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

223 lines
6.6 KiB
JavaScript
Raw Normal View History

const { AzureOpenAiEmbedder } = require("../../EmbeddingEngines/azureOpenAi");
const { chatPrompt } = require("../../chats");
const { writeResponseChunk } = require("../../chats/stream");
class AzureOpenAiLLM {
constructor(embedder = null, _modelPreference = null) {
const { OpenAIClient, AzureKeyCredential } = require("@azure/openai");
if (!process.env.AZURE_OPENAI_ENDPOINT)
throw new Error("No Azure API endpoint was set.");
if (!process.env.AZURE_OPENAI_KEY)
throw new Error("No Azure API key was set.");
this.openai = new OpenAIClient(
process.env.AZURE_OPENAI_ENDPOINT,
new AzureKeyCredential(process.env.AZURE_OPENAI_KEY)
);
this.model = process.env.OPEN_MODEL_PREF;
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
if (!embedder)
console.warn(
"No embedding provider defined for AzureOpenAiLLM - falling back to AzureOpenAiEmbedder for embedding!"
);
this.embedder = !embedder ? new AzureOpenAiEmbedder() : embedder;
this.defaultTemp = 0.7;
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
// Sure the user selected a proper value for the token limit
// could be any of these https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-models
// and if undefined - assume it is the lowest end.
promptWindowLimit() {
return !!process.env.AZURE_OPENAI_TOKEN_LIMIT
? Number(process.env.AZURE_OPENAI_TOKEN_LIMIT)
: 4096;
}
isValidChatCompletionModel(_modelName = "") {
// The Azure user names their "models" as deployments and they can be any name
// so we rely on the user to put in the correct deployment as only they would
// know it.
return true;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async isSafe(_input = "") {
// Not implemented by Azure OpenAI so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!this.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 messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
);
const textResponse = await this.openai
.getChatCompletions(this.model, messages, {
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
})
.then((res) => {
if (!res.hasOwnProperty("choices"))
throw new Error("AzureOpenAI chat: No results!");
if (res.choices.length === 0)
throw new Error("AzureOpenAI 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 streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!this.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 messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
);
const stream = await this.openai.streamChatCompletions(
this.model,
messages,
{
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
}
);
return stream;
}
async getChatCompletion(messages = [], { temperature = 0.7 }) {
if (!this.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(this.model, messages, {
temperature,
});
if (!data.hasOwnProperty("choices")) return null;
return data.choices[0].message.content;
}
async streamGetChatCompletion(messages = [], { temperature = 0.7 }) {
if (!this.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 stream = await this.openai.streamChatCompletions(
this.model,
messages,
{
temperature,
n: 1,
}
);
return stream;
}
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise(async (resolve) => {
let fullText = "";
for await (const event of stream) {
for (const choice of event.choices) {
const delta = choice.delta?.content;
if (!delta) continue;
fullText += delta;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: delta,
close: false,
error: false,
});
}
}
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
resolve(fullText);
});
}
// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
}
module.exports = {
AzureOpenAiLLM,
};