# Future Improvements for TDZ C64 Knowledge Base
**Status:** ✅ ALL ORIGINAL IMPROVEMENTS COMPLETED!
**Last Updated:** December 18, 2025
**Current Version:** v2.14.0
**Current State:** Production-ready with all Priority 1-6 features fully implemented + URL scraping.
---
## 🎉 Completion Summary
**ALL SUGGESTED IMPROVEMENTS FROM THIS DOCUMENT HAVE BEEN IMPLEMENTED!**
This document represents the original improvement roadmap which has been fully completed across versions v2.0.0 through v2.11.0.
**For next-generation features, see:** [FUTURE_IMPROVEMENTS_2025.md](FUTURE_IMPROVEMENTS_2025.md)
---
## ✅ Implementation Status Overview
| Priority | Category | Status | Version |
|----------|----------|--------|---------|
| Priority 1 | High-Impact Quick Wins | ✅ Complete | v2.0.0 - v2.4.0 |
| Priority 2 | Search & Discovery | ✅ Complete | v2.2.0 - v2.4.0 |
| Priority 3 | Performance & Scalability | ✅ Complete | v2.3.0 |
| Priority 4 | Content Enhancements | ✅ Complete | v2.1.0 |
| Priority 5 | User Experience | ✅ Complete | v2.4.0 - v2.11.0 |
| Priority 6 | Operational Excellence | ✅ Complete | v2.0.0 - v2.5.0 |
---
## 🎯 Priority 1: High-Impact, Quick Wins
### 1.1 Hybrid Search (Combine FTS5 + Semantic)
**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** ⭐⭐ | **Time:** 2-4 hours
**Current State:** FTS5 and semantic work independently
**Proposed:** Combine results for better precision + recall
```python
def hybrid_search(self, query: str, max_results: int = 10,
semantic_weight: float = 0.3, tags=None):
"""
Combine FTS5 (keyword) and semantic (meaning) search.
Args:
semantic_weight: 0.0 = pure keyword, 1.0 = pure semantic, 0.3 = balanced
"""
# Get both result sets (2x max to allow for merging)
fts_results = self.search(query, max_results=max_results*2, tags=tags)
sem_results = self.semantic_search(query, max_results=max_results*2, tags=tags)
# Normalize scores to 0-1 range
fts_normalized = self._normalize_scores(fts_results)
sem_normalized = self._normalize_scores(sem_results)
# Merge with weighted scores
merged = {}
for result in fts_normalized:
doc_id = result['doc_id']
merged[doc_id] = {
'fts_score': result['score'],
'sem_score': 0.0,
**result
}
for result in sem_normalized:
doc_id = result['doc_id']
if doc_id in merged:
merged[doc_id]['sem_score'] = result['score']
else:
merged[doc_id] = {
'fts_score': 0.0,
'sem_score': result['score'],
**result
}
# Calculate hybrid score
for doc_id, data in merged.items():
data['hybrid_score'] = (
(1 - semantic_weight) * data['fts_score'] +
semantic_weight * data['sem_score']
)
# Sort and return top N
sorted_results = sorted(merged.values(),
key=lambda x: x['hybrid_score'],
reverse=True)
return sorted_results[:max_results]
```
**Benefits:**
- Best of both worlds: exact matching + conceptual understanding
- Configurable balance (tune for your use case)
- Better handles typos, synonyms, and technical terms simultaneously
- Example: "6502 assembler" finds both exact matches AND related content about machine code, opcodes, etc.
**MCP Tool:**
```python
Tool(
name="hybrid_search",
description="Search using combined keyword + semantic understanding",
inputSchema={
"properties": {
"query": {"type": "string"},
"max_results": {"type": "integer", "default": 10},
"semantic_weight": {"type": "number", "default": 0.3, "minimum": 0, "maximum": 1},
"tags": {"type": "array"}
}
}
)
```
---
### 1.2 Query Autocompletion ✅ **COMPLETED in v2.4.0**
**Impact:** ⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 4-6 hours
**Implemented:** Query autocomplete with FTS5-powered suggestions
```sql
-- New table for autocomplete dictionary
CREATE VIRTUAL TABLE query_suggestions USING fts5(
term,
frequency, -- How often it appears
category -- hardware, instruction, register, etc.
