Intelligent Customer Service System Integration Solution
A key step in getting Search-R1 into the customer service system:
- Data preparation phase::
- Organize the domain knowledge base as
corpus.jsonlspecification - Labeling typical user problems as training data
- Organize the domain knowledge base as
- Model Tuning::
- fulfillment
python scripts/data_process/nq_search.pyGenerating Domain Data - increase
"ability": "customer-service"Special Abilities Label
- fulfillment
- system integration::
- Encapsulating the model inference interface through FastAPI
- set up
uvicornService Listening Port
- Online Deployment::
- utilization
infer.pyScripts to handle real-time queries - Configuring Load Balancing for High Concurrency
- utilization
Typical optimization strategies:
- set up
cache_dirCache answers to high-frequency questions - exist
extra_infoAdd product category tags to - Combining rule engines to handle simple queries
Effectiveness evaluation: It can reduce the rate of manual customer service intervention by about 40%, with an average response time of <2 seconds.
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 " How to apply Search-R1 to realize real-time question answering in intelligent customer service scenarios?
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