Docs Update (#2591)
This commit is contained in:
Prateek Chhikara
@@ -0,0 +1,189 @@
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from collections import defaultdict
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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from mem0 import MemoryClient
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import json
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import time
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from jinja2 import Template
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from openai import OpenAI
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from prompts import ANSWER_PROMPT_GRAPH, ANSWER_PROMPT
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import os
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from dotenv import load_dotenv
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load_dotenv()
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class MemorySearch:
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def __init__(self, output_path='results.json', top_k=10, filter_memories=False, is_graph=False):
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self.mem0_client = MemoryClient(
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api_key=os.getenv("MEM0_API_KEY"),
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org_id=os.getenv("MEM0_ORGANIZATION_ID"),
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project_id=os.getenv("MEM0_PROJECT_ID")
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)
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self.top_k = top_k
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self.openai_client = OpenAI()
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self.results = defaultdict(list)
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self.output_path = output_path
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self.filter_memories = filter_memories
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self.is_graph = is_graph
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if self.is_graph:
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self.ANSWER_PROMPT = ANSWER_PROMPT_GRAPH
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else:
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self.ANSWER_PROMPT = ANSWER_PROMPT
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def search_memory(self, user_id, query, max_retries=3, retry_delay=1):
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start_time = time.time()
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retries = 0
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while retries < max_retries:
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try:
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if self.is_graph:
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print("Searching with graph")
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memories = self.mem0_client.search(query, user_id=user_id, top_k=self.top_k,
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filter_memories=self.filter_memories, enable_graph=True, output_format='v1.1')
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else:
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memories = self.mem0_client.search(query, user_id=user_id, top_k=self.top_k,
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filter_memories=self.filter_memories)
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break
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except Exception as e:
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print("Retrying...")
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retries += 1
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if retries >= max_retries:
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raise e
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time.sleep(retry_delay)
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end_time = time.time()
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if not self.is_graph:
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semantic_memories = [{'memory': memory['memory'],
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'timestamp': memory['metadata']['timestamp'],
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'score': round(memory['score'], 2)}
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for memory in memories]
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graph_memories = None
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else:
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semantic_memories = [{'memory': memory['memory'],
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'timestamp': memory['metadata']['timestamp'],
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'score': round(memory['score'], 2)} for memory in memories['results']]
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graph_memories = [{"source": relation['source'], "relationship": relation['relationship'], "target": relation['target']} for relation in memories['relations']]
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return semantic_memories, graph_memories, end_time - start_time
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def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category):
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speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(speaker_1_user_id, question)
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speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(speaker_2_user_id, question)
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search_1_memory = [f"{item['timestamp']}: {item['memory']}"
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for item in speaker_1_memories]
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search_2_memory = [f"{item['timestamp']}: {item['memory']}"
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for item in speaker_2_memories]
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template = Template(self.ANSWER_PROMPT)
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answer_prompt = template.render(
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speaker_1_user_id=speaker_1_user_id.split('_')[0],
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speaker_2_user_id=speaker_2_user_id.split('_')[0],
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speaker_1_memories=json.dumps(search_1_memory, indent=4),
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speaker_2_memories=json.dumps(search_2_memory, indent=4),
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speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4),
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speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4),
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question=question
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)
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t1 = time.time()
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response = self.openai_client.chat.completions.create(
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model=os.getenv("MODEL"),
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messages=[
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{"role": "system", "content": answer_prompt}
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],
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temperature=0.0
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)
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t2 = time.time()
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response_time = t2 - t1
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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
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def process_question(self, val, speaker_a_user_id, speaker_b_user_id):
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question = val.get('question', '')
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answer = val.get('answer', '')
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category = val.get('category', -1)
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evidence = val.get('evidence', [])
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adversarial_answer = val.get('adversarial_answer', '')
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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(
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speaker_a_user_id,
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speaker_b_user_id,
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question,
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answer,
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category
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)
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result = {
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"question": question,
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"answer": answer,
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"category": category,
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"evidence": evidence,
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"response": response,
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"adversarial_answer": adversarial_answer,
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"speaker_1_memories": speaker_1_memories,
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"speaker_2_memories": speaker_2_memories,
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'num_speaker_1_memories': len(speaker_1_memories),
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'num_speaker_2_memories': len(speaker_2_memories),
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'speaker_1_memory_time': speaker_1_memory_time,
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'speaker_2_memory_time': speaker_2_memory_time,
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"speaker_1_graph_memories": speaker_1_graph_memories,
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"speaker_2_graph_memories": speaker_2_graph_memories,
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"response_time": response_time
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}
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# Save results after each question is processed
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with open(self.output_path, 'w') as f:
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json.dump(self.results, f, indent=4)
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return result
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def process_data_file(self, file_path):
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with open(file_path, 'r') as f:
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data = json.load(f)
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for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
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qa = item['qa']
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conversation = item['conversation']
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speaker_a = conversation['speaker_a']
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speaker_b = conversation['speaker_b']
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speaker_a_user_id = f"{speaker_a}_{idx}"
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speaker_b_user_id = f"{speaker_b}_{idx}"
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for question_item in tqdm(qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False):
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result = self.process_question(
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question_item,
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speaker_a_user_id,
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speaker_b_user_id
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)
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self.results[idx].append(result)
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# Save results after each question is processed
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with open(self.output_path, 'w') as f:
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json.dump(self.results, f, indent=4)
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# Final save at the end
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with open(self.output_path, 'w') as f:
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json.dump(self.results, f, indent=4)
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def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1):
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def process_single_question(val):
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result = self.process_question(val, speaker_a_user_id, speaker_b_user_id)
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# Save results after each question is processed
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with open(self.output_path, 'w') as f:
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json.dump(self.results, f, indent=4)
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return result
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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results = list(tqdm(
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executor.map(process_single_question, qa_list),
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total=len(qa_list),
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desc="Answering Questions"
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))
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# Final save at the end
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with open(self.output_path, 'w') as f:
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json.dump(self.results, f, indent=4)
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return results
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