Implemented Abstract Processing Pipeline with AI Agents

📅 2025-02-08 — Session: Implemented Abstract Processing Pipeline with AI Agents

🕒 16:30–17:50
🏷️ Labels: Abstract Processing, Ai Agents, Pipeline, Error Handling, State Management
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to integrate and implement an abstract processing pipeline using AI agents for structured extraction and screening of research abstracts.

Key Activities:

• Adapted existing frameworks to incorporate abstract processing, focusing on class and function modifications for state management.
• Developed the AbstractManager class for storing and retrieving abstracts, ensuring JSON validity and preventing duplicates.
• Created the AbstractsState class to manage abstract processing stages, enhancing traceability.
• Transformed ChunkHandler into AbstractProcessor to process abstracts using DOI identifiers and AI extraction functions.
• Updated schemas and function mappings for AI agents, including the Abstract Reader and Screening Agent.
• Implemented the Abstract Processing Pipeline with process_abstracts() and run_pipeline() functions.
• Addressed network issues with CrossRef API by generating a mock dataset for demonstration.
• Updated AbstractsState and AbstractManager for AI output handling, focusing on DOI-based indexing.
• Revised process_abstracts function for improved error handling and local storage integration.
• Fixed errors related to AbstractsState initialization and method calls in AbstractProcessor.

Achievements:

• Successfully executed a mock abstract processing and screening workflow, demonstrating the pipeline’s capability to fetch, process, and evaluate abstracts.
• Enhanced error handling and state management in the abstract processing functions.

Pending Tasks:

• Validate and integrate the updated schemas and function mappings for AI agents in the production environment.
• Resolve network issues with CrossRef API for live data fetching.