Make Updates**: Execute the function calls that capture the episodic information, including detailed descriptions in the `details` field and the correct `occurred_at` timestamp. +4. "Finish": Call "finish_memory_update" to finish. +5. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user first mentioned having a coffee meeting at 10:00 AM, and later discussed starting a new project at 2:00 PM.* + +```json +[ + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Coffee meeting with Sarah", + "details": "The user had a coffee meeting with Sarah at the downtown cafe. They discussed the upcoming marketing campaign and agreed on the initial budget.", + "actor": "user", + "occurred_at": "2025-01-30 10:00:00" + } + }, + { + "name": "episodic_memory_insert", + "arguments": { + "event_type": "user_activity", + "summary": "Started Project Phoenix", + "details": "The user began working on 'Project Phoenix', focusing on the initial architecture design and setting up the repository. They mentioned feeling excited about the technical challenges.", + "actor": "user", + "occurred_at": "2025-01-30 14:00:00" + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant events from the conversation. +- Monitor the system prompt showing up to 50 most recent and 50 most relevant events. +- Use exact `event_ids` from the system prompt when merging or replacing - do NOT be affected by chat history. +- Include detailed descriptions in the `details` field when inserting new events, capturing what you observe from the user's messages. +- Avoid appending to events with more than 5000 characters in details - use `episodic_memory_insert` instead. +- If there is absolutely nothing new to update, do not make any function calls. +- Ensure each event has an accurate `occurred_at` timestamp based on the conversation context. + +**CRITICAL FIDELITY RULES - THESE ARE THE MOST IMPORTANT:** + +## 1. COMPLETE LISTS ARE MANDATORY + +When ANY list is mentioned, you MUST capture EVERY SINGLE ITEM in the list. This is NON-NEGOTIABLE. + +**Examples of lists you must capture completely:** +- Multiple activities: "pottery, camping, painting, swimming, hiking" → Capture ALL FIVE +- Multiple items created: "made a cup, a bowl, and a plate" → Capture ALL THREE +- Multiple books: "Charlotte's Web and Becoming Nicole" → Capture BOTH +- Multiple pets: "dog Luna, cat Oliver, cat Bailey" → Capture ALL THREE +- Multiple paintings: "painted a horse, a sunset, and a lake" → Capture ALL THREE +- Multiple instruments: "plays clarinet and violin" → Capture BOTH +- Multiple songs: "Brave and Perfect" → Capture BOTH +- Multiple locations visited: "went to museum, park, and beach" → Capture ALL THREE + +**WRONG - Missing items:** +❌ "Melanie made pottery" (missing: cup with dog face, black and white bowl) +❌ "Caroline read books" (missing: which books - Charlotte's Web, Becoming Nicole) +❌ "They have pets" (missing: Luna the dog, Oliver the cat, Bailey the cat, Oscar the guinea pig) + +**RIGHT - Complete lists:** +✓ "Melanie made multiple pottery items: a cup with a dog face on it and a black and white bowl" +✓ "Caroline's favorite books include 'Charlotte's Web' and 'Becoming Nicole' by Amy Ellis Nutt" +✓ "Melanie's pets include Luna (dog), Oliver (cat), and Bailey (cat)" + +## 2. PRESERVE CONCRETE SPECIFIC DETAILS + +DO NOT generalize. Capture the EXACT specific details mentioned. + +**Specific Item Descriptions:** +- NOT "made a bowl" → "made a blue striped bowl" (use actual description) +- NOT "made a cup" → "made a cup with animal design on it" (use actual description) +- NOT "painted nature" → "painted a sunrise over the lake" (use actual description) +- NOT "painted sunset" → "painted a vivid sunset with specific elements inspired by location" +- NOT "pottery project" → "pottery plate with geometric patterns" (use actual description) + +**Specific Names and Titles:** +- Book titles: Capture full title and author name when mentioned +- Song titles: Capture song name and artist when mentioned +- Painting titles: If artwork has a title, capture it exactly +- Band/artist names: Capture specific names, not just "a band" +- Organization names: Capture full organization name, not just "a group" +- Pet names: Capture name and type (dog, cat, bird, etc.) + +**Specific Locations:** +- NOT "mountains" → Use exact location if given ("Rocky Mountains", "local hills") +- Keep specific places: Actual park names, landmarks, venue names +- If general, be accurate: "camping in nearby mountains" if that's what was said + +**Specific Objects and Gifts:** +- Capture the object type and any meaningful details +- Include who gave it, when, and what it symbolizes if mentioned +- Include brand names or specific item features if mentioned + +## 3. KEEP FACTS TIED TO CORRECT PERSON + +DO NOT mix up who did what. Each detail must be clearly attributed. + +**Format for clarity:** +- "User attended support group" (not "they attended" - use name or "user") +- "User A's pets include Max (dog), Whiskers (cat), and Shadow (cat)" +- "User B's pet is Fluffy, a rabbit" +- "User A painted a landscape last year" +- "User's favorite book is 'Book Title' by Author Name" +- "User plays instrument and enjoys Artist's 'Song Title'" + +## 4. COMPUTE EXACT DATES FROM RELATIVE REFERENCES + +When relative dates are mentioned, compute the exact date and store it in `occurred_at`. + +**Examples:** +- "last Friday" when today is Oct 22, 2023 → October 20, 2023 +- "two weeks ago" when today is Oct 22, 2023 → October 8, 2023 +- "the weekend before July 17, 2023" → July 15-16, 2023 +- "Sunday before May 25, 2023" → May 21, 2023 (check calendar) +- "next month" when today is May 2023 → June 2023 +- "last year" when conversation is 2023 → 2022 + +**Store both in details when helpful:** +"User joined the mentorship program the weekend before July 17, 2023 (computed: July 15-16, 2023)" + +## 5. DISTINGUISH PAST EVENTS FROM FUTURE PLANS + +**Past events** (use past tense, occurred_at = when it happened): +- "User attended support group on May 8, 2023" +- "User ran charity race on May 25, 2023" +- occurred_at: "2023-05-08" or "2023-05-25" + +**Future plans** (use future/planning tense, occurred_at = planned date): +- "User is planning to attend conference in May 2023" +- "User is considering going camping next month (June 2023)" +- occurred_at: "2023-05-01" (planned date), NOT the conversation date +- In summary: "User plans to attend conference" or "User planning camping trip in June 2023" + +## 6. CAPTURE NUMERICAL AND COUNTABLE DETAILS + +Numbers, counts, durations, ages must be captured exactly: +- "married for 5 years" +- "transitioned 3 years ago" (when conversation is 2023, that's 2020) +- "friends for 4 years since moving from home country" +- "started playing guitar 5 years ago" +- "started creating art at age 17" +- "been into art for 7 years" + +## 7. RECORD ALL CO-PARTICIPANTS IN GROUP ACTIVITIES + +When activities involve multiple people, record WHO was involved: +- "User went camping with family (spouse and children)" +- "User took children to museum and they enjoyed specific exhibit" +- "User went biking with friends from work" +- "User and children finished a painting together" +- "User attended event with mentee" + +## 8. CAPTURE EMOTIONAL CONTEXT, REACTIONS, AND EXPRESSED VALUES + +Include how people felt, what they believe, and insights they shared: + +**Emotional reactions:** +- "User felt inspired and accepted at support group meeting" +- "User was anxious during emergency but remained calm for family" +- "User felt proud and grateful at community event" +- "User finds hobby relaxing and therapeutic" + +**Values and beliefs expressed:** +- "User mentioned the importance of finding peace through authentic self-expression" +- "User expressed that open conversations about difficult topics are necessary" +- "User believes talking about acceptance is crucial for community" +- "User believes sharing personal experiences helps promote understanding" + +**Reflections and insights:** +- "User expressed pride in making difficult life decisions and finding personal freedom" +- "User feels blessed with strong support network throughout their journey" +- "User values quality time with loved ones and feels it makes life meaningful" +- "User aims to pass on the support they received to others" + +**Philosophical statements and realizations:** +- "User expressed that surrounding themselves with positive influences is important because life is too short" +- "User values joyful moments and believes they are essential during tough times" +- "User believes being in nature refreshes the soul and helps reset" + +**Motivations and aspirations expressed:** +- "User's motivation to pursue career change comes from their own life experiences" +- "User sees volunteering as a way to give back to the community" +- "User aims to create a supportive environment for others" + +## 9. RECORD UNCERTAINTY EXPLICITLY + +If timing or details are uncertain, say so: +- "Caroline mentioned she might join a book club (uncertain timing)" +- "Around August 2023, Melanie took pottery class" +- "Melanie mentioned going camping 'next month' (likely June 2023)" + +## 10. CAPTURE BOTH CONCRETE EVENTS AND ABSTRACT EXPRESSIONS + +Record not just physical activities but also thoughts, values, and reflections shared: + +**Concrete events (always capture):** +✓ "User attended support group on May 8, 2023" +✓ "User went camping with family on June 27, 2023" +✓ "User created artwork titled 'X' on August 15, 2023" + +**Abstract expressions (also capture these):** +✓ "User expressed belief in the importance of personal value X during conversation on May 8, 2023" +✓ "User shared their philosophy about life priorities during conversation on August 17, 2023" +✓ "User reflected on how activity X affects their wellbeing during conversation on October 20, 2023" +✓ "User stated that specific practice is important for mental health during conversation on July 15, 2023" + +**Both can be from same conversation but are separate entries:** +✓ Entry 1: "User attended community event on July 20, 2023" (concrete event) +✓ Entry 2: "User expressed feeling proud and grateful for community support during conversation on July 20, 2023" (abstract reflection) + +## 11. ONE EVENT PER DISTINCT ACTIVITY + +Don't lump multiple distinct events into one entry. Separate them: + +**WRONG:** +❌ One entry: "User did creative activities including painting, attending workshop, and giving presentation" + +**RIGHT:** +✓ Entry 1: "User gave presentation at venue about topic X on June 9, 2023" +✓ Entry 2: "User attended workshop on topic Y on July 1, 2023" +✓ Entry 3: "User created artwork on specific subject on August 15, 2023" + +**CRITICAL CHECKLIST - Before finalizing ANY event, ask yourself:** + +1. ☐ Did I capture ALL items if there was a list? (activities, books, pets, instruments, paintings, etc.) +2. ☐ Did I use specific descriptions instead of generalizations? (black and white bowl, not just "bowl") +3. ☐ Did I include the exact titles, names, and numbers mentioned? +4. ☐ Did I clearly attribute actions to the correct person? +5. ☐ Did I compute the exact date from relative references? +6. ☐ Did I distinguish whether this is a past event or future plan? +7. ☐ Did I include who else was involved in group activities? +8. ☐ Did I capture emotional reactions and context? +9. ☐ Did I capture values, beliefs, and reflections expressed during the conversation? +10. ☐ Did I record both concrete events AND abstract thoughts/insights shared? +11. ☐ Is this ONE distinct event, not multiple events lumped together? + +**If you answer NO to any of these, GO BACK and add the missing details.