/**
* OpenAI Embedding Provider
*
* Uses text-embedding-3-small for high-quality embeddings.
*/
import type { EmbeddingProvider, EmbeddingResult, BatchEmbeddingResult } from "./types.js";
import { createLogger } from "../shared/logger.js";
import { withSpan, SpanAttributes, SpanOperations } from "../shared/index.js";
import { validateOpenAIKey, maskApiKey } from "../config/validation.js";
const logger = createLogger("OpenAIEmbeddings");
const DEFAULT_MODEL = "text-embedding-3-small";
const MODEL_DIMENSIONS: Record<string, number> = {
"text-embedding-3-small": 1536,
"text-embedding-3-large": 3072,
"text-embedding-ada-002": 1536,
};
export class OpenAIEmbeddingProvider implements EmbeddingProvider {
readonly providerId = "openai";
readonly modelId: string;
readonly fullModelName: string;
readonly dimension: number;
private readonly apiKey: string;
private readonly baseUrl = "https://api.openai.com/v1";
constructor(apiKey: string, model?: string) {
// Validate API key format early
validateOpenAIKey(apiKey);
this.apiKey = apiKey;
this.modelId = model ?? DEFAULT_MODEL;
this.fullModelName = `openai:${this.modelId}`;
this.dimension = MODEL_DIMENSIONS[this.modelId] ?? 1536;
logger.debug("OpenAI embedding provider initialized", {
model: this.modelId,
dimension: this.dimension,
maskedKey: maskApiKey(apiKey),
});
}
async isAvailable(): Promise<boolean> {
try {
// Simple API key validation - make a minimal request
const response = await fetch(`${this.baseUrl}/models`, {
headers: { Authorization: `Bearer ${this.apiKey}` },
});
return response.ok;
} catch {
return false;
}
}
async embed(text: string): Promise<EmbeddingResult> {
return withSpan(
`embedding.generate.openai`,
SpanOperations.EMBEDDING_GENERATE,
async (span) => {
span?.setAttribute(SpanAttributes.EMBEDDING_PROVIDER, "openai");
span?.setAttribute(SpanAttributes.EMBEDDING_MODEL, this.modelId);
span?.setAttribute(SpanAttributes.EMBEDDING_DIMENSIONS, this.dimension);
const result = await this.embedBatch([text]);
const first = result.embeddings[0];
if (!first) {
throw new Error("No embedding returned from OpenAI");
}
return first;
}
);
}
async embedBatch(texts: string[]): Promise<BatchEmbeddingResult> {
return withSpan(
`embedding.generate.batch.openai`,
SpanOperations.EMBEDDING_GENERATE,
async (span) => {
span?.setAttribute(SpanAttributes.EMBEDDING_PROVIDER, "openai");
span?.setAttribute(SpanAttributes.EMBEDDING_MODEL, this.modelId);
span?.setAttribute(SpanAttributes.EMBEDDING_DIMENSIONS, this.dimension);
span?.setAttribute(SpanAttributes.EMBEDDING_BATCH_SIZE, texts.length);
logger.debug("Generating embeddings", {
count: texts.length,
model: this.modelId,
});
const response = await fetch(`${this.baseUrl}/embeddings`, {
method: "POST",
headers: {
Authorization: `Bearer ${this.apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: this.modelId,
input: texts,
}),
});
if (!response.ok) {
const error = await response.text();
throw new Error(`OpenAI API error: ${response.status} - ${error}`);
}
const data = (await response.json()) as OpenAIEmbeddingResponse;
return {
embeddings: data.data.map((item) => ({
embedding: item.embedding,
model: this.fullModelName,
dimension: this.dimension,
})),
totalTokens: data.usage?.total_tokens,
};
}
);
}
}
interface OpenAIEmbeddingResponse {
data: Array<{ embedding: number[]; index: number }>;
// eslint-disable-next-line @typescript-eslint/naming-convention
usage?: { total_tokens: number };
}
