Model Pipeline - Vector database operations (CRUD)
This pipeline shows how data vectors are created, stored, updated, retrieved, and deleted in a vector database. It helps machines find similar items quickly by comparing vector distances.
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Vector database operations (CRUD) in Prompt Engineering / GenAI - Model Pipeline Trace
Data Flow - 6 Stages
1Data in
1000 rows x 1 text column→Raw text data collected for embedding→1000 rows x 1 text column
"A photo of a cat"
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2Preprocessing
1000 rows x 1 text column→Convert text to vector embeddings using a model→1000 rows x 512 vector dimensions
[0.12, -0.03, 0.45, ..., 0.07]
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3Feature Engineering
1000 rows x 512 vector dimensions→Normalize vectors to unit length for similarity search→1000 rows x 512 vector dimensions
[0.11, -0.028, 0.42, ..., 0.065]
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4Model Trains
N/A→No training, vectors stored directly in database→N/A
Vectors indexed for fast search
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5Metrics Improve
N/A→Evaluate retrieval accuracy and speed→N/A
Recall@10 = 0.85, Query time = 5ms
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6Prediction
1 query vector of 512 dimensions→Search database for closest vectors using cosine similarity→Top 5 closest vectors with distances
[{"id": 23, "distance": 0.12}, {"id": 87, "distance": 0.15}, ...]
Training Trace - Epoch by Epoch
No training loss to show for vector database operations
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | N/A | N/A | No model training; vectors stored directly |
Prediction Trace - 3 Layers
Layer 1: Input query vector
Layer 2: Similarity search
Layer 3: Return results
Model Quiz - 3 Questions
Test your understanding
What shape does the data have after converting text to vectors?
Key Insight
Vector databases store data as vectors to quickly find similar items by comparing distances. They do not require training but rely on efficient search algorithms to retrieve relevant results.