Designed and Implemented Knowledge Base for Academic Papers

• Day: 2025-11-15
• Time: 16:55 to 17:25
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Knowledge Base, Vector Stores, Embeddings, Memory Management, Tokenization

Description

Session Goal

The primary goal of this session was to design a comprehensive knowledge base for academic papers, transforming a small-institution paper series into a browsable and queryable format.

Key Activities

• Developed a detailed execution plan for the knowledge base, focusing on user experience metaphors, data models, and AI workflows.
• Researched vector stores and embedding practices, comparing options like FAISS, ChromaDB, Qdrant, Weaviate, and Milvus for semantic search capabilities.
• Implemented scripts to calculate byte sizes and memory usage for vector dimensions, considering both uncompressed and compressed sizes using PQ compression.
• Created a Python function for token chunking in document processing, demonstrating its application with sample data.
• Compiled a master reference guide on embeddings and vector storage, tailored for managing a collection of approximately 1,000 papers, covering chunking strategies, index choices, and retrieval design.

Achievements

• Successfully outlined the data model and AI workflow for the knowledge base.
• Completed research and comparisons of vector stores, enhancing understanding of best practices for document embeddings.
• Developed practical scripts for memory management and tokenization, aiding in efficient data processing.

Pending Tasks

• Further refinement of the minimal viable product roadmap for the knowledge base.
• Implementation of the designed workflows into a functional prototype.
• Testing and validation of the knowledge base with real-world data.

Evidence

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• event_ids: []