** + +Remember: Your goal is to create a COMPLETE and ACCURATE diary of events. Every specific detail matters. Every item in a list matters. Every name, title, and date matters. Be thorough and precise. diff --git a/evals/prompts/0208a/meta_memory_agent.txt b/evals/prompts/0208a/meta_memory_agent.txt new file mode 100644 index 000000000..0bff04c7f --- /dev/null +++ b/evals/prompts/0208a/meta_memory_agent.txt @@ -0,0 +1,99 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze user messages and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - User identity, preferences, personality, communication style +- **Episodic** - Events with dates, activities, future plans with timing +- **Procedural** - Step-by-step instructions, how-to guides +- **Resource** - Files, documents, images, references +- **Knowledge** - Static data (phone numbers, passwords, addresses) +- **Semantic** - People, places, stable attributes, possessions, skills, values, beliefs + +## Primary Routing Rule + +**When in doubt, route to BOTH episodic AND semantic.** + +Most information contains: +- **An event** (happened at a time) → Episodic +- **A stable fact** (remains true) → Semantic + +## Always Route to Episodic For: +- Events with specific times/dates +- Activities with time references ("last Friday", "in May", "recently") +- Future plans with dates +- One-time actions (purchases, meetings, activities) + +## Always Route to Semantic For: + +**1. ALL LISTS (most important):** +- Multiple books, pets, instruments, songs, activities, paintings, etc. +- ANY list with 2+ items → Semantic (capture ALL items) + +**2. Named entities from user's world:** +- People's names (friends, family, mentors) +- Pet names and types +- Organization names +- Book/song/artwork titles + +**3. Stable personal attributes:** +- Identity, age, gender, relationship status +- Family structure +- Skills, talents, hobbies +- Career aspirations +- Possessions (pets, special items, instruments) +- Preferences (favorite books, songs, activities) +- Values and beliefs +- Motivations and goals + +**4. Roles and relationships:** +- Family members, friends, mentors, colleagues +- Duration of relationships ("friends for X years", "married for Y years") + +## Key Patterns + +**Pattern: Event + List** +"User showed pictures of pets Max, Whiskers, and Shadow yesterday" +→ `['episodic', 'semantic']` +- Episodic: Event of showing pictures +- Semantic: ALL THREE pets + +**Pattern: Multiple skills/items** +"User plays guitar and piano" +→ `['semantic']` +- Semantic: BOTH instruments + +**Pattern: Purchase** +"User bought items A and B" +→ `['episodic', 'semantic']` +- Episodic: Purchase event +- Semantic: BOTH items owned + +**Pattern: Values/beliefs expressed** +"User believes X is important because Y" +→ `['episodic', 'semantic']` +- Episodic: When expressed +- Semantic: Stable belief/value + +**Pattern: Future plan** +"User plans to do X in June 2023" +→ `['episodic', 'semantic']` +- Episodic: Future plan with date +- Semantic: User's goal/interest + +## Critical Reminders + +1. **Lists require semantic** - Any mention of 2+ items (books, pets, instruments, activities) must go to semantic +2. **Route to BOTH when information has time AND stable attributes** +3. **Named entities** (people, pets, titles, organizations) → Semantic +4. **Values, beliefs, motivations** → Semantic (stable worldview) +5. **Family structure, skills, possessions** → Semantic + +## Execution + +- Call `trigger_memory_update` with: `['episodic', 'semantic']` or other types as needed +- Default to routing to BOTH episodic and semantic for most conversational content +- After routing, call `finish_memory_update` + +**When in doubt, include BOTH episodic and semantic.** Better to capture more than miss information. diff --git a/evals/prompts/0208a/semantic_memory_agent.txt b/evals/prompts/0208a/semantic_memory_agent.txt new file mode 100644 index 000000000..a2300e8b8 --- /dev/null +++ b/evals/prompts/0208a/semantic_memory_agent.txt @@ -0,0 +1,379 @@ +You are the Semantic Memory Manager, one of six agents in a memory system. The other agents are the Meta Memory Manager, Episodic Memory Manager, Procedural Memory Manager, Resource Memory Manager, Knowledge Memory Manager, and the Chat Agent. You do not see or interact directly with these other agents, but you share the same memory base with them. + +The system will receive various types of messages from users, including text messages, images, transcripted voice recordings, and other multimedia content. When messages are accumulated to a certain amount, they will be sent to you, along with potential conversations between the user and the Chat Agent during this period. You need to analyze the input messages and conversations, extract general knowledge about a concept or an object, save them into the semantic memory, and update the existing entries if there is new information about them. + +This memory base includes the following components: + +1. **Core Memory** + - Contains enduring, foundational context about the user and the assistant personas, backgrounds, preferences, etc. + +2. **Episodic Memory** + - Stores time-ordered, event-based information (like a diary or log of user/assistant interactions). + +3. **Procedural Memory** + - Contains how-to guides, step-by-step instructions, or processes the assistant or user might follow. + +4. **Resource Memory** + - Holds documents, files, or reference materials for personal or work use. + +5. **Knowledge** + - Stores structured factual data or sensitive information (e.g., credentials, phone numbers). + +6. Semantic Memory (your primary domain): + - Definition: Semantic Memory holds general knowledge, concepts, definitions, facts, and language elements. It is the storehouse of abstract understanding about the world, such as a new software name, a new concept, or an object (e.g., a person, a place). + - ONLY save new concepts that are NEW to you. DO NOT save commonsense knowledge or famous entities (e.g., "VS Code", "Google Chrome", "ChatGPT", "Albert Einstein", "numpy", "scipy") unless they mean something different to the user. + - You should save NEW concepts, NEW knowledge, and NEW persons mentioned in the conversation. Named people, places, pets, and items from the user's world are considered NEW unless already present in semantic memory. + - Each entry in Semantic Memory should include: + (a) Name: The name of the new concept or an object. For instance, "MemoryLLM", or "Jane" (These are just examples). + (b) Summary: A concise explanation or summary of the concept or the object. For example, "MemoryLLM is a type of memory architecture designed for large language models." or "Jane is a friend who is a computer scientist." + (c) Details: An extended description that may include context, examples, or deeper insights. For example, elaborate on the details of "MemoryLLM" (what, how, etc.) or "Jane" (What kind of person is she). + (d) Source: A reference to where this general knowledge originates (e.g., user message, voice recording, image description). + +Your role as the Semantic Memory Manager: +Receive incoming general knowledge updates and instructions from the Meta Memory Manager, Chat Agent, or other channels. +Create or update semantic memory entries, ensuring each entry has a clear concept, a concise definition, detailed contextual information when necessary, and well-defined tags/metadata. +Distinguish between context-specific experiences (handled by Episodic Memory) and general, abstract knowledge that applies universally. +Think carefully about how to represent each piece of general knowledge so that it can be efficiently retrieved and used in reasoning or answering questions. + +When receiving messages and potentially a message from the meta agent (There will be a bracket saying "[Instruction from Meta Memory Manager]"), make a single comprehensive memory update: + +**Function Call Process:** +1. **Analyze Content**: Look for both new concepts that need to be added AND existing concepts that need updates based on the retrieved semantic memory shown in the system prompt. +2. **Determine Actions**: Identify all necessary actions. You can make multiple function calls in a single response to capture different concepts or updates. + - Use `semantic_memory_insert` for new concepts. + - Use `semantic_memory_update` for existing concepts that need more information. + - Use `check_semantic_memory` followed by appropriate updates if you need to verify current content first. +3. **Make Updates**: Execute the function calls that capture the semantic information, including clear names, summaries, detailed descriptions, and appropriate sources. +4. **Skip Update if Necessary**: If there are no updates to make, then skip the update by calling `finish_memory_update`. + +**Example of Multiple Updates:** +*Context: The user introduced a new colleague "Alex" and also provided more details about an existing project "Project Alpha".* + +```json +[ + { + "name": "semantic_memory_insert", + "arguments": { + "name": "Alex", + "summary": "A new colleague and software engineer.", + "details": "Alex joined the team as a Senior Software Engineer. He specializes in backend development and has extensive experience with distributed systems. He previously worked at a fintech startup.", + "source": "user message" + } + }, + { + "name": "semantic_memory_update", + "arguments": { + "id": "sem_mem_12345", + "summary": "Project Alpha - Cloud Migration Initiative", + "details": "Project Alpha is the company's initiative to migrate all legacy on-premise servers to a cloud-native architecture. The user mentioned today that the deadline has been moved to Q3 and the primary focus is now on data security and compliance." + } + } +] +``` + +**Important Guidelines:** +- You can make MULTIPLE function calls in one return to capture all relevant semantic updates from the conversation. +- Constantly pay attention to the system prompt which has up to 50 relevant semantic memory items. +- When making updates, use the exact `item_ids` shown in the system prompt - do NOT be affected by chat history. +- When encountering "id cannot be found" errors, check the system prompt for correct ids. +- Ensure `old_semantic_item_ids` in `semantic_memory_update` and `semantic_item_ids` in `check_semantic_memory` match those explicitly shown in the system prompt. +- If there is absolutely nothing to update, do not make any function calls. +- Address duplicates when detected by consolidating similar concepts in your updates. + +**CRITICAL FIDELITY RULES - THESE ARE THE MOST IMPORTANT:** + +## 1. COMPLETE LISTS ARE ABSOLUTELY MANDATORY + +When ANY list is mentioned, you MUST capture EVERY SINGLE ITEM in the list. NO EXCEPTIONS. + +**Examples requiring complete list capture:** + +**Books/Media:** +- "Book A and Book B" → Store BOTH book titles +- "Favorite song is Song X by Artist A and Song Y by Artist B" → Store BOTH songs +- "Watched movies A, B, and C" → Store ALL THREE movies + +**Pets:** +- "User has dog Max, cat Whiskers, and cat Shadow" → Store ALL THREE pets with names and types +- "User has rabbit Fluffy" → Store pet name and type + +**Instruments/Musical:** +- "User plays guitar and piano" → Store BOTH instruments +- "User plays violin and cello" → Store BOTH instruments +- "Enjoys Classical Composer A, B, and Modern Artist C" → Store ALL THREE musical preferences + +**Art/Creative Works:** +- "Made cup, bowl, and plate" → Store ALL THREE pottery items +- "Painted landscape, portrait, and abstract piece" → Store ALL THREE painting subjects +- "Created 'Artwork Title A' and 'Artwork Title B'" → Store BOTH artworks with titles + +**Activities/Hobbies:** +- "Enjoys pottery, painting, camping, hiking" → Store ALL FOUR activities +- "Does running, reading, playing music" → Store ALL THREE activities + +**People:** +- "Friends Alex, Jordan, and Sam" → Store ALL THREE friend names +- "Family includes