102
DOCUMENT PROCESSING AND KNOWLEDGE RETRIEVAL
PC Chat integrates Azure AI Document Intelligence with Microsoft Kernel Memory to transform our document repos-
itories into an intelligent, searchable knowledge base. The pipeline begins when a user uploads a document and ends
with accurate, context-aware responses to natural language queries.
The key stages of this pipeline are:
• File Ingestion and Validation: The system accepts
a broad range of formats: PDFs, Word documents,
Excel spreadsheets, and others, and determines the
optimal processing strategy for each.
• Retrieval-Augmented Generation (RAG): Kernel
Memory implements a RAG framework that
combines the broad capabilities of large language
models with the specific, authoritative content in
our document repository, ensuring responses are
both accurate and contextually relevant.
• Document Embedding and Vectorization:
Documents are transformed into high-dimensional
vector representations at multiple levels of
granularity, from individual clauses to full sections,
which enables precise retrieval while maintaining
broader document context
• Vector Database Storage: Embeddings are stored
in a SQL vector database alongside rich metadata
(source, date, matter associations, legal categories),
supporting sophisticated filtering, relevance ranking,
and rapid retrieval at scale.