);
```
```python
def build_suggestion_dictionary(self):
"""Extract common terms/phrases for autocomplete."""
# Extract important terms from all chunks
terms = defaultdict(int)
for chunk in self._get_chunks_db():
# Extract technical terms (ALL CAPS, $-prefixed, hyphenated)
tech_terms = re.findall(r'\b[A-Z]{2,}(?:-[A-Z]+)?\b', chunk.content) # VIC-II, SID
registers = re.findall(r'\$[0-9A-F]{4}', chunk.content) # $D000
instructions = re.findall(r'\b(?:LDA|STA|JMP|JSR|RTS|...)\b', chunk.content)
for term in tech_terms + registers + instructions:
terms[term] += 1
# Store top N terms in suggestions table
for term, freq in sorted(terms.items(), key=lambda x: x[1], reverse=True)[:1000]:
# categorize and store
def get_query_suggestions(self, partial: str, max_suggestions: int = 5):
"""Get autocomplete suggestions for partial query."""
cursor = self.db_conn.cursor()
cursor.execute("""
SELECT term, frequency, category
FROM query_suggestions
WHERE term MATCH ?
ORDER BY rank, frequency DESC
LIMIT ?
""", (f"{partial}*", max_suggestions))
return [{'term': row[0], 'category': row[2]} for row in cursor.fetchall()]
```
**Example:**
- User types: "VIC"
- Suggestions: ["VIC-II sprites", "VIC-II registers", "VIC-II programming", "VIC chip", "VIC-II colors"]
**Benefits:**
- Discover searchable content
- Reduce typos
- Learn proper terminology (VIC-II vs VIC II vs VIC2)
**MCP Tool:**
```python
Tool(name="suggest_queries", ...)
```
---
### 1.3 Enhanced Result Snippets
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐ | **Time:** 2-3 hours
**Current:** Basic text extraction with keyword highlighting
**Proposed:** Context-aware snippets
```python
def _extract_smart_snippet(self, chunk_text: str, query: str,
context_window: int = 200) -> str:
"""Extract optimal snippet with better context."""
query_terms = set(query.lower().split())
# Find best position (highest term density)
best_score = 0
best_start = 0
words = chunk_text.split()
for i in range(len(words) - context_window):
window = ' '.join(words[i:i+context_window]).lower()
score = sum(window.count(term) for term in query_terms)
if score > best_score:
best_score = score
best_start = i
# Extract full sentences around best position
snippet = ' '.join(words[max(0, best_start-20):best_start+context_window+20])
# Special handling for code blocks
if ' ' in snippet or snippet.count('\n') > 3: # Likely code
# Preserve formatting
lines = snippet.split('\n')
# Find lines with matches
matched_lines = [l for l in lines if any(term in l.lower() for term in query_terms)]
snippet = '\n'.join(matched_lines[:5]) # Up to 5 lines of code
# Add ellipsis
if best_start > 20:
snippet = '...' + snippet
if best_start + context_window + 20 < len(words):
snippet = snippet + '...'
return snippet
```
**Benefits:**
- Better context (complete sentences)
- Code blocks stay formatted
- Multiple snippets if term appears often
- Related terms highlighted (not just exact matches)
---
## Priority 2: Search & Discovery Enhancements
### 2.1 Faceted Search & Filtering ✅ **COMPLETED in v2.2.0**
**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Proposed:** Multi-dimensional filtering
```python
def faceted_search(self, query: str, facets: dict, max_results: int = 10):
"""
Search with multiple filters.
facets = {
"tags": ["assembly", "reference"],
"file_type": ["pdf"],
"page_range": {"min": 100, "max": 500},
"hardware": ["SID", "VIC-II"], # extracted from content
"year": [1983, 1984] # from metadata
}
"""
# Build WHERE clause from facets
# ...
# Extract facet values during indexing
def _extract_facets(self, text: str) -> dict:
"""Extract categorizable terms for faceting."""
