Enterprise Knowledge Management Guide 2025: AI-Powered Automation, Security & Global Orchestration
Enterprise Knowledge Management Guide 2025: AI-Powered Automation, Security & Global Orchestration
Enterprise knowledge management in the modern era demands sophisticated AI-driven systems that can automatically discover, classify, curate, and deliver knowledge at scale across global organizations. This comprehensive guide transforms traditional Knowledge-Centered Support (KCS) principles into enterprise-grade knowledge orchestration platforms with machine learning, natural language processing, and intelligent automation capabilities.
Table of Contents
- Enterprise Knowledge Architecture Overview
- AI-Powered Knowledge Discovery Framework
- Automated Content Generation and Curation
- Intelligent Knowledge Classification
- Advanced Search and Retrieval Systems
- Knowledge Security and Access Control
- Global Knowledge Orchestration
- Analytics and Performance Metrics
- Compliance and Governance Framework
- Integration with Enterprise Systems
- Quality Assurance and Validation
- Multi-language Knowledge Management
- Knowledge Lifecycle Management
- Advanced Troubleshooting and Optimization
- Best Practices and Strategic Implementation
Enterprise Knowledge Architecture Overview
Modern Knowledge Management Requirements
Enterprise knowledge management systems must handle millions of articles, support thousands of concurrent users, and provide intelligent knowledge discovery across diverse organizational contexts while maintaining security, compliance, and performance standards.
# enterprise-knowledge-architecture.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: enterprise-knowledge-config
namespace: knowledge-management
data:
architecture.yaml: |
knowledge_platform:
core_components:
- name: "knowledge-discovery-engine"
type: "ai-powered"
technologies: ["elasticsearch", "transformers", "bert"]
capacity: 100000000 # 100M documents
- name: "content-generation-service"
type: "ai-automation"
technologies: ["gpt-4", "langchain", "vector-db"]
capacity: 10000 # documents per hour
- name: "knowledge-graph-engine"
type: "semantic-search"
technologies: ["neo4j", "rdf", "sparql"]
relationships: 50000000 # 50M relationships
- name: "analytics-platform"
type: "real-time"
technologies: ["kafka", "flink", "clickhouse"]
events_per_second: 100000
ai_capabilities:
natural_language_processing:
- sentiment_analysis
- topic_modeling
- entity_extraction
- intent_classification
- language_detection
machine_learning:
- content_recommendation
- knowledge_gap_detection
- quality_scoring
- usage_prediction
- expert_identification
computer_vision:
- document_ocr
- diagram_analysis
- screenshot_annotation
- video_content_extraction
security_framework:
access_control: "rbac"
encryption: "aes-256"
audit_logging: "comprehensive"
compliance: ["gdpr", "sox", "hipaa"]
threat_detection: "ai-powered"
global_deployment:
regions:
- "us-east-1"
- "eu-west-1"
- "ap-southeast-1"
- "us-west-2"
content_distribution: "edge-cached"
search_latency_target: "50ms"
availability_target: "99.99%"
Knowledge Platform Architecture Framework
#!/usr/bin/env python3
"""
Enterprise Knowledge Management Platform
Core architecture and orchestration system
"""
import asyncio
import logging
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import json
import uuid
from pathlib import Path
import aiohttp
import asyncpg
import elasticsearch
import redis
import neo4j
from sentence_transformers import SentenceTransformer
import openai
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.text_splitter import RecursiveCharacterTextSplitter
@dataclass
class KnowledgeArticle:
id: str
title: str
content: str
tags: List[str]
category: str
author: str
created_at: datetime
updated_at: datetime
version: int
language: str
confidence_score: float
quality_score: float
usage_count: int
feedback_score: float
related_articles: List[str] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class KnowledgeQuery:
id: str
query_text: str
user_id: str
timestamp: datetime
language: str
context: Dict[str, Any]
results: List[str] = field(default_factory=list)
satisfaction_score: Optional[float] = None
@dataclass
class UserProfile:
user_id: str
department: str
role: str
expertise_areas: List[str]
preferred_language: str
access_level: str
usage_patterns: Dict[str, Any] = field(default_factory=dict)
class EnterpriseKnowledgeManager:
def __init__(self, config_path: str):
self.config = self._load_config(config_path)
self.logger = self._setup_logging()
self.db_pool = None
self.redis_client = None
self.elasticsearch_client = None
self.neo4j_driver = None
self.embedding_model = None
self.vector_store = None
def _load_config(self, config_path: str) -> Dict:
"""Load platform configuration"""
with open(config_path, 'r') as f:
return json.load(f)
