智能客服系统集成方案
将Search-R1接入客服系统的关键步骤：
- Data preparation phase::
- 整理领域知识库为
corpus.jsonlspecification - 标注典型用户问题作为训练数据
- 整理领域知识库为
- Model Tuning::
- fulfillment
python scripts/data_process/nq_search.py生成领域数据 - increase
"ability": "customer-service"特殊能力标签
- fulfillment
- system integration::
- 通过FastAPI封装模型推理接口
- set up
uvicorn服务监听端口
- 线上部署::
- utilization
infer.py脚本处理实时查询 - 配置负载均衡应对高并发
- utilization
典型优化策略：
- set up
cache_dir缓存高频问题答案 - exist
extra_info中添加产品分类标签 - 结合规则引擎处理简单查询
效果评估：可降低人工客服介入率约40%，平均响应时间<2秒。
This answer comes from the articleSearch-R1: A Tool for Reinforcement Learning to Train Large Models for Search and ReasoningThe
May not be reproduced without permission:AI productivity tools " 在智能客服场景中如何应用Search-R1实现实时问题解答？
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