# Future Improvements 2025 - Next Generation Features
**Status:** Phase 1 ✅ (100%) | Phase 2 ✅ (100%) | Phase 3 ✅ (100%) | Production Mode 🎯
**Last Updated:** January 3, 2026
**Current Version:** v2.23.15 - RAG Question Answering Complete, Fuzzy Search, Smart Tagging, Phase 2 & 3 Complete
---
## 🚀 Next Generation Roadmap
### Phase 1: AI-Powered Intelligence (Q1 2025)
#### 1.1 RAG-Based Question Answering ⭐⭐⭐⭐⭐ ✅ COMPLETED (v2.23.0)
**Impact:** Game changer | **Effort:** ⭐⭐⭐⭐ | **Time:** 16-24 hours
**Status:** ✅ Complete - Natural language question answering using LLMs
```python
def answer_question(self, question: str, context_chunks: int = 5,
model: str = "claude-3-haiku") -> dict:
"""
Answer questions using RAG (Retrieval Augmented Generation).
Args:
question: Natural language question
context_chunks: Number of relevant chunks to include
model: LLM model to use (claude-3-haiku, gpt-4, etc.)
Returns:
{
'answer': 'The SID chip has 3 voices...',
'confidence': 0.95,
'sources': [list of source documents],
'context_used': [relevant chunks]
}
"""
# 1. Search for relevant context
results = self.hybrid_search(question, max_results=context_chunks)
# 2. Build context from top results
context = "\n\n".join([
f"[Source: {r['title']}, Page {r['page']}]\n{r['content']}"
for r in results
])
# 3. Send to LLM with prompt
prompt = f"""Based on the following documentation about the Commodore 64,
answer this question: {question}
Context:
{context}
Provide a detailed answer citing specific sources."""
# 4. Call LLM API (Claude, OpenAI, etc.)
response = self._call_llm(prompt, model)
return {
'answer': response,
'sources': [r['doc_id'] for r in results],
'confidence': self._calculate_confidence(results)
}
```
**MCP Tool:**
```python
Tool(
name="ask_question",
description="Ask natural language questions about C64 documentation",
inputSchema={
"properties": {
"question": {"type": "string"},
"model": {"type": "string", "default": "claude-3-haiku"}
}
}
)
```
**Example Queries:**
- "How do I program sprites on the VIC-II chip?"
- "What's the difference between CIA1 and CIA2?"
- "How does the SID chip generate sound?"
**Benefits:**
- Natural language interaction
- Synthesizes information across multiple documents
- Citations to source material
- Better than simple keyword search for conceptual questions
**Configuration:**
```bash
export LLM_PROVIDER=anthropic # or openai, local
export LLM_API_KEY=sk-ant-...
export LLM_MODEL=claude-3-haiku-20240307
```
---
#### 1.2 Automatic Document Summarization ✅ COMPLETED
**Status:** ✅ Complete (v2.13.0) | **Impact:** High | **Effort:** ⭐⭐⭐ | **Time:** 8-12 hours
**Status:** Generate concise summaries of documents - IMPLEMENTED
```python
def generate_summary(self, doc_id: str, summary_type: str = 'brief') -> str:
"""
Generate AI-powered summary of document.
Args:
doc_id: Document to summarize
summary_type: 'brief' (1-2 paragraphs), 'detailed' (1 page),
'bullet' (key points)
Returns:
Formatted summary text
"""
# Get full document content
doc = self.get_document(doc_id)
full_text = "\n\n".join([c['content'] for c in doc['chunks']])
# Call LLM for summarization
prompt = self._build_summary_prompt(full_text, summary_type)
summary = self._call_llm(prompt)
# Store summary in database for caching
self._save_summary(doc_id, summary, summary_type)
return summary
def _build_summary_prompt(self, text: str, summary_type: str) -> str:
"""Build appropriate prompt for summary type."""