def _setup_logging(self) -> logging.Logger:
"""Configure comprehensive logging"""
logger = logging.getLogger('enterprise-knowledge')
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
# Console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# File handler
file_handler = logging.FileHandler('/var/log/knowledge/platform.log')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
return logger
async def initialize(self):
"""Initialize all platform components"""
await self._init_database()
await self._init_redis()
await self._init_elasticsearch()
await self._init_neo4j()
await self._init_ai_models()
self.logger.info("Enterprise Knowledge Platform initialized successfully")
async def _init_database(self):
"""Initialize PostgreSQL connection pool"""
self.db_pool = await asyncpg.create_pool(
host=self.config['database']['host'],
port=self.config['database']['port'],
user=self.config['database']['user'],
password=self.config['database']['password'],
database=self.config['database']['name'],
min_size=10,
max_size=100
)
async def _init_redis(self):
"""Initialize Redis connection"""
self.redis_client = redis.Redis(
host=self.config['redis']['host'],
port=self.config['redis']['port'],
password=self.config['redis']['password'],
decode_responses=True
)
async def _init_elasticsearch(self):
"""Initialize Elasticsearch client"""
self.elasticsearch_client = elasticsearch.AsyncElasticsearch(
[{'host': self.config['elasticsearch']['host'],
'port': self.config['elasticsearch']['port']}],
http_auth=(
self.config['elasticsearch']['user'],
self.config['elasticsearch']['password']
)
)
async def _init_neo4j(self):
"""Initialize Neo4j driver"""
self.neo4j_driver = neo4j.GraphDatabase.driver(
self.config['neo4j']['uri'],
auth=(
self.config['neo4j']['user'],
self.config['neo4j']['password']
)
)
async def _init_ai_models(self):
"""Initialize AI models and vector stores"""
# Initialize embedding model
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
# Initialize OpenAI
openai.api_key = self.config['openai']['api_key']
# Initialize vector store (Pinecone)
embeddings = OpenAIEmbeddings(openai_api_key=self.config['openai']['api_key'])
self.vector_store = Pinecone.from_existing_index(
index_name=self.config['pinecone']['index_name'],
embedding=embeddings
)
async def create_knowledge_article(self, article: KnowledgeArticle) -> str:
"""Create new knowledge article with AI enhancement"""
try:
# Generate embeddings
content_embedding = self.embedding_model.encode(article.content)
# AI-powered content enhancement
enhanced_content = await self._enhance_content_with_ai(article.content)
article.content = enhanced_content
# Extract entities and topics
entities = await self._extract_entities(article.content)
topics = await self._extract_topics(article.content)
# Calculate quality score
quality_score = await self._calculate_quality_score(article)
article.quality_score = quality_score
# Store in database
async with self.db_pool.acquire() as conn:
article_id = await conn.fetchval("""
INSERT INTO knowledge_articles
(id, title, content, tags, category, author, language,
quality_score, content_embedding, entities, topics)
VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)
RETURNING id
""",
article.id, article.title, article.content, article.tags,
article.category, article.author, article.language,
article.quality_score, content_embedding.tolist(),
json.dumps(entities), json.dumps(topics)
)
# Index in Elasticsearch
await self._index_article_elasticsearch(article)
# Add to knowledge graph
await self._add_to_knowledge_graph(article, entities, topics)
# Add to vector store
await self._add_to_vector_store(article)
# Update cache
await self._update_article_cache(article)
self.logger.info(f"Created knowledge article: {article_id}")
return article_id
except Exception as e:
self.logger.error(f"Error creating knowledge article: {str(e)}")
raise
async def _enhance_content_with_ai(self, content: str) -> str:
"""Enhance content using AI"""
try:
prompt = f"""
Enhance the following technical content by:
1. Improving clarity and readability
2. Adding relevant technical context
3. Ensuring proper formatting
4. Adding helpful examples where appropriate
5. Maintaining technical accuracy
Original content:
{content}
Enhanced content:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=2000,
temperature=0.3
)
return response.choices[0].message.content.strip()
except Exception as e:
self.logger.warning(f"AI content enhancement failed: {str(e)}")
return content # Return original if enhancement fails
async def _extract_entities(self, content: str) -> List[Dict]:
"""Extract named entities from content"""
try:
# Use NLP model for entity extraction
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp(content)
entities = []
for ent in doc.ents:
entities.append({
'text': ent.text,
'label': ent.label_,
'start': ent.start_char,
'end': ent.end_char,