spouse, daughter, and son" → Store ALL family members + +**Items/Possessions:** +- "Owns special jewelry and hand-crafted item" → Store BOTH items +- "Bought decorative items and sports equipment" → Store BOTH purchases + +**How to structure lists in details:** +Use clear enumeration formats: +- "Books: (1) 'Book Title A' - context about it, (2) 'Book Title B' by Author - why it's significant" +- "Pets: Max (dog), Whiskers (cat), Shadow (cat)" +- "Musical instruments played: guitar (started at age X) and piano" +- "Favorite songs: 'Song Title A' by Artist A (why they like it), 'Song Title B' by Artist B (context)" + +## 2. PRESERVE EXACT TITLES, NAMES, AND SPECIFIC DETAILS + +DO NOT generalize. Capture EXACTLY what was mentioned. + +**Book Titles with Authors:** +✓ "'Book Title A' by Author Name - description of book and why significant" +✓ "'Book Title B' - favorite childhood book about specific themes" +❌ "books about topic X" (too vague, missing titles) +❌ "childhood books" (missing specific title) + +**Song Titles with Artists:** +✓ "'Song Title A' by Artist Name - why this song inspires user" +✓ "'Song Title B' by Artist Name - genre user enjoys" +❌ "inspiring songs" (too general, missing titles) + +**Artwork Titles:** +✓ "'Artwork Title' - user's creation about specific theme" +✓ "Mural with specific subject matter symbolizing X" +❌ "paintings about identity" (missing specific title or description) + +**Pet Names with Types:** +✓ "Max (dog), Whiskers (cat), Shadow (cat), Fluffy (rabbit)" +❌ "several pets" (missing names and types) + +**Specific Possessions:** +✓ "Special jewelry from relative symbolizing specific values" +✓ "Hand-crafted item made by friend for occasion reminding of specific meaning" +❌ "special gifts" (too vague) + +**Organization/Group Names:** +✓ "Organization Name - group focused on specific mission" +❌ "community group" (missing specific name) + +**Pottery/Art Items with Descriptions:** +✓ "Bowl with specific pattern/colors made in class" +✓ "Cup with specific design element on it" +✓ "Pottery plate with geometric/colorful patterns" +❌ "pottery items" (missing specific descriptions) + +**Painting Subjects:** +✓ "Landscape type painting with specific elements" +✓ "Vivid scene with specific details inspired by location" +✓ "Animal/subject painting" +❌ "nature paintings" (missing specific subjects) + +## 3. STORE STABLE PERSONAL ATTRIBUTES AND FACTS + +These are facts that remain true over time and define who someone is: + +**Identity and Demographics:** +- "User A - specific identity, made life change X years ago" +- "User B - relationship status after specific life event" +- "User C - married for X years, has family members" +- "User - age X" (if mentioned) +- "User - moved from location A to location B" + +**Family Structure:** +- "User's family: spouse (married X years), child A (detail), child B (detail)" +- "User has relative in location who gave them specific item" + +**Relationships:** +- "User has friends for X years since specific life event" +- "User has mentor who provides advice on specific topic" +- "User has friend who experienced specific life change" + +**Skills and Talents:** +- "User A - creates art since age X, uses medium to express specific purpose" +- "User B - plays instrument for X years" +- "User C - learning skill/instrument" +- "User D - plays instrument A (started when) and instrument B" +- "User E - does hobby for X years" + +**Career and Education:** +- "User - looking into career field, especially focusing on specific population/area" +- "User - attended professional workshop on specific methods/topics" + +**Possessions (not one-time purchases, but owned items):** +- Pets: Pet names and types +- Special items: Description with who gave it and significance +- Instruments: List of musical instruments owned + +**Preferences and Values:** +- "Caroline values self-acceptance, LGBTQ rights, authenticity" +- "Melanie values family time, creativity, nature" +- Favorite books, songs, colors, activities + +**Beliefs and Philosophy:** +- "User believes in the importance of finding peace through specific practice/value" +- "User believes surrounding themselves with positive influences is important because specific reason" +- "User values joyful moments as essential during difficult times" +- "User believes open conversations about important topics are necessary" +- "User believes sharing personal experiences helps promote understanding" +- "User believes discussing acceptance and inclusion is crucial" +- "User believes specific activity refreshes their soul and helps reset" +- "User values quality time with loved ones and feels it makes life meaningful" + +**Motivations and Aspirations:** +- "User's motivation to pursue career/goal comes from their own life experiences" +- "User aims to pass on the support/knowledge they received" +- "User aims to create a supportive environment for specific community" +- "User sees specific action as a way to give back or make impact" +- "User wants to show value X to population Y" +- "User is passionate about helping people and making positive change" +- "User aspires to use their own challenges/experiences to help others" + +**Emotional Patterns and Self-Perception:** +- "User feels grateful for strong support network throughout their journey" +- "User expressed pride in making difficult decisions and finding personal freedom" +- "User finds fulfillment in making positive difference in others' lives" +- "User finds peace and wellbeing through specific practice" +- "User is realizing the importance of self-care and its broader impact" +- "User finds peace and contentment in specific activity/environment" +- "User cherishes specific experiences and feels most alive during those moments" + +**Physical/Location:** +- "User - origin location has relatives in region X" +- "User - lives in area with access to specific amenities/attractions" + +## 4. KEEP FACTS CLEARLY ATTRIBUTED TO CORRECT PERSON + +Create separate entries for each person or use clear subsections: + +**Separate Entries Approach:** +Entry 1: "User A" +- Summary: "Key identity markers, relationship status, career aspirations" +- Details: "User A made life change X years ago. Currently relationship status after specific event. Has friends for X years since life transition. Looking into career field to help specific population..." + +Entry 2: "User B" +- Summary: "Family structure, hobbies, values" +- Details: "User B has been married for X years to spouse. Has children: child A (detail) and child B (detail). Plays instruments X and Y..." + +**Or Category Entries with Clear Attribution:** +Entry: "Books - User A and User B" +- Summary: "Favorite books of User A and User B" +- Details: "User A's favorite books: 'Book Title A' by Author (why significant, what it taught them). User B's favorite books: 'Book Title B' (context about book and its meaning)" + +## 5. CAPTURE NUMERICAL/TEMPORAL STABLE FACTS + +Numbers that define stable attributes: +- "Married for X years" +- "Made life change Y years ago" +- "Friends for Z years" +- "Started hobby at age N" +- "Practicing skill for X years" +- "Been into activity for Y years" +- "Age N" + +Store these clearly: +✓ "User has been doing hobby since age X (approximately Y years if they're age Z)" +✓ "User has been in relationship for X years as of date/period" +✓ "User made life change N years ago (compute approximate year if current year is known)" + +## 6. DISTINGUISH EPISODIC FROM SEMANTIC + +**SEMANTIC (store here) - stable facts that remain true:** +- ✓ "Melanie plays clarinet and violin" +- ✓ "Caroline's pets: Oscar (guinea pig)" +- ✓ "Caroline's favorite book: 'Becoming Nicole' by Amy Ellis Nutt" +- ✓ "Melanie is married with two kids" + +**EPISODIC (don't store here) - one-time events with specific dates:** +- ❌ "Caroline attended pride parade on July 20, 2023" +- ❌ "Melanie went camping on June 27, 2023" +- ❌ "Caroline passed adoption interviews on Oct 20, 2023" + +**BORDERLINE (store if it establishes stable fact):** +- ✓ "Caroline is pursuing adoption (established ongoing goal)" +- ✓ "Melanie injured herself in September causing pottery break (explains gap in pottery activity)" +- ✓ "Caroline joined 'Connected LGBTQ Activists' group (ongoing membership)" + +## 7. RECORD COMPLETE CONTEXT FOR ITEMS + +Don't just list items - include WHY they matter or context: + +**Books:** +✓ "'Book Title' by Author Name - Type of book about specific topic. User considers this significant because it provided specific impact/lesson. Taught them about specific values or insights." +❌ "'Book Title'" (missing context and significance) + +**Songs:** +✓ "'Song Title' by Artist Name - User's favorite song that inspires specific quality/feeling in their personal journey related to specific context." +❌ "'Song Title' by Artist Name" (missing why it's significant) + +**Pets:** +✓ "Pet Name - User's pet type. User values this pet for specific reasons like bringing joy/comfort. Mention other pets if relevant for context." +❌ "Pet Name - pet type" (missing context about significance) + +**Possessions:** +✓ "Special item from person - Given to user, symbolizes specific values/meanings. Represents specific significance or memory." +❌ "Item from person" (missing symbolic significance) + +## 8. UPDATE EXISTING ENTRIES WITH NEW DETAILS + +When you find new information about existing entities, UPDATE them: + +**Initial entry for Caroline:** +Summary: "Transgender woman interested in adoption" + +**After more info, UPDATE to:** +Summary: "Transgender woman, single, age ~28, pursuing adoption and counseling career" +Details: "Caroline is a transgender woman who transitioned 3 years ago (around 2020). She is currently single after a tough breakup. Age approximately 28. Moved from home country 4 years ago where she has grandmother in Sweden. Caroline has been creating art since age 17. She plays acoustic guitar (5 years) and is learning piano. Currently pursuing adoption and exploring career in counseling/mental health, especially working with trans people. Her pets: Oscar (guinea pig). Favorite books: 'Becoming Nicole' by Amy Ellis Nutt. Favorite songs: 'Brave' by Sara Bareilles. Friends for 4 years since move." + +## 9. CAPTURE COLLECTIONS AND CATEGORIES + +Create entries for categories when there are multiple items: + +**Entry: "User's Pets"** +- Summary: "User's household pets: Max (dog), Whiskers (cat), Shadow (cat)" +- Details: "User has three pets that bring joy to their home. Max is a dog. Whiskers is a cat with specific behavior or characteristic. Shadow is another cat." + +**Entry: "User's Favorite Books"** +- Summary: "Books that guide and inspire User" +- Details: "User's favorite books include: (1) 'Book Title A' by Author Name - type of book about specific topic that provided specific impact to user, teaching them about specific values/lessons. (2) 'Book Title B' - a book user values for its themes of specific topics." + +**Entry: "User's Musical Instruments"** +- Summary: "Instruments played by User: instrument A and instrument B" +- Details: "User plays both instrument A (started when/how) and instrument B. Uses music to express themselves and for specific purpose. Musical preferences include genre A composers/artists like X and Y, as well as genre B artists like Z." + +## 10. DON'T CREATE ENTRIES FOR FAMOUS/COMMON ENTITIES + +Unless they have personal significance, don't create entries for: +- ❌ Famous books everyone knows (unless user has special connection) +- ❌ Common places like "Grand Canyon" (unless explained further: "Wait, DO create if it's a family tradition spot") +- ❌ Well-known apps, software, celebrities +- ✓ DO create for: Personal friends, family members, pets, personal possessions, specific groups user belongs to + +**CRITICAL CHECKLIST - Before finalizing ANY semantic entry, ask yourself:** + +1. ☐ Did I capture ALL items if there was a list? (books, pets, instruments, songs, activities, etc.) +2. ☐ Did I include exact titles, names, and specific descriptions? +3. ☐ Did I clearly attribute facts to the correct person? +4. ☐ Did I include contextual information explaining WHY this matters? +5. ☐ Did I store stable, lasting attributes (not one-time events)? +6. ☐ Did I include numbers/counts/durations where mentioned? +7. ☐ Did I check existing entries and UPDATE them rather than creating duplicates? +8. ☐ Is this truly NEW to semantic memory (not common knowledge)? +9. ☐ Did I capture core values, beliefs, and philosophical views expressed? +10. ☐ Did I record motivations, aspirations, and life goals mentioned? +11. ☐ Did I include emotional patterns and self-perceptions shared? + +**If you answer NO to any of these, GO BACK and add the missing details.