if summary_type == 'brief':
return f"Summarize this C64 technical document in 1-2 paragraphs:\n\n{text}"
elif summary_type == 'bullet':
return f"Extract the key technical points as bullet points:\n\n{text}"
# ... etc
```
**Storage:**
```sql
CREATE TABLE document_summaries (
doc_id TEXT,
summary_type TEXT,
summary TEXT,
generated_at TEXT,
model TEXT,
PRIMARY KEY (doc_id, summary_type)
);
```
**Benefits:**
- Quick overview of long documents
- Better search result previews
- Document discovery ("show summaries of all SID documents")
---
#### 1.3 Smart Auto-Tagging ⭐⭐⭐⭐
**Impact:** High | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Proposed:** AI-generated tags from content analysis
```python
def auto_tag_document(self, doc_id: str, confidence_threshold: float = 0.7) -> list[str]:
"""
Generate tags automatically using LLM analysis.
Returns:
List of suggested tags with confidence scores
"""
doc = self.get_document(doc_id)
# Use first 3 chunks for analysis (representative sample)
sample_text = "\n\n".join([c['content'] for c in doc['chunks'][:3]])
prompt = f"""Analyze this C64 technical documentation and suggest relevant tags.
Consider: hardware components, programming topics, difficulty level, document type.
Text:
{sample_text}
Return as JSON:
{{"tags": [{{"tag": "sid-programming", "confidence": 0.95}}, ...]}}
"""
response = self._call_llm(prompt)
suggested_tags = json.loads(response)
# Filter by confidence threshold
high_confidence_tags = [
t['tag'] for t in suggested_tags['tags']
if t['confidence'] >= confidence_threshold
]
return high_confidence_tags
def auto_tag_all_documents(self, reindex: bool = False):
"""Bulk auto-tag all documents."""
for doc_id in self.documents.keys():
# Skip if already has tags (unless reindex=True)
if self.documents[doc_id].tags and not reindex:
continue
suggested_tags = self.auto_tag_document(doc_id)
# Add tags (don't replace existing)
current_tags = set(self.documents[doc_id].tags)
new_tags = list(current_tags | set(suggested_tags))
self.update_document_tags(doc_id, new_tags)
```
**Benefits:**
- Consistent tagging across documents
- Discover unexpected connections
- Better organization with minimal manual effort
- Multi-level tags (hardware/sid, programming/assembly, level/beginner)
---
### Phase 2: Advanced Search & Discovery (Q2 2025) ✅ COMPLETE
#### 2.1 Natural Language Query Translation ⭐⭐⭐⭐ ✅ COMPLETED (v2.17.0)
**Impact:** Very High | **Effort:** ⭐⭐⭐ | **Time:** 8-12 hours
**Status:** ✅ Complete - Convert natural language to optimized search queries
```python
def natural_language_search(self, nl_query: str, max_results: int = 10) -> list:
"""
Translate natural language to optimized search.
Examples:
"Show me everything about how sprites work"
→ faceted_search("sprite", facets={'hardware': ['VIC-II']})
"I need assembly code examples for the SID chip"
→ search_code("SID", block_type="assembly")
"What memory addresses control screen colors?"
→ hybrid_search("screen color control", ...) + filter by memory refs
"""
# 1. Analyze query intent
analysis = self._analyze_query_intent(nl_query)
# 2. Route to appropriate search method
if analysis['intent'] == 'code_examples':
return self.search_code(
analysis['extracted_query'],
block_type=analysis.get('code_type')
)
elif analysis['intent'] == 'hardware_reference':
return self.faceted_search(
analysis['extracted_query'],
facet_filters={'hardware': analysis['hardware_components']}
)
# ... more routing logic
# 3. Fallback to hybrid search
return self.hybrid_search(analysis['extracted_query'], max_results)
def _analyze_query_intent(self, query: str) -> dict:
"""Use LLM to understand query intent."""
prompt = f"""Analyze this search query and extract:
1. Intent: code_examples, hardware_reference, tutorial, troubleshooting, etc.
2. Main search terms
3. Hardware components mentioned (SID, VIC-II, CIA, etc.)
4. Code type if relevant (BASIC, Assembly, Hex)
Query: {query}
Return as JSON."""
