Docs Update (#2591)

2025-04-29 08:15:25 -07:00
parent 6d13e83001
commit 393a4fd5a6
111 changed files with 2296 additions and 99 deletions
--- a/evaluation/run_experiments.py
+++ b/evaluation/run_experiments.py
@@ -0,0 +1,102 @@
+import os
+import json
+from src.memzero.add import MemoryADD
+from src.memzero.search import MemorySearch
+from src.utils import TECHNIQUES, METHODS
+import argparse
+from src.rag import RAGManager
+from src.langmem import LangMemManager
+from src.zep.search import ZepSearch
+from src.zep.add import ZepAdd
+from src.openai.predict import OpenAIPredict
+
+
+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()
+
+    # Add your experiment logic here
+    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()