Model Pipeline - Embedding models for semantic search
This pipeline uses embedding models to turn text into numbers that capture meaning. Then it finds similar texts by comparing these numbers, helping to search by meaning, not just words.
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Embedding models for semantic search in Agentic AI - Model Pipeline Trace
Data Flow - 6 Stages
1Raw Text Input
1000 rows x 1 column→Collect sentences or documents to search→1000 rows x 1 column
"How to bake a cake?"
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2Text Preprocessing
1000 rows x 1 column→Lowercase, remove punctuation, tokenize→1000 rows x variable tokens
["how", "to", "bake", "a", "cake"]
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3Embedding Generation
1000 rows x variable tokens→Convert tokens to fixed-size vectors using embedding model→1000 rows x 512 columns
[0.12, -0.05, 0.33, ..., 0.07]
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4Indexing Embeddings
1000 rows x 512 columns→Store vectors in a search index for fast similarity lookup→1000 rows x 512 columns
Vector index ready for similarity search
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5Query Embedding
1 row x 1 column→Preprocess and embed user query text→1 row x 512 columns
"Best cake recipes" -> [0.10, -0.02, 0.30, ..., 0.05]
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6Similarity Search
1 row x 512 columns (query) + 1000 rows x 512 columns (index)→Calculate cosine similarity between query and indexed embeddings→Top 5 rows x 1 column (most similar)
Top 5 similar texts with similarity scores
Training Trace - Epoch by Epoch
Loss
1.0 |****
0.8 |****
0.6 |****
0.4 |****
0.2 |****
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1 2 3 4 5 Epochs| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 0.85 | 0.45 | Model starts learning basic semantic relations |
| 2 | 0.65 | 0.60 | Loss decreases as embeddings capture better meaning |
| 3 | 0.50 | 0.72 | Accuracy improves, embeddings more meaningful |
| 4 | 0.40 | 0.80 | Model converging, semantic similarity clearer |
| 5 | 0.35 | 0.85 | Final embeddings ready for semantic search |
Prediction Trace - 5 Layers
Layer 1: Input Query Text
Layer 2: Tokenization
Layer 3: Embedding Model
Layer 4: Similarity Calculation
Layer 5: Return Results
Model Quiz - 3 Questions
Test your understanding
What does the embedding model output represent?
Key Insight
Embedding models transform text into numbers that capture meaning, enabling search systems to find results based on semantic similarity rather than exact words. Training improves these embeddings so similar meanings get closer vectors, making search smarter and more flexible.