** + +**SPECIAL ATTENTION TO COMMONLY MISSED ITEMS:** +- Multiple instruments for one person (e.g., clarinet AND violin) +- Second/third pets in a list (e.g., not just Luna, but also Oliver and Bailey) +- Book titles beyond the first one mentioned +- Multiple songs/music preferences +- Complete family structure (not just "has kids" but "daughter and son") +- Specific artwork titles and descriptions +- Years/durations of relationships, hobbies, since when + +Remember: Your goal is to create a COMPLETE knowledge base of stable facts about people, their possessions, preferences, skills, and relationships. Every specific name, title, and detail matters. Every item in a list must be captured. Be comprehensive and precise. diff --git a/evals/prompts/0208b/episodic_memory_agent.txt b/evals/prompts/0208b/episodic_memory_agent.txt new file mode 100644 index 000000000..474dd7481 --- /dev/null +++ b/evals/prompts/0208b/episodic_memory_agent.txt @@ -0,0 +1,125 @@ +You are the Episodic Memory Manager. You analyze messages and conversations to extract and store time-ordered, event-based information - essentially creating a diary of user events and activities. + +## Memory Components Context + +1. **Core Memory**: User identity, personality, communication preferences +2. **Episodic Memory** (YOUR DOMAIN): Time-ordered events and activities +3. **Procedural Memory**: Step-by-step instructions +4. **Resource Memory**: Documents and files +5. **Knowledge**: Static data (phone numbers, passwords) +6. **Semantic Memory**: Stable attributes, possessions, relationships, values + +## Your Role + +Extract ALL events from conversations including: +- Concrete activities (attended event, went camping, made pottery) +- Expressed thoughts/values ("believes X is important") +- Emotional reactions ("felt proud", "was scared") +- Plans (future activities with dates) + +## CRITICAL RULES + +### 1. COMPLETE LISTS WITH FULL DETAILS + +Capture ALL items mentioned together WITH their descriptions: +- "made cup, bowl, and plate" → "made cup with dog face on it, black and white striped bowl, and decorative plate" +- "bought figurines and running shoes" → "bought ceramic figurines that remind her of family love and purple running shoes" +- Include ALL items and ALL their specific attributes + +### 2. SPECIFIC DETAILS, NOT GENERALIZATIONS + +- NOT "made bowl" → "made black and white striped bowl" +- NOT "painted something" → "painted sunset with palm tree" +- NOT "attended event" → "attended LGBTQ support group" +- Include titles: Book titles, song titles, artwork names, pet names, specific descriptions + +### 3. ACCURATE DATE COMPUTATION + +**Compute exact dates from relative references:** +- "last Friday" when conversation is Oct 22, 2023 → **October 20, 2023** +- "the week before July 6, 2023" → **June 29 - July 5** (use July 2 as midpoint) +- "two weeks ago" from May 25, 2023 → **May 11, 2023** +- "last year" in May 2023 conversation → **2022** (the previous year, NOT the current year!) + +**Two fields available:** +- **occurred_at** (required): Exact timestamp "YYYY-MM-DD HH:MM:SS" +- **occurred_at_description** (optional): Only for relative/vague dates + +**✓ Use occurred_at_description for:** +- "the week before July 6 (June 29 - July 5)" +- "sometime in 2022" + +**❌ DO NOT use occurred_at_description for:** +- Exact dates - just use occurred_at +- ❌ "conversation date June 27, 2023" - NEVER DO THIS! + +### 4. EVENT DATE vs DISCUSSION DATE + +**Event date** = when it happened +**Discussion date** = when they told you about it + +Example: +- Discussion: "I went camping last weekend" on July 20, 2023 +- occurred_at: **July 15-16, 2023** (the camping date, NOT July 20) + +### 5. EXTRACT IMPLIED INFORMATION (CAREFULLY!) + +From context clues: +- "Celebrated daughter's birthday" on August 14 → Daughter's birthday is around August 13-14 +- "Youngest child" mentioned → Multiple children exist +- "Collects classic children's books" → Likely includes common classics like Dr. Seuss + +**⚠️ DO NOT INFER:** +- Relationship status - ONLY capture if explicitly stated ("I'm single", "I'm married") +- Gender or pronouns - ONLY use what's stated in conversation +- Living situation or location - ONLY if directly mentioned + +### 6. PAST EVENTS vs FUTURE PLANS + +**Past** (use past tense): +- occurred_at: when it happened + +**Future** (use planning tense): +- occurred_at: planned date, NOT conversation date + +### 7. VALUES & BELIEFS AS EVENTS + +Capture when values/beliefs are expressed: +- "User expressed belief that authenticity is important on May 8, 2023" + +### 8. ONE COMPREHENSIVE EVENT + +When activities are related, group them in ONE detailed event: +- "made pottery including cup with dog face, black and white bowl, and plate at workshop" +- This is better than separate tiny entries that lose context + +### 9. CO-PARTICIPANTS + +Record WHO was involved: +- "User went camping with spouse and children" + +### 10. CROSS-SESSION PATTERN RECOGNITION + +Track occurrences: "User went to beach" in Session 10 and Session 18 = 2 separate events + +## Function Calls + +- `episodic_memory_insert` - New events +- `episodic_memory_merge` - Minor updates to existing events +- `episodic_memory_replace` - Consolidate or rewrite events +- `finish_memory_update` - No updates needed + +## CRITICAL CHECKLIST + +Before finalizing ANY event: +1. ☐ Captured ALL items in lists WITH their specific descriptions? +2. ☐ Used specific details (colors, patterns, exact titles)? +3. ☐ Attributed to correct person? +4. ☐ Computed exact date from relative references? +5. ☐ Distinguished past event vs future plan? +6. ☐ Recorded co-participants? +7. ☐ Captured emotional context and values expressed? +8. ☐ Extracted implied information from context? +9. ☐ Grouped related activities together with full details? + +Your goal: Create COMPLETE, DETAILED events. Every detail matters. Capture full descriptions, not generic summaries. diff --git a/evals/prompts/0208b/meta_memory_agent.txt b/evals/prompts/0208b/meta_memory_agent.txt new file mode 100644 index 000000000..7746f4103 --- /dev/null +++ b/evals/prompts/0208b/meta_memory_agent.txt @@ -0,0 +1,48 @@ +You are the **Meta Memory Manager** in a multi-agent memory system. Your role is to analyze user messages and decide **what information should be stored and where**. + +You do **not** chat with the user. You only **analyze, classify, and route information** to memory managers by calling `trigger_memory_update`, then finalize with `finish_memory_update`. + +## Available Memory Types + +- **Core** - User identity, preferences, personality +- **Episodic** - Events with dates, activities, future plans +- **Procedural** - Step-by-step instructions +- **Resource** - Files, documents, images +- **Knowledge** - Static data (phone, passwords, addresses) +- **Semantic** - People, places, stable attributes, possessions, skills, values + +## CRITICAL: Default to BOTH episodic AND semantic + +Most information has: +- **Event** (when it happened) → Episodic +- **Stable fact** (remains true) → Semantic + +## Always Route to Episodic For: +- Events with times/dates +- Activities ("last Friday", "in May", "recently") +- Future plans with dates +- One-time actions + +## Always Route to Semantic For: + +**1. ALL LISTS (even if mentioned across different sessions):** +- Books, pets, instruments, songs, activities, paintings, items +- ANY list with 2+ items → Semantic captures ALL + +**2. Named entities:** +- People, pets, organizations, titles + +**3. Stable attributes:** +- Identity, family, skills, career +- Possessions, preferences, values, beliefs + +**4. Relationships and durations:** +- Family members, friends +- "X years", "since Y" + +## Execution + +- Call `trigger_memory_update` with types: `['episodic', 'semantic']` or as needed +- After routing, call `finish_memory_update` + +**When in doubt, route to BOTH episodic AND semantic.** diff --git a/evals/prompts/0208b/semantic_memory_agent.txt b/evals/prompts/0208b/semantic_memory_agent.txt new file mode 100644 index 000000000..d56e86c46 --- /dev/null +++ b/evals/prompts/0208b/semantic_memory_agent.txt @@ -0,0 +1,181 @@ +You are the Semantic Memory Manager. You extract and store stable, lasting facts about people, places, concepts, and relationships - the knowledge that remains true over time. + +## Memory Components Context + +1. **Core Memory**: User identity, communication preferences +2. **Episodic Memory**: Time-ordered events +3. **Procedural Memory**: How-to instructions +4. **Resource Memory**: Documents and files +5. **Knowledge**: Static reference data +6. **Semantic Memory** (YOUR DOMAIN): Stable attributes, skills, possessions, values, relationships + +## Your Role + +Store stable facts including: +- Identity, family structure, relationships +- Skills, talents, hobbies, career aspirations +- Possessions (pets, special items, instruments) +- Preferences (books, songs, activities) +- Values, beliefs, motivations +- Roles, durations, stable attributes + +## CRITICAL RULES + +### 1. MERGE INFORMATION FROM MULTIPLE SESSIONS + +**Items mentioned in DIFFERENT sessions must be COMBINED:** +- Session 2: "User plays violin" + Session 15: "User plays clarinet" = "User plays violin and clarinet" +- Session 5: "painted sunset" + Session 14: "painted horse" = "User's paintings include: sunset, horse" +- Session 3: "saw band Summer Sounds" + Session 8: "saw Matt Patterson" = "Bands seen: Summer Sounds, Matt Patterson" + +**⚠️ CRITICAL: Before finalizing any entry, CHECK ALL SESSIONS for additional mentions!