'confidence': float(ent._.confidence) if hasattr(ent._, 'confidence') else 1.0
})
return entities
except Exception as e:
self.logger.error(f"Entity extraction failed: {str(e)}")
return []
async def _extract_topics(self, content: str) -> List[Dict]:
"""Extract topics from content using topic modeling"""
try:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation
# Simple topic extraction (in production, use more sophisticated models)
vectorizer = TfidfVectorizer(max_features=100, stop_words='english')
doc_matrix = vectorizer.fit_transform([content])
lda = LatentDirichletAllocation(n_components=5, random_state=42)
lda.fit(doc_matrix)
feature_names = vectorizer.get_feature_names_out()
topics = []
for topic_idx, topic in enumerate(lda.components_):
top_words = [feature_names[i] for i in topic.argsort()[-10:]]
topics.append({
'topic_id': topic_idx,
'words': top_words,
'weight': float(topic.max())
})
return topics
except Exception as e:
self.logger.error(f"Topic extraction failed: {str(e)}")
return []
async def _calculate_quality_score(self, article: KnowledgeArticle) -> float:
"""Calculate article quality score using multiple factors"""
try:
score = 0.0
max_score = 100.0
# Content length factor (20 points)
content_length = len(article.content.split())
if content_length > 500:
score += 20
elif content_length > 200:
score += 15
elif content_length > 100:
score += 10
else:
score += 5
# Title quality (15 points)
title_words = len(article.title.split())
if 5 <= title_words <= 15:
score += 15
elif 3 <= title_words <= 20:
score += 10
else:
score += 5
# Tags quality (10 points)
if len(article.tags) >= 3:
score += 10
elif len(article.tags) >= 1:
score += 5
# Content structure (20 points)
has_headers = bool(re.search(r'#+\s+', article.content))
has_code_blocks = bool(re.search(r'```', article.content))
has_bullets = bool(re.search(r'^\s*[\-\*\+]\s+', article.content, re.MULTILINE))
structure_score = 0
if has_headers:
structure_score += 8
if has_code_blocks:
structure_score += 6
if has_bullets:
structure_score += 6
score += structure_score
# Readability (15 points)
readability_score = await self._calculate_readability(article.content)
score += readability_score * 15
# Technical accuracy (20 points) - simplified heuristic
technical_keywords = ['error', 'solution', 'steps', 'configuration', 'install', 'setup']
keyword_matches = sum(1 for keyword in technical_keywords if keyword.lower() in article.content.lower())
score += min(keyword_matches * 4, 20)
return min(score, max_score)
except Exception as e:
self.logger.error(f"Quality score calculation failed: {str(e)}")
return 50.0 # Default score
async def _calculate_readability(self, text: str) -> float:
"""Calculate text readability score"""
try:
import textstat
# Use Flesch Reading Ease
flesch_score = textstat.flesch_reading_ease(text)
# Normalize to 0-1 range
if flesch_score >= 90:
return 1.0
elif flesch_score >= 80:
return 0.9
elif flesch_score >= 70:
return 0.8
elif flesch_score >= 60:
return 0.7
elif flesch_score >= 50:
return 0.6
else:
return 0.5
except Exception as e:
self.logger.error(f"Readability calculation failed: {str(e)}")
return 0.7 # Default readability score
async def _index_article_elasticsearch(self, article: KnowledgeArticle):
"""Index article in Elasticsearch for fast search"""
try:
doc = {
'id': article.id,
'title': article.title,
'content': article.content,
'tags': article.tags,
'category': article.category,
'author': article.author,
'language': article.language,
'quality_score': article.quality_score,
'created_at': article.created_at.isoformat(),
'updated_at': article.updated_at.isoformat()
}
await self.elasticsearch_client.index(
index=f"knowledge-articles-{article.language}",
id=article.id,
body=doc
)
except Exception as e:
self.logger.error(f"Elasticsearch indexing failed: {str(e)}")
async def _add_to_knowledge_graph(self, article: KnowledgeArticle, entities: List[Dict], topics: List[Dict]):
"""Add article and relationships to knowledge graph"""
try:
with self.neo4j_driver.session() as session:
# Create article node
session.run("""
CREATE (a:Article {
id: $id,
title: $title,
category: $category,
author: $author,
language: $language,
quality_score: $quality_score,
created_at: $created_at
})
""",
id=article.id,
title=article.title,
category=article.category,
author=article.author,
language=article.language,
quality_score=article.quality_score,
created_at=article.created_at.isoformat()
)
# Create entity relationships
for entity in entities:
session.run("""
MATCH (a:Article {id: $article_id})
MERGE (e:Entity {name: $entity_name, type: $entity_type})
CREATE (a)-[:MENTIONS {confidence: $confidence}]->(e)
""",
article_id=article.id,
entity_name=entity['text'],
entity_type=entity['label'],
confidence=entity['confidence']
)
# Create topic relationships
for topic in topics:
session.run("""
MATCH (a:Article {id: $article_id})
