Improve contract search accuracy with auto-generated filters in Amazon Bedrock
AIDA (AI-Driven Annotation) is an AWS-powered solution that transforms unstructured legal contracts into searchable, actionable intelligence using a RAG architecture on Amazon Bedrock Knowledge Bases The system introduces implicit and explicit filtering mechanisms that apply metadata-based constraints before semantic search, significantly improving retrieval accuracy over standard RAG approaches Metadata-enriched chunking breaks contracts into semantically meaningful segments while preserving do
Analysis
TL;DR
- AIDA (AI-Driven Annotation) is an AWS-powered solution that transforms unstructured legal contracts into searchable, actionable intelligence using a RAG architecture on Amazon Bedrock Knowledge Bases
- The system introduces implicit and explicit filtering mechanisms that apply metadata-based constraints before semantic search, significantly improving retrieval accuracy over standard RAG approaches
- Metadata-enriched chunking breaks contracts into semantically meaningful segments while preserving document-level context, addressing the challenge of legal documents being highly contextual
- The architecture uses Amazon OpenSearch Service or Amazon S3 Vectors for vector storage, Amazon Bedrock Guardrails for security, and IAM policies for role-based access control
- The workflow spans document ingestion, metadata configuration, chunking, vector storage, query embedding, filtering, semantic search, prompt augmentation, and LLM response generation
Why It Matters
This solution addresses a critical pain point for enterprises managing thousands of complex legal agreements, where manual contract review is time-consuming, costly, and difficult to scale—particularly in industries like entertainment and media. By combining metadata-driven filtering with semantic search, AIDA demonstrates how RAG systems can be enhanced beyond naive vector retrieval to handle the contextual nuances of legal documents, offering a blueprint for enterprise-grade document intelligence systems.
Technical Details
- Architecture: RAG-based system built on Amazon Bedrock Knowledge Bases with a two-stage retrieval process: metadata filtering followed by semantic similarity search using cosine similarity
- Data Ingestion Pipeline: Contracts are synced with structured metadata (parties, effective date, termination date, jurisdiction) into Amazon Bedrock Knowledge Bases, then chunked into semantically meaningful segments optimized for retrieval
- Vector Storage Options: Amazon OpenSearch Service or Amazon S3 Vectors, both configured with encryption at rest; data transmission secured via HTTPS/TLS 1.2+
- Security & Access Control: Amazon Bedrock Guardrails protect against prompt injections and data leaks; AWS IAM policies enforce role-based, project-scoped access; Amazon CloudWatch maintains audit trails for compliance
- Query Flow: User queries are embedded using Amazon Bedrock embedding models, filtered through implicit (automatic metadata constraints) and explicit filtering mechanisms, then augmented into prompts with relevant contract excerpts and metadata context before LLM response generation
Industry Insight
- Enterprises should prioritize metadata enrichment during document ingestion rather than treating it as an afterthought; structured metadata is the foundation for effective pre-search filtering that dramatically improves RAG accuracy in domain-specific applications
- The implicit + explicit two-stage filtering approach demonstrated by AIDA should be considered a best practice for any RAG system handling contextual, clause-heavy documents like legal agreements, financial reports, or compliance records
- As contract management scales across jurisdictions and parties, combining vector search with metadata constraints and role-based access control becomes essential for both accuracy and regulatory compliance—organizations should evaluate similar architectures before building custom solutions
Disclaimer: The above content is generated by AI and is for reference only.