mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-15 19:00:33 +01:00
1f29cec918
* Remove LangchainJS for chat support chaining Implement runtime LLM selection Implement AzureOpenAI Support for LLM + Emebedding WIP on frontend Update env to reflect the new fields * Remove LangchainJS for chat support chaining Implement runtime LLM selection Implement AzureOpenAI Support for LLM + Emebedding WIP on frontend Update env to reflect the new fields * Replace keys with LLM Selection in settings modal Enforce checks for new ENVs depending on LLM selection
100 lines
2.9 KiB
JavaScript
100 lines
2.9 KiB
JavaScript
class AzureOpenAi {
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constructor() {
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const { OpenAIClient, AzureKeyCredential } = require("@azure/openai");
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const openai = new OpenAIClient(
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process.env.AZURE_OPENAI_ENDPOINT,
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new AzureKeyCredential(process.env.AZURE_OPENAI_KEY)
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);
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this.openai = openai;
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}
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isValidChatModel(_modelName = "") {
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// The Azure user names their "models" as deployments and they can be any name
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// so we rely on the user to put in the correct deployment as only they would
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// know it.
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return true;
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}
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async isSafe(_input = "") {
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// Not implemented by Azure OpenAI so must be stubbed
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return { safe: true, reasons: [] };
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}
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async sendChat(chatHistory = [], prompt, workspace = {}) {
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const model = process.env.OPEN_MODEL_PREF;
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if (!model)
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throw new Error(
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"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."
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);
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const textResponse = await this.openai
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.getChatCompletions(
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model,
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[
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{ role: "system", content: "" },
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...chatHistory,
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{ role: "user", content: prompt },
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],
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{
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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n: 1,
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}
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)
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.then((res) => {
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if (!res.hasOwnProperty("choices"))
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throw new Error("OpenAI chat: No results!");
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if (res.choices.length === 0)
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throw new Error("OpenAI chat: No results length!");
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return res.choices[0].message.content;
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})
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.catch((error) => {
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console.log(error);
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throw new Error(
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`AzureOpenAI::getChatCompletions failed with: ${error.message}`
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);
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});
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return textResponse;
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}
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async getChatCompletion(messages = [], { temperature = 0.7 }) {
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const model = process.env.OPEN_MODEL_PREF;
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if (!model)
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throw new Error(
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"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."
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);
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const data = await this.openai.getChatCompletions(model, messages, {
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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}
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async embedTextInput(textInput) {
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const result = await this.embedChunks(textInput);
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return result?.[0] || [];
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}
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async embedChunks(textChunks = []) {
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const textEmbeddingModel =
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process.env.EMBEDDING_MODEL_PREF || "text-embedding-ada-002";
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if (!textEmbeddingModel)
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throw new Error(
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"No EMBEDDING_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an embedding model."
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);
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const { data = [] } = await this.openai.getEmbeddings(
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textEmbeddingModel,
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textChunks
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);
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return data.length > 0 &&
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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}
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}
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module.exports = {
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AzureOpenAi,
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};
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