return {
'hardware': self._extract_hardware_refs(text),
'instructions': self._extract_6502_instructions(text),
'registers': self._extract_registers(text),
}
def _extract_hardware_refs(self, text: str) -> list[str]:
"""Find hardware component mentions."""
hardware = []
patterns = {
'SID': r'\b(?:SID|6581|Sound\s+Interface\s+Device)\b',
'VIC-II': r'\b(?:VIC-II|VIC\s*2|6569|6567)\b',
'CIA': r'\b(?:CIA|6526|Complex\s+Interface\s+Adapter)\b',
'6502': r'\b6502\b',
}
for component, pattern in patterns.items():
if re.search(pattern, text, re.IGNORECASE):
hardware.append(component)
return hardware
```
**New Schema:**
```sql
CREATE TABLE document_facets (
doc_id TEXT,
facet_type TEXT, -- 'hardware', 'instruction', 'register'
facet_value TEXT,
PRIMARY KEY (doc_id, facet_type, facet_value)
);
```
**MCP Tool:**
```python
Tool(
name="faceted_search",
description="Search with multiple filters (tags, hardware, year, etc.)",
...
)
```
**Benefits:**
- Narrow large result sets quickly
- Explore by category ("Show me all SID programming documents")
- Power-user feature
---
### 2.2 Search History & Analytics ✅ **COMPLETED in v2.2.0**
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 4-6 hours
**Proposed:** Learn from usage patterns
```sql
CREATE TABLE search_log (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT DEFAULT CURRENT_TIMESTAMP,
query TEXT,
search_mode TEXT, -- fts5, semantic, hybrid
results_count INTEGER,
clicked_doc_id TEXT, -- which result was used
user_agent TEXT -- MCP client identifier
);
CREATE INDEX idx_search_log_query ON search_log(query);
CREATE INDEX idx_search_log_timestamp ON search_log(timestamp);
```
```python
def log_search(self, query: str, mode: str, results: list, clicked_doc: str = None):
"""Log search for analytics."""
cursor = self.db_conn.cursor()
cursor.execute("""
INSERT INTO search_log (query, search_mode, results_count, clicked_doc_id)
VALUES (?, ?, ?, ?)
""", (query, mode, len(results), clicked_doc))
self.db_conn.commit()
def get_search_analytics(self, days: int = 7) -> dict:
"""Analyze recent search patterns."""
cursor = self.db_conn.cursor()
# Most common queries
cursor.execute("""
SELECT query, COUNT(*) as count
FROM search_log
WHERE timestamp > datetime('now', '-' || ? || ' days')
GROUP BY query
ORDER BY count DESC
LIMIT 10
""", (days,))
popular_queries = cursor.fetchall()
# Queries with no results
cursor.execute("""
SELECT DISTINCT query
FROM search_log
WHERE results_count = 0
AND timestamp > datetime('now', '-' || ? || ' days')
LIMIT 10
""", (days,))
no_results = cursor.fetchall()
# Average results per mode
cursor.execute("""
SELECT search_mode, AVG(results_count) as avg_results
FROM search_log
WHERE timestamp > datetime('now', '-' || ? || ' days')
GROUP BY search_mode
""", (days,))
mode_performance = cursor.fetchall()
return {
'popular_queries': popular_queries,
'no_results_queries': no_results,
'search_mode_stats': mode_performance
}
```
**MCP Tool:**
```python
Tool(name="search_analytics", description="View search usage patterns and trends", ...)
```
**Benefits:**
- Identify knowledge gaps (queries with no results)
- Popular topics
- Improve autocomplete suggestions
- Optimize search modes
**Privacy:** All data stays local, never transmitted.
---
## Priority 3: Performance & Scalability
### 3.1 Incremental Embeddings ✅ COMPLETED in v2.3.0
**Impact:** ⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 4-6 hours
**Current:** Full rebuild when adding documents
**Proposed:** Incremental updates
```python
def add_document(self, filepath: str, ...):
doc = # ... normal add ...
