anything-llm/server/utils/EmbeddingEngines/voyageAi/index.js

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class VoyageAiEmbedder {
constructor() {
if (!process.env.VOYAGEAI_API_KEY)
throw new Error("No Voyage AI API key was set.");
const {
VoyageEmbeddings,
} = require("@langchain/community/embeddings/voyage");
const voyage = new VoyageEmbeddings({
apiKey: process.env.VOYAGEAI_API_KEY,
});
this.voyage = voyage;
this.model = process.env.EMBEDDING_MODEL_PREF || "voyage-large-2-instruct";
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.batchSize = 128; // Voyage AI's limit per request is 128 https://docs.voyageai.com/docs/rate-limits#use-larger-batches
this.embeddingMaxChunkLength = this.#getMaxEmbeddingLength();
}
// https://docs.voyageai.com/docs/embeddings
#getMaxEmbeddingLength() {
switch (this.model) {
case "voyage-large-2-instruct":
case "voyage-law-2":
case "voyage-code-2":
case "voyage-large-2":
return 16_000;
case "voyage-2":
return 4_000;
default:
return 4_000;
}
}
async embedTextInput(textInput) {
const result = await this.voyage.embedDocuments(
Array.isArray(textInput) ? textInput : [textInput],
{ modelName: this.model }
);
// If given an array return the native Array[Array] format since that should be the outcome.
// But if given a single string, we need to flatten it so that we have a 1D array.
return (Array.isArray(textInput) ? result : result.flat()) || [];
}
async embedChunks(textChunks = []) {
try {
const embeddings = await this.voyage.embedDocuments(textChunks, {
modelName: this.model,
batchSize: this.batchSize,
});
return embeddings;
} catch (error) {
console.error("Voyage AI Failed to embed:", error);
if (
error.message.includes(
"Cannot read properties of undefined (reading '0')"
)
)
throw new Error("Voyage AI failed to embed: Rate limit reached");
throw error;
}
}
}
module.exports = {
VoyageAiEmbedder,
};