centralize all prompts

src/routes/api/llm.ts

@@ -13,6 +13,7 @@ import contextService from "../../services/llm/context_service.js";
 import sql from "../../services/sql.js";
 // Import the index service for knowledge base management
 import indexService from "../../services/llm/index_service.js";
+import { CONTEXT_PROMPTS } from '../../services/llm/llm_prompt_constants.js';
 
 // LLM service constants
 export const LLM_CONSTANTS = {
@@ -504,7 +505,7 @@ async function findRelevantNotes(content: string, contextNoteId: string | null =
 }
 
 /**
- * Build context from notes
+ * Build a prompt with context from relevant notes
  */
 function buildContextFromNotes(sources: NoteSource[], query: string): string {
     console.log("Building context from notes with query:", query);
@@ -529,14 +530,10 @@ function buildContextFromNotes(sources: NoteSource[], query: string): string {
         return query || '';
     }
 
-    // Build a complete context prompt with clearer instructions
-    return `I'll provide you with relevant information from my notes to help answer your question.
-
-${noteContexts}
-
-When referring to information from these notes in your response, please cite them by their titles (e.g., "According to your note on [Title]...") rather than using labels like "Note 1" or "Note 2".
-
-Now, based on the above information, please answer: ${query}`;
+    // Use the template from the constants file, replacing placeholders
+    return CONTEXT_PROMPTS.CONTEXT_NOTES_WRAPPER
+        .replace('{noteContexts}', noteContexts)
+        .replace('{query}', query);
 }
 
 /**

src/services/llm/agent_tools/contextual_thinking_tool.ts

@@ -15,6 +15,7 @@
 
 import log from "../../log.js";
 import aiServiceManager from "../ai_service_manager.js";
+import { AGENT_TOOL_PROMPTS } from '../llm_prompt_constants.js';
 
 /**
  * Represents a single reasoning step taken by the agent
@@ -73,17 +74,17 @@ export class ContextualThinkingTool {
         // Initialize with some starter thinking steps
         this.addThinkingStep(thinkingId, {
             type: 'observation',
-            content: `Starting analysis of the query: "${query}"`
+            content: AGENT_TOOL_PROMPTS.CONTEXTUAL_THINKING.STARTING_ANALYSIS(query)
         });
 
         this.addThinkingStep(thinkingId, {
             type: 'question',
-            content: `What are the key components of this query that need to be addressed?`
+            content: AGENT_TOOL_PROMPTS.CONTEXTUAL_THINKING.KEY_COMPONENTS
         });
 
         this.addThinkingStep(thinkingId, {
             type: 'observation',
-            content: `Breaking down the query to understand its requirements and context.`
+            content: AGENT_TOOL_PROMPTS.CONTEXTUAL_THINKING.BREAKING_DOWN
         });
 
         return thinkingId;

src/services/llm/agent_tools/query_decomposition_tool.ts

@@ -13,6 +13,7 @@
  */
 
 import log from '../../log.js';
+import { AGENT_TOOL_PROMPTS } from '../llm_prompt_constants.js';
 
 export interface SubQuery {
     id: string;
@@ -66,7 +67,7 @@ export class QueryDecompositionTool {
                 const mainSubQuery = {
                     id: this.generateSubQueryId(),
                     text: query,
-                    reason: 'Direct question that can be answered without decomposition',
+                    reason: AGENT_TOOL_PROMPTS.QUERY_DECOMPOSITION.SUB_QUERY_DIRECT,
                     isAnswered: false
                 };
 
@@ -74,7 +75,7 @@ export class QueryDecompositionTool {
                 const genericQuery = {
                     id: this.generateSubQueryId(),
                     text: `Information related to ${query}`,
-                    reason: "Generic exploration to find related content",
+                    reason: AGENT_TOOL_PROMPTS.QUERY_DECOMPOSITION.SUB_QUERY_GENERIC,
                     isAnswered: false
                 };
 