return json.loads(self._call_llm(prompt))
```
**Benefits:**
- Users don't need to know query syntax
- Better results through intelligent routing
- Learns from usage patterns
---
#### 2.2 Fuzzy Search with Typo Tolerance ⭐⭐⭐ ✅ COMPLETED (v2.23.0)
**Impact:** Medium | **Effort:** ⭐⭐⭐ | **Time:** 6-8 hours
**Status:** ✅ Complete - Handle misspellings and variations
```python
from rapidfuzz import fuzz, process
def fuzzy_search(self, query: str, max_results: int = 10,
similarity_threshold: int = 80) -> list:
"""
Search with typo tolerance using fuzzy string matching.
Examples:
"VIC2" → finds "VIC-II"
"asembly" → finds "assembly"
"6052" → finds "6502"
"""
# 1. Try exact search first
exact_results = self.search(query, max_results)
if len(exact_results) >= max_results:
return exact_results
# 2. Build vocabulary from all indexed terms
if not hasattr(self, '_search_vocabulary'):
self._build_search_vocabulary()
# 3. Find closest matches to query terms
query_terms = query.split()
corrected_terms = []
for term in query_terms:
# Find best match in vocabulary
matches = process.extract(
term,
self._search_vocabulary,
scorer=fuzz.ratio,
limit=1
)
if matches and matches[0][1] >= similarity_threshold:
corrected_terms.append(matches[0][0])
else:
corrected_terms.append(term)
# 4. Search with corrected query
corrected_query = ' '.join(corrected_terms)
if corrected_query != query:
self.logger.info(f"Fuzzy search: '{query}' → '{corrected_query}'")
return self.search(corrected_query, max_results)
def _build_search_vocabulary(self):
"""Extract all unique terms from indexed content."""
vocabulary = set()
# Get all chunks
chunks = self._get_chunks_db()
for chunk in chunks:
# Extract words (lowercase, alphanumeric + hyphen)
words = re.findall(r'\b[a-z0-9-]+\b', chunk.content.lower())
vocabulary.update(words)
# Add known technical terms
vocabulary.update(['VIC-II', 'SID', 'CIA', '6502', 'sprite', 'raster'])
self._search_vocabulary = list(vocabulary)
```
**Benefits:**
- Better user experience (forgive typos)
- Handles variant spellings (VIC-II, VIC2, VICII)
- Useful for technical terms users might misremember
---
#### 2.3 Search Within Results ⭐⭐⭐ ✅ COMPLETED (v2.23.0)
**Impact:** Medium | **Effort:** ⭐⭐ | **Time:** 4-6 hours
**Status:** ✅ Complete - Refine search results with additional filters
```python
def search_within_results(self, previous_results: list,
refinement_query: str) -> list:
"""
Search within a previous result set.
Example workflow:
1. results = search("VIC-II") # 50 results
2. refined = search_within_results(results, "sprite collision") # 8 results
"""
# Extract doc_ids from previous results
doc_ids = list(set([r['doc_id'] for r in previous_results]))
# Search only within those documents
cursor = self.db_conn.cursor()
# Build FTS5 query restricted to doc_ids
placeholders = ','.join(['?'] * len(doc_ids))
cursor.execute(f"""
SELECT doc_id, chunk_id, content,
bm25(chunks_fts) as score
FROM chunks_fts
WHERE chunks_fts MATCH ?
AND doc_id IN ({placeholders})
ORDER BY score DESC
LIMIT 20
""", (refinement_query, *doc_ids))
# Format results
results = []
for row in cursor.fetchall():
# ... format result ...
results.append(result)
return results
```
**Benefits:**
- Progressive refinement of searches
- Explore large result sets
- "Drill down" workflow
---
### Phase 3: Content Intelligence (Q3 2025) ✅ COMPLETE
#### 3.1 Document Version Tracking ⭐⭐⭐⭐ ✅ COMPLETED (v2.20.0-v2.21.0)
**Impact:** High | **Effort:** ⭐⭐⭐⭐ | **Time:** 12-16 hours
**Status:** ✅ Complete - Track changes to indexed documents
```python
def check_for_updates(self) -> dict:
"""
Check if any indexed files have changed on disk.
