anything-llm/server/utils/AiProviders/groq/index.js
Sean Hatfield 0634013788
[FEAT] Groq LLM support (#865)
* Groq LLM support complete

* update useGetProvidersModels for groq models

* Add definiations
update comments and error log reports
add example envs

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
2024-03-06 14:48:38 -08:00

208 lines
5.9 KiB
JavaScript

const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { chatPrompt } = require("../../chats");
const { handleDefaultStreamResponse } = require("../../helpers/chat/responses");
class GroqLLM {
constructor(embedder = null, modelPreference = null) {
const { Configuration, OpenAIApi } = require("openai");
if (!process.env.GROQ_API_KEY) throw new Error("No Groq API key was set.");
const config = new Configuration({
basePath: "https://api.groq.com/openai/v1",
apiKey: process.env.GROQ_API_KEY,
});
this.openai = new OpenAIApi(config);
this.model =
modelPreference || process.env.GROQ_MODEL_PREF || "llama2-70b-4096";
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
this.embedder = !embedder ? new NativeEmbedder() : 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 ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
promptWindowLimit() {
switch (this.model) {
case "llama2-70b-4096":
return 4096;
case "mixtral-8x7b-32768":
return 32_768;
default:
return 4096;
}
}
async isValidChatCompletionModel(modelName = "") {
const validModels = ["llama2-70b-4096", "mixtral-8x7b-32768"];
const isPreset = validModels.some((model) => modelName === model);
if (isPreset) return true;
const model = await this.openai
.retrieveModel(modelName)
.then((res) => res.data)
.catch(() => null);
return !!model;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async isSafe(_input = "") {
// Not implemented so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`Groq chat: ${this.model} is not valid for chat completion!`
);
const textResponse = await this.openai
.createChatCompletion({
model: this.model,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
})
.then((json) => {
const res = json.data;
if (!res.hasOwnProperty("choices"))
throw new Error("GroqAI chat: No results!");
if (res.choices.length === 0)
throw new Error("GroqAI chat: No results length!");
return res.choices[0].message.content;
})
.catch((error) => {
throw new Error(
`GroqAI::createChatCompletion failed with: ${error.message}`
);
});
return textResponse;
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`GroqAI:streamChat: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.openai.createChatCompletion(
{
model: this.model,
stream: true,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
},
{ responseType: "stream" }
);
return streamRequest;
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`GroqAI:chatCompletion: ${this.model} is not valid for chat completion!`
);
const { data } = await this.openai
.createChatCompletion({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.response.data.error.message);
});
if (!data.hasOwnProperty("choices")) return null;
return data.choices[0].message.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`GroqAI:streamChatCompletion: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.openai.createChatCompletion(
{
model: this.model,
stream: true,
messages,
temperature,
},
{ responseType: "stream" }
);
return streamRequest;
}
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponse(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 = {
GroqLLM,
};