📅 2025-08-16 — Session: Enhanced RAG pipeline and retrieval systems
🕒 22:30–23:40
🏷️ Labels: RAG, Python, Retrieval, CLI, Hugging Face
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session focused on enhancing the Retrieval-Augmented Generation (RAG) pipeline and related retrieval systems, aiming to improve flexibility, scalability, and observability.
Key Activities
- Enhancements to RAG.py: Introduced new dataclasses and functionalities for generating run reports and per-question metrics.
- Code Review and Fixes: Addressed issues in the query engine builder, improving imports, argument handling, and model management.
- Decoupling Pipeline Components: Refactored retrieval pipeline components for flexible configuration of storage, embeddings, and processing.
- CLI Implementation: Developed a
main()function for a pluggable builder with CLI flags, enhancing document processing and retrieval. - CLI Playbook: Created a comprehensive CLI playbook for RAG pipeline setup and execution.
- Model Management: Implemented embedding model selection and fallback mechanisms, including error handling for Hugging Face models.
- VectorStoreIndex Fix: Provided a solution for version-safe creation of a VectorStoreIndex in llama_index.
- Future-Proof Retrieval Pipeline: Built a robust retrieval pipeline addressing API differences and multilingual support.
Achievements
- Successfully enhanced the RAG pipeline with new reporting and metric functionalities.
- Improved the flexibility and scalability of retrieval systems through decoupling and modularization.
- Developed robust CLI tools and playbooks for easier pipeline management.
- Implemented effective error handling and fallback strategies for model management.
Pending Tasks
- Further testing and validation of the new retrieval pipeline configurations.
- Continued optimization of model selection and error handling strategies.