Pain Point Identification
Deep Searcher solves this problem by using a triple optimization mechanism to solve the problem of semantic bias in general-purpose search tools, as technical documents contain a large number of technical terms and code fragments.
Optimization solutions
- Embedding model selection::
- Code2vec is recommended for code class documentation.
- BERT-base was selected for the theoretical documentation - Data preprocessing::
- Extract the API parameter tables from the documentation
- Adding type annotations to code blocks - hybrid search strategy::
- Keyword search ensures recall
- Vector Search Improves Accuracy
- Setting up a domain terminology whitelist
Implementation steps
- Configure multimodal embedding in the configuration module
- Use data partitions to store different document types
- pass (a bill or inspection etc)
query("解释XXX函数参数")test effect
Validation metrics
- Mean Reciprocal Rank (MRR) reaches 0.82+
- First 3 results hit 90%+
- Jargon Recognition Accuracy 95%+
This answer comes from the articleDeep Searcher: Efficient Retrieval of Enterprise Private Documents and Intelligent Q&AThe
May not be reproduced without permission:AI productivity tools " How to optimize Deep Searcher's retrieval accuracy in technical document management?
