import json import os import time from collections import defaultdict from concurrent.futures import ThreadPoolExecutor from dotenv import load_dotenv from jinja2 import Template from openai import OpenAI from prompts import ANSWER_PROMPT, ANSWER_PROMPT_GRAPH from tqdm import tqdm from mem0 import MemoryClient load_dotenv() class MemorySearch: def __init__(self, output_path="results.json", top_k=10, filter_memories=False, is_graph=False): self.mem0_client = MemoryClient( api_key=os.getenv("MEM0_API_KEY"), org_id=os.getenv("MEM0_ORGANIZATION_ID"), project_id=os.getenv("MEM0_PROJECT_ID"), ) self.top_k = top_k self.openai_client = OpenAI() self.results = defaultdict(list) self.output_path = output_path self.filter_memories = filter_memories self.is_graph = is_graph if self.is_graph: self.ANSWER_PROMPT = ANSWER_PROMPT_GRAPH else: self.ANSWER_PROMPT = ANSWER_PROMPT def search_memory(self, user_id, query, max_retries=3, retry_delay=1): start_time = time.time() retries = 0 while retries < max_retries: try: if self.is_graph: print("Searching with graph") memories = self.mem0_client.search( query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories, enable_graph=True, output_format="v1.1", ) else: memories = self.mem0_client.search( query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories ) break except Exception as e: print("Retrying...") retries += 1 if retries >= max_retries: raise e time.sleep(retry_delay) end_time = time.time() if not self.is_graph: semantic_memories = [ { "memory": memory["memory"], "timestamp": memory["metadata"]["timestamp"], "score": round(memory["score"], 2), } for memory in memories ] graph_memories = None else: semantic_memories = [ { "memory": memory["memory"], "timestamp": memory["metadata"]["timestamp"], "score": round(memory["score"], 2), } for memory in memories["results"] ] graph_memories = [ {"source": relation["source"], "relationship": relation["relationship"], "target": relation["target"]} for relation in memories["relations"] ] return semantic_memories, graph_memories, end_time - start_time def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category): speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory( speaker_1_user_id, question ) speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory( speaker_2_user_id, question ) search_1_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_1_memories] search_2_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_2_memories] template = Template(self.ANSWER_PROMPT) answer_prompt = template.render( speaker_1_user_id=speaker_1_user_id.split("_")[0], speaker_2_user_id=speaker_2_user_id.split("_")[0], speaker_1_memories=json.dumps(search_1_memory, indent=4), speaker_2_memories=json.dumps(search_2_memory, indent=4), speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4), speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4), question=question, ) t1 = time.time() response = self.openai_client.chat.completions.create( model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0 ) t2 = time.time() response_time = t2 - t1 return ( response.choices[0].message.content, speaker_1_memories, speaker_2_memories, speaker_1_memory_time, speaker_2_memory_time, speaker_1_graph_memories, speaker_2_graph_memories, response_time, ) def process_question(self, val, speaker_a_user_id, speaker_b_user_id): question = val.get("question", "") answer = val.get("answer", "") category = val.get("category", -1) evidence = val.get("evidence", []) adversarial_answer = val.get("adversarial_answer", "") ( response, speaker_1_memories, speaker_2_memories, speaker_1_memory_time, speaker_2_memory_time, speaker_1_graph_memories, speaker_2_graph_memories, response_time, ) = self.answer_question(speaker_a_user_id, speaker_b_user_id, question, answer, category) result = { "question": question, "answer": answer, "category": category, "evidence": evidence, "response": response, "adversarial_answer": adversarial_answer, "speaker_1_memories": speaker_1_memories, "speaker_2_memories": speaker_2_memories, "num_speaker_1_memories": len(speaker_1_memories), "num_speaker_2_memories": len(speaker_2_memories), "speaker_1_memory_time": speaker_1_memory_time, "speaker_2_memory_time": speaker_2_memory_time, "speaker_1_graph_memories": speaker_1_graph_memories, "speaker_2_graph_memories": speaker_2_graph_memories, "response_time": response_time, } # Save results after each question is processed with open(self.output_path, "w") as f: json.dump(self.results, f, indent=4) return result def process_data_file(self, file_path): with open(file_path, "r") as f: data = json.load(f) for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"): qa = item["qa"] conversation = item["conversation"] speaker_a = conversation["speaker_a"] speaker_b = conversation["speaker_b"] speaker_a_user_id = f"{speaker_a}_{idx}" speaker_b_user_id = f"{speaker_b}_{idx}" for question_item in tqdm( qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False ): result = self.process_question(question_item, speaker_a_user_id, speaker_b_user_id) self.results[idx].append(result) # Save results after each question is processed with open(self.output_path, "w") as f: json.dump(self.results, f, indent=4) # Final save at the end with open(self.output_path, "w") as f: json.dump(self.results, f, indent=4) def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1): def process_single_question(val): result = self.process_question(val, speaker_a_user_id, speaker_b_user_id) # Save results after each question is processed with open(self.output_path, "w") as f: json.dump(self.results, f, indent=4) return result with ThreadPoolExecutor(max_workers=max_workers) as executor: results = list( tqdm(executor.map(process_single_question, qa_list), total=len(qa_list), desc="Answering Questions") ) # Final save at the end with open(self.output_path, "w") as f: json.dump(self.results, f, indent=4) return results