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get rid of this unused file too

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perf3ct 2025-03-19 19:44:04 +00:00
parent d5efcfe0a9
commit 23480960c0
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2 changed files with 36 additions and 239 deletions
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50
src/services/llm/context/index.ts
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@ -10,7 +10,33 @@ import { chunkContent, semanticChunking } from './content_chunking.js';
import type { ContentChunk, ChunkOptions } from './content_chunking.js';
import { summarizeContent, extractKeyPoints } from './summarization.js';
import { getParentNotes, getParentContext, getChildContext, getLinkedNotesContext } from './hierarchy.js';
import { getSemanticContext } from './semantic_context.js';
/**
* Get semantic context
* This is now a wrapper that redirects to the new context service
* @param noteId - The ID of the note to get context for
* @param options - Options for semantic context
* @returns Semantic context string
*/
async function getSemanticContext(
noteId: string,
options: { maxSimilarNotes?: number } = {}
): Promise<string> {
// Use the context service
try {
const { default: aiServiceManager } = await import('../ai_service_manager.js');
const contextService = aiServiceManager.getInstance().getContextService();
if (!contextService) {
return "Semantic context service not available.";
}
return await contextService.getSemanticContext(noteId, "", options.maxSimilarNotes || 5);
} catch (error) {
console.error("Error getting semantic context:", error);
return "Error retrieving semantic context.";
}
}
/**
* Options for context extraction
@ -332,20 +358,16 @@ export class ContextExtractor {
/**
* Get semantic context
* This is now a wrapper that redirects to the new context service
* @param noteId - The ID of the note to get context for
* @param options - Options for semantic context
* @returns Semantic context string
*/
static async getSemanticContext(
noteId: string,
maxSimilarNotesOrQuery: number | string = 5
options: { maxSimilarNotes?: number } = {}
): Promise<string> {
// Handle both the new (number) and old (string query) parameter types
if (typeof maxSimilarNotesOrQuery === 'string') {
// Old API: The second parameter was a query string
// For backward compatibility, we'll still accept this
return getSemanticContext(noteId, { maxSimilarNotes: 5 });
} else {
// New API: The second parameter is maxSimilarNotes
return getSemanticContext(noteId, { maxSimilarNotes: maxSimilarNotesOrQuery });
}
return getSemanticContext(noteId, options);
}
/**
@ -353,9 +375,9 @@ export class ContextExtractor {
*/
async getSemanticContext(
noteId: string,
maxSimilarNotesOrQuery: number | string = 5
options: { maxSimilarNotes?: number } = {}
): Promise<string> {
return ContextExtractor.getSemanticContext(noteId, maxSimilarNotesOrQuery);
return ContextExtractor.getSemanticContext(noteId, options);
}
/**
@ -436,7 +458,7 @@ export class ContextExtractor {
if (config.includeSimilar) {
const semanticContext = await ContextExtractor.getSemanticContext(
noteId,
config.maxSimilarNotes
{ maxSimilarNotes: config.maxSimilarNotes }
);
if (semanticContext && !semanticContext.includes("No semantically similar notes found.")) {

