DocMason – Agent Knowledge Base for local complex office files

Processes locally-stored office documents (DOCX, XLSX, PPTX, PDF) without cloud transmission by implementing format-specific parsers that extract structured content, metadata, and formatting information. Uses a local-first architecture where files remain on-device throughout parsing, enabling privacy-preserving document analysis for sensitive corporate documents. The system builds an internal representation of document structure that preserves hierarchical relationships (sections, tables, embedded objects) for downstream agent reasoning.

Breaks parsed documents into semantically meaningful chunks using a hybrid approach that respects document structure (sections, paragraphs, tables) rather than naive token-count splitting. The system analyzes content boundaries, preserves context relationships, and creates overlapping chunks with metadata tags indicating source location, document type, and semantic role. This enables agents to retrieve contextually relevant document fragments without losing structural coherence or breaking mid-sentence.

Generates embeddings for document chunks using configurable embedding models (local or API-based) and stores them in a vector database for semantic search. The system supports multiple embedding backends (sentence-transformers for local inference, OpenAI/Anthropic APIs for cloud-based) and implements efficient indexing strategies (FAISS, Chroma, or Pinecone) that enable sub-100ms semantic similarity queries. Maintains bidirectional links between embeddings and source chunks, enabling retrieval of both vector representations and original document content.

Enables LLM agents to query the document knowledge base through a conversational interface that maintains multi-turn context and conversation history. The agent uses semantic search to retrieve relevant chunks, synthesizes information across multiple documents, and can ask clarifying questions or perform follow-up searches based on initial results. Implements a retrieval-augmented generation (RAG) loop where the agent decides when to search, what to search for, and how to synthesize results into coherent answers with source attribution.

Enables agents to synthesize information across multiple documents and resolve cross-references by tracking relationships between chunks from different sources. The system maintains a document relationship graph that identifies when information in one document references or contradicts information in another, allowing agents to provide comprehensive answers that integrate insights from multiple sources. Implements conflict detection and resolution strategies to flag contradictions and help users understand document relationships.

Monitors source documents for changes and incrementally updates the knowledge base without re-processing the entire collection. Uses file modification timestamps and content hashing to detect changes, re-parses only modified documents, and updates affected chunks in the vector index. Maintains a change log with timestamps and version information, enabling agents to understand document evolution and retrieve historical versions if needed.

Allows customization of agent behavior through configuration of reasoning strategy (chain-of-thought, tree-of-thought, direct answer), response style (formal/casual, verbose/concise), and domain-specific instructions. Implements a prompt template system that injects custom instructions into the agent's reasoning loop, enabling teams to adapt the agent's behavior for different use cases (legal document analysis, technical documentation, financial reports) without code changes. Supports role-based prompting where the agent adopts a specific persona (e.g., 'legal analyst', 'technical writer') to influence reasoning and response generation.

Enables export of indexed documents, chunks, and agent conversation histories in multiple formats (JSON, CSV, Markdown) for integration with external tools and workflows. Supports integration with note-taking systems (Obsidian, Notion), project management tools (Jira, Asana), and communication platforms (Slack, Teams) through API connectors or file-based exports. Maintains export format consistency and metadata preservation to ensure downstream tools can process exported content correctly.

Tracks agent query performance metrics (latency, retrieval quality, answer accuracy) and provides analytics dashboards showing query patterns, popular documents, and agent effectiveness. Implements logging of all queries, retrieved chunks, and agent reasoning steps for debugging and optimization. Supports A/B testing of different retrieval strategies or agent configurations by comparing performance metrics across variants.