MemTensor
diff --git a/‎README.md‎
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diff --git a/‎evaluation/scripts/locomo/locomo_rag.py‎
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@@ -252,6 +252,7 @@ MemOS is licensed under the [Apache 2.0 License](./LICENSE).
 Stay up to date with the latest MemOS announcements, releases, and community highlights.
 
 
+- **2025-09-10** - 🎉 *MemOS v1.0.1 (Group Q&A Bot)*: Group Q&A bot based on MemOS Cube, updated KV-Cache performance comparison data across different GPU deployment schemes, optimized test benchmarks and statistics, added plaintext memory Reranker sorting, optimized plaintext memory hallucination issues, and Playground version updates. [Try PlayGround](https://memos-playground.openmem.net/login/)
 - **2025-08-07** - 🎉 *MemOS v1.0.0 (MemCube Release)*: First MemCube with word game demo, LongMemEval evaluation, BochaAISearchRetriever integration, NebulaGraph support, enhanced search capabilities, and official Playground launch.
 - **2025-07-29** – 🎉 *MemOS v0.2.2 (Nebula Update)*: Internet search+Nebula DB integration, refactored memory scheduler, KV Cache stress tests, MemCube Cookbook release (CN/EN), and 4b/1.7b/0.6b memory ops models.
 - **2025-07-21** – 🎉 *MemOS v0.2.1 (Neo Release)*: Lightweight Neo version with plaintext+KV Cache functionality, Docker/multi-tenant support, MCP expansion, and new Cookbook/Mud game examples.
 
@@ -0,0 +1,337 @@
+"""
+Modify the code from the mem0 project
+"""
+
+import argparse
+import concurrent.futures
+import json
+import os
+import threading
+import time
+
+from collections import defaultdict
+
+import numpy as np
+import tiktoken
+
+from dotenv import load_dotenv
+from jinja2 import Template
+from openai import OpenAI
+from tqdm import tqdm
+
+
+load_dotenv()
+
+PROMPT = """
+# Question:
+{{QUESTION}}
+
+# Context:
+{{CONTEXT}}
+
+# Short answer:
+"""
+
+TECHNIQUES = ["mem0", "rag"]
+
+
+class RAGManager:
+    def __init__(self, data_path="data/locomo/locomo10_rag.json", chunk_size=500, k=2):
+        self.model = os.getenv("MODEL")
+        self.client = OpenAI()
+        self.data_path = data_path
+        self.chunk_size = chunk_size
+        self.k = k
+
+    def generate_response(self, question, context):
+        template = Template(PROMPT)
+        prompt = template.render(CONTEXT=context, QUESTION=question)
+
+        max_retries = 3
+        retries = 0
+
+        while retries <= max_retries:
+            try:
+                t1 = time.time()
+                response = self.client.chat.completions.create(
+                    model=self.model,
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": "You are a helpful assistant that can answer "
+                            "questions based on the provided context."
+                            "If the question involves timing, use the conversation date for reference."
+                            "Provide the shortest possible answer."
+                            "Use words directly from the conversation when possible."
+                            "Avoid using subjects in your answer.",
+                        },
+                        {"role": "user", "content": prompt},
+                    ],
+                    temperature=0,
+                )
+                t2 = time.time()
+                if response and response.choices:
+                    content = response.choices[0].message.content
+                    if content is not None:
+                        return content.strip(), t2 - t1
+                    else:
+                        return "No content returned", t2 - t1
+                        print("❎ No content returned!")
+                else:
+                    return "Empty response", t2 - t1
+            except Exception as e:
+                retries += 1
+                if retries > max_retries:
+                    raise e
+                time.sleep(1)  # Wait before retrying
+
+    def clean_chat_history(self, chat_history):
+        cleaned_chat_history = ""
+        for c in chat_history:
+            cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n"
+
+        return cleaned_chat_history
+
+    def calculate_embedding(self, document):
+        response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
+        return response.data[0].embedding
+
+    def calculate_similarity(self, embedding1, embedding2):
+        return np.dot(embedding1, embedding2) / (
+            np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
+        )
+
+    def search(self, query, chunks, embeddings, k=1):
+        """
+        Search for the top-k most similar chunks to the query.
