296 lines
11 KiB
JavaScript
296 lines
11 KiB
JavaScript
|
"use strict";
|
||
|
|
Object.defineProperty(exports, "__esModule", { value: true });
|
||
|
|
exports.ChatLlamaCpp = void 0;
|
||
|
|
/* eslint-disable import/no-extraneous-dependencies */
|
||
|
|
const node_llama_cpp_1 = require("node-llama-cpp");
|
||
|
|
const chat_models_1 = require("@langchain/core/language_models/chat_models");
|
||
|
|
const messages_1 = require("@langchain/core/messages");
|
||
|
|
const outputs_1 = require("@langchain/core/outputs");
|
||
|
|
const llama_cpp_js_1 = require("../utils/llama_cpp.cjs");
|
||
|
|
/**
|
||
|
|
* To use this model you need to have the `node-llama-cpp` module installed.
|
||
|
|
* This can be installed using `npm install -S node-llama-cpp` and the minimum
|
||
|
|
* version supported in version 2.0.0.
|
||
|
|
* This also requires that have a locally built version of Llama2 installed.
|
||
|
|
* @example
|
||
|
|
* ```typescript
|
||
|
|
* // Initialize the ChatLlamaCpp model with the path to the model binary file.
|
||
|
|
* const model = new ChatLlamaCpp({
|
||
|
|
* modelPath: "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin",
|
||
|
|
* temperature: 0.5,
|
||
|
|
* });
|
||
|
|
*
|
||
|
|
* // Call the model with a message and await the response.
|
||
|
|
* const response = await model.invoke([
|
||
|
|
* new HumanMessage({ content: "My name is John." }),
|
||
|
|
* ]);
|
||
|
|
*
|
||
|
|
* // Log the response to the console.
|
||
|
|
* console.log({ response });
|
||
|
|
*
|
||
|
|
* ```
|
||
|
|
*/
|
||
|
|
class ChatLlamaCpp extends chat_models_1.SimpleChatModel {
|
||
|
|
static lc_name() {
|
||
|
|
return "ChatLlamaCpp";
|
||
|
|
}
|
||
|
|
constructor(inputs) {
|
||
|
|
super(inputs);
|
||
|
|
Object.defineProperty(this, "maxTokens", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "temperature", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "topK", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "topP", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "trimWhitespaceSuffix", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "_model", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "_context", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "_session", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "lc_serializable", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: true
|
||
|
|
});
|
||
|
|
this.maxTokens = inputs?.maxTokens;
|
||
|
|
this.temperature = inputs?.temperature;
|
||
|
|
this.topK = inputs?.topK;
|
||
|
|
this.topP = inputs?.topP;
|
||
|
|
this.trimWhitespaceSuffix = inputs?.trimWhitespaceSuffix;
|
||
|
|
this._model = (0, llama_cpp_js_1.createLlamaModel)(inputs);
|
||
|
|
this._context = (0, llama_cpp_js_1.createLlamaContext)(this._model, inputs);
|
||
|
|
this._session = null;
|
||
|
|
}
|
||
|
|
_llmType() {
|
||
|
|
return "llama2_cpp";
|
||
|
|
}
|
||
|
|
/** @ignore */
|
||
|
|
_combineLLMOutput() {
|
||
|
|
return {};
|
||
|
|
}
|
||
|
|
invocationParams() {
|
||
|
|
return {
|
||
|
|
maxTokens: this.maxTokens,
|
||
|
|
temperature: this.temperature,
|
||
|
|
topK: this.topK,
|
||
|
|
topP: this.topP,
|
||
|
|
trimWhitespaceSuffix: this.trimWhitespaceSuffix,
|
||
|
|
};
|
||
|
|
}
|
||
|
|
/** @ignore */
|
||
|
|
async _call(messages, options, runManager) {
|
||
|
|
let prompt = "";
|
||
|
|
if (messages.length > 1) {
|
||
|
|
// We need to build a new _session
|
||
|