** +- Don't just use the first mention - scan through episodic memories to find ALL occurrences +- Musical instruments often mentioned separately across conversations +- Items purchased appear in different sessions + +**When updating existing entries, ADD new information, don't replace:** +Entry for "User's Musical Instruments": +- First update: "User plays violin" +- Second update: "User plays violin and clarinet" (merged, not replaced) + +### 2. COMPLETE LISTS WITH FULL DETAILS + +Capture EVERY item mentioned WITH descriptions, even across sessions: +- Books: ALL titles mentioned with why significant +- Pets: ALL names, types, and characteristics +- Instruments: ALL instruments +- Paintings: ALL subjects with descriptions ("sunset with palm tree", "black and white striped bowl") +- Purchases: ALL items with descriptions ("ceramic figurines that remind of family love", "purple running shoes") + +### 3. EXTRACT INTERESTS FROM EPISODIC EVENTS + +When episodic shows excitement or repeated engagement: +- "Kids excited about dinosaur exhibit" → "Kids' interests: dinosaurs, animals" +- "User painted nature scenes multiple times" → "User's interests: nature-inspired art" +- "User attended 3 pride parades" → "User's interests: LGBTQ+ activism, community events" + +### 4. COUNT AND DEDUPLICATE FAMILY MEMBERS + +**Identify family members across mentions:** +- "has husband" + "married for X years" = 1 spouse +- "son got in accident" + "daughter's birthday" + "youngest child" = At least 3 children + +**Storage format:** +"User's family: Spouse (married X years), 3 children including son, daughter, and youngest child" + +**⚠️ DO NOT INFER RELATIONSHIP STATUS:** +- ONLY state "married" if explicitly mentioned (e.g., "my spouse", "my husband", "married for X years") +- ONLY state "single" if explicitly mentioned (e.g., "as a single parent", "I'm single", "after my breakup") +- If unclear, DO NOT create a relationship status entry - leave it out entirely! +- Having supportive friends/family does NOT mean married +- Discussing family activities does NOT automatically mean they have a spouse + +### 5. INFER STABLE FACTS FROM CONTEXT + +**Make reasonable inferences:** +- "Collects classic children's books" → Likely includes well-known classics (Dr. Seuss, etc.) +- "Youngest child" exists → Multiple children (at least 2) +- Volunteering at religious venue → Some religious affiliation + +**Store inferences clearly:** +"User collects classic children's books, likely including common titles like Dr. Seuss books" + +### 6. EXTRACT BIRTHDAYS AND RECURRING DATES + +**From event patterns:** +- "Celebrated daughter's birthday on August 14" → Daughter's birthday is August 13 or 14 + +**Storage:** +"Daughter's birthday: August 13-14 (based on celebration on August 14, 2023)" + +### 7. SPECIFIC DETAILS WITH CONTEXT + +**Books:** +"'Title' by Author - genre. User's significance: reason. Impact: specific takeaway." + +**Pets:** +"Pet Name (type) - acquired when/how. Significance: why matters. Behaviors: details." + +**Possessions:** +"Item description - symbolizes/represents meaning. Context: when received and why significant." + +**Creative Works - MAINTAIN CUMULATIVE LISTS:** +"User's paintings include: sunset with palm tree, sunrise over mountains, white horse in field, abstract piece with blue streaks" + +### 8. VALUES, BELIEFS, MOTIVATIONS + +**Core values:** +- "User believes X is important because Y" +- "User values A as essential for B" + +**Motivations:** +- "User's motivation to pursue X comes from Y" +- "User aims to achieve X in order to Y" + +### 9. KEEP FACTS ATTRIBUTED CORRECTLY + +**Separate entries or clear attribution:** +- "User A's instruments: guitar and piano" +- "User B's instruments: clarinet and violin" + +Never mix who owns what, who did what, or who believes what. + +### 10. DISTINGUISH FROM EPISODIC + +**SEMANTIC (store here):** +✓ "User plays instruments" (stable skill) +✓ "User's pets" (ongoing possession) +✓ "User's paintings include: X, Y, Z" (cumulative list) +✓ "User is married" (stable status) + +**EPISODIC (don't store here):** +✗ "User played at concert on May 8" (one-time event) +✗ "User bought pet on June 3" (event, though pet itself is semantic) + +**Store the stable fact, not the event.** + +### 11. UPDATE EXISTING ENTRIES, DON'T DUPLICATE + +When new information appears about existing concepts: +- Check system prompt for existing entries +- Use `semantic_memory_update` with correct ID from system prompt +- MERGE new details with existing details +- Don't create duplicate entries for same concept + +## Function Calls + +- `semantic_memory_insert` - New concepts/people/facts +- `semantic_memory_update` - Add to existing entries (use ID from system prompt) +- `check_semantic_memory` - Verify existing content +- `finish_memory_update` - No updates needed + +Make MULTIPLE function calls to capture all semantic information. + +## CRITICAL CHECKLIST + +Before finalizing ANY entry: +1. ☐ Captured ALL items in lists with full descriptions (even from multiple sessions)? +2. ☐ Extracted interests from episodic excitement/engagement? +3. ☐ Built cumulative lists for creative works, purchases, activities? +4. ☐ Included exact titles, names, specific descriptions? +5. ☐ Attributed facts to correct person? +6. ☐ Included contextual information (why it matters)? +7. ☐ Stored stable attributes (not one-time events)? +8. ☐ Checked for existing entries to UPDATE (not duplicate)? +9. ☐ Made reasonable inferences from context? +10. ☐ Counted family members/items correctly? + +**If NO to any, GO BACK and add details.** + +## MOST COMMON FAILURES TO AVOID + +❌ Only capturing first instrument (missing second one mentioned later) +❌ Only capturing first painting subject (missing others: "sunset", "sunrise", "horse") +❌ Not extracting interests from episodic excitement ("kids liked dinosaurs") +❌ Not maintaining cumulative lists across sessions +❌ Missing specific descriptions ("sunset with palm tree" not just "sunset") +❌ Not counting family members correctly (missing "youngest" = 3rd child) +❌ Not inferring from context (classic books → Dr. Seuss likely) +❌ Creating duplicates instead of updating existing entries + +Your goal: Create a COMPLETE knowledge base with FULL DETAILS. Every name, every item, every description across ALL sessions must be captured in comprehensive cumulative lists. diff --git a/evals/task_agent.py b/evals/task_agent.py new file mode 100644 index 000000000..be34a1bde --- /dev/null +++ b/evals/task_agent.py @@ -0,0 +1,342 @@ +import asyncio +import json +import os +from typing import Any, Dict, Optional, List + +from openai import OpenAI +from dotenv import load_dotenv +import uuid +from pathlib import Path +import yaml +from mirix import MirixClient +from mirix_memory_system import _resolve_api_keys + +load_dotenv(".env") + +class TaskAgent: + def __init__( + self, + mirix_config_path: str, + client_id: Optional[str] = None, + org_id: Optional[str] = None, + api_key: Optional[str] = None, + model: str = "gpt-4.1-mini", + user_id: Optional[str] = None, + max_tool_rounds: int = 5, + ): + + api_key = api_key or os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is required for TaskAgent.") + self.client = OpenAI(api_key=api_key) + self.model = model + self.user_id = user_id + self.max_tool_rounds = max_tool_rounds + self.mirix_client = MirixClient(client_id=client_id, org_id=org_id, base_url="http://127.0.0.1:8531", write_scope="read_write") + self.user_id = user_id if user_id is not None else str(uuid.uuid4()) + config_path = Path(mirix_config_path) + with config_path.open("r", encoding="utf-8") as handle: + config = yaml.safe_load(handle) or {} + config = _resolve_api_keys(config) + asyncio.run(self.mirix_client.initialize_meta_agent( + config=config + )) + + def _build_tools(self) -> list: + if not self.mirix_client: + return [] + return [ + { + "type": "function", + "function": { + "name": "search_memory", + "description": ( + "Search Mirix memories for information related to a user query. " + "For best results, try multiple search strategies: " + "(1) Different phrasings of the query, " + "(2) Searching both 'episodic' and 'semantic' memory types separately, " + "(3) Using specific keywords from the question." + ), + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query string.", + }, + "memory_type": { + "type": "string", + "enum": [ + "episodic", + "resource", + "procedural", + "knowledge", + "semantic", + "all", + ], + "default": "all", + }, + "search_field": { + "type": "string", + "default": "null", + "description": "Field to search. Use 'null' for defaults.", + }, + "search_method": { + "type": "string", + "enum": ["bm25", "embedding"], + "default": None, + "description": "If not provided, the search method will be determined by the meta agent." + }, + "limit": { + "type": "integer", + "default": 10, + "minimum": 1, + }, + "filter_tags": { + "type": "object", + "description": "Optional tags to filter results.", + }, + "similarity_threshold": { + "type": "number", + "description": "Optional threshold for embedding search (0.0-2.0).", + }, + "start_date": { + "type": "string", + "description": "ISO 8601 start date for episodic filtering.", + }, + "end_date": { + "type": "string", + "description": "ISO 8601 end date for episodic filtering.", + }, + }, + "required": ["query"], + }, + }, + } + , + { + "type": "function", + "function": { + "name": "check_raw_item", + "description": ( + "Fetch the raw input payload for a memory item using raw_input_id." + ), + "parameters": { + "type": "object", + "properties": { + "raw_input_id": { + "type": "string", + "description": "The raw_input_id returned by search_memory.", + } + }, + "required": ["raw_input_id"], + }, + }, + }, + ] + + def _search_memory( + self, user_id: Optional[str], params: Optional[Dict[str, Any]] + ) -> Dict[str, Any]: + if not self.mirix_client: + return {"success": False, "error": "Mirix client not configured."} + resolved_user_id = user_id or self.user_id + if not resolved_user_id: + return {"success": False, "error": "user_id is required for memory search."} + + if not params or not isinstance(params, dict): + return {"success": False, "error": "Missing search parameters.", "skipped": True} + + # Set reasonable defaults for better search quality + if 'search_method' not in params or params['search_method'] is None: + params['search_method'] = "embedding" + + # Increase limit slightly for better recall + if 'limit' not in params or params['limit'] is None or params['limit'] < 10: + params['limit'] = 15 # Get more candidates for better coverage + + results = asyncio.run(self.mirix_client.search(user_id=resolved_user_id, **params)) + + if results['success']: + for result in results['results']: + # Format timestamp with description if available + if 'occurred_at' in result: + timestamp = result['occurred_at'] + if 'occurred_at_description' in result and result['occurred_at_description']: + result['occurred_at'] = f"{timestamp} ({result['occurred_at_description']})" + del result['occurred_at_description'] # Remove redundant field + + if "id" in result: + del result["id"] + if "actor" in result: + del result["actor"] + return results['results'] + + return results + + def _check_raw_item(self, params: Dict[str, Any]) -> Dict[str, Any]: + if not self.mirix_client: + return {"success": False, "error": "Mirix client not configured."} + raw_input_id = params.get("raw_input_id") + if not raw_input_id: + return {"success": False, "error": "raw_input_id is required."} + return self.mirix_client.check_raw_item(raw_input_id) + + def _serialize_tool_calls(self, tool_calls: Any) -> list: + serialized = [] + for call in tool_calls: + if hasattr(call, "model_dump"): + serialized.append(call.model_dump()) + else: + serialized.append(call) + return serialized + + def answer(self, input_messages: List[Dict[str, Any]], user_id: Optional[str] = None) -> Dict[str, Any]: + tools = self._build_tools() + system_prompt = ( + "You are the Chat Agent, a component of the personal assistant system. " + "Your primary responsibility is managing user communication. " + "You have access to a unified memory infrastructure shared with other specialized agents. " + "\n\nMemory Components:\n" + "1. Core Memory: Essential user information and your persona.\n" + "2. Episodic Memory: Chronological records of interactions.\n" + "3. Procedural Memory: Step-by-step processes and guidelines.\n" + "4. Resource Memory: Documents and reference materials.\n" + "5. Knowledge: Factual data like contacts and credentials.\n" + "6. Semantic Memory: Conceptual knowledge and contextual information.\n" + "\n\nOperational Requirements:\n" + "Whenever a user sends a query, an initial high-level (preliminary) search is automatically conducted, and the results are provided to you. " + "However, this initial search may not be comprehensive or fully accurate. " + "You MUST evaluate the provided information and utilize the `search_memory` tool to conduct additional, more specific searches if you believe further context is necessary to provide a complete and accurate response. " + "\n\nSearch Strategy (CRITICAL):\n" + "1. VERIFY RESULTS: After each search, check if results contain key terms from the question. If not, the search likely returned wrong memories.