Returns:
{
'modified': [list of docs that changed],
'deleted': [list of docs no longer on disk],
'unchanged': count
}
"""
changes = {'modified': [], 'deleted': [], 'unchanged': 0}
for doc_id, doc_meta in self.documents.items():
filepath = Path(doc_meta.filepath)
if not filepath.exists():
changes['deleted'].append({
'doc_id': doc_id,
'title': doc_meta.title,
'filepath': str(filepath)
})
continue
# Check modification time
current_mtime = filepath.stat().st_mtime
indexed_mtime = doc_meta.indexed_at
if current_mtime > indexed_mtime:
# File modified since indexing
changes['modified'].append({
'doc_id': doc_id,
'title': doc_meta.title,
'filepath': str(filepath),
'indexed_at': indexed_mtime,
'modified_at': current_mtime
})
else:
changes['unchanged'] += 1
return changes
def reindex_modified_documents(self, auto_backup: bool = True):
"""Re-index all modified documents."""
changes = self.check_for_updates()
if auto_backup and changes['modified']:
self.create_backup(self.data_dir / 'backups')
results = {'reindexed': [], 'failed': []}
for doc_info in changes['modified']:
try:
# Remove old version
self.remove_document(doc_info['doc_id'])
# Re-add updated version
new_doc = self.add_document(
doc_info['filepath'],
tags=doc_info.get('tags', [])
)
results['reindexed'].append(new_doc)
except Exception as e:
results['failed'].append({
'doc': doc_info,
'error': str(e)
})
return results
```
**Schema:**
```sql
-- Track document versions
CREATE TABLE document_versions (
doc_id TEXT,
version INTEGER,
indexed_at TEXT,
file_mtime REAL,
content_hash TEXT, -- MD5 of content
change_description TEXT,
PRIMARY KEY (doc_id, version)
);
```
**MCP Tool:**
```python
Tool(
name="check_updates",
description="Check if indexed documents have been modified on disk"
)
```
**Benefits:**
- Know when documentation is outdated
- Automatic re-indexing
- Change history tracking
- Rollback capability
---
#### 3.2 Entity Extraction & Recognition ⭐⭐⭐⭐ ✅ COMPLETED (v2.15-v2.22.0)
**Impact:** High | **Effort:** ⭐⭐⭐⭐ | **Time:** 16-20 hours
**Status:** ✅ Complete - Extract and categorize entities (people, companies, products)
```python
def extract_entities(self, doc_id: str) -> dict:
"""
Extract named entities from document.
Returns:
{
'people': ['Bob Yannes', 'Jack Tramiel', ...],
'companies': ['Commodore', 'MOS Technology', ...],
'products': ['VIC-20', 'C128', ...],
'locations': ['West Chester PA', ...],
'dates': ['1982', '1985', ...]
}
"""
doc = self.get_document(doc_id)
full_text = "\n".join([c['content'] for c in doc['chunks']])
# Use NER model or LLM
prompt = f"""Extract named entities from this C64 documentation:
People (engineers, programmers, authors)
Companies (Commodore, MOS Technology, software houses)
Products (computers, peripherals, software)
Technical Terms (chips, registers, commands)
Dates (years, product releases)
Text:
{full_text[:5000]} # Sample
Return as JSON with categories."""
entities = json.loads(self._call_llm(prompt))
# Store in database
self._save_entities(doc_id, entities)
return entities
def search_by_entity(self, entity_type: str, entity_value: str) -> list:
"""Find all documents mentioning a specific entity."""
cursor = self.db_conn.cursor()
cursor.execute("""
SELECT DISTINCT doc_id
FROM document_entities
WHERE entity_type = ? AND entity_value LIKE ?