|
prompt = this._buildSession(messages);
|
||
|
|
}
|
||
|
|
else if (!this._session) {
|
||
|
|
prompt = this._buildSession(messages);
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if (typeof messages[0].content !== "string") {
|
||
|
|
throw new Error("ChatLlamaCpp does not support non-string message content in sessions.");
|
||
|
|
}
|
||
|
|
// If we already have a session then we should just have a single prompt
|
||
|
|
prompt = messages[0].content;
|
||
|
|
}
|
||
|
|
try {
|
||
|
|
const promptOptions = {
|
||
|
|
signal: options.signal,
|
||
|
|
onToken: async (tokens) => {
|
||
|
|
options.onToken?.(tokens);
|
||
|
|
await runManager?.handleLLMNewToken(this._context.decode(tokens));
|
||
|
|
},
|
||
|
|
maxTokens: this?.maxTokens,
|
||
|
|
temperature: this?.temperature,
|
||
|
|
topK: this?.topK,
|
||
|
|
topP: this?.topP,
|
||
|
|
trimWhitespaceSuffix: this?.trimWhitespaceSuffix,
|
||
|
|
};
|
||
|
|
// @ts-expect-error - TS2531: Object is possibly 'null'.
|
||
|
|
const completion = await this._session.prompt(prompt, promptOptions);
|
||
|
|
return completion;
|
||
|
|
}
|
||
|
|
catch (e) {
|
||
|
|
if (typeof e === "object") {
|
||
|
|
const error = e;
|
||
|
|
if (error.message === "AbortError") {
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
throw new Error("Error getting prompt completion.");
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async *_streamResponseChunks(input, _options, runManager) {
|
||
|
|
const promptOptions = {
|
||
|
|
temperature: this?.temperature,
|
||
|
|
topK: this?.topK,
|
||
|
|
topP: this?.topP,
|
||
|
|
};
|
||
|
|
const prompt = this._buildPrompt(input);
|
||
|
|
const stream = await this.caller.call(async () => this._context.evaluate(this._context.encode(prompt), promptOptions));
|
||
|
|
for await (const chunk of stream) {
|
||
|
|
yield new outputs_1.ChatGenerationChunk({
|
||
|
|
text: this._context.decode([chunk]),
|
||
|
|
message: new messages_1.AIMessageChunk({
|
||
|
|
content: this._context.decode([chunk]),
|
||
|
|
}),
|
||
|
|
generationInfo: {},
|
||
|
|
});
|
||
|
|
await runManager?.handleLLMNewToken(this._context.decode([chunk]) ?? "");
|
||
|
|
}
|
||
|
|
}
|
||
|
|
// This constructs a new session if we need to adding in any sys messages or previous chats
|
||
|
|
_buildSession(messages) {
|
||
|
|
let prompt = "";
|
||
|
|
let sysMessage = "";
|
||
|
|
let noSystemMessages = [];
|
||
|
|
let interactions = [];
|
||
|
|
// Let's see if we have a system message
|
||
|
|
if (messages.findIndex((msg) => msg._getType() === "system") !== -1) {
|
||
|
|
const sysMessages = messages.filter((message) => message._getType() === "system");
|
||
|
|
const systemMessageContent = sysMessages[sysMessages.length - 1].content;
|
||
|
|
if (typeof systemMessageContent !== "string") {
|
||
|
|
throw new Error("ChatLlamaCpp does not support non-string message content in sessions.");
|
||
|
|
}
|
||
|
|
// Only use the last provided system message
|
||
|
|
sysMessage = systemMessageContent;
|
||
|
|
// Now filter out the system messages
|
||
|
|
noSystemMessages = messages.filter((message) => message._getType() !== "system");
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
noSystemMessages = messages;
|
||
|
|
}
|
||
|
|
// Lets see if we just have a prompt left or are their previous interactions?
|
||
|
|
if (noSystemMessages.length > 1) {
|
||
|
|
// Is the last message a prompt?