# Generate embeddings for new chunks only
if self.use_semantic and self.embeddings_index is not None:
new_chunks = self._get_chunks_db(doc.doc_id)
self._add_chunks_to_embeddings(new_chunks)
return doc
def _add_chunks_to_embeddings(self, chunks: list[DocumentChunk]):
"""Add new chunks to existing FAISS index."""
if not chunks:
return
# Generate embeddings for new chunks
texts = [c.content for c in chunks]
new_embeddings = self.embeddings_model.encode(texts, convert_to_numpy=True)
faiss.normalize_L2(new_embeddings)
# Add to existing index
self.embeddings_index.add(new_embeddings)
# Update mapping
for chunk in chunks:
self.embeddings_doc_map.append((chunk.doc_id, chunk.chunk_id))
# Save updated index
self._save_embeddings()
```
**Benefits:**
- Fast document addition (seconds instead of minutes)
- Scales to 10k+ documents
- No interruption to search
---
### 3.2 Parallel Document Processing ✅ COMPLETED in v2.3.0
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 4-6 hours
**Proposed:** Process multiple PDFs simultaneously
```python
from concurrent.futures import ThreadPoolExecutor, as_completed
def add_documents_bulk(self, directory: str, max_workers: int = 4, **kwargs):
"""Process multiple documents in parallel."""
files = list(Path(directory).glob(kwargs.get('pattern', '**/*.{pdf,txt}')))
results = {'added': [], 'skipped': [], 'failed': []}
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit all tasks
future_to_file = {
executor.submit(self._add_document_safe, str(f), **kwargs): f
for f in files
}
# Collect results as they complete
for future in as_completed(future_to_file):
file_path = future_to_file[future]
try:
doc = future.result()
results['added'].append(doc)
except Exception as e:
results['failed'].append({'file': str(file_path), 'error': str(e)})
return results
def _add_document_safe(self, filepath: str, **kwargs):
"""Thread-safe wrapper for add_document."""
# Use thread-local DB connection
# Or use lock for shared connection
with self._db_lock:
return self.add_document(filepath, **kwargs)
```
**Configuration:**
```bash
# Control parallelism
export MAX_WORKERS=4 # CPU count
```
**Benefits:**
- 3-4x faster bulk imports
- Better CPU utilization
- Especially helpful for large collections
---
## Priority 4: Content Enhancements
### 4.1 Table Extraction from PDFs
**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐⭐⭐ | **Time:** 12-16 hours
**Critical for C64 docs:** Memory maps, register tables, opcode tables
```python
import pdfplumber
def _extract_tables(self, pdf_path: str) -> list[dict]:
"""Extract structured tables from PDF."""
tables = []
with pdfplumber.open(pdf_path) as pdf:
for page_num, page in enumerate(pdf.pages):
page_tables = page.extract_tables()
for table in page_tables:
if not table or len(table) < 2:
continue
# Convert to structured format
headers = table[0]
rows = table[1:]
tables.append({
'page': page_num + 1,
'headers': headers,
'rows': rows,
'markdown': self._table_to_markdown(headers, rows),
'text': self._table_to_text(headers, rows)
})
return tables
def _table_to_markdown(self, headers: list, rows: list) -> str:
"""Convert table to markdown format."""
md = '| ' + ' | '.join(headers) + ' |\n'
md += '| ' + ' | '.join(['---'] * len(headers)) + ' |\n'
for row in rows:
md += '| ' + ' | '.join(str(cell or '') for cell in row) + ' |\n'
return md
```
**Storage:**
```sql
CREATE TABLE document_tables (
doc_id TEXT,
table_id INTEGER,
page INTEGER,
markdown TEXT,
searchable_text TEXT,
PRIMARY KEY (doc_id, table_id)
);
CREATE VIRTUAL TABLE tables_fts USING fts5(
doc_id UNINDEXED,
table_id UNINDEXED,
searchable_text
);
```
**Search Enhancement:**
```python
def search_tables(self, query: str, max_results: int = 5):
"""Search specifically in tables."""