225
src/services/llm/context/semantic_context.ts
View File

@ -1,225 +0,0 @@
/**
* Contains functions for semantic context extraction
* Uses more intelligent methods to determine relevant context
*/
import { sanitizeHtmlContent } from './note_content.js';
import becca from '../../../becca/becca.js';
import { getNoteContent } from './note_content.js';
/**
* Options for semantic context extraction
*/
export interface SemanticContextOptions {
/**
* Maximum number of similar notes to include
*/
maxSimilarNotes?: number;
/**
* Whether to include note content snippets
*/
includeContent?: boolean;
/**
* Maximum length of content snippets
*/
snippetLength?: number;
/**
* Minimum similarity score (0-1) to include a note
*/
minSimilarity?: number;
}
/**
* Default options for semantic context extraction
*/
const DEFAULT_SEMANTIC_CONTEXT_OPTIONS: Required<SemanticContextOptions> = {
maxSimilarNotes: 5,
includeContent: true,
snippetLength: 200,
minSimilarity: 0.7
};
/**
* Retrieve semantically similar notes to provide context
* This is a simplified version without vector store integration
* Use vector_store for actual semantic search
*/
export async function getSemanticContext(
noteId: string,
options: SemanticContextOptions = {}
): Promise<string> {
// Merge provided options with defaults
const config: Required<SemanticContextOptions> = {
...DEFAULT_SEMANTIC_CONTEXT_OPTIONS,
...options
};
try {
// Get the current note
const note = becca.getNote(noteId);
if (!note) {
return "Note not found.";
}
// Get note content for comparison
const noteContent = await getNoteContent(noteId);
if (!noteContent) {
return "No content available for similarity comparison.";
}
// Get potential related notes (simplified method)
// In real implementation, this would use vector_store.similarity methods
const relatedNotes = await findRelatedNotes(noteId, noteContent, config);
// Format the semantic context result
let context = `Semantically related notes to "${note.title}":\n\n`;
if (relatedNotes.length === 0) {
context += "No semantically similar notes found.";
return context;
}
// Add each related note to the context
for (const relatedNote of relatedNotes) {
context += `## ${relatedNote.title}\n`;
if (config.includeContent && relatedNote.snippet) {
context += `${relatedNote.snippet}\n\n`;
}
}
return context;
} catch (error) {
console.error(`Error getting semantic context for ${noteId}:`, error);
return "Error retrieving semantic context.";
}
}
/**
* Find related notes based on simple heuristics
* This is a placeholder for semantic search that would normally use vector embeddings
*/
async function findRelatedNotes(
noteId: string,
noteContent: string,
options: Required<SemanticContextOptions>
): Promise<{ id: string, title: string, snippet: string | null, score: number }[]> {
const results: { id: string, title: string, snippet: string | null, score: number }[] = [];
const note = becca.getNote(noteId);
if (!note) {
return results;
}
// 1. Check siblings (notes with the same parent)
const parentBranches = note.getParentBranches();
const processedNotes = new Set<string>();
processedNotes.add(noteId); // Don't include the current note
// Process parent branches to find siblings
for (const branch of parentBranches) {
if (!branch.parentNote) {
continue;
}
const parentNote = branch.parentNote;
const siblingNotes = parentNote.getChildNotes().filter(n => n.noteId !== noteId);
for (const siblingNote of siblingNotes) {
if (processedNotes.has(siblingNote.noteId)) {
continue;
}
processedNotes.add(siblingNote.noteId);
const siblingContent = await getNoteContent(siblingNote.noteId);
if (!siblingContent) {
continue;
}
// Calculate a very simple similarity score
const score = calculateSimpleTextSimilarity(noteContent, siblingContent);
if (score >= options.minSimilarity) {
results.push({
id: siblingNote.noteId,
title: siblingNote.title,
snippet: siblingContent.substring(0, options.snippetLength) + '...',
score
});
}
}
}
// 2. Check notes connected by relations
const relations = note.getRelations();
for (const relation of relations) {
const targetNoteId = relation.value;
if (!targetNoteId || processedNotes.has(targetNoteId)) {
continue;
}
processedNotes.add(targetNoteId);
const targetNote = becca.getNote(targetNoteId);
if (!targetNote) {
continue;
}
const targetContent = await getNoteContent(targetNoteId);
if (!targetContent) {
continue;
}
// Relations are already semantically connected, so give them a boost
const score = calculateSimpleTextSimilarity(noteContent, targetContent) + 0.2;
results.push({
id: targetNoteId,
title: targetNote.title,
snippet: targetContent.substring(0, options.snippetLength) + '...',
score: Math.min(score, 1.0) // Cap at 1.0
});
}
// Sort by similarity score (highest first) and limit
return results
.sort((a, b) => b.score - a.score)
.slice(0, options.maxSimilarNotes);
}
/**
* Calculate a simple text similarity based on shared words
* This is a very basic implementation and should be replaced with actual embedding similarity
*/
function calculateSimpleTextSimilarity(text1: string, text2: string): number {
// Clean and tokenize the texts
const cleanText1 = sanitizeHtmlContent(text1).toLowerCase();
const cleanText2 = sanitizeHtmlContent(text2).toLowerCase();
// Get unique words (case insensitive)
const words1 = new Set(cleanText1.split(/\W+/).filter(w => w.length > 3));
const words2 = new Set(cleanText2.split(/\W+/).filter(w => w.length > 3));
// No meaningful comparison possible if either text has no significant words
if (words1.size === 0 || words2.size === 0) {
return 0;
}
// Count shared words
let sharedCount = 0;
for (const word of words1) {
if (words2.has(word)) {
sharedCount++;
}
}
// Jaccard similarity: intersection size / union size
return sharedCount / (words1.size + words2.size - sharedCount);
}