Engineering Realization Scheme for Multi-Round Dialogue
The following key technical points need to be considered for building a production-level dialog system:
- Dialog state management
- devise
DialogStateObject logging: user intent/entities/historical conversations - Storing Conversation Contexts with Caches like Redis
- devise
- Context splicing technology
- Use special markers to separate multiple rounds of dialog:
"[Round1]用户:xxn[Round2]AI:yyn[Round3]用户:zz" - Implement a sliding window mechanism to avoid exceeding the max_length limit.
- Use special markers to separate multiple rounds of dialog:
- Response quality control
- set up
repetition_penalty=1.2Avoid duplicate content - Add business rule filters to block violation responses
- set up
- performance optimization
- Pre-generated answer templates for high-frequency questions
- Implementing Asynchronous Response Streaming Returns
Recommended architecture: Front-end access layer → Dialogue management middleware → DeepSeek model microservices → Business system integration. Conversation logs should be updated monthly for continuous model optimization.
This answer comes from the articleDeepSeek-V3.1-Base: a large-scale language model for efficiently processing complex tasksThe
May not be reproduced without permission:AI productivity tools " How to Apply DeepSeek-V3.1-Base for Context Holding in an Enterprise Conversation System?
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