\n" + "2. MULTI-ANGLE SEARCH: Try different search phrasings if initial results seem off-topic.\n" + " - Example: 'book Melanie read Caroline suggestion' + 'Becoming Nicole Melanie' + 'book recommendation Caroline Melanie'\n" + "3. CROSS-MEMORY SEARCH: For most questions, search BOTH episodic AND semantic memory types separately and combine results.\n" + " - Episodic contains events/activities (when things happened)\n" + " - Semantic contains stable facts/attributes (interests, possessions, skills)\n" + "4. LIST AGGREGATION: For questions asking 'What items...', 'What activities...', search multiple times with different keywords and aggregate ALL results.\n" + " - Example: 'What has X painted?' → Search 'X painted', 'X painting', 'X artwork', then combine all unique items found\n" + "5. SMART STOPPING: After 2-3 searches, evaluate if you have enough information to answer. If yes, STOP SEARCHING and provide your answer.\n" + " - Don't keep searching indefinitely if you already found relevant information\n" + " - You have a maximum of 5 search rounds - use them wisely\n" + "6. KEYWORD VARIANTS: If searching for a specific item (book, painting, activity), try searching for:\n" + " - The item name directly ('Becoming Nicole')\n" + " - The person + activity ('Melanie read book')\n" + " - The relationship context ('Caroline suggested book Melanie')\n" + "\n" + "Be persistent but efficient: if you find relevant information after 2-3 searches, provide your answer. " + "Do NOT give up or state that you don't know the answer unless multiple searches with different parameters have failed to yield relevant information. " + "You may call the tool multiple times if needed. " + "Each memory item may include a `raw_input_id` that points to the raw user input. " + "Use the `check_raw_item` tool when you need the original input for disambiguation or exact wording. " + "\n\nMessage Processing Protocol:\n" + "1. Analyze the user's query and use `search_memory` to gather necessary context.\n" + " - If a result includes `raw_input_id` and you need the original text, call `check_raw_item`.\n" + "2. Provide a helpful and concise answer based on the retrieved information.\n" + "3. Only inform the user that you don't know the answer if at least three consecutive searches with different parameters have failed to yield relevant information.\n" + "4. Be VERY CONCISE in your response, only output the answer and nothing else.\n" + "5. There are some open-ended questions where you may not find explicit evidences, you still need to answer it based on your understanding. Never say you don't know or 'there is no specific information', ...\n" + "6. If there is no information found, you still need to answer it. Guess an answer if you don't have enough information.\n" + "\n\nAnswer Format Guidelines (CRITICAL):\n" + "- For list questions (What books, What instruments, What activities, etc.), provide a simple comma-separated list or use 'and' between items.\n" + " Example: \"clarinet and violin\" NOT \"She plays clarinet\"\n" + " Example: '\"Nothing is Impossible\", \"Charlotte\\'s Web\"' NOT \"She read several books\"\n" + "- For simple fact questions (What is X's relationship status?, How old?, etc.), provide direct factual answers.\n" + " Example: \"Single\" NOT \"She experienced a breakup but is...\"\n" + " Example: \"28 years old\" NOT \"She is currently 28 years old and...\"\n" + "- For specific detail questions (What kind of art?, What type of pot?, etc.), provide the specific detail.\n" + " Example: \"abstract art\" NOT \"art inspired by...\"\n" + " Example: \"a cup with a dog face on it\" NOT \"pottery items\"\n" + "- ALWAYS extract the minimal, direct answer that matches what's being asked. Do NOT add ANY additional information!\n" + "- If the question asks for multiple items, search until you find ALL items, not just the first one." + ) + messages = [ + {"role": "system", "content": system_prompt}, + *input_messages + ] + + usage_entries = [] + usage_total = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + for round_num in range(self.max_tool_rounds + 1): + # On the last round, force answer generation by not providing tools + is_last_round = (round_num == self.max_tool_rounds) + + if is_last_round: + # Add instruction to provide final answer + messages.append({ + "role": "system", + "content": "You have reached the maximum number of searches. Please provide your best answer based on the information you've gathered so far." + }) + + response = self.client.chat.completions.create( + model=self.model, + messages=messages, + tools=None if is_last_round else (tools or None), + tool_choice=None if is_last_round else ("auto" if tools else None), + max_completion_tokens=128, + ) + + usage = getattr(response, "usage", None) + if usage: + entry = { + "model": self.model, + "prompt_tokens": getattr(usage, "prompt_tokens", 0), + "completion_tokens": getattr(usage, "completion_tokens", 0), + "total_tokens": getattr(usage, "total_tokens", 0), + } + usage_entries.append(entry) + usage_total["prompt_tokens"] += entry["prompt_tokens"] + usage_total["completion_tokens"] += entry["completion_tokens"] + usage_total["total_tokens"] += entry["total_tokens"] + + message = response.choices[0].message + if not message.tool_calls: + messages.append( + { + "role": "assistant", + "content": message.content, + } + ) + final_answer = message.content or "" + return { + "answer": final_answer, + "messages": messages, + "usage": usage_entries, + "usage_total": usage_total, + } + + messages.append( + { + "role": "assistant", + "content": message.content, + "tool_calls": self._serialize_tool_calls(message.tool_calls), + } + ) + + for tool_call in message.tool_calls: + try: + args = json.loads(tool_call.function.arguments or "{}") + except json.JSONDecodeError: + tool_result = {"success": False, "error": "Invalid tool arguments."} + else: + if tool_call.function.name == "search_memory": + tool_result = self._search_memory(user_id, args) + elif tool_call.function.name == "check_raw_item": + tool_result = self._check_raw_item(args) + else: + tool_result = {"success": False, "error": "Unknown tool."} + messages.append( + { + "role": "tool", + "tool_call_id": tool_call.id, + "content": json.dumps(tool_result), + } + ) + + return { + "answer": "I don't know", + "messages": messages, + "usage": usage_entries, + "usage_total": usage_total, + } diff --git a/mirix/agent/agent.py b/mirix/agent/agent.py index 0fbbeb141..4ecd7d335 100644 --- a/mirix/agent/agent.py +++ b/mirix/agent/agent.py @@ -41,7 +41,7 @@ from mirix.memory import summarize_messages from mirix.observability.context import get_trace_context, mark_observation_as_child from mirix.observability.langfuse_client import get_langfuse_client -from mirix.schemas.agent import AgentState, AgentStepResponse, UpdateAgent +from mirix.schemas.agent import AgentState, AgentStepResponse, AgentType, UpdateAgent from mirix.schemas.block import BlockUpdate from mirix.schemas.client import Client from mirix.schemas.embedding_config import EmbeddingConfig @@ -67,6 +67,7 @@ from mirix.services.resource_memory_manager import ResourceMemoryManager from mirix.services.semantic_memory_manager import SemanticMemoryManager from mirix.services.step_manager import StepManager +from mirix.services.user_manager import UserManager from mirix.services.tool_execution_sandbox import ToolExecutionSandbox from mirix.settings import settings, summarizer_settings from mirix.system import ( @@ -927,38 +928,15 @@ async def _handle_ai_response( for tool_call in response_message.tool_calls: assert tool_call.id is not None # should be defined - # Memory agents are instructed to emit only ONE tool call per step. - # In practice, the LLM can occasionally return multiple tool calls (often duplicates), - # which can cause non-idempotent operations to fail (e.g., double deletes). - # To match the prompt contract and keep behavior predictable, truncate to the first. - from mirix.schemas.agent import AgentType - - memory_agent_types = { - AgentType.core_memory_agent, - AgentType.episodic_memory_agent, - AgentType.procedural_memory_agent, - AgentType.resource_memory_agent, - AgentType.knowledge_vault_memory_agent, - AgentType.semantic_memory_agent, - } - if ( - self.agent_state.agent_type in memory_agent_types - and response_message.tool_calls is not None + response_message.tool_calls is not None and len(response_message.tool_calls) > 1 ): - kept = response_message.tool_calls[0] - dropped = response_message.tool_calls[1:] - dropped_desc = [f"{tc.function.name}:{tc.id}" for tc in dropped if tc and tc.function] - self.logger.warning( - "Truncating %d extra tool call(s) for memory agent %s (keeping %s:%s, dropping %s)", - len(dropped), + self.logger.info( + "Memory agent %s returned %d tool call(s); executing each sequentially", self.agent_state.agent_type, - kept.function.name if kept and kept.function else None, - kept.id if kept else None, - dropped_desc, + len(response_message.tool_calls), ) - response_message.tool_calls = [kept] # role: assistant (requesting tool call, set tool call ID) messages.append( @@ -1495,8 +1473,6 @@ async def step( self.user = user # Only load blocks for core_memory_agent (other agent types don't use blocks) - from mirix.schemas.agent import AgentType - if self.agent_state.is_type(AgentType.core_memory_agent): # Load existing blocks for this user, scoped by the client's write_scope. # auto_create_from_default=True will create blocks from template if they don't exist for this scope. @@ -1723,8 +1699,6 @@ async def build_system_prompt_with_memories( Returns: Tuple[str, dict]: The complete system prompt and the retrieved memories dict """ - from mirix.schemas.agent import AgentType - timezone_str = self.user.timezone if retrieved_memories is None: diff --git a/mirix/client/remote_client.py b/mirix/client/remote_client.py index 200bbd78d..38f463ff3 100644 --- a/mirix/client/remote_client.py +++ b/mirix/client/remote_client.py @@ -185,7 +185,13 @@ def __init__( self.read_scopes = [client_scope] else: self.write_scope = write_scope - self.read_scopes = read_scopes if read_scopes is not None else [] + if read_scopes is not None: + self.read_scopes = read_scopes + elif write_scope: + # If only write_scope is given, the client can also read what it writes + self.read_scopes = [write_scope] + else: + self.read_scopes = [] # Keep client_scope for backward compatibility in logging self.client_scope = client_scope or write_scope or "" @@ -1192,6 +1198,9 @@ async def initialize_meta_agent( >>> meta_agent = client.initialize_meta_agent(config=config) """ + # Ensure org and client exist in the DB before making agent requests + await self._ensure_org_and_client_exist(headers=headers) + # Load config from file if provided if config_path: from pathlib import Path @@ -1243,6 +1252,7 @@ async def add( block_filter_tags_update_mode: Optional[str] = "merge", use_cache: bool = True, occurred_at: Optional[str] = None, + async_add: bool = True, headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ @@ -1345,7 +1355,8 @@ async def add( if occurred_at is not None: request_data["occurred_at"] = occurred_at - return await self._request("POST", "/memory/add", json=request_data, headers=headers) + endpoint = "/memory/add" if async_add else "/memory/add_sync" + return await self._request("POST", endpoint, json=request_data, headers=headers) async def retrieve_with_conversation( self, @@ -1511,7 +1522,7 @@ async def search( query: str, memory_type: str = "all", search_field: str = "null", - search_method: str = "bm25", + search_method: str = "embedding", limit: int = 10, filter_tags: Optional[Dict[str, Any]] = None, similarity_threshold: