semantic memory
Knowledge Layer — PDF Pipeline with Semantic Intelligence
| Metric | Value | Significance | |
|---|---|---|---|
| Chunking Strategy | 3-Level | Document → Section → Paragraph hierarchy preserves semantic meaning | Chunking Strategy 3-Level Document → Section → Paragraph hierarchy preserves semantic meaning |
| Search Accuracy | Hybrid | Combines keyword and vector search for optimal retrieval | Search Accuracy Hybrid Combines keyword and vector search for optimal retrieval |
| Integration | 2-way bridge | Connects static PDFs to cognitive-memory's dynamic memory layer | Integration 2-way bridge Connects static PDFs to cognitive-memory's dynamic memory layer |
Challenge
How can PDFs with curated knowledge be efficiently ingested by LLMs when naive chunking breaks semantic boundaries and fixed-size approaches destroy context?
Solution
3-Level Chunking Pipeline (document → section → paragraph) with semantic boundary detection and hybrid search (lexical + semantic) integration, bridging raw PDFs to actionable knowledge for cognitive-memory.
Designed a 3-level chunking pipeline (Document → Section → Paragraph) with semantic boundary detection. Integrated hybrid search combining lexical and semantic approaches for optimal retrieval.
Semantic Memory: Knowledge Layer — PDF Pipeline with Semantic Intelligence
How Can PDF Knowledge Be Ingested Without Destroying Meaning?
Semantic Memory is a PDF knowledge pipeline implementing 3-level chunking (Document → Section → Paragraph) with semantic boundary detection. Unlike typical document processing systems that use fixed-size approaches and fragment meaning, Semantic Memory preserves context through intelligent chunking that respects document structure. Creates a bridge between static PDF knowledge and cognitive-memory's dynamic memory layer through hybrid search combining lexical and semantic approaches.
The Problem: Naive Chunking Destroys Context
Fixed-size chunking approaches break semantic boundaries and destroy context when processing PDFs for LLM consumption. Document structure and meaning are lost when text is split arbitrarily.
The Solution: 3-Level Semantic Chunking
Semantic Memory implements a hierarchical chunking pipeline (Document → Section → Paragraph) with semantic boundary detection. Hybrid search combines keyword and vector approaches for optimal retrieval.
Key Features
- 3-Level Chunking: Document → Section → Paragraph hierarchy preserves semantic meaning
- Hybrid Search: Combines keyword (lexical) and vector (semantic) approaches
- Semantic Boundary Detection: Intelligently identifies document structure
- 2-Way Bridge: Connects static PDFs to dynamic cognitive-memory layer
Technical Stack
- Python, LangChain
- Unstructured (document processing)
- Qdrant (vector database)
- PyPDF2, numpy
Impact
Created a bridge between static PDF knowledge and dynamic AI memory layers. Preserves semantic meaning through intelligent chunking that respects document structure.
Technologies & Skills Demonstrated: PDF Processing, Document Chunking, Vector Databases, LangChain, Semantic Search
Timeline: 2025 | Role: Developer
Screenshots
Backend
Tools & Services
AI Stack Connections
Impact
Created a bridge between static PDFs and dynamic cognitive-memory layer. Preserves semantic meaning through intelligent chunking strategy.
Key Learnings
- Structure-aware chunking: 3-Level hierarchy (Document → Section → Paragraph) preserves semantic meaning—fixed-size chunking destroys context
- Hybrid search balance: Combining keyword and vector search provides optimal retrieval—pure semantic or pure lexical approaches each miss relevant results
- Document structure matters: Semantic boundary detection intelligently identifies headings and sections for clean chunking