Performance Bottleneck Analysis
Legal documents are characterized by the presence of many technical terms and the relevance of clauses, resulting in the limited accuracy of traditional search methods. dsRAG has verified an accuracy of 96.6% in FinanceBench, and its optimization path includes:
Key technology applications
- Customized Embedding Models: Selection of legal domain-specific embeddings (e.g. LexNLP Embeddings) instead of generic models
- forced segmentation strategy: Settings
max_segment_length=500Ensuring separate coding of legal texts - Hybrid Search Mode: combining semantic search with traditional keyword search (via the
hybrid_search=True(Parameter enabled)
Implementation steps
- Initialize the knowledge base:
kb = KnowledgeBase('legal_db', embed_model='LexNLP') - Chain add files:
kb.add_document('contract.docx').add_document('clause.md') - Enable relevance feedback:
query('termination clause', expand_terms=True)Auto-expanding synonyms
caveat
Regular use is recommendedkb.optimize()Rebuild the index and pair it with GPT-4 as auto_context_model to handle cross-references.
This answer comes from the articledsRAG: A Retrieval Engine for Unstructured Data and Complex QueriesThe
May not be reproduced without permission:AI productivity tools " How do I improve the accuracy of my legal document quiz system to 90% or higher?
