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Interface: EmbeddingRequest

Defined in: packages/agentos/src/rag/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.

EmbeddingRequest

Properties​

collectionId?​

optional collectionId: string

Defined in: packages/agentos/src/rag/IEmbeddingManager.ts:86

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.

Optional​

Example​

"financial_reports_q3_2024"

customParameters?​

optional customParameters: Record<string, any>

Defined in: packages/agentos/src/rag/IEmbeddingManager.ts:96

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.

Optional​

Example​

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

modelId?​

optional modelId: string

Defined in: packages/agentos/src/rag/IEmbeddingManager.ts:54

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).

Optional​

Example​

"text-embedding-3-small"

providerId?​

optional providerId: string

Defined in: packages/agentos/src/rag/IEmbeddingManager.ts:65

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.

Optional​

Example​

"openai"

texts​

texts: string | string[]

Defined in: packages/agentos/src/rag/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/rag/IEmbeddingManager.ts:75

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.

Optional​

Example​

"user-12345"