const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const {
LLMPerformanceMonitor,
} = require("../../helpers/chat/LLMPerformanceMonitor");
const {
handleDefaultStreamResponseV2,
} = require("../../helpers/chat/responses");
const { MODEL_MAP } = require("../modelMap");
class GroqLLM {
constructor(embedder = null, modelPreference = null) {
const { OpenAI: OpenAIApi } = require("openai");
if (!process.env.GROQ_API_KEY) throw new Error("No Groq API key was set.");
this.openai = new OpenAIApi({
baseURL: "https://api.groq.com/openai/v1",
apiKey: process.env.GROQ_API_KEY,
});
this.model =
modelPreference || process.env.GROQ_MODEL_PREF || "llama-3.1-8b-instant";
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("")
);
}
#log(text, ...args) {
console.log(`\x1b[32m[GroqAi]\x1b[0m ${text}`, ...args);
}
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
static promptWindowLimit(modelName) {
return MODEL_MAP.groq[modelName] ?? 8192;
}
promptWindowLimit() {
return MODEL_MAP.groq[this.model] ?? 8192;
}
async isValidChatCompletionModel(modelName = "") {
return !!modelName; // name just needs to exist
}
/**
* Generates appropriate content array for a message + attachments.
* @param {{userPrompt:string, attachments: import("../../helpers").Attachment[]}}
* @returns {string|object[]}
*/
#generateContent({ userPrompt, attachments = [] }) {
if (!attachments.length) return userPrompt;
const content = [{ type: "text", text: userPrompt }];
for (let attachment of attachments) {
content.push({
type: "image_url",
image_url: {
url: attachment.contentString,
},
});
}
return content.flat();
}
/**
* Last Updated: October 21, 2024
* According to https://console.groq.com/docs/vision
* the vision models supported all make a mess of prompting depending on the model.
* Currently the llama3.2 models are only in preview and subject to change and the llava model is deprecated - so we will not support attachments for that at all.
*
* Since we can only explicitly support the current models, this is a temporary solution.
* If the attachments are empty or the model is not a vision model, we will return the default prompt structure which will work for all models.
* If the attachments are present and the model is a vision model - we only return the user prompt with attachments - see comment at end of function for more.
*
* Historical attachments are also omitted from prompt chat history for the reasons above. (TDC: Dec 30, 2024)
*/
#conditionalPromptStruct({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [], // This is the specific attachment for only this prompt
}) {
const VISION_MODELS = [
"llama-3.2-90b-vision-preview",
"llama-3.2-11b-vision-preview",
];
const DEFAULT_PROMPT_STRUCT = [
{
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
},
...chatHistory,
{ role: "user", content: userPrompt },
];
// If there are no attachments or model is not a vision model, return the default prompt structure
// as there is nothing to attach or do and no model limitations to consider
if (!attachments.length) return DEFAULT_PROMPT_STRUCT;
if (!VISION_MODELS.includes(this.model)) {
this.#log(
`${this.model} is not an explicitly supported vision model! Will omit attachments.`
);
return DEFAULT_PROMPT_STRUCT;
}
return [
// Why is the system prompt and history commented out?
// The current vision models for Groq perform VERY poorly with ANY history or text prior to the image.
// In order to not get LLM refusals for every single message, we will not include the "system prompt" or even the chat history.
// This is a temporary solution until Groq fixes their vision models to be more coherent and also handle context prior to the image.
// Note for the future:
// Groq vision models also do not support system prompts - which is why you see the user/assistant emulation used instead of "system".
// This means any vision call is assessed independently of the chat context prior to the image.
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// {
// role: "user",
// content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
// },
// {
// role: "assistant",
// content: "OK",
// },
// ...chatHistory,
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
/**
* Construct the user prompt for this model.
* @param {{attachments: import("../../helpers").Attachment[]}} param0
* @returns
*/
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [], // This is the specific attachment for only this prompt
}) {
// NOTICE: SEE GroqLLM.#conditionalPromptStruct for more information on how attachments are handled with Groq.
return this.#conditionalPromptStruct({
systemPrompt,
contextTexts,
chatHistory,
userPrompt,
attachments,
});
}
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 result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage.prompt_tokens || 0,
completion_tokens: result.output.usage.completion_tokens || 0,
total_tokens: result.output.usage.total_tokens || 0,
outputTps:
result.output.usage.completion_tokens /
result.output.usage.completion_time,
duration: result.output.usage.total_time,
},
};
}
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 measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages,
false
);
return measuredStreamRequest;
}
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 = {
GroqLLM,
};