anything-llm/server/utils/EmbeddingEngines/openAi/index.js
Timothy Carambat 658e7fa390
chore: Better VectorDb and Embedder error messages (#620)
* chore: propogate embedder and vectordb errors during document mutations

* add default value for errors on addDocuments
2024-01-18 11:40:48 -08:00

88 lines
2.7 KiB
JavaScript

const { toChunks } = require("../../helpers");
class OpenAiEmbedder {
constructor() {
const { Configuration, OpenAIApi } = require("openai");
if (!process.env.OPEN_AI_KEY) throw new Error("No OpenAI API key was set.");
const config = new Configuration({
apiKey: process.env.OPEN_AI_KEY,
});
const openai = new OpenAIApi(config);
this.openai = openai;
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 500;
this.embeddingMaxChunkLength = 1_000;
}
async embedTextInput(textInput) {
const result = await this.embedChunks(textInput);
return result?.[0] || [];
}
async embedChunks(textChunks = []) {
// Because there is a hard POST limit on how many chunks can be sent at once to OpenAI (~8mb)
// we concurrently execute each max batch of text chunks possible.
// Refer to constructor maxConcurrentChunks for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai
.createEmbedding({
model: "text-embedding-ada-002",
input: chunk,
})
.then((res) => {
resolve({ data: res.data?.data, error: null });
})
.catch((e) => {
e.type =
e?.response?.data?.error?.code ||
e?.response?.status ||
"failed_to_embed";
e.message = e?.response?.data?.error?.message || e.message;
resolve({ data: [], error: e });
});
})
);
}
const { data = [], error = null } = await Promise.all(
embeddingRequests
).then((results) => {
// If any errors were returned from OpenAI abort the entire sequence because the embeddings
// will be incomplete.
const errors = results
.filter((res) => !!res.error)
.map((res) => res.error)
.flat();
if (errors.length > 0) {
let uniqueErrors = new Set();
errors.map((error) =>
uniqueErrors.add(`[${error.type}]: ${error.message}`)
);
return {
data: [],
error: Array.from(uniqueErrors).join(", "),
};
}
return {
data: results.map((res) => res?.data || []).flat(),
error: null,
};
});
if (!!error) throw new Error(`OpenAI Failed to embed: ${error}`);
return data.length > 0 &&
data.every((embd) => embd.hasOwnProperty("embedding"))
? data.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
OpenAiEmbedder,
};