""", (entity_type, f'%{entity_value}%'))
doc_ids = [row[0] for row in cursor.fetchall()]
return [self.documents[doc_id] for doc_id in doc_ids]
```
**Schema:**
```sql
CREATE TABLE document_entities (
doc_id TEXT,
entity_type TEXT, -- person, company, product, location, date
entity_value TEXT,
context TEXT, -- surrounding text
confidence REAL,
PRIMARY KEY (doc_id, entity_type, entity_value)
);
CREATE INDEX idx_entities_value ON document_entities(entity_type, entity_value);
```
**Example Queries:**
- "Show all documents written by Bob Yannes"
- "Find documentation about MOS Technology products"
- "What was released in 1985?"
**Benefits:**
- Rich metadata extraction
- Historical research
- Author/source attribution
- Product family navigation
---
### Phase 4+: Archived Features (Not Pursued)
**Note:** Phase 4 and beyond are archived for reference only. Current focus is production stability and maintenance of Phase 1-3 features.
### Phase 4: C64-Specific Features (Q4 2025) [ARCHIVED]
#### 4.1 VICE Emulator Integration ⭐⭐⭐⭐⭐
**Impact:** Very High for C64 enthusiasts | **Effort:** ⭐⭐⭐⭐⭐ | **Time:** 20-30 hours
**Proposed:** Link documentation to VICE emulator
```python
class VICEIntegration:
"""Integration with VICE C64 emulator."""
def __init__(self, vice_monitor_port: int = 6510):
"""Connect to VICE remote monitor."""
self.monitor_port = vice_monitor_port
self.connection = None
def connect_to_vice(self):
"""Establish connection to VICE monitor."""
import socket
self.connection = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.connection.connect(('localhost', self.monitor_port))
def peek_memory(self, address: str) -> int:
"""Read memory address from running emulator."""
# Send command to VICE monitor
cmd = f"m {address}\n"
self.connection.send(cmd.encode())
response = self.connection.recv(1024).decode()
# Parse response
value = int(response.split(':')[1].strip().split()[0], 16)
return value
def find_docs_for_address(self, address: str) -> list:
"""Find documentation for a memory address."""
# Search cross-references
results = kb.find_by_reference('memory_address', address)
return results
def annotate_memory_dump(self, start_addr: str, end_addr: str) -> str:
"""
Create annotated memory dump with inline documentation.
Example output:
$D000: 14 # VIC-II: Sprite 0 X position (low byte)
$D001: 32 # VIC-II: Sprite 0 Y position
$D015: FF # VIC-II: Sprite enable register (all sprites on)
"""
start = int(start_addr.replace('$', ''), 16)
end = int(end_addr.replace('$', ''), 16)
output = []
for addr in range(start, end + 1):
hex_addr = f"${addr:04X}"
value = self.peek_memory(hex_addr)
# Find documentation for this address
docs = self.find_docs_for_address(hex_addr)
# Extract description from docs
description = ""
if docs:
# Use first result's context
description = docs[0].get('context', '')[:60]
output.append(f"{hex_addr}: {value:02X} # {description}")
return "\n".join(output)
# MCP Tool
Tool(
name="vice_memory_lookup",
description="Look up documentation for memory address in running VICE emulator",
inputSchema={
"properties": {
"address": {"type": "string", "pattern": "^\\$[0-9A-F]{4}$"},
"action": {"enum": ["peek", "docs", "annotate"]}
}
}
)
```
**Benefits:**
- Real-time documentation while programming
- Understand memory contents in running programs
- Learn by exploration
- Debug with documentation context
---
#### 4.2 PRG File Analysis ⭐⭐⭐⭐
**Impact:** High | **Effort:** ⭐⭐⭐⭐ | **Time:** 12-16 hours
**Proposed:** Analyze C64 program files
```python
def analyze_prg_file(self, filepath: str) -> dict:
"""
Analyze C64 PRG file and extract metadata.
