📅 2025-02-12 — Session: Developed and Optimized AI Query and Processing Models
🕒 19:55–23:10
🏷️ Labels: AI, Query Model, Workflow, Langgraph, Text Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to explore and develop hybrid models for AI query and processing, focusing on enhancing data retrieval, workflow optimization, and text processing capabilities.
Key Activities:
- Hybrid Functional and AI Action Query Model: Discussed a hybrid model integrating functional queries with AI action queries to improve data retrieval.
- AI Action Wrapper: Designed an AI action wrapper for unified query retrieval and dynamic AI processing.
- Refactored AIActionWrapper: Enhanced text processing by refactoring the AIActionWrapper class for compatibility with TextManager.
- LangGraph State Persistence: Explored state persistence and checkpointing in LangGraph using MemorySaver and RedisSaver.
- Scaling AI Execution Pipelines: Outlined strategies for scaling AI execution pipelines to manage multiple workflows efficiently.
- Unified AI Function Execution: Refactored Python functions for flexible text processing and enhanced input handling.
- VectorStoreManager Integration: Integrated VectorStoreManager with AI processing pipelines for seamless text retrieval and processing.
Achievements:
- Developed a comprehensive framework for hybrid query and AI processing models.
- Implemented state persistence techniques in LangGraph.
- Optimized AI function execution for single and multi-chunk processing.
- Integrated VectorStoreManager with AI pipelines.