+
+        Args:
+            query: The query string
+            chunks: List of text chunks
+            embeddings: List of embeddings for each chunk
+            k: Number of top chunks to return (default: 1)
+
+        Returns:
+            combined_chunks: The combined text of the top-k chunks
+            search_time: Time taken for the search
+        """
+        t1 = time.time()
+        query_embedding = self.calculate_embedding(query)
+        similarities = [
+            self.calculate_similarity(query_embedding, embedding) for embedding in embeddings
+        ]
+
+        # Get indices of top-k most similar chunks
+        top_indices = [np.argmax(similarities)] if k == 1 else np.argsort(similarities)[-k:][::-1]
+        # Combine the top-k chunks
+        combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
+
+        t2 = time.time()
+        return combined_chunks, t2 - t1
+
+    def create_chunks(self, chat_history, chunk_size=500):
+        """
+        Create chunks using tiktoken for more accurate token counting
+        """
+        # Get the encoding for the model
+        encoding = tiktoken.encoding_for_model(os.getenv("EMBEDDING_MODEL"))
+
+        documents = self.clean_chat_history(chat_history)
+
+        if chunk_size == -1:
+            return [documents], []
+
+        chunks = []
+
+        # Encode the document
+        tokens = encoding.encode(documents)
+
+        # Split into chunks based on token count
+        for i in range(0, len(tokens), chunk_size):
+            chunk_tokens = tokens[i : i + chunk_size]
+            chunk = encoding.decode(chunk_tokens)
+            chunks.append(chunk)
+
+        embeddings = []
+        for chunk in chunks:
+            embedding = self.calculate_embedding(chunk)
+            embeddings.append(embedding)
+
+        return chunks, embeddings
+
+    def process_all_conversations(self, output_file_path):
+        with open(self.data_path) as f:
+            data = json.load(f)
+
+        final_results = defaultdict(list)
+        for key, value in tqdm(data.items(), desc="Processing conversations"):
+            chat_history = value["conversation"]
+            questions = value["question"]
+
+            chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
+
+            for item in tqdm(questions, desc="Answering questions", leave=False):
+                question = item["question"]
+                answer = item.get("answer", "")
+                category = item["category"]
+
+                if self.chunk_size == -1:
+                    context = chunks[0]
+                    search_time = 0
+                else:
+                    context, search_time = self.search(question, chunks, embeddings, k=self.k)
+                response, response_time = self.generate_response(question, context)
+
+                final_results[key].append(
+                    {
+                        "question": question,
+                        "answer": answer,
+                        "category": category,
+                        "context": context,
+                        "response": response,
+                        "search_time": search_time,
+                        "response_time": response_time,
+                    }
+                )
+                with open(output_file_path, "w+") as f:
+                    json.dump(final_results, f, indent=4)
+
+        # Save results
+        with open(output_file_path, "w+") as f:
+            json.dump(final_results, f, indent=4)
+        print("The original rag file have been generated!")
+
+
+class Experiment:
+    def __init__(self, technique_type, chunk_size):
+        self.technique_type = technique_type
+        self.chunk_size = chunk_size
+
+    def run(self):
+        print(
+            f"Running experiment with technique: {self.technique_type}, chunk size: {self.chunk_size}"
+        )
+
+
+def process_item(item_data):
+    k, v = item_data
+    local_results = defaultdict(list)
+
+    for item in tqdm(v):
+        gt_answer = str(item["answer"])
+        pred_answer = str(item["response"])
+        category = str(item["category"])
+        question = str(item["question"])
+        search_time = str(item["search_time"])
+        response_time = str(item["response_time"])
+        search_context = str(item["context"])
+
+        # Skip category 5
+        if category == "5":
+            continue
+
+        local_results[k].append(
+            {
+                "question": question,
+                "golden_answer": gt_answer,
+                "answer": pred_answer,
+                "category": int(category),
+                "response_duration_ms": float(response_time) * 1000,
+                "search_duration_ms": float(search_time) * 1000,
+                "search_context": search_context,
+                # "llm_score_std":np.std(llm_score)
+            }
+        )
+
+    return local_results
+
+
+def rename_json_keys(file_path):
+    with open(file_path, encoding="utf-8") as f:
+        data = json.load(f)
+
+    new_data = {}
+    for old_key in data:
+        new_key = f"locomo_exp_user_{old_key}"
+        new_data[new_key] = data[old_key]
+
+    with open(file_path, "w", encoding="utf-8") as f:
+        json.dump(new_data, f, indent=2, ensure_ascii=False)
+
+
+def generate_response_file(file_path):
+    parser = argparse.ArgumentParser(description="Evaluate RAG results")
+
+    parser.add_argument(
+        "--output_folder",
+        type=str,
+        default="default_locomo_responses.json",
+        help="Path to save the evaluation results",
+    )
+    parser.add_argument(
+        "--max_workers", type=int, default=10, help="Maximum number of worker threads"
+    )
+    parser.add_argument("--chunk_size", type=int, default=2000, help="Chunk size for processing")
+    parser.add_argument("--num_chunks", type=int, default=2, help="Number of chunks to process")
+
+    args = parser.parse_args()
+    with open(file_path) as f:
+        data = json.load(f)
+
+    results = defaultdict(list)
+    results_lock = threading.Lock()
+
+    # Use ThreadPoolExecutor with specified workers
+    with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = [executor.submit(process_item, item_data) for item_data in data.items()]
+
+        for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
+            local_results = future.result()
+            with results_lock:
+                for k, items in local_results.items():
+                    results[k].extend(items)
+
+    # Save results to JSON file
+    with open(file_path, "w") as f:
+        json.dump(results, f, indent=4)
+
+    rename_json_keys(file_path)
+    print(f"Results saved to {file_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run memory experiments")
+    parser.add_argument(
+        "--technique_type", choices=TECHNIQUES, default="rag", help="Memory technique to use"
+    )
+    parser.add_argument("--chunk_size", type=int, default=2000, help="Chunk size for processing")
+    parser.add_argument(
+        "--output_folder",
+        type=str,
+        default="results/locomo/mem0-default/",
+        help="Output path for results",
+    )
+    parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve")
+    parser.add_argument("--num_chunks", type=int, default=2, help="Number of chunks to process")
+    parser.add_argument("--frame", type=str, default="mem0")
+    parser.add_argument("--version", type=str, default="default")
+
+    args = parser.parse_args()
+
+    response_path = f"{args.frame}_locomo_responses.json"
+
+    if args.technique_type == "rag":
+        output_file_path = os.path.join(args.output_folder, response_path)
+        rag_manager = RAGManager(
+            data_path="data/locomo/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks
+        )
+        rag_manager.process_all_conversations(output_file_path)
+        """Generate response files"""
+        generate_response_file(output_file_path)
+
+
+if __name__ == "__main__":
+    start = time.time()
+    main()
+    end = time.time()
+    print(f"Execution time is:{end - start}")