@@ -110,7 +111,7 @@ export class QueryDecompositionTool {
                 subQueries: [{
                     id: this.generateSubQueryId(),
                     text: query,
-                    reason: 'Error in decomposition, treating as simple query',
+                    reason: AGENT_TOOL_PROMPTS.QUERY_DECOMPOSITION.SUB_QUERY_ERROR,
                     isAnswered: false
                 }],
                 status: 'pending',
@@ -290,7 +291,7 @@ export class QueryDecompositionTool {
             return [{
                 id: this.generateSubQueryId(),
                 text: query,
-                reason: 'Direct analysis of note details',
+                reason: AGENT_TOOL_PROMPTS.QUERY_DECOMPOSITION.SUB_QUERY_DIRECT_ANALYSIS,
                 isAnswered: false
             }];
         }
@@ -299,7 +300,7 @@ export class QueryDecompositionTool {
         subQueries.push({
             id: this.generateSubQueryId(),
             text: query,
-            reason: 'Original query',
+            reason: AGENT_TOOL_PROMPTS.QUERY_DECOMPOSITION.ORIGINAL_QUERY,
             isAnswered: false
         });
 

src/services/llm/base_ai_service.ts

@@ -1,5 +1,6 @@
 import options from '../options.js';
 import type { AIService, ChatCompletionOptions, ChatResponse, Message } from './ai_interface.js';
+import { DEFAULT_SYSTEM_PROMPT } from './llm_prompt_constants.js';
 
 export abstract class BaseAIService implements AIService {
     protected name: string;
@@ -19,11 +20,7 @@ export abstract class BaseAIService implements AIService {
     }
 
     protected getSystemPrompt(customPrompt?: string): string {
-        // Default system prompt if none is provided
-        return customPrompt ||
-            "You are a helpful assistant embedded in the Trilium Notes application. " +
-            "You can help users with their notes, answer questions, and provide information. " +
-            "Keep your responses concise and helpful. " +
-            "You're currently chatting with the user about their notes.";
+        // Use prompt from constants file if no custom prompt is provided
+        return customPrompt || DEFAULT_SYSTEM_PROMPT;
     }
 }

src/services/llm/context/modules/context_formatter.ts

@@ -1,5 +1,6 @@
 import sanitizeHtml from 'sanitize-html';
 import log from '../../../log.js';
+import { CONTEXT_PROMPTS } from '../../llm_prompt_constants.js';
 
 // Constants for context window sizes, defines in-module to avoid circular dependencies
 const CONTEXT_WINDOW = {
@@ -23,10 +24,8 @@ export class ContextFormatter {
      */
     async buildContextFromNotes(sources: any[], query: string, providerId: string = 'default'): Promise<string> {
         if (!sources || sources.length === 0) {
-            // Return a default context instead of empty string
-            return "I am an AI assistant helping you with your Trilium notes. " +
-                   "I couldn't find any specific notes related to your query, but I'll try to assist you " +
-                   "with general knowledge about Trilium or other topics you're interested in.";
+            // Return a default context from constants instead of empty string
+            return CONTEXT_PROMPTS.NO_NOTES_CONTEXT;
         }
 
         try {
@@ -42,8 +41,8 @@ export class ContextFormatter {
 
             // Start with different headers based on provider
             let context = isAnthropicFormat
-                ? `I'm your AI assistant helping with your Trilium notes database. For your query: "${query}", I found these relevant notes:\n\n`
-                : `I've found some relevant information in your notes that may help answer: "${query}"\n\n`;
+                ? CONTEXT_PROMPTS.CONTEXT_HEADERS.ANTHROPIC(query)
+                : CONTEXT_PROMPTS.CONTEXT_HEADERS.DEFAULT(query);
 
             // Sort sources by similarity if available to prioritize most relevant
             if (sources[0] && sources[0].similarity !== undefined) {
@@ -97,8 +96,8 @@ export class ContextFormatter {
 
             // Add closing to provide instructions to the AI
             const closing = isAnthropicFormat
-                ? "\n\nPlease use this information to answer the user's query. If the notes don't contain enough information, you can use your general knowledge as well."
-                : "\n\nBased on this information from the user's notes, please provide a helpful response.";
+                ? CONTEXT_PROMPTS.CONTEXT_CLOSINGS.ANTHROPIC
+                : CONTEXT_PROMPTS.CONTEXT_CLOSINGS.DEFAULT;
 