# Use tables_fts for targeted table search
# Return formatted markdown tables in results
```
**Benefits:**
- ✅ Crucial for C64 memory maps ($D000-$DFFF register tables)
- ✅ Opcode tables
- ✅ Better formatting in results
- ✅ Searchable table content
---
### 4.2 Code Block Detection
**Impact:** ⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Proposed:** Identify and preserve code
```python
def _detect_code_blocks(self, text: str) -> list[dict]:
"""Detect assembly code, BASIC, hex dumps."""
code_blocks = []
# Pattern 1: BASIC (line numbers)
basic_pattern = r'^\d{1,5}\s+[A-Z]+.*$'
# Pattern 2: Assembly (mnemonics + addressing modes)
asm_pattern = r'^\s*(?:LDA|STA|JMP|JSR|...)\s+[#$%]?[\w,()]+\s*;?.*$'
# Pattern 3: Hex dumps
hex_pattern = r'^[\dA-F]{4}:\s+(?:[\dA-F]{2}\s+)+.*$'
lines = text.split('\n')
current_block = []
block_type = None
for line in lines:
if re.match(basic_pattern, line, re.IGNORECASE):
if block_type != 'basic':
if current_block:
code_blocks.append({'type': block_type, 'code': '\n'.join(current_block)})
current_block = []
block_type = 'basic'
current_block.append(line)
elif re.match(asm_pattern, line, re.IGNORECASE):
if block_type != 'assembly':
if current_block:
code_blocks.append({'type': block_type, 'code': '\n'.join(current_block)})
current_block = []
block_type = 'assembly'
current_block.append(line)
# ... similar for hex dumps ...
return code_blocks
def _format_code_snippet(self, code: str, lang: str) -> str:
"""Format code for display with syntax highlighting hints."""
return f"```{lang}\n{code}\n```"
```
**Benefits:**
- Preserve formatting in search results
- "Find assembly examples for LDA instruction"
- Better snippet quality
- Syntax highlighting in supported clients
---
### 4.3 Cross-Reference Detection ✅ COMPLETED in v2.3.0
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 8-10 hours
**Proposed:** Link related content
```python
def _extract_references(self, text: str) -> dict:
"""Extract cross-references: memory addresses, register names, page refs."""
refs = {
'memory_addresses': set(),
'registers': set(),
'page_numbers': set(),
'chips': set()
}
# Memory addresses: $D000-$D3FF
refs['memory_addresses'] = set(re.findall(r'\$[0-9A-F]{4}', text))
# Register references: VIC+0, SID+4
refs['registers'] = set(re.findall(r'(?:VIC|SID|CIA)\+\d+', text, re.IGNORECASE))
# Page references: "see page 156"
refs['page_numbers'] = set(re.findall(r'page\s+(\d+)', text, re.IGNORECASE))
# Chip mentions
chips = ['6502', '6581', 'SID', '6569', '6567', 'VIC-II', '6526', 'CIA']
for chip in chips:
if chip.lower() in text.lower():
refs['chips'].add(chip)
return refs
```
**Schema:**
```sql
CREATE TABLE cross_references (
doc_id TEXT,
ref_type TEXT, -- 'memory', 'register', 'page', 'chip'
ref_value TEXT,
chunk_id INTEGER,
PRIMARY KEY (doc_id, ref_type, ref_value, chunk_id)
);
CREATE INDEX idx_xref_value ON cross_references(ref_type, ref_value);
```
**MCP Tool:**
```python
Tool(
name="find_by_reference",
description="Find all documents mentioning a specific address/register/chip",
inputSchema={
"properties": {
"ref_type": {"enum": ["memory", "register", "chip"]},
"ref_value": {"type": "string"} # e.g., "$D000" or "SID"
}
}
)
```
**Benefits:**
- "Show all docs about $D000"
- Build knowledge graph
- Navigate by hardware component
---
## Priority 5: User Experience
### 5.1 Export Results ✅ **COMPLETED in v2.4.0**
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Implemented:** Export search results to Markdown, JSON, and HTML formats
```python
def export_search_results(self, results: list, format: str = 'markdown') -> str:
"""Export results to various formats."""
if format == 'markdown':
output = f"# Search Results\n\n"
output += f"**Query:** {results['query']}\n"
output += f"**Results:** {len(results['items'])}\n\n"
for i, result in enumerate(results['items'], 1):
output += f"## {i}. {result['title']}\n\n"
output += f"**Score:** {result['score']:.2f}\n\n"
output += f"{result['snippet']}\n\n"
output += f"**Source:** {result['filename']} (page {result['page']})\n\n"
output += "---\n\n"
return output
elif format == 'json':
return json.dumps(results, indent=2)
elif format == 'html':
# HTML template with styling
...
