anything-llm/server/utils/AiProviders/azureOpenAi/index.js

145 lines
4.7 KiB
JavaScript

const { toChunks } = require("../../helpers");
class AzureOpenAi {
constructor() {
const { OpenAIClient, AzureKeyCredential } = require("@azure/openai");
const openai = new OpenAIClient(
process.env.AZURE_OPENAI_ENDPOINT,
new AzureKeyCredential(process.env.AZURE_OPENAI_KEY)
);
this.openai = openai;
// The maximum amount of "inputs" that OpenAI API can process in a single call.
// https://learn.microsoft.com/en-us/azure/ai-services/openai/faq#i-am-trying-to-use-embeddings-and-received-the-error--invalidrequesterror--too-many-inputs--the-max-number-of-inputs-is-1---how-do-i-fix-this-:~:text=consisting%20of%20up%20to%2016%20inputs%20per%20API%20request
this.embeddingChunkLimit = 16;
}
isValidChatModel(_modelName = "") {
// The Azure user names their "models" as deployments and they can be any name
// so we rely on the user to put in the correct deployment as only they would
// know it.
return true;
}
async isSafe(_input = "") {
// Not implemented by Azure OpenAI so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}) {
const model = process.env.OPEN_MODEL_PREF;
if (!model)
throw new Error(
"No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5."
);
const textResponse = await this.openai
.getChatCompletions(
model,
[
{ role: "system", content: "" },
...chatHistory,
{ role: "user", content: prompt },
],
{
temperature: Number(workspace?.openAiTemp ?? 0.7),
n: 1,
}
)
.then((res) => {
if (!res.hasOwnProperty("choices"))
throw new Error("OpenAI chat: No results!");
if (res.choices.length === 0)
throw new Error("OpenAI chat: No results length!");
return res.choices[0].message.content;
})
.catch((error) => {
console.log(error);
throw new Error(
`AzureOpenAI::getChatCompletions failed with: ${error.message}`
);
});
return textResponse;
}
async getChatCompletion(messages = [], { temperature = 0.7 }) {
const model = process.env.OPEN_MODEL_PREF;
if (!model)
throw new Error(
"No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5."
);
const data = await this.openai.getChatCompletions(model, messages, {
temperature,
});
if (!data.hasOwnProperty("choices")) return null;
return data.choices[0].message.content;
}
async embedTextInput(textInput) {
const result = await this.embedChunks(textInput);
return result?.[0] || [];
}
async embedChunks(textChunks = []) {
const textEmbeddingModel =
process.env.EMBEDDING_MODEL_PREF || "text-embedding-ada-002";
if (!textEmbeddingModel)
throw new Error(
"No EMBEDDING_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an embedding model."
);
// Because there is a limit on how many chunks can be sent at once to Azure OpenAI
// we concurrently execute each max batch of text chunks possible.
// Refer to constructor embeddingChunkLimit for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.embeddingChunkLimit)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai
.getEmbeddings(textEmbeddingModel, chunk)
.then((res) => {
resolve({ data: res.data, error: null });
})
.catch((e) => {
resolve({ data: [], error: e?.error });
});
})
);
}
const { data = [], error = null } = await Promise.all(
embeddingRequests
).then((results) => {
// If any errors were returned from Azure abort the entire sequence because the embeddings
// will be incomplete.
const errors = results
.filter((res) => !!res.error)
.map((res) => res.error)
.flat();
if (errors.length > 0) {
return {
data: [],
error: `(${errors.length}) Embedding Errors! ${errors
.map((error) => `[${error.type}]: ${error.message}`)
.join(", ")}`,
};
}
return {
data: results.map((res) => res?.data || []).flat(),
error: null,
};
});
if (!!error) throw new Error(`Azure OpenAI Failed to embed: ${error}`);
return data.length > 0 &&
data.every((embd) => embd.hasOwnProperty("embedding"))
? data.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
AzureOpenAi,
};