anything-llm/server/utils/AiProviders/genericOpenAi/index.js
Timothy Carambat 547d4859ef
Bump openai package to latest (#1234)
* Bump `openai` package to latest
Tested all except localai

* bump LocalAI support with latest image

* add deprecation notice

* linting
2024-04-30 12:33:42 -07:00

189 lines
5.6 KiB
JavaScript

const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { chatPrompt } = require("../../chats");
const {
handleDefaultStreamResponseV2,
} = require("../../helpers/chat/responses");
class GenericOpenAiLLM {
constructor(embedder = null, modelPreference = null) {
const { OpenAI: OpenAIApi } = require("openai");
if (!process.env.GENERIC_OPEN_AI_BASE_PATH)
throw new Error(
"GenericOpenAI must have a valid base path to use for the api."
);
this.basePath = process.env.GENERIC_OPEN_AI_BASE_PATH;
this.openai = new OpenAIApi({
baseURL: this.basePath,
apiKey: process.env.GENERIC_OPEN_AI_API_KEY ?? null,
});
this.model =
modelPreference ?? process.env.GENERIC_OPEN_AI_MODEL_PREF ?? null;
if (!this.model)
throw new Error("GenericOpenAI must have a valid model set.");
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 GenericOpenAiLLM - falling back to NativeEmbedder for embedding!"
);
this.embedder = !embedder ? new NativeEmbedder() : embedder;
this.defaultTemp = 0.7;
this.log(`Inference API: ${this.basePath} Model: ${this.model}`);
}
log(text, ...args) {
console.log(`\x1b[36m[${this.constructor.name}]\x1b[0m ${text}`, ...args);
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
// Ensure the user set a value for the token limit
// and if undefined - assume 4096 window.
promptWindowLimit() {
const limit = process.env.GENERIC_OPEN_AI_MODEL_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No token context limit was set.");
return Number(limit);
}
// Short circuit since we have no idea if the model is valid or not
// in pre-flight for generic endpoints
isValidChatCompletionModel(_modelName = "") {
return true;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async isSafe(_input = "") {
// Not implemented so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
const textResponse = await this.openai.chat.completions
.create({
model: this.model,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
})
.then((result) => {
if (!result.hasOwnProperty("choices"))
throw new Error("GenericOpenAI chat: No results!");
if (result.choices.length === 0)
throw new Error("GenericOpenAI chat: No results length!");
return result.choices[0].message.content;
})
.catch((error) => {
throw new Error(
`GenericOpenAI::createChatCompletion failed with: ${error.message}`
);
});
return textResponse;
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
const streamRequest = await this.openai.chat.completions.create({
model: this.model,
stream: true,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
});
return streamRequest;
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.response.data.error.message);
});
if (!result.hasOwnProperty("choices") || result.choices.length === 0)
return null;
return result.choices[0].message.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
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 = {
GenericOpenAiLLM,
};