```
**MCP Tool:**
```python
Tool(name="export_results", ...)
```
**Use Cases:**
- Create custom reference guides
- Share findings
- Print for offline use
---
## Priority 6: Operational Excellence
### 6.1 Health Monitoring
**Impact:** ⭐⭐⭐ | **Effort:** ⭐⭐ | **Time:** 3-4 hours
```python
def health_check(self) -> dict:
"""Comprehensive health status."""
health = {
'status': 'healthy',
'timestamp': datetime.now().isoformat(),
'database': {'status': 'unknown'},
'indexes': {},
'disk': {},
'performance': {}
}
try:
# Database connectivity
cursor = self.db_conn.cursor()
cursor.execute("SELECT COUNT(*) FROM documents")
doc_count = cursor.fetchone()[0]
health['database'] = {
'status': 'healthy',
'documents': doc_count
}
# FTS5 index status
if self.use_fts5:
cursor.execute("SELECT COUNT(*) FROM chunks_fts")
fts_count = cursor.fetchone()[0]
health['indexes']['fts5'] = {
'status': 'healthy' if fts_count > 0 else 'empty',
'entries': fts_count
}
# Embeddings status
if self.use_semantic:
health['indexes']['embeddings'] = {
'status': 'loaded' if self.embeddings_index else 'not_loaded',
'vectors': self.embeddings_index.ntotal if self.embeddings_index else 0
}
# Disk usage
data_dir_size = sum(f.stat().st_size for f in Path(self.data_dir).rglob('*'))
health['disk'] = {
'data_dir_size_mb': data_dir_size / (1024 * 1024),
'db_size_mb': os.path.getsize(self.db_path) / (1024 * 1024)
}
except Exception as e:
health['status'] = 'unhealthy'
health['error'] = str(e)
return health
```
**MCP Tool:**
```python
Tool(name="health_check", description="Get system health status", ...)
```
---
### 6.2 Automated Backup ✅ **COMPLETED in v2.5.0**
**Impact:** ⭐⭐⭐⭐ | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Implemented:** Full backup and restore functionality with compression, verification, and GUI interface
```python
import shutil
from datetime import datetime
def create_backup(self, dest_dir: str, compress: bool = True) -> str:
"""Create full backup of knowledge base."""
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
backup_name = f"kb_backup_{timestamp}"
backup_path = Path(dest_dir) / backup_name
backup_path.mkdir(parents=True, exist_ok=True)
# Backup database
shutil.copy2(self.db_path, backup_path / "knowledge_base.db")
# Backup embeddings if they exist
if self.embeddings_index:
self._save_embeddings_to(backup_path)
# Export metadata
metadata = {
'timestamp': timestamp,
'document_count': len(self.documents),
'version': self._get_version()
}
with open(backup_path / "metadata.json", 'w') as f:
json.dump(metadata, f, indent=2)
if compress:
# Create zip file
shutil.make_archive(str(backup_path), 'zip', backup_path)
shutil.rmtree(backup_path) # Remove uncompressed
return f"{backup_path}.zip"
return str(backup_path)
def restore_from_backup(self, backup_path: str):
"""Restore knowledge base from backup."""
