Enhanced Query Engine Design and AI Integration

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📅 2025-02-12 — Session: Enhanced Query Engine Design and AI Integration

🕒 19:50–23:11
🏷️ Labels: Queryengine, AI, Langgraph, Text Processing, Workflow, Integration
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the design and functionality of a Query Engine and integrate it with AI-driven models and workflows.

Key Activities

  • Designed a comprehensive Query Engine supporting semantic and hybrid search, metadata filtering, and domain-specific retrieval.
  • Developed a hybrid model integrating functional and AI action queries for improved data retrieval.
  • Outlined an AI action wrapper for integrating structured query retrieval with AI processing tasks.
  • Refactored the AIActionWrapper class for enhanced text processing capabilities.
  • Explored LangGraph state persistence and checkpointing strategies.
  • Implemented scaling strategies for AI execution pipelines to manage multiple workflows.
  • Developed methods for persisting compiled LangGraph workflows.
  • Differentiated between invoke() and stream() methods in LangGraph workflows.
  • Planned initial workflows for a hybrid invoke/stream system.
  • Refactored Python functions for unified AI function execution and enhanced input handling.
  • Integrated VectorStoreManager with the AI processing pipeline for seamless text retrieval and processing.

Achievements

  • Successfully designed and planned enhancements for a Query Engine and AI integration.
  • Implemented and refactored AI functionalities for improved text processing and workflow management.

Pending Tasks

  • Further testing and validation of the enhanced Query Engine and AI models.
  • Continued development of workflows for the hybrid invoke/stream system.

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