Returns:
{
'load_address': '$0801',
'size_bytes': 2048,
'likely_type': 'BASIC program', # or machine code
'entry_point': '$0810',
'referenced_addresses': ['$D020', '$D021'],
'strings_found': ['HELLO WORLD', ...],
'suggested_docs': [list of relevant documentation]
}
"""
with open(filepath, 'rb') as f:
data = f.read()
# Extract load address (first 2 bytes, little-endian)
load_addr = data[0] + (data[1] << 8)
load_addr_hex = f"${load_addr:04X}"
# Detect program type
if load_addr == 0x0801:
prog_type = 'BASIC program'
elif load_addr >= 0xC000:
prog_type = 'Cartridge/ROM'
else:
prog_type = 'Machine code program'
# Extract referenced memory addresses
code = data[2:] # Skip load address
# Find $XXXX patterns (memory addresses)
referenced_addrs = set()
for i in range(len(code) - 1):
# Look for address-like patterns
addr = code[i] + (code[i+1] << 8)
if 0xD000 <= addr <= 0xDFFF: # I/O range
referenced_addrs.add(f"${addr:04X}")
# Extract visible strings
strings = []
current_string = []
for byte in code:
if 32 <= byte <= 126: # Printable ASCII
current_string.append(chr(byte))
elif current_string:
if len(current_string) >= 4:
strings.append(''.join(current_string))
current_string = []
# Find relevant documentation
suggested_docs = []
for addr in referenced_addrs:
docs = self.find_by_reference('memory_address', addr, max_results=1)
if docs:
suggested_docs.extend(docs)
return {
'load_address': load_addr_hex,
'size_bytes': len(data),
'likely_type': prog_type,
'referenced_addresses': list(referenced_addrs),
'strings_found': strings[:10], # Top 10
'suggested_docs': suggested_docs
}
```
**Benefits:**
- Understand what documentation is relevant for a program
- Reverse engineering assistance
- Learn from existing programs
- Link code to documentation
---
#### 4.3 SID Music File Metadata ⭐⭐⭐
**Impact:** Medium | **Effort:** ⭐⭐⭐ | **Time:** 8-10 hours
**Proposed:** Extract metadata from SID music files
```python
def analyze_sid_file(self, filepath: str) -> dict:
"""
Parse SID/PSID music file and extract metadata.
Returns:
{
'title': 'Monty on the Run',
'author': 'Rob Hubbard',
'copyright': '1985 Gremlin Graphics',
'format': 'PSID',
'version': 2,
'load_address': '$1000',
'init_address': '$1000',
'play_address': '$1003',
'songs': 1,
'default_song': 1,
'speed': 'CIA',
'sid_model': '6581',
'relevant_docs': [...] # SID programming docs
}
"""
# Parse PSID/RSID format
# ... implementation ...
# Find relevant SID documentation
sid_docs = self.faceted_search(
"SID programming music",
facet_filters={'hardware': ['SID']}
)
return metadata
```
---
### Phase 5: Collaboration & Integration (2026)
#### 5.1 REST API Server ⭐⭐⭐⭐
**Impact:** Very High | **Effort:** ⭐⭐⭐⭐ | **Time:** 16-20 hours
**Proposed:** Full REST API for integration
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI(title="TDZ C64 Knowledge API", version="3.0.0")
class SearchRequest(BaseModel):
query: str
max_results: int = 10
search_mode: str = "hybrid" # fts5, semantic, hybrid
tags: list[str] = None
class SearchResponse(BaseModel):
results: list[dict]
total_found: int
search_time_ms: float
search_mode: str
@app.post("/api/v1/search", response_model=SearchResponse)
async def search(request: SearchRequest):
"""Search endpoint."""
start_time = time.time()
if request.search_mode == "hybrid":
results = kb.hybrid_search(request.query, request.max_results, request.tags)
elif request.search_mode == "semantic":
results = kb.semantic_search(request.query, request.max_results, request.tags)
else:
results = kb.search(request.query, request.max_results, request.tags)
search_time = (time.time() - start_time) * 1000
return SearchResponse(
results=results,
total_found=len(results),
search_time_ms=search_time,
search_mode=request.search_mode
)
@app.get("/api/v1/documents/{doc_id}")
async def get_document(doc_id: str):
"""Get document by ID."""
