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Interface: EmbeddingRequest

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:33

Represents a request to generate embeddings. This structure encapsulates the text(s) to be embedded and any parameters that might influence the embedding process, such as model selection hints or user context.

Interface​

EmbeddingRequest

Properties​

collectionId?​

optional collectionId: string

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:86

Optional

Optional: Identifier for a data collection or namespace. This can be used by dynamic model selection strategies (e.g., 'dynamic_collection_preference') to choose a model best suited for the content of a specific collection.

Example​

"financial_reports_q3_2024"

customParameters?​

optional customParameters: Record<string, any>

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:96

Optional

Optional: Custom parameters to pass through to the embedding generation process. This could include provider-specific options or hints for the EmbeddingManager. The exact interpretation of these parameters is implementation-dependent.

Example​

{ "priority": "high", "target_latency_ms": 500 }

modelId?​

optional modelId: string

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:54

Optional

Optional: The explicit ID of the embedding model to use. If not provided, the EmbeddingManager will select a model based on its configured strategy (e.g., default model, dynamic selection).

Example​

"text-embedding-3-small"

providerId?​

optional providerId: string

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:65

Optional

Optional: The explicit ID of the LLM provider to use. This is typically used in conjunction with modelId. If modelId is provided and has a configured provider, this field might be used for validation or override if the architecture supports it. Generally, the model's configured provider is preferred.

Example​

"openai"

texts​

texts: string | string[]

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:44

The text content to be embedded. Can be a single string or an array of strings for batch processing.

Example​

// Single text
const requestOne: EmbeddingRequest = { texts: "Hello, world!" };
// Batch of texts
const requestBatch: EmbeddingRequest = { texts: ["First document.", "Second document."] };

userId?​

optional userId: string

Defined in: packages/agentos/src/core/embeddings/IEmbeddingManager.ts:75

Optional

Optional: Identifier for the user making the request. This can be used for logging, auditing, or if the underlying LLM provider requires user-specific API keys or applies user-based rate limits.

Example​

"user-12345"