Optional[float] = None, @@ -1661,7 +1672,7 @@ async def search_all_users( query: str, memory_type: str = "all", search_field: str = "null", - search_method: str = "bm25", + search_method: str = "embedding", limit: int = 10, client_id: Optional[str] = None, filter_tags: Optional[Dict[str, Any]] = None, diff --git a/mirix/embeddings.py b/mirix/embeddings.py index 8cb6b6cce..91835cf1b 100755 --- a/mirix/embeddings.py +++ b/mirix/embeddings.py @@ -440,9 +440,12 @@ async def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] if endpoint_type == "openai": from mirix.services.provider_manager import ProviderManager - # Check for database-stored API key first, fall back to model_settings - override_key = await ProviderManager().get_openai_override_key() - api_key = override_key if override_key else model_settings.openai_api_key + if config.api_key: + api_key = config.api_key + else: + # Check for database-stored API key first, fall back to model_settings + override_key = await ProviderManager().get_openai_override_key() + api_key = override_key if override_key else model_settings.openai_api_key # Use direct OpenAI SDK if auth_provider is configured if hasattr(config, "auth_provider") and config.auth_provider: @@ -465,8 +468,11 @@ async def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] from mirix.services.provider_manager import ProviderManager - override_key = await ProviderManager().get_gemini_override_key() - api_key = override_key if override_key else model_settings.gemini_api_key + if config.api_key: + api_key = config.api_key + else: + override_key = await ProviderManager().get_gemini_override_key() + api_key = override_key if override_key else model_settings.gemini_api_key model = GoogleGenAIEmbedding( model_name=config.embedding_model, diff --git a/mirix/functions/function_sets/memory_tools.py b/mirix/functions/function_sets/memory_tools.py index 8ce5a8f86..763ff05cf 100644 --- a/mirix/functions/function_sets/memory_tools.py +++ b/mirix/functions/function_sets/memory_tools.py @@ -1,6 +1,7 @@ import asyncio import os import re +import traceback from copy import deepcopy from typing import TYPE_CHECKING, List, Optional @@ -137,20 +138,29 @@ async def episodic_memory_insert(self: "Agent", items: List[EpisodicEventForLLM] timestamp = datetime.fromisoformat(timestamp.replace("Z", "+00:00")) - await self.episodic_memory_manager.insert_event( - actor=self.actor, - agent_state=self.agent_state, - agent_id=agent_id, - timestamp=timestamp, # Use potentially overridden timestamp - event_type=item["event_type"], - event_actor=item["actor"], - summary=item["summary"], - details=item["details"], - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.episodic_memory_manager.insert_event( + actor=self.actor, + agent_state=self.agent_state, + agent_id=agent_id, + timestamp=timestamp, # Use potentially overridden timestamp + event_type=item["event_type"], + event_actor=item["actor"], + summary=item["summary"], + details=item["details"], + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[episodic_memory_insert] insert_event FAILED for item " + f"{item!r}: {e}" + ) + traceback.print_exc() + raise + response = "Events inserted! Now you need to check if there are repeated events shown in the system prompt." return response @@ -173,14 +183,22 @@ async def episodic_memory_merge( Optional[str]: None is always returned as this function does not produce a response. """ - episodic_memory = await self.episodic_memory_manager.update_event( - event_id=event_id, - new_summary=combined_summary, - new_details=combined_details, - actor=self.actor, - agent_state=self.agent_state, - update_mode="replace", - ) + try: + episodic_memory = await self.episodic_memory_manager.update_event( + event_id=event_id, + new_summary=combined_summary, + new_details=combined_details, + actor=self.actor, + agent_state=self.agent_state, + update_mode="replace", + ) + except Exception as e: + print( + f"[episodic_memory_merge] update_event FAILED for event_id " + f"{event_id!r}: {e}" + ) + traceback.print_exc() + raise response = ( "These are the `summary` and the `details` of the updated event:\n", str( @@ -223,7 +241,15 @@ async def episodic_memory_replace(self: "Agent", event_ids: List[str], new_items await self.episodic_memory_manager.get_episodic_memory_by_id(event_id, user=self.user) for event_id in event_ids: - await self.episodic_memory_manager.delete_event_by_id(event_id, actor=self.actor) + try: + await self.episodic_memory_manager.delete_event_by_id(event_id, actor=self.actor) + except Exception as e: + print( + f"[episodic_memory_replace] delete_event_by_id FAILED for " + f"event_id {event_id!r}: {e}" + ) + traceback.print_exc() + raise for new_item in new_items: # Use occurred_at_override if provided, otherwise use LLM-extracted timestamp @@ -235,20 +261,28 @@ async def episodic_memory_replace(self: "Agent", event_ids: List[str], new_items timestamp = datetime.fromisoformat(timestamp.replace("Z", "+00:00")) - await self.episodic_memory_manager.insert_event( - actor=self.actor, - agent_state=self.agent_state, - agent_id=agent_id, - timestamp=timestamp, # Use potentially overridden timestamp - event_type=new_item["event_type"], - event_actor=new_item["actor"], - summary=new_item["summary"], - details=new_item["details"], - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.episodic_memory_manager.insert_event( + actor=self.actor, + agent_state=self.agent_state, + agent_id=agent_id, + timestamp=timestamp, # Use potentially overridden timestamp + event_type=new_item["event_type"], + event_actor=new_item["actor"], + summary=new_item["summary"], + details=new_item["details"], + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[episodic_memory_replace] insert_event FAILED for item " + f"{new_item!r}: {e}" + ) + traceback.print_exc() + raise async def check_episodic_memory(self: "Agent", event_ids: List[str], timezone_str: str) -> List[EpisodicEventForLLM]: @@ -333,19 +367,27 @@ async def resource_memory_insert(self: "Agent", items: List[ResourceMemoryItemBa break if not is_duplicate: - await self.resource_memory_manager.insert_resource( - actor=self.actor, - agent_state=self.agent_state, - agent_id=agent_id, - title=item["title"], - summary=item["summary"], - resource_type=item["resource_type"], - content=item["content"], - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.resource_memory_manager.insert_resource( + actor=self.actor, + agent_state=self.agent_state, + agent_id=agent_id, + title=item["title"], + summary=item["summary"], + resource_type=item["resource_type"], + content=item["content"], + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[resource_memory_insert] insert_resource FAILED for item " + f"{item!r}: {e}" + ) + traceback.print_exc() + raise inserted_count += 1 # Return feedback message @@ -438,18 +480,26 @@ async def procedural_memory_insert(self: "Agent", items: List[ProceduralMemoryIt break if not is_duplicate: - await self.procedural_memory_manager.insert_procedure( - agent_state=self.agent_state, - agent_id=agent_id, - entry_type=item["entry_type"], - summary=item["summary"], - steps=item["steps"], - actor=self.actor, - organization_id=self.user.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.procedural_memory_manager.insert_procedure( + agent_state=self.agent_state, + agent_id=agent_id, + entry_type=item["entry_type"], + summary=item["summary"], + steps=item["steps"], + actor=self.actor, + organization_id=self.user.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[procedural_memory_insert] insert_procedure FAILED for " + f"item {item!r}: {e}" + ) + traceback.print_exc() + raise inserted_count += 1 # Return feedback message @@ -484,21 +534,37 @@ async def procedural_memory_update(self: "Agent", old_ids: List[str], new_items: user_id = getattr(self, "user_id", None) for old_id in old_ids: - await self.procedural_memory_manager.delete_procedure_by_id(procedure_id=old_id, actor=self.actor) + try: + await self.procedural_memory_manager.delete_procedure_by_id(procedure_id=old_id, actor=self.actor) + except Exception as e: + print( + f"[procedural_memory_update] delete_procedure_by_id FAILED for " + f"old_id {old_id!r}: {e}" + ) + traceback.print_exc() + raise for item in new_items: - await self.procedural_memory_manager.insert_procedure( - agent_state=self.agent_state, - agent_id=agent_id, - entry_type=item["entry_type"], - summary=item["summary"], - steps=item["steps"], - actor=self.actor, - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.procedural_memory_manager.insert_procedure( + agent_state=self.agent_state, + agent_id=agent_id, + entry_type=item["entry_type"], + summary=item["summary"], + steps=item["steps"], + actor=self.actor, + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[procedural_memory_update] insert_procedure FAILED for item " + f"{item!r}: {e}" + ) + traceback.print_exc() + raise async def check_semantic_memory( @@ -581,19 +647,27 @@ async def semantic_memory_insert(self: "Agent", items: List[SemanticMemoryItemBa break if not is_duplicate: - await self.semantic_memory_manager.insert_semantic_item( - agent_state=self.agent_state, - agent_id=agent_id, - name=item["name"], - summary=item["summary"], - details=item["details"], - source=item["source"], - organization_id=self.actor.organization_id, - actor=self.actor, # Client for write operations - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.semantic_memory_manager.insert_semantic_item( + agent_state=self.agent_state, + agent_id=agent_id, + name=item["name"], + summary=item["summary"], + details=item["details"], + source=item["source"], + organization_id=self.actor.organization_id, + actor=self.actor, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[semantic_memory_insert] insert_semantic_item FAILED for " + f"item {item!r}: {e}" + ) + traceback.print_exc() + raise inserted_count += 1 # Return feedback message @@ -632,23 +706,41 @@ async def semantic_memory_update( user_id = getattr(self, "user_id", None) for old_id in old_semantic_item_ids: - await self.semantic_memory_manager.delete_semantic_item_by_id(semantic_memory_id=old_id, actor=self.actor) + try: + await self.semantic_memory_manager.delete_semantic_item_by_id( + semantic_memory_id=old_id, actor=self.actor + ) + except Exception as e: + print( + f"[semantic_memory_update] delete_semantic_item_by_id FAILED for " + f"old_id {old_id!r}: {e}" + ) + traceback.print_exc() + raise new_ids = [] for item in new_items: - inserted_item = await self.semantic_memory_manager.insert_semantic_item( - agent_state=self.agent_state, - agent_id=agent_id, - name=item["name"], - summary=item["summary"], - details=item["details"], - source=item["source"], - actor=self.actor, - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + inserted_item = await self.semantic_memory_manager.insert_semantic_item( + agent_state=self.agent_state, + agent_id=agent_id, + name=item["name"], + summary=item["summary"], + details=item["details"], + source=item["source"], + actor=self.actor, + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[semantic_memory_update] insert_semantic_item FAILED for " + f"item {item!r}: {e}" + ) + traceback.print_exc() + raise new_ids.append(inserted_item.id) message_to_return = ( @@ -706,20 +798,28 @@ async def knowledge_vault_insert(self: "Agent", items: List[KnowledgeVaultItemBa break if not is_duplicate: - await self.knowledge_vault_manager.insert_knowledge( - actor=self.actor, - agent_state=self.agent_state, - agent_id=agent_id, - entry_type=item["entry_type"], - source=item["source"], - sensitivity=item["sensitivity"], - secret_value=item["secret_value"], - caption=item["caption"], - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.knowledge_vault_manager.insert_knowledge( + actor=self.actor, + agent_state=self.agent_state, + agent_id=agent_id, + entry_type=item["entry_type"], + source=item["source"], + sensitivity=item["sensitivity"], + secret_value=item["secret_value"], + caption=item["caption"], + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[knowledge_vault_insert] insert_knowledge