# Extract if compressed
# Validate backup integrity
# Restore database
# Restore embeddings
# Reload index
```
---
## ✅ Summary: All Items Completed
### ✅ Immediate (1-2 weeks) - COMPLETED
1. ✅ **Hybrid Search** (v2.0.0) - Best ROI, easy to implement
2. ✅ **Enhanced Snippets** (v2.0.0) - Better UX
3. ✅ **Health Monitoring** (v2.0.0) - Operational necessity
### ✅ Short Term (1 month) - COMPLETED
4. ✅ **Query Autocomplete** (v2.4.0) - Great UX improvement
5. ✅ **Faceted Search** (v2.2.0) - Essential for growing collection
6. ✅ **Table Extraction** (v2.1.0) - Critical for C64 memory maps
### ✅ Medium Term (2-3 months) - COMPLETED
7. ✅ **Search Analytics** (v2.2.0) - Learn usage patterns
8. ✅ **Code Block Detection** (v2.1.0) - Better for technical docs
9. ✅ **Incremental Embeddings** (v2.3.0) - Scalability
10. ✅ **Export Results** (v2.4.0) - Sharing capability
### ✅ Long Term (3-6 months) - COMPLETED
11. ✅ **Cross-References** (v2.3.0) - Build knowledge graph
12. ✅ **Parallel Processing** (v2.3.0) - Performance at scale
13. ✅ **Automated Backups** (v2.5.0) - Data safety
### ✅ Additional Completed Features
14. ✅ **Bulk Operations** (v2.6.0) - Tag management, bulk delete/export
15. ✅ **Document Relationships** (v2.7.0) - Link related documents
16. ✅ **Relationship Graph Visualization** (v2.8.0) - Interactive network graphs
17. ✅ **Excel File Support** (v2.9.0) - .xlsx/.xls ingestion
18. ✅ **HTML File Support** (v2.10.0) - .html/.htm ingestion
19. ✅ **GUI Improvements** (v2.11.0) - File path input, duplicate detection, enhanced viewer
20. ✅ **Version Management** (v2.11.0) - Centralized versioning system
21. ✅ **URL Scraping & Web Ingestion** (v2.14.0) - Scrape documentation websites with mdscrape
---
## 🎊 Achievement Unlocked: 100% Completion
**The knowledge base has achieved ALL original improvement goals!**
Current capabilities:
1. ✅ **Better Search Quality** - Hybrid, autocomplete, facets, semantic, FTS5
2. ✅ **Richer Content** - Tables, code, cross-refs, HTML, Excel
3. ✅ **Scalability** - Incremental updates, parallelization, lazy loading
4. ✅ **Operations** - Monitoring, backups, analytics, health checks
5. ✅ **User Experience** - Full GUI, file viewer, progress indicators
6. ✅ **Data Management** - Relationships, bulk operations, version tracking
---
## 🚀 What's Next?
**This roadmap is complete.** For next-generation features, see:
### [FUTURE_IMPROVEMENTS_2025.md](FUTURE_IMPROVEMENTS_2025.md)
**Featuring:**
- 🤖 RAG-Based Question Answering (AI assistant)
- 🏷️ Smart Auto-Tagging (LLM-powered)
- 🔍 Fuzzy Search / Typo Tolerance
- 📊 Document Version Tracking
- 🎮 VICE Emulator Integration (C64-specific!)
- 📦 PRG File Analysis
- 🎵 SID Music Metadata
- 🌐 REST API Server
- 🔌 Plugin System
**Recommended Start for 2025:** RAG Question Answering - Transform from search tool to intelligent assistant!
---
## 📈 Version History
- **v2.14.0** (Dec 2025) - URL scraping & web ingestion with mdscrape integration
- **v2.11.0** (Dec 2025) - GUI improvements, version management
- **v2.10.0** (Dec 2025) - HTML file support
- **v2.9.0** (Dec 2025) - Excel file support
- **v2.8.0** (Dec 2025) - CI/CD, relationship visualization
- **v2.7.0** (Dec 2025) - Document relationships, tag management
- **v2.6.0** (Dec 2025) - Bulk operations
- **v2.5.0** (Dec 2025) - Backup/restore, GUI admin
- **v2.4.0** (Dec 2025) - Query autocomplete, export
- **v2.3.0** (Dec 2025) - Incremental embeddings, parallel processing, cross-references
- **v2.2.0** (Dec 2025) - Faceted search, analytics
- **v2.1.0** (Dec 2024) - Table extraction, code detection
- **v2.0.0** (Dec 2024) - Hybrid search, enhanced snippets, health monitoring
---
**Status:** 🎉 MISSION ACCOMPLISHED! Ready for next-generation features!