doc = kb.get_document(doc_id)
if not doc:
raise HTTPException(status_code=404, detail="Document not found")
return doc
@app.post("/api/v1/documents")
async def add_document(filepath: str, title: str = None, tags: list[str] = None):
"""Add new document."""
doc = kb.add_document(filepath, title, tags)
return doc
# More endpoints...
```
**Benefits:**
- Integration with web apps
- Third-party tool integration
- Mobile app development
- Custom frontends
---
#### 5.2 Plugin System ⭐⭐⭐⭐
**Impact:** High | **Effort:** ⭐⭐⭐⭐⭐ | **Time:** 20-24 hours
**Proposed:** Extensible plugin architecture
```python
class Plugin:
"""Base plugin class."""
def __init__(self, kb: KnowledgeBase):
self.kb = kb
def on_document_added(self, doc: DocumentMeta):
"""Called when document is added."""
pass
def on_search(self, query: str, results: list) -> list:
"""Called after search, can modify results."""
return results
def add_tools(self) -> list[Tool]:
"""Return custom MCP tools."""
return []
# Example plugin
class SlackNotificationPlugin(Plugin):
"""Send Slack notifications when documents are added."""
def on_document_added(self, doc: DocumentMeta):
"""Notify Slack channel."""
webhook_url = os.environ.get('SLACK_WEBHOOK_URL')
if webhook_url:
requests.post(webhook_url, json={
'text': f'New document added: {doc.title}'
})
# Load plugins
class PluginManager:
def __init__(self, kb: KnowledgeBase):
self.kb = kb
self.plugins = []
def load_plugins(self, plugin_dir: str):
"""Dynamically load plugins from directory."""
for file in Path(plugin_dir).glob('*.py'):
# Import and instantiate plugin
# ...
self.plugins.append(plugin)
```
**Benefits:**
- Community contributions
- Custom extractors
- Integration hooks
- Extensibility without modifying core
---
## Summary: 2025+ Roadmap
### Q1 2025 - AI Intelligence ✅ COMPLETE
- ✅ RAG Question Answering (v2.23.0)
- ✅ Auto-Summarization (v2.13.0)
- ✅ Smart Auto-Tagging (v2.23.0)
- ✅ Natural Language Query (v2.17.0)
### Q2 2025 - Advanced Search ✅ COMPLETE
- ✅ Fuzzy Search / Typo Tolerance (v2.23.0)
- ✅ Search Within Results (v2.23.0)
- ✅ Multi-language Support (v2.18.0+)
### Q3 2025 - Content Intelligence ✅ COMPLETE
- ✅ Document Version Tracking (v2.20.0-v2.21.0)
- ✅ Entity Extraction (v2.15-v2.22.0)
- ✅ Change Detection / Anomaly Detection (v2.21.0)
## Project Completion
**v2.23.15 Release (January 2026):**
All planned features through Phase 3 are complete and production-ready:
- RAG-based question answering with citations
- Fuzzy search with typo tolerance
- Progressive search refinement
- Smart document tagging system
- Entity extraction & relationship mapping
- Document version tracking & anomaly detection
- Complete REST API (v2.18.0+)
- Natural language query translation
- Comprehensive entity analytics
**Focus:** Stability, maintenance, and optimization of existing features.
## Configuration
All AI features support multiple providers:
```bash
# LLM Provider Configuration
export LLM_PROVIDER=anthropic # or openai, local
export LLM_API_KEY=sk-ant-...
export LLM_MODEL=claude-3-haiku-20240307
# Feature Toggles
export ENABLE_RAG=1
export ENABLE_AUTO_TAGGING=1
export ENABLE_FUZZY_SEARCH=1
# Cost Controls
export MAX_LLM_CALLS_PER_DAY=1000
export LLM_CACHE_ENABLED=1
```
---
**Ready to implement any of these features!** Which phase interests you most?
