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

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const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const {
writeResponseChunk,
clientAbortedHandler,
} = require("../../helpers/chat/responses");
class LiteLLM {
constructor(embedder = null, modelPreference = null) {
const { OpenAI: OpenAIApi } = require("openai");
if (!process.env.LITE_LLM_BASE_PATH)
throw new Error(
"LiteLLM must have a valid base path to use for the api."
);
this.basePath = process.env.LITE_LLM_BASE_PATH;
this.openai = new OpenAIApi({
baseURL: this.basePath,
apiKey: process.env.LITE_LLM_API_KEY ?? null,
});
this.model = modelPreference ?? process.env.LITE_LLM_MODEL_PREF ?? null;
this.maxTokens = process.env.LITE_LLM_MODEL_TOKEN_LIMIT ?? 1024;
if (!this.model) throw new Error("LiteLLM must have a valid model set.");
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;
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 ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
// Ensure the user set a value for the token limit
// and if undefined - assume 4096 window.
promptWindowLimit() {
const limit = process.env.LITE_LLM_MODEL_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No token context limit was set.");
return Number(limit);
}
// Short circuit since we have no idea if the model is valid or not
// in pre-flight for generic endpoints
isValidChatCompletionModel(_modelName = "") {
return true;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${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 getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
max_tokens: parseInt(this.maxTokens), // LiteLLM requires int
})
.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 }) {
const streamRequest = await this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
max_tokens: parseInt(this.maxTokens), // LiteLLM requires int
});
return streamRequest;
}
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise(async (resolve) => {
let fullText = "";
const handleAbort = () => clientAbortedHandler(resolve, fullText);
response.on("close", handleAbort);
for await (const chunk of stream) {
const message = chunk?.choices?.[0];
const token = message?.delta?.content;
if (token) {
fullText += token;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
// LiteLLM does not give a finish reason in stream until the final chunk
if (message.finish_reason || message.finish_reason === "stop") {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
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 = {
LiteLLM,
};