             // Check if adding the closing would exceed our limit
             if (totalSize + closing.length <= maxTotalLength) {
@@ -108,7 +107,7 @@ export class ContextFormatter {
             return context;
         } catch (error) {
             log.error(`Error building context from notes: ${error}`);
-            return "I'm your AI assistant helping with your Trilium notes. I'll try to answer based on what I know.";
+            return CONTEXT_PROMPTS.ERROR_FALLBACK_CONTEXT;
         }
     }
 

src/services/llm/context/modules/context_service.ts

@@ -6,6 +6,7 @@ import queryEnhancer from './query_enhancer.js';
 import contextFormatter from './context_formatter.js';
 import aiServiceManager from '../../ai_service_manager.js';
 import { ContextExtractor } from '../index.js';
+import { CONTEXT_PROMPTS } from '../../llm_prompt_constants.js';
 
 /**
  * Main context service that integrates all context-related functionality
@@ -84,8 +85,7 @@ export class ContextService {
                 log.error(`Failed to initialize ContextService: ${error}`);
                 // Return a fallback response if initialization fails
                 return {
-                    context: "I am an AI assistant helping you with your Trilium notes. " +
-                             "I'll try to assist you with general knowledge about your query.",
+                    context: CONTEXT_PROMPTS.NO_NOTES_CONTEXT,
                     notes: [],
                     queries: [userQuestion]
                 };
@@ -175,8 +175,7 @@ export class ContextService {
         } catch (error) {
             log.error(`Error processing query: ${error}`);
             return {
-                context: "I am an AI assistant helping you with your Trilium notes. " +
-                         "I'll try to assist you with general knowledge about your query.",
+                context: CONTEXT_PROMPTS.NO_NOTES_CONTEXT,
                 notes: [],
                 queries: [userQuestion]
             };

src/services/llm/context/modules/query_enhancer.ts

@@ -1,17 +1,14 @@
 import log from '../../../log.js';
 import cacheManager from './cache_manager.js';
 import type { Message } from '../../ai_interface.js';
+import { CONTEXT_PROMPTS } from '../../llm_prompt_constants.js';
 
 /**
  * Provides utilities for enhancing queries and generating search queries
  */
 export class QueryEnhancer {
-    // Default meta-prompt for query enhancement
-    private metaPrompt = `You are an AI assistant that decides what information needs to be retrieved from a user's knowledge base called TriliumNext Notes to answer the user's question.
-Given the user's question, generate 3-5 specific search queries that would help find relevant information.
-Each query should be focused on a different aspect of the question.
-Format your answer as a JSON array of strings, with each string being a search query.
-Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`;
+    // Use the centralized query enhancer prompt
+    private metaPrompt = CONTEXT_PROMPTS.QUERY_ENHANCER;
 
     /**
      * Generate search queries to find relevant information for the user question

src/services/llm/index_service.ts

@@ -20,6 +20,7 @@ import type { NoteEmbeddingContext } from "./embeddings/embeddings_interface.js"
 import type { OptionDefinitions } from "../options_interface.js";
 import sql from "../sql.js";
 import sqlInit from "../sql_init.js";
+import { CONTEXT_PROMPTS } from './llm_prompt_constants.js';
 
 class IndexService {
     private initialized = false;
@@ -691,7 +692,7 @@ class IndexService {
             );
 
             if (similarNotes.length === 0) {
-                return "I'm an AI assistant helping with your Trilium notes. I couldn't find specific notes related to your query, but I'll try to assist based on general knowledge.";
+                return CONTEXT_PROMPTS.INDEX_NO_NOTES_CONTEXT;
             }
 