|
||
|
|
if (noSystemMessages[noSystemMessages.length - 1]._getType() === "human") {
|
||
|
|
const finalMessageContent = noSystemMessages[noSystemMessages.length - 1].content;
|
||
|
|
if (typeof finalMessageContent !== "string") {
|
||
|
|
throw new Error("ChatLlamaCpp does not support non-string message content in sessions.");
|
||
|
|
}
|
||
|
|
prompt = finalMessageContent;
|
||
|
|
interactions = this._convertMessagesToInteractions(noSystemMessages.slice(0, noSystemMessages.length - 1));
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
interactions = this._convertMessagesToInteractions(noSystemMessages);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if (typeof noSystemMessages[0].content !== "string") {
|
||
|
|
throw new Error("ChatLlamaCpp does not support non-string message content in sessions.");
|
||
|
|
}
|
||
|
|
// If there was only a single message we assume it's a prompt
|
||
|
|
prompt = noSystemMessages[0].content;
|
||
|
|
}
|
||
|
|
// Now lets construct a session according to what we got
|
||
|
|
if (sysMessage !== "" && interactions.length > 0) {
|
||
|
|
this._session = new node_llama_cpp_1.LlamaChatSession({
|
||
|
|
context: this._context,
|
||
|
|
conversationHistory: interactions,
|
||
|
|
systemPrompt: sysMessage,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
else if (sysMessage !== "" && interactions.length === 0) {
|
||
|
|
this._session = new node_llama_cpp_1.LlamaChatSession({
|
||
|
|
context: this._context,
|
||
|
|
systemPrompt: sysMessage,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
else if (sysMessage === "" && interactions.length > 0) {
|
||
|
|
this._session = new node_llama_cpp_1.LlamaChatSession({
|
||
|
|
context: this._context,
|
||
|
|
conversationHistory: interactions,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
this._session = new node_llama_cpp_1.LlamaChatSession({
|
||
|
|
context: this._context,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
return prompt;
|
||
|
|
}
|
||
|
|
// This builds a an array of interactions
|
||
|
|
_convertMessagesToInteractions(messages) {
|
||
|
|
const result = [];
|
||
|
|
for (let i = 0; i < messages.length; i += 2) {
|
||
|
|
if (i + 1 < messages.length) {
|
||
|
|
const prompt = messages[i].content;
|
||
|
|
const response = messages[i + 1].content;
|
||
|
|
if (typeof prompt !== "string" || typeof response !== "string") {
|
||
|
|
throw new Error("ChatLlamaCpp does not support non-string message content.");
|
||
|
|
}
|
||
|
|
result.push({
|
||
|
|
prompt,
|
||
|
|
response,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return result;
|
||
|
|
}
|
||
|
|
_buildPrompt(input) {
|
||
|
|
const prompt = input
|
||
|
|
.map((message) => {
|
||
|
|
let messageText;
|
||
|
|
if (message._getType() === "human") {
|
||
|
|
messageText = `[INST] ${message.content} [/INST]`;
|
||
|
|
}
|
||
|
|
else if (message._getType() === "ai") {
|
||
|
|
messageText = message.content;
|
||
|
|
}
|
||
|
|
else if (message._getType() === "system") {
|
||
|
|
messageText = `<<SYS>> ${message.content} <</SYS>>`;
|
||
|
|
}
|
||
|
|
else if (messages_1.ChatMessage.isInstance(message)) {
|
||
|
|
messageText = `\n\n${message.role[0].toUpperCase()}${message.role.slice(1)}: ${message.content}`;
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
console.warn(`Unsupported message type passed to llama_cpp: "${message._getType()}"`);
|
||
|
|
messageText = "";
|
||
|
|
}
|
||
|
|
return messageText;
|
||
|
|
})
|
||
|
|
.join("\n");
|
||
|
|
return prompt;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
exports.ChatLlamaCpp = ChatLlamaCpp;
|