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

PHASE 2: API INTEGRATIONS (Days 11-25)

In this phase, you'll build the data collection foundation of your PAAS by implementing integrations with all your target information sources. Each integration will follow a similar pattern: first understanding the API and data structure, then implementing core functionality, and finally optimizing and extending the integration. You'll apply the foundational patterns established in Phase 1 while adapting to the unique characteristics of each source. By the end of this phase, your system will be able to collect data from all major research, code, patent, and financial news sources.

Day 14-15: arXiv Integration

During these two days, you'll focus on creating a robust integration with arXiv, one of the primary sources of research papers in AI, ML, and other technical fields. You'll develop a comprehensive understanding of arXiv's API capabilities and limitations, learning how to efficiently retrieve and process papers across different categories. You'll build systems to extract key information from papers including abstracts, authors, and citations. You'll also implement approaches for processing the full PDF content of papers to enable deeper analysis and understanding of research trends.

• Morning (3h): Study arXiv API and data structure

  • API documentation: Thoroughly review the arXiv API documentation, focusing on endpoints for search, metadata retrieval, and category browsing that will enable systematic monitoring of new research. Understand rate limits, response formats, and sorting options that will affect your ability to efficiently monitor new papers.
  • Paper metadata extraction: Study the metadata schema used by arXiv, identifying key fields like authors, categories, publication dates, and citation information that are critical for organizing and analyzing research papers. Create data models that will store this information in a standardized format in your system.
  • PDF processing libraries: Research libraries like PyPDF2, pdfminer, and PyMuPDF that can extract text, figures, and tables from PDF papers, understanding their capabilities and limitations. Develop a strategy for efficiently processing PDFs to extract full text while preserving document structure and handling common OCR challenges in scientific papers.

• Afternoon (3h): Implement arXiv paper retrieval

  • Query recent papers by categories: Build functions that can systematically query arXiv for recent papers across categories relevant to AI, machine learning, computational linguistics, and other fields of interest. Implement filters for timeframes, sorting by relevance or recency, and tracking which papers have already been processed.
  • Extract metadata and abstracts: Create parsers that extract structured information from arXiv API responses, correctly handling author lists, affiliations, and category classifications. Implement text processing for abstracts to identify key topics, methodologies, and claimed contributions.
  • Store paper information for processing: Develop storage mechanisms for paper metadata and content that support efficient retrieval, update tracking, and integration with your vector database. Create processes for updating information when papers are revised and for maintaining links between papers and their citations.