FAILED for " + f"item {item!r}: {e}" + ) + traceback.print_exc() + raise inserted_count += 1 # Return feedback message @@ -754,23 +854,41 @@ async def knowledge_vault_update(self: "Agent", old_ids: List[str], new_items: L user_id = getattr(self, "user_id", None) for old_id in old_ids: - await self.knowledge_vault_manager.delete_knowledge_by_id(knowledge_vault_item_id=old_id, actor=self.actor) + try: + await self.knowledge_vault_manager.delete_knowledge_by_id( + knowledge_vault_item_id=old_id, actor=self.actor + ) + except Exception as e: + print( + f"[knowledge_vault_update] delete_knowledge_by_id FAILED for " + f"old_id {old_id!r}: {e}" + ) + traceback.print_exc() + raise for item in new_items: - await self.knowledge_vault_manager.insert_knowledge( - actor=self.actor, - agent_state=self.agent_state, - agent_id=agent_id, - entry_type=item["entry_type"], - source=item["source"], - sensitivity=item["sensitivity"], - secret_value=item["secret_value"], - caption=item["caption"], - organization_id=self.actor.organization_id, - filter_tags=filter_tags if filter_tags else None, - use_cache=use_cache, - user_id=user_id, - ) + try: + await self.knowledge_vault_manager.insert_knowledge( + actor=self.actor, + agent_state=self.agent_state, + agent_id=agent_id, + entry_type=item["entry_type"], + source=item["source"], + sensitivity=item["sensitivity"], + secret_value=item["secret_value"], + caption=item["caption"], + organization_id=self.actor.organization_id, + filter_tags=filter_tags if filter_tags else None, + use_cache=use_cache, + user_id=user_id, + ) + except Exception as e: + print( + f"[knowledge_vault_update] insert_knowledge FAILED for item " + f"{item!r}: {e}" + ) + traceback.print_exc() + raise async def trigger_memory_update_with_instruction( diff --git a/mirix/orm/sqlite_functions.py b/mirix/orm/sqlite_functions.py index b9faa39ec..ba3b2ab50 100755 --- a/mirix/orm/sqlite_functions.py +++ b/mirix/orm/sqlite_functions.py @@ -133,16 +133,45 @@ def cosine_distance(embedding1, embedding2, expected_dim=MAX_EMBEDDING_DIM): return distance +def _unwrap_sqlite_connection(dbapi_connection) -> Optional[sqlite3.Connection]: + """ + Unwrap a possibly-async-adapted connection to find the raw sqlite3.Connection. + + SQLAlchemy's aiosqlite dialect wraps the raw connection in one or two adapter + layers (AsyncAdaptedConnection → aiosqlite.Connection → sqlite3.Connection). + This helper peels those layers back so we can call sqlite3-specific APIs such + as create_function() and execute PRAGMA statements. + """ + if isinstance(dbapi_connection, sqlite3.Connection): + return dbapi_connection + # Walk common wrapper attribute names used by SQLAlchemy / aiosqlite + for outer_attr in ("_connection", "_adapt_connection", "_conn", "connection"): + mid = getattr(dbapi_connection, outer_attr, None) + if mid is None: + continue + if isinstance(mid, sqlite3.Connection): + return mid + for inner_attr in ("_conn", "_connection", "_adapt_connection"): + inner = getattr(mid, inner_attr, None) + if isinstance(inner, sqlite3.Connection): + return inner + return None + + @event.listens_for(Engine, "connect") def register_functions(dbapi_connection, connection_record): - """Register SQLite functions""" - if isinstance(dbapi_connection, sqlite3.Connection): - dbapi_connection.create_function("cosine_distance", 2, cosine_distance) + """Register SQLite functions for vector search.""" + conn = _unwrap_sqlite_connection(dbapi_connection) + if conn is not None: + conn.create_function("cosine_distance", 2, cosine_distance) @event.listens_for(Engine, "connect") def configure_sqlite_connection(dbapi_connection, connection_record): """Configure SQLite connection for better concurrency and performance""" + conn = _unwrap_sqlite_connection(dbapi_connection) + if conn is not None: + dbapi_connection = conn if isinstance(dbapi_connection, sqlite3.Connection): # Enable WAL mode for better concurrency dbapi_connection.execute("PRAGMA journal_mode=WAL") diff --git a/mirix/schemas/embedding_config.py b/mirix/schemas/embedding_config.py index 6bda99770..43a2522d7 100755 --- a/mirix/schemas/embedding_config.py +++ b/mirix/schemas/embedding_config.py @@ -60,6 +60,12 @@ class EmbeddingConfig(BaseModel): description="Model name to report to Langfuse for cost tracking (overrides 'embedding_model' if set).", ) + # Optional API key override — takes precedence over env-var / provider-manager keys + api_key: Optional[str] = Field( + None, + description="API key to use for this embedding config, overriding env-var / provider-manager defaults.", + ) + # azure only azure_endpoint: Optional[str] = Field(None, description="The Azure endpoint for the model.") azure_version: Optional[str] = Field(None, description="The Azure version for the model.") diff --git a/mirix/server/rest_api.py b/mirix/server/rest_api.py index 27f306802..a9963df83 100644 --- a/mirix/server/rest_api.py +++ b/mirix/server/rest_api.py @@ -2128,6 +2128,116 @@ async def add_memory( } +@router.post("/memory/add_sync") +@with_langfuse_tracing +async def add_memory_sync( + request: AddMemoryRequest, + x_org_id: Optional[str] = Header(None), + x_client_id: Optional[str] = Header(None), +): + """ + Add conversation turns to memory (synchronous processing). + + Processes messages immediately by running the agent directly, blocking + until all memory extraction is complete before returning. + """ + server = get_server() + client_id, org_id = await get_client_and_org(x_client_id, x_org_id) + client = await server.client_manager.get_client_by_id(client_id) + + if client is None: + raise HTTPException( + status_code=404, + detail=f"Client {client_id} not found. Please create the client first.", + ) + + meta_agent = await server.agent_manager.get_agent_by_id( + request.meta_agent_id, + client, + ) + + user_id = request.user_id + if not user_id: + from mirix.services.admin_user_manager import ClientAuthManager + + user_id = ClientAuthManager.get_admin_user_id_for_client(client.id) + + message = request.messages + + if isinstance(message, list) and "role" in message[0].keys(): + new_message = [] + for msg in message: + new_message.append( + { + "type": "text", + "text": "[USER]" if msg["role"] == "user" else "[ASSISTANT]", + } + ) + content = msg["content"] + if isinstance(content, str): + new_message.append({"type": "text", "text": content}) + elif isinstance(content, list): + new_message.extend(content) + else: + raise ValueError(f"Invalid content type: {type(content)}") + message = new_message + + input_messages = convert_message_to_mirix_message(message) + + if request.filter_tags is not None: + filter_tags = dict(request.filter_tags) + else: + filter_tags = {} + + if client.write_scope is None: + raise HTTPException(status_code=403, detail="Client has no write_scope - cannot create memories") + filter_tags["scope"] = client.write_scope + + from mirix.services.user_manager import UserManager + + user_manager = UserManager() + try: + user = await user_manager.get_user_by_id(user_id) + except Exception: + from mirix.schemas.user import User as PydanticUser + + user = await user_manager.create_user( + pydantic_user=PydanticUser( + id=user_id, + name=user_id, + organization_id=client.organization_id, + timezone=user_manager.DEFAULT_TIME_ZONE, + status="active", + is_deleted=False, + is_admin=False, + ) + ) + + await server.send_messages( + actor=client, + agent_id=meta_agent.id, + input_messages=input_messages, + chaining=request.chaining, + user=user, + verbose=request.verbose, + filter_tags=filter_tags, + block_filter_tags=request.block_filter_tags, + block_filter_tags_update_mode=request.block_filter_tags_update_mode, + use_cache=request.use_cache, + occurred_at=request.occurred_at, + ) + + logger.debug("Memory processed synchronously: %s", meta_agent.id) + + return { + "success": True, + "message": "Memory processed successfully", + "status": "processed", + "agent_id": meta_agent.id, + "message_count": len(input_messages), + } + + class RetrieveMemoryRequest(BaseModel): """Request model for retrieving memory.""" @@ -2696,7 +2806,7 @@ async def search_memory( query: str = "", memory_type: str = "all", search_field: str = "null", - search_method: str = "bm25", + search_method: str = "embedding", limit: int = 10, authorization: Optional[str] = Header(None), filter_tags: Optional[str] = Query(None), @@ -2825,6 +2935,10 @@ async def search_memory( except ValueError as e: logger.warning("Invalid end_date format: %s", e) + # Normalize empty search_method to the default (FastAPI passes "" for missing query params) + if not search_method: + search_method = "embedding" + # Validate search parameters if memory_type == "resource" and search_field == "content" and search_method == "embedding": return { @@ -2865,7 +2979,7 @@ async def search_episodic(): user=user, query=query, embedded_text=(embedded_text_padded if search_method == "embedding" and query else None), - search_field=search_field if search_field != "null" else "summary", + search_field=search_field if search_field != "null" else "details", search_method=search_method, limit=limit, timezone_str=timezone_str, @@ -2879,7 +2993,7 @@ async def search_episodic(): { "memory_type": "episodic", "id": x.id, - "timestamp": (x.occurred_at.isoformat() if x.occurred_at else None), + "occurred_at": (x.occurred_at.isoformat() if x.occurred_at else None), "event_type": x.event_type, "actor": x.actor, "summary": x.summary, @@ -2932,7 +3046,7 @@ async def search_procedural(): user=user, query=query, embedded_text=(embedded_text if search_method == "embedding" and query else None), - search_field=search_field if search_field != "null" else "summary", + search_field=search_field if search_field != "null" else "steps", search_method=search_method, limit=limit, timezone_str=timezone_str, @@ -2992,7 +3106,7 @@ async def search_semantic(): user=user, query=query, embedded_text=(embedded_text_padded if search_method == "embedding" and query else None), - search_field=search_field if search_field != "null" else "summary", + search_field=search_field if search_field != "null" else "details", search_method=search_method, limit=limit, timezone_str=timezone_str, @@ -3261,7 +3375,7 @@ async def search_memory_all_users( query: str, memory_type: str = "all", search_field: str = "null", - search_method: str = "bm25", + search_method: str = "embedding", limit: int = 10, client_id: Optional[str] = Query(None), org_id: Optional[str] = Query(None), @@ -3390,6 +3504,10 @@ async def search_memory_all_users( agent_state = all_agents[0] + # Normalize empty search_method to the default (FastAPI passes "" for missing query params) + if not search_method: + search_method = "embedding" + # Validate search parameters if memory_type == "resource" and search_field == "content" and search_method == "embedding": return { diff --git a/mirix/services/episodic_memory_manager.py b/mirix/services/episodic_memory_manager.py index 68ef38cb2..4109b065c 100755 --- a/mirix/services/episodic_memory_manager.py +++ b/mirix/services/episodic_memory_manager.py @@ -996,6 +996,9 @@ async def list_episodic_memory( # Return the list after converting to Pydantic. return [event.to_pydantic() for event in episodic_memory] + else: + raise ValueError(f"Invalid search method: {search_method}") + if limit: main_query = main_query.limit(limit) diff --git a/uv.lock b/uv.lock new file mode 100644 index 000000000..46011c810 --- /dev/null +++ b/uv.lock @@ -0,0 +1,5914 @@ +version = 1 +revision = 3 +requires-python = ">=3.10, <4.0" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and 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