| import argparse |
| import os |
|
|
| from src.langmem import LangMemManager |
| from src.memzero.add import MemoryADD |
| from src.memzero.search import MemorySearch |
| from src.openai.predict import OpenAIPredict |
| from src.rag import RAGManager |
| from src.utils import METHODS, TECHNIQUES |
| from src.zep.add import ZepAdd |
| from src.zep.search import ZepSearch |
|
|
|
|
| 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 main(): |
| parser = argparse.ArgumentParser(description="Run memory experiments") |
| parser.add_argument("--technique_type", choices=TECHNIQUES, default="mem0", help="Memory technique to use") |
| parser.add_argument("--method", choices=METHODS, default="add", help="Method to use") |
| parser.add_argument("--chunk_size", type=int, default=1000, help="Chunk size for processing") |
| parser.add_argument("--output_folder", type=str, default="results/", help="Output path for results") |
| parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve") |
| parser.add_argument("--filter_memories", action="store_true", default=False, help="Whether to filter memories") |
| parser.add_argument("--is_graph", action="store_true", default=False, help="Whether to use graph-based search") |
| parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks to process") |
|
|
| args = parser.parse_args() |
|
|
| |
| print(f"Running experiments with technique: {args.technique_type}, chunk size: {args.chunk_size}") |
|
|
| if args.technique_type == "mem0": |
| if args.method == "add": |
| memory_manager = MemoryADD(data_path="dataset/locomo10.json", is_graph=args.is_graph) |
| memory_manager.process_all_conversations() |
| elif args.method == "search": |
| output_file_path = os.path.join( |
| args.output_folder, |
| f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json", |
| ) |
| memory_searcher = MemorySearch(output_file_path, args.top_k, args.filter_memories, args.is_graph) |
| memory_searcher.process_data_file("dataset/locomo10.json") |
| elif args.technique_type == "rag": |
| output_file_path = os.path.join(args.output_folder, f"rag_results_{args.chunk_size}_k{args.num_chunks}.json") |
| rag_manager = RAGManager(data_path="dataset/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks) |
| rag_manager.process_all_conversations(output_file_path) |
| elif args.technique_type == "langmem": |
| output_file_path = os.path.join(args.output_folder, "langmem_results.json") |
| langmem_manager = LangMemManager(dataset_path="dataset/locomo10_rag.json") |
| langmem_manager.process_all_conversations(output_file_path) |
| elif args.technique_type == "zep": |
| if args.method == "add": |
| zep_manager = ZepAdd(data_path="dataset/locomo10.json") |
| zep_manager.process_all_conversations("1") |
| elif args.method == "search": |
| output_file_path = os.path.join(args.output_folder, "zep_search_results.json") |
| zep_manager = ZepSearch() |
| zep_manager.process_data_file("dataset/locomo10.json", "1", output_file_path) |
| elif args.technique_type == "openai": |
| output_file_path = os.path.join(args.output_folder, "openai_results.json") |
| openai_manager = OpenAIPredict() |
| openai_manager.process_data_file("dataset/locomo10.json", output_file_path) |
| else: |
| raise ValueError(f"Invalid technique type: {args.technique_type}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