             // Build context from the similar notes

src/services/llm/llm_prompt_constants.ts

@@ -0,0 +1,98 @@
+/**
+ * LLM Prompt Constants
+ *
+ * This file centralizes all LLM/AI prompts used throughout the application.
+ * When adding new prompts, please add them here rather than hardcoding them in other files.
+ *
+ * Prompts are organized by their usage context (e.g., service, feature, etc.)
+ */
+
+// Base system prompt used when no custom prompt is provided
+export const DEFAULT_SYSTEM_PROMPT =
+    "You are a helpful assistant embedded in the Trilium Notes application. " +
+    "You can help users with their notes, answer questions, and provide information. " +
+    "Keep your responses concise and helpful. " +
+    "You're currently chatting with the user about their notes.";
+
+// Context-specific prompts
+export const CONTEXT_PROMPTS = {
+    // Query enhancer prompt for generating better search terms
+    QUERY_ENHANCER:
+        `You are an AI assistant that decides what information needs to be retrieved from a user's knowledge base called TriliumNext Notes to answer the user's question.
+Given the user's question, generate 3-5 specific search queries that would help find relevant information.
+Each query should be focused on a different aspect of the question.
+Format your answer as a JSON array of strings, with each string being a search query.
+Example: ["exact topic mentioned", "related concept 1", "related concept 2"]`,
+
+    // Used to format notes context when providing responses
+    CONTEXT_NOTES_WRAPPER:
+        `I'll provide you with relevant information from my notes to help answer your question.
+
+{noteContexts}
+
+When referring to information from these notes in your response, please cite them by their titles (e.g., "According to your note on [Title]...") rather than using labels like "Note 1" or "Note 2".
+
+Now, based on the above information, please answer: {query}`,
+
+    // Default fallback when no notes are found
+    NO_NOTES_CONTEXT:
+        "I am an AI assistant helping you with your Trilium notes. " +
+        "I couldn't find any specific notes related to your query, but I'll try to assist you " +
+        "with general knowledge about Trilium or other topics you're interested in.",
+
+    // Fallback when context building fails
+    ERROR_FALLBACK_CONTEXT:
+        "I'm your AI assistant helping with your Trilium notes. I'll try to answer based on what I know.",
+
+    // Headers for context (by provider)
+    CONTEXT_HEADERS: {
+        ANTHROPIC: (query: string) =>
+            `I'm your AI assistant helping with your Trilium notes database. For your query: "${query}", I found these relevant notes:\n\n`,
+        DEFAULT: (query: string) =>
+            `I've found some relevant information in your notes that may help answer: "${query}"\n\n`
+    },
+
+    // Closings for context (by provider)
+    CONTEXT_CLOSINGS: {
+        ANTHROPIC:
+            "\n\nPlease use this information to answer the user's query. If the notes don't contain enough information, you can use your general knowledge as well.",
+        DEFAULT:
+            "\n\nBased on this information from the user's notes, please provide a helpful response."
+    },
+
+    // Context for index service
+    INDEX_NO_NOTES_CONTEXT:
+        "I'm an AI assistant helping with your Trilium notes. I couldn't find specific notes related to your query, but I'll try to assist based on general knowledge."
+};
+
+// Agent tool prompts
+export const AGENT_TOOL_PROMPTS = {
+    // Prompts for query decomposition
+    QUERY_DECOMPOSITION: {
+        SUB_QUERY_DIRECT: 'Direct question that can be answered without decomposition',
+        SUB_QUERY_GENERIC: 'Generic exploration to find related content',
+        SUB_QUERY_ERROR: 'Error in decomposition, treating as simple query',
+        SUB_QUERY_DIRECT_ANALYSIS: 'Direct analysis of note details',
+        ORIGINAL_QUERY: 'Original query'
+    },
+
+    // Prompts for contextual thinking tool
+    CONTEXTUAL_THINKING: {
+        STARTING_ANALYSIS: (query: string) => `Starting analysis of the query: "${query}"`,
+        KEY_COMPONENTS: 'What are the key components of this query that need to be addressed?',
+        BREAKING_DOWN: 'Breaking down the query to understand its requirements and context.'
+    }
+};
+
+// Provider-specific prompt modifiers
+export const PROVIDER_PROMPTS = {
+    ANTHROPIC: {
+        // Any Anthropic Claude-specific prompt modifications would go here
+    },
+    OPENAI: {
+        // Any OpenAI-specific prompt modifications would go here
+    },
+    OLLAMA: {
+        // Any Ollama-specific prompt modifications would go here
+    }
+};