Add initial system prompt in ChatHandler and completion

src/chat-handler.ts

@@ -13,10 +13,12 @@ import type { BaseChatModel } from '@langchain/core/language_models/chat_models'
 import {
   AIMessage,
   HumanMessage,
-  mergeMessageRuns
+  mergeMessageRuns,
+  SystemMessage
 } from '@langchain/core/messages';
 import { UUID } from '@lumino/coreutils';
 import { getErrorMessage } from './llm-models';
+import { chatSystemPrompt } from './provider';
 import { IAIProvider } from './token';
 
 export type ConnectionMessage = {
@@ -28,15 +30,28 @@ export class ChatHandler extends ChatModel {
   constructor(options: ChatHandler.IOptions) {
     super(options);
     this._aiProvider = options.aiProvider;
+    this._prompt = chatSystemPrompt(this._aiProvider.name);
+
     this._aiProvider.modelChange.connect(() => {
       this._errorMessage = this._aiProvider.chatError;
+      this._prompt = chatSystemPrompt(this._aiProvider.name);
     });
   }
 
   get provider(): BaseChatModel | null {
     return this._aiProvider.chatModel;
   }
 
+  /**
+   * Getter and setter for the initial prompt.
+   */
+  get prompt(): string {
+    return this._prompt;
+  }
+  set prompt(value: string) {
+    this._prompt = value;
+  }
+
   async sendMessage(message: INewMessage): Promise<boolean> {
     message.id = UUID.uuid4();
     const msg: IChatMessage = {
@@ -62,8 +77,9 @@ export class ChatHandler extends ChatModel {
 
     this._history.messages.push(msg);
 
-    const messages = mergeMessageRuns(
-      this._history.messages.map(msg => {
+    const messages = mergeMessageRuns([new SystemMessage(this._prompt)]);
+    messages.push(
+      ...this._history.messages.map(msg => {
         if (msg.sender.username === 'User') {
           return new HumanMessage(msg.body);
         }
@@ -117,6 +133,7 @@ export class ChatHandler extends ChatModel {
   }
 
   private _aiProvider: IAIProvider;
+  private _prompt: string;
   private _errorMessage: string = '';
   private _history: IChatHistory = { messages: [] };
   private _defaultErrorMessage = 'AI provider not configured';

src/llm-models/anthropic-completer.ts

@@ -7,6 +7,7 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 import { AIMessage, SystemMessage } from '@langchain/core/messages';
 
 import { BaseCompleter, IBaseCompleter } from './base-completer';
+import { COMPLETION_SYSTEM_PROMPT } from '../provider';
 
 export class AnthropicCompleter implements IBaseCompleter {
   constructor(options: BaseCompleter.IOptions) {
@@ -17,6 +18,16 @@ export class AnthropicCompleter implements IBaseCompleter {
     return this._anthropicProvider;
   }
 
+  /**
+   * Getter and setter for the initial prompt.
+   */
+  get prompt(): string {
+    return this._prompt;
+  }
+  set prompt(value: string) {
+    this._prompt = value;
+  }
+
   async fetch(
     request: CompletionHandler.IRequest,
     context: IInlineCompletionContext
@@ -28,9 +39,7 @@ export class AnthropicCompleter implements IBaseCompleter {
     const trimmedPrompt = prompt.trim();
 
     const messages = [
-      new SystemMessage(
-        'You are a code-completion AI completing the following code from a Jupyter Notebook cell.'
-      ),
+      new SystemMessage(this._prompt),
       new AIMessage(trimmedPrompt)
     ];
 
@@ -62,4 +71,5 @@ export class AnthropicCompleter implements IBaseCompleter {
   }
 
   private _anthropicProvider: ChatAnthropic;
+  private _prompt: string = COMPLETION_SYSTEM_PROMPT;
 }

src/llm-models/base-completer.ts

@@ -11,6 +11,11 @@ export interface IBaseCompleter {
    */
   provider: BaseLanguageModel;
 
+  /**
+   * The completion prompt.
+   */
+  prompt: string;
+
   /**
    * The function to fetch a new completion.
    */

src/llm-models/codestral-completer.ts

@@ -8,6 +8,7 @@ import { Throttler } from '@lumino/polling';
 import { CompletionRequest } from '@mistralai/mistralai';
 
 import { BaseCompleter, IBaseCompleter } from './base-completer';
+import { COMPLETION_SYSTEM_PROMPT } from '../provider';
 
 /**
  * The Mistral API has a rate limit of 1 request per second
@@ -66,6 +67,16 @@ export class CodestralCompleter implements IBaseCompleter {
     return this._mistralProvider;
   }
 
+  /**
+   * Getter and setter for the initial prompt.
+   */
+  get prompt(): string {
+    return this._prompt;
+  }
+  set prompt(value: string) {
+    this._prompt = value;
+  }
+
   set requestCompletion(value: () => void) {
     this._requestCompletion = value;
   }
@@ -109,5 +120,6 @@ export class CodestralCompleter implements IBaseCompleter {
   private _requestCompletion?: () => void;
   private _throttler: Throttler;
   private _mistralProvider: MistralAI;
+  private _prompt: string = COMPLETION_SYSTEM_PROMPT;
   private _currentData: CompletionRequest | null = null;
 }

src/provider.ts

@@ -8,6 +8,30 @@ import { CompletionProvider } from './completion-provider';
 import { getChatModel, IBaseCompleter } from './llm-models';
 import { IAIProvider } from './token';
 
+export const chatSystemPrompt = (provider_name: string) => `
+You are Jupyternaut, a conversational assistant living in JupyterLab to help users.
+You are not a language model, but rather an application built on a foundation model from ${provider_name}.
+You are talkative and you provide lots of specific details from the foundation model's context.
+You may use Markdown to format your response.
+If your response includes code, they must be enclosed in Markdown fenced code blocks (with triple backticks before and after).
+If your response includes mathematical notation, they must be expressed in LaTeX markup and enclosed in LaTeX delimiters.
+All dollar quantities (of USD) must be formatted in LaTeX, with the \`$\` symbol escaped by a single backslash \`\\\`.
+- Example prompt: \`If I have \\\\$100 and spend \\\\$20, how much money do I have left?\`
+- **Correct** response: \`You have \\(\\$80\\) remaining.\`
+- **Incorrect** response: \`You have $80 remaining.\`
+If you do not know the answer to a question, answer truthfully by responding that you do not know.
+The following is a friendly conversation between you and a human.
+`;
+
+export const COMPLETION_SYSTEM_PROMPT = `
+You are an application built to provide helpful code completion suggestions.
+You should only produce code. Keep comments to minimum, use the
+programming language comment syntax. Produce clean code.
+The code is written in JupyterLab, a data analysis and code development
+environment which can execute code extended with additional syntax for
+interactive features, such as magics.
+`;
+
 export class AIProvider implements IAIProvider {
   constructor(options: AIProvider.IOptions) {
     this._completionProvider = new CompletionProvider({