File size: 4,392 Bytes
f8b5d42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | const { toChunks, maximumChunkLength } = require("../../helpers");
class GenericOpenAiEmbedder {
constructor() {
if (!process.env.EMBEDDING_BASE_PATH)
throw new Error(
"GenericOpenAI must have a valid base path to use for the api."
);
const { OpenAI: OpenAIApi } = require("openai");
this.basePath = process.env.EMBEDDING_BASE_PATH;
this.openai = new OpenAIApi({
baseURL: this.basePath,
apiKey: process.env.GENERIC_OPEN_AI_EMBEDDING_API_KEY ?? null,
});
this.model = process.env.EMBEDDING_MODEL_PREF ?? null;
this.embeddingMaxChunkLength = maximumChunkLength();
// this.maxConcurrentChunks is delegated to the getter below.
// Refer to your specific model and provider you use this class with to determine a valid maxChunkLength
this.log(`Initialized ${this.model}`, {
baseURL: this.basePath,
maxConcurrentChunks: this.maxConcurrentChunks,
embeddingMaxChunkLength: this.embeddingMaxChunkLength,
});
}
log(text, ...args) {
console.log(`\x1b[36m[GenericOpenAiEmbedder]\x1b[0m ${text}`, ...args);
}
/**
* returns the `GENERIC_OPEN_AI_EMBEDDING_API_DELAY_MS` env variable as a number or null if the env variable is not set or is not a number.
* The minimum delay is 500ms.
*
* For some implementation this is necessary to avoid 429 errors due to rate limiting or
* hardware limitations where a single-threaded process is not able to handle the requests fast enough.
* @returns {number}
*/
get apiRequestDelay() {
if (!("GENERIC_OPEN_AI_EMBEDDING_API_DELAY_MS" in process.env)) return null;
if (isNaN(Number(process.env.GENERIC_OPEN_AI_EMBEDDING_API_DELAY_MS)))
return null;
const delayTimeout = Number(
process.env.GENERIC_OPEN_AI_EMBEDDING_API_DELAY_MS
);
if (delayTimeout < 500) return 500; // minimum delay of 500ms
return delayTimeout;
}
/**
* runs the delay if it is set and valid.
* @returns {Promise<void>}
*/
async runDelay() {
if (!this.apiRequestDelay) return;
this.log(`Delaying new batch request for ${this.apiRequestDelay}ms`);
await new Promise((resolve) => setTimeout(resolve, this.apiRequestDelay));
}
/**
* returns the `GENERIC_OPEN_AI_EMBEDDING_MAX_CONCURRENT_CHUNKS` env variable as a number
* or 500 if the env variable is not set or is not a number.
* @returns {number}
*/
get maxConcurrentChunks() {
if (!process.env.GENERIC_OPEN_AI_EMBEDDING_MAX_CONCURRENT_CHUNKS)
return 500;
if (
isNaN(Number(process.env.GENERIC_OPEN_AI_EMBEDDING_MAX_CONCURRENT_CHUNKS))
)
return 500;
return Number(process.env.GENERIC_OPEN_AI_EMBEDDING_MAX_CONCURRENT_CHUNKS);
}
async embedTextInput(textInput) {
const result = await this.embedChunks(
Array.isArray(textInput) ? textInput : [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 sequentially execute each max batch of text chunks possible.
// Refer to constructor maxConcurrentChunks for more info.
const allResults = [];
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
const { data = [], error = null } = await new Promise((resolve) => {
this.openai.embeddings
.create({
model: this.model,
input: chunk,
})
.then((result) => resolve({ data: result?.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 });
});
});
// If any errors were returned from OpenAI abort the entire sequence because the embeddings
// will be incomplete.
if (error)
throw new Error(`GenericOpenAI Failed to embed: ${error.message}`);
allResults.push(...(data || []));
if (this.apiRequestDelay) await this.runDelay();
}
return allResults.length > 0 &&
allResults.every((embd) => embd.hasOwnProperty("embedding"))
? allResults.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
GenericOpenAiEmbedder,
};
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