feat Dev mcp code

evaluation/.env-example

@@ -1,3 +1,4 @@
+# memory process model
 MODEL="gpt-4o-mini"
 OPENAI_API_KEY="sk-***REDACTED***"
 OPENAI_BASE_URL="http://***.***.***.***:3000/v1"
@@ -6,10 +7,18 @@ MEM0_API_KEY="m0-***REDACTED***"
 
 ZEP_API_KEY="z_***REDACTED***"
 
+# response model
 CHAT_MODEL="gpt-4o-mini"
 CHAT_MODEL_BASE_URL="http://***.***.***.***:3000/v1"
 CHAT_MODEL_API_KEY="sk-***REDACTED***"
 
+MEMOS_KEY="Token mpg-xxxxx"
+MEMOS_URL="https://apigw-pre.memtensor.cn/api/openmem/v1"
+PRE_SPLIT_CHUNK=false  # pre split chunk in client end
+
+MEMOBASE_API_KEY="xxxxx"
+MEMOBASE_PROJECT_URL="http://xxx.xxx.xxx.xxx:8019"
+
 # Configuration Only For Scheduler
 # RabbitMQ Configuration
 MEMSCHEDULER_RABBITMQ_HOST_NAME=rabbitmq-cn-***.cn-***.amqp-32.net.mq.amqp.aliyuncs.com
@@ -29,4 +38,4 @@ MEMSCHEDULER_GRAPHDBAUTH_URI=bolt://localhost:7687
 MEMSCHEDULER_GRAPHDBAUTH_USER=neo4j
 MEMSCHEDULER_GRAPHDBAUTH_PASSWORD=***
 MEMSCHEDULER_GRAPHDBAUTH_DB_NAME=neo4j
-MEMSCHEDULER_GRAPHDBAUTH_AUTO_CREATE=true
+MEMSCHEDULER_GRAPHDBAUTH_AUTO_CREATE=true

evaluation/README.md

@@ -34,3 +34,21 @@ This repository provides tools and scripts for evaluating the LoCoMo dataset usi
 ```
 
 ✍️ For evaluating OpenAI's native memory feature with the LoCoMo dataset, please refer to the detailed guide: [OpenAI Memory on LoCoMo - Evaluation Guide](./scripts/locomo/openai_memory_locomo_eval_guide.md).
+
+### LongMemEval Evaluation
+First prepare the dataset `longmemeval_s` from https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned
+, and save it as `data/longmemeval/longmemeval_s.json`
+
+```bash
+# Edit the configuration in ./scripts/run_lme_eval.sh
+# Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
+./scripts/run_lme_eval.sh
+```
+
+### prefEval Evaluation
+
+### personaMem Evaluation
+get `questions_32k.csv` and `shared_contexts_32k.jsonl` from https://huggingface.co/datasets/bowen-upenn/PersonaMem and save them at `data/personamem/`
+```bash
+./scripts/run_pm_eval.sh
+```