Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 3: ADVANCED AGENT CAPABILITIES (Days 26-40)

Day 35-37: Information Summarization

These three days will focus on building sophisticated summarization capabilities for your PAAS, enabling it to condense large volumes of information into concise, insightful summaries. You'll learn advanced summarization techniques that go beyond simple extraction to provide true synthesis of information across multiple sources. You'll develop systems that can identify key trends, breakthroughs, and connections that might not be obvious from individual documents. You'll create topic modeling and clustering algorithms that can organize information into meaningful categories. Throughout, you'll leverage Rust for performance-critical processing while using LLMs for natural language generation.

• Morning (3h): Learn summarization techniques with Rust acceleration

  • Extractive vs. abstractive summarization: Study different summarization approaches, from simple extraction of key sentences to more sophisticated abstractive techniques that generate new text capturing essential information. Implement baseline extractive summarization in Rust using TF-IDF and TextRank algorithms, leveraging Rust's performance for processing large document collections efficiently.
  • Multi-document summarization: Explore methods for synthesizing information across multiple documents, identifying common themes, contradictions, and unique contributions from each source. Develop Rust components for cross-document analysis that can efficiently process thousands of documents to extract patterns and relationships between concepts.
  • Topic modeling and clustering with Rust: Learn techniques for automatically organizing documents into thematic groups using approaches like Latent Dirichlet Allocation (LDA) and transformer-based embeddings. Implement efficient topic modeling in Rust, using libraries like rust-bert for embeddings generation and custom clustering algorithms optimized for high-dimensional vector spaces.

• Afternoon (3h): Implement summarization pipeline

  • Build topic clustering system: Create a document organization system that automatically groups related content across different sources, identifying emerging research areas and technology trends. Implement hierarchical clustering in Rust that can adapt its granularity based on the diversity of the document collection, providing both broad categories and fine-grained subcategories.
  • Create multi-source summarization: Develop components that can synthesize information from arXiv papers, GitHub repositories, patent filings, and news articles into coherent narratives about emerging technologies. Build a pipeline that extracts key information from each source type using specialized extractors, then combines these insights using LLMs prompted with structured context.
  • Generate trend reports with Tauri UI: Implement report generation capabilities that produce clear, concise summaries of current developments in areas of interest, highlighting significant breakthroughs and connections. Create a Tauri/Svelte interface for configuring and viewing these reports, with Rust backend processing for data aggregation and LLM integration for natural language generation.