Summary#

Experience with Qdrant vector database for semantic search, similarity matching, and RAG application backends. Integrated with Python applications for knowledge base search.

How I Apply This Skill#

• Worked with Qdrant for production vector storage
• Stored 1,000+ note embeddings for semantic search across knowledge base
• Implemented similarity matching with 0.70 threshold for auto-linking notes
• Built RAG retrieval pipeline fetching relevant context for AI responses
• Created bidirectional note links (2,757 total) based on semantic similarity

Key Strengths#

• Vector Storage: Collections, payloads, indexing strategies
• Similarity Search: Cosine similarity, threshold tuning, filtering
• Integration: Python client, persistence configuration
• RAG Integration: Retrieval pipelines, context assembly

Related Projects#

• Obsidian Notes Pipeline - 1,000+ notes, 2,757 auto-links