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86 lines
2.7 KiB
YAML
86 lines
2.7 KiB
YAML
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post:
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tags:
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- Flow Services
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summary: Embeddings - text to vector conversion
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description: |
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Convert text to embedding vectors for semantic similarity search.
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## Embeddings Overview
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Embeddings transform text into dense vector representations that:
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- Capture semantic meaning
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- Enable similarity comparisons via cosine distance
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- Support semantic search and retrieval
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- Power RAG systems
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## Use Cases
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- **Document indexing**: Convert documents to vectors for storage
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- **Query encoding**: Convert search queries for similarity matching
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- **Semantic similarity**: Find related texts via vector distance
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- **Clustering**: Group similar content
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- **Classification**: Use as features for ML models
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## Vector Dimensions
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Dimension count depends on embedding model:
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- text-embedding-ada-002: 1536 dimensions
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- text-embedding-3-small: 1536 dimensions
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- text-embedding-3-large: 3072 dimensions
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- Custom models: Varies
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## Single Request
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Unlike batch embedding APIs, this endpoint processes one text at a time.
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For bulk operations, use document-load or text-load services.
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operationId: embeddingsService
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security:
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- bearerAuth: []
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parameters:
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- name: flow
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in: path
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required: true
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schema:
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type: string
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description: Flow instance ID
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example: my-flow
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requestBody:
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required: true
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content:
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application/json:
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schema:
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$ref: '../../components/schemas/embeddings/EmbeddingsRequest.yaml'
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examples:
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shortText:
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summary: Short text embedding
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value:
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text: Machine learning
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sentence:
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summary: Sentence embedding
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value:
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text: Quantum computing uses quantum mechanics principles for computation.
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paragraph:
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summary: Paragraph embedding
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value:
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text: |
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Neural networks are computing systems inspired by biological neural networks.
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They consist of interconnected nodes (neurons) organized in layers.
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Through training, they learn to recognize patterns and make predictions.
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responses:
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'200':
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description: Successful response
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content:
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application/json:
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schema:
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$ref: '../../components/schemas/embeddings/EmbeddingsResponse.yaml'
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examples:
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embeddingVector:
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summary: Embedding vector
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value:
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vectors: [0.023, -0.142, 0.089, 0.234, -0.067, 0.156, 0.201, -0.178, 0.045, 0.312]
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'401':
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$ref: '../../components/responses/Unauthorized.yaml'
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'500':
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$ref: '../../components/responses/Error.yaml'
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