Sector
Legal & Compliance Services, Professional Publishing, Enterprise Compliance, and Corporate Training
Status
Illustrative
Problem Statement
The challenge addressed
Legislative analysis requires relevant context from multiple knowledge sources including prior legislation, legal mementos, case law, and client data. Finding relevant information across large knowled... Legislative analysis requires relevant context from multiple knowledge sources including prior legislation, legal mementos, case law, and client data. Finding relevant information across large knowledge bases is computationally intensive and requires sophisticated semantic search. Without RAG, agents lack the context needed for accurate analysis.
Core Logic
How the agent solves it
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
The Knowledge Retriever Agent performs semantic vector search across con... Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
The Knowledge Retriever Agent performs semantic vector search across configured knowledge sources using embedding models. It generates query embeddings from analysis context, searches vector database (simulating Pinecone) with configurable topK results and similarity threshold, applies reranking to optimize relevance ordering, and returns retrieved chunks with full metadata. Each chunk includes content, source name and type (legislation, memento, case_law, client_data), relevance score, and metadata (title, date, author, section, page number). Highlights show matching text for quick verification. The agent tracks search metrics: query embedding time, search time, rerank time, total time, documents scanned, and chunks returned. Reasoning traces show: initiating vector search across configured sources with query preview, and concluding with result count, top sources, and average relevance percentage.
