anything-llm/server/utils/AiProviders/openAi/index.js
2024-05-22 09:58:10 -05:00

178 lines
5.1 KiB
JavaScript

const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const {
handleDefaultStreamResponseV2,
} = require("../../helpers/chat/responses");
class OpenAiLLM {
constructor(embedder = null, modelPreference = null) {
if (!process.env.OPEN_AI_KEY) throw new Error("No OpenAI API key was set.");
const { OpenAI: OpenAIApi } = require("openai");
this.openai = new OpenAIApi({
apiKey: process.env.OPEN_AI_KEY,
});
this.model = modelPreference || process.env.OPEN_MODEL_PREF || "gpt-4o";
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
this.embedder = embedder ?? new NativeEmbedder();
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 ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
promptWindowLimit() {
switch (this.model) {
case "gpt-3.5-turbo":
case "gpt-3.5-turbo-1106":
return 16_385;
case "gpt-4o":
case "gpt-4-turbo":
case "gpt-4-1106-preview":
case "gpt-4-turbo-preview":
return 128_000;
case "gpt-4":
return 8_192;
case "gpt-4-32k":
return 32_000;
default:
return 4_096; // assume a fine-tune 3.5?
}
}
// Short circuit if name has 'gpt' since we now fetch models from OpenAI API
// via the user API key, so the model must be relevant and real.
// and if somehow it is not, chat will fail but that is caught.
// we don't want to hit the OpenAI api every chat because it will get spammed
// and introduce latency for no reason.
async isValidChatCompletionModel(modelName = "") {
const isPreset = modelName.toLowerCase().includes("gpt");
if (isPreset) return true;
const model = await this.openai.models
.retrieve(modelName)
.then((modelObj) => modelObj)
.catch(() => null);
return !!model;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async isSafe(input = "") {
const { flagged = false, categories = {} } = await this.openai.moderations
.create({ input })
.then((res) => {
if (!res.hasOwnProperty("results"))
throw new Error("OpenAI moderation: No results!");
if (res.results.length === 0)
throw new Error("OpenAI moderation: No results length!");
return res.results[0];
})
.catch((error) => {
throw new Error(
`OpenAI::CreateModeration failed with: ${error.message}`
);
});
if (!flagged) return { safe: true, reasons: [] };
const reasons = Object.keys(categories)
.map((category) => {
const value = categories[category];
if (value === true) {
return category.replace("/", " or ");
} else {
return null;
}
})
.filter((reason) => !!reason);
return { safe: false, reasons };
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenAI chat: ${this.model} is not valid for chat completion!`
);
const result = await this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.message);
});
if (!result.hasOwnProperty("choices") || result.choices.length === 0)
return null;
return result.choices[0].message.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenAI chat: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
});
return streamRequest;
}
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
// 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 = {
OpenAiLLM,
};