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llama_cpp.ts
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llama_cpp.ts
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import {
LlamaModel,
LlamaContext,
LlamaChatSession,
type ConversationInteraction,
} from "node-llama-cpp";
import {
SimpleChatModel,
type BaseChatModelParams,
} from "@langchain/core/language_models/chat_models";
import type { BaseLanguageModelCallOptions } from "@langchain/core/language_models/base";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import {
BaseMessage,
AIMessageChunk,
ChatMessage,
} from "@langchain/core/messages";
import { ChatGenerationChunk } from "@langchain/core/outputs";
import {
LlamaBaseCppInputs,
createLlamaModel,
createLlamaContext,
} from "../utils/llama_cpp.js";
/**
* Note that the modelPath is the only required parameter. For testing you
* can set this in the environment variable `LLAMA_PATH`.
*/
export interface LlamaCppInputs
extends LlamaBaseCppInputs,
BaseChatModelParams {}
export interface LlamaCppCallOptions extends BaseLanguageModelCallOptions {
/** The maximum number of tokens the response should contain. */
maxTokens?: number;
/** A function called when matching the provided token array */
onToken?: (tokens: number[]) => void;
}
/**
* 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.call([
* new HumanMessage({ content: "My name is John." }),
* ]);
*
* // Log the response to the console.
* console.log({ response });
*
* ```
*/
export class ChatLlamaCpp extends SimpleChatModel<LlamaCppCallOptions> {
static inputs: LlamaCppInputs;
maxTokens?: number;
temperature?: number;
topK?: number;
topP?: number;
trimWhitespaceSuffix?: boolean;
_model: LlamaModel;
_context: LlamaContext;
_session: LlamaChatSession | null;
lc_serializable = true;
static lc_name() {
return "ChatLlamaCpp";
}
constructor(inputs: LlamaCppInputs) {
super(inputs);
this.maxTokens = inputs?.maxTokens;
this.temperature = inputs?.temperature;
this.topK = inputs?.topK;
this.topP = inputs?.topP;
this.trimWhitespaceSuffix = inputs?.trimWhitespaceSuffix;
this._model = createLlamaModel(inputs);
this._context = 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: BaseMessage[],
options: this["ParsedCallOptions"]
): Promise<string> {
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 = {
onToken: options.onToken,
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) {
throw new Error("Error getting prompt completion.");
}
}
async *_streamResponseChunks(
input: BaseMessage[],
_options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): AsyncGenerator<ChatGenerationChunk> {
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 ChatGenerationChunk({
text: this._context.decode([chunk]),
message: new 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
protected _buildSession(messages: BaseMessage[]): string {
let prompt = "";
let sysMessage = "";
let noSystemMessages: BaseMessage[] = [];
let interactions: ConversationInteraction[] = [];
// 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 LlamaChatSession({
context: this._context,
conversationHistory: interactions,
systemPrompt: sysMessage,
});
} else if (sysMessage !== "" && interactions.length === 0) {
this._session = new LlamaChatSession({
context: this._context,
systemPrompt: sysMessage,
});
} else if (sysMessage === "" && interactions.length > 0) {
this._session = new LlamaChatSession({
context: this._context,
conversationHistory: interactions,
});
} else {
this._session = new LlamaChatSession({
context: this._context,
});
}
return prompt;
}
// This builds a an array of interactions
protected _convertMessagesToInteractions(
messages: BaseMessage[]
): ConversationInteraction[] {
const result: ConversationInteraction[] = [];
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;
}
protected _buildPrompt(input: BaseMessage[]): string {
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>> ${message.content} <</SYS>>`;
} else if (ChatMessage.isInstance(message)) {
messageText = `\n\n${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;
}
}