Building Graph-Aware Retrieval for Contract Reasoning
The article discusses the limitations of vector-based retrieval systems for answering questions about legal contracts, and how the authors developed a hybrid graph-plus-vector retrieval system called EngramDB to address this challenge.
Why it matters
This work highlights the limitations of traditional retrieval methods for complex, structured documents like legal contracts, and demonstrates the need for more advanced techniques that can capture the full context required for accurate reasoning.
Key Points
- 1Vector-based retrieval systems often fail to capture the complete context needed to answer questions about legal contracts
- 2Contracts have hierarchical, definitional, and referential structures that are not well-represented by flat text embeddings
- 3The authors built EngramDB, a hybrid retrieval system that combines graph-based and vector-based approaches to enable multi-hop reasoning over contract documents
Details
The article explains that the authors initially tried a standard vector-based retrieval pipeline to answer questions about legal contracts, but found that this approach often returned incomplete or misleading answers. This is because contracts have complex structures, including hierarchical organization, defined terms, and cross-references, that are not well-captured by simple text similarity. The authors realized that the real objective in contract QA is to reconstruct the full reasoning path across these structural elements, rather than just finding the most semantically similar passage. This led them to develop EngramDB, a hybrid retrieval system that combines graph-based and vector-based approaches to enable multi-hop reasoning over contract documents. EngramDB models the structure of contracts as a graph and uses this representation, along with dense embeddings, to retrieve the relevant sections needed to answer a given query.
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