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Semantic Log Indexing & Search
Intro
Intro (0:43)
Getting Started: Semantic Search for Your Logs (3:07)
Data Pipeline Details & Starting It Up
Dissecting the Pipeline Monitor Architecture: FastAPI, Qdrant & DuckDB (3:49)
Beginner’s Guide to Qdrant Collections and Similarity Search (3:27)
Your First Glimpse at the Project Code Structure on GitHub (2:54)
Building and Launching the Pipeline with Docker Compose (4:36)
Turning Logs Into Searchable Vectors in Qdrant
Writing JSON Logs to FastAPI: Bulk Upload Explained (1:41)
How FastAPI Parses LogEntry Models and Prepares Embeddings (4:36)
Embeddings 101: Turning Your Logs into Searchable Vectors (2:05)
Querying Data & Improving Search Performance
Querying Qdrant: From Playground to Streamlit Dashboard (3:54)
Hands-On Embedding Tuning: Boost Your Log Search Accuracy (3:53)
Deploying Improved Embeddings and Measuring Improvement (5:34)
Summary
What We Built and Why It Matters (2:52)
Bonus: DuckDB - SQL and Dashboards
How DuckDB Fits into Your Data Observability Stack (1:27)
Writing to DuckDB with a Write-Ahead Log (5:02)
Docker & DuckDB: Implementing WAL to Solve File Lock Errors (3:41)
Embeddings 101: Turning Your Logs into Searchable Vectors
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