anything-llm/server/utils/AiProviders/native/index.js
2024-02-07 08:15:14 -08:00

232 lines
6.8 KiB
JavaScript

const fs = require("fs");
const path = require("path");
const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { chatPrompt } = require("../../chats");
const { writeResponseChunk } = require("../../chats/stream");
// Docs: https://api.js.langchain.com/classes/chat_models_llama_cpp.ChatLlamaCpp.html
const ChatLlamaCpp = (...args) =>
import("langchain/chat_models/llama_cpp").then(
({ ChatLlamaCpp }) => new ChatLlamaCpp(...args)
);
class NativeLLM {
constructor(embedder = null, modelPreference = null) {
if (!process.env.NATIVE_LLM_MODEL_PREF)
throw new Error("No local Llama model was set.");
this.model = modelPreference || process.env.NATIVE_LLM_MODEL_PREF || null;
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
this.embedder = embedder || new NativeEmbedder();
this.cacheDir = path.resolve(
process.env.STORAGE_DIR
? path.resolve(process.env.STORAGE_DIR, "models", "downloaded")
: path.resolve(__dirname, `../../../storage/models/downloaded`)
);
// Make directory when it does not exist in existing installations
if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir);
this.defaultTemp = 0.7;
}
async #initializeLlamaModel(temperature = 0.7) {
const modelPath = path.join(this.cacheDir, this.model);
if (!fs.existsSync(modelPath))
throw new Error(
`Local Llama model ${this.model} was not found in storage!`
);
global.llamaModelInstance = await ChatLlamaCpp({
modelPath,
temperature,
useMlock: true,
});
}
// If the model has been loaded once, it is in the memory now
// so we can skip re-loading it and instead go straight to inference.
// Note: this will break temperature setting hopping between workspaces with different temps.
async #llamaClient({ temperature = 0.7 }) {
if (global.llamaModelInstance) return global.llamaModelInstance;
await this.#initializeLlamaModel(temperature);
return global.llamaModelInstance;
}
#convertToLangchainPrototypes(chats = []) {
const {
HumanMessage,
SystemMessage,
AIMessage,
} = require("langchain/schema");
const langchainChats = [];
const roleToMessageMap = {
system: SystemMessage,
user: HumanMessage,
assistant: AIMessage,
};
for (const chat of chats) {
if (!roleToMessageMap.hasOwnProperty(chat.role)) continue;
const MessageClass = roleToMessageMap[chat.role];
langchainChats.push(new MessageClass({ content: chat.content }));
}
return langchainChats;
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
// Ensure the user set a value for the token limit
promptWindowLimit() {
const limit = process.env.NATIVE_LLM_MODEL_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No NativeAI token context limit was set.");
return Number(limit);
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${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 = []) {
try {
const messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
);
const model = await this.#llamaClient({
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
});
const response = await model.call(messages);
return response.content;
} catch (error) {
throw new Error(
`NativeLLM::createChatCompletion failed with: ${error.message}`
);
}
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
const model = await this.#llamaClient({
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
});
const messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
);
const responseStream = await model.stream(messages);
return responseStream;
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const model = await this.#llamaClient({ temperature });
const response = await model.call(messages);
return response.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const model = await this.#llamaClient({ temperature });
const responseStream = await model.stream(messages);
return responseStream;
}
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise(async (resolve) => {
let fullText = "";
for await (const chunk of stream) {
if (chunk === undefined)
throw new Error(
"Stream returned undefined chunk. Aborting reply - check model provider logs."
);
const content = chunk.hasOwnProperty("content") ? chunk.content : chunk;
fullText += content;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: content,
close: false,
error: false,
});
}
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
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);
const compressedMessages = await messageArrayCompressor(
this,
messageArray,
rawHistory
);
return this.#convertToLangchainPrototypes(compressedMessages);
}
}
module.exports = {
NativeLLM,
};