MERGE (t:Topic {words: $topic_words})
CREATE (a)-[:COVERS {weight: $weight}]->(t)
""",
article_id=article.id,
topic_words=str(topic['words']),
weight=topic['weight']
)
except Exception as e:
self.logger.error(f"Knowledge graph update failed: {str(e)}")
async def _add_to_vector_store(self, article: KnowledgeArticle):
"""Add article to vector store for semantic search"""
try:
# Split content into chunks for better retrieval
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = text_splitter.split_text(article.content)
# Add each chunk to vector store
for i, chunk in enumerate(chunks):
metadata = {
'article_id': article.id,
'title': article.title,
'chunk_index': i,
'category': article.category,
'author': article.author,
'language': article.language,
'quality_score': article.quality_score
}
await self.vector_store.aadd_texts(
texts=[chunk],
metadatas=[metadata],
ids=[f"{article.id}_{i}"]
)
except Exception as e:
self.logger.error(f"Vector store update failed: {str(e)}")
async def _update_article_cache(self, article: KnowledgeArticle):
"""Update article in Redis cache"""
try:
cache_key = f"article:{article.id}"
article_data = {
'id': article.id,
'title': article.title,
'content': article.content,
'tags': article.tags,
'category': article.category,
'author': article.author,
'language': article.language,
'quality_score': article.quality_score,
'created_at': article.created_at.isoformat(),
'updated_at': article.updated_at.isoformat()
}
await self.redis_client.setex(
cache_key,
timedelta(hours=24),
json.dumps(article_data)
)
except Exception as e:
self.logger.error(f"Cache update failed: {str(e)}")
async def search_knowledge(self, query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Intelligent knowledge search with personalization"""
try:
# Multi-modal search combining different approaches
results = []
# 1. Semantic search using vector similarity
semantic_results = await self._semantic_search(query, user_profile)
results.extend(semantic_results)
# 2. Full-text search using Elasticsearch
fulltext_results = await self._fulltext_search(query, user_profile)
results.extend(fulltext_results)
# 3. Graph-based search using Neo4j
graph_results = await self._graph_search(query, user_profile)
results.extend(graph_results)
# 4. Personalized recommendations
recommendation_results = await self._get_personalized_recommendations(query, user_profile)
results.extend(recommendation_results)
# Deduplicate and rank results
final_results = await self._rank_and_deduplicate_results(results, query, user_profile)
# Log search analytics
await self._log_search_analytics(query, final_results, user_profile)
return final_results[:20] # Return top 20 results
except Exception as e:
self.logger.error(f"Knowledge search failed: {str(e)}")
return []
async def _semantic_search(self, query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Perform semantic search using vector similarity"""
try:
# Filter by language and access level
filter_dict = {
'language': user_profile.preferred_language,
'quality_score': {'$gte': 70} # Minimum quality threshold
}
# Perform similarity search
docs = await self.vector_store.asimilarity_search_with_score(
query.query_text,
k=10,
filter=filter_dict
)
articles = []
for doc, score in docs:
if score > 0.7: # Similarity threshold
article = await self._get_article_by_id(doc.metadata['article_id'])
if article:
article.confidence_score = score
articles.append(article)
return articles
except Exception as e:
self.logger.error(f"Semantic search failed: {str(e)}")
return []
async def _fulltext_search(self, query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Perform full-text search using Elasticsearch"""
try:
search_body = {
'query': {
'bool': {
'must': [
{
'multi_match': {
'query': query.query_text,
'fields': ['title^3', 'content', 'tags^2'],
'type': 'best_fields',
'fuzziness': 'AUTO'
}
}
],
'filter': [
{'term': {'language': user_profile.preferred_language}},
{'range': {'quality_score': {'gte': 70}}}
]
}
},
'sort': [
{'_score': {'order': 'desc'}},
{'quality_score': {'order': 'desc'}},
{'updated_at': {'order': 'desc'}}
],
'size': 10
}
response = await self.elasticsearch_client.search(
index=f"knowledge-articles-{user_profile.preferred_language}",
body=search_body
)
articles = []
for hit in response['hits']['hits']:
article = await self._convert_es_hit_to_article(hit)
if article:
article.confidence_score = hit['_score'] / 10 # Normalize score
articles.append(article)
return articles
except Exception as e:
self.logger.error(f"Full-text search failed: {str(e)}")
return []
async def _graph_search(self, query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Perform graph-based search using Neo4j"""
try:
with self.neo4j_driver.session() as session:
# Extract entities from query
query_entities = await self._extract_entities(query.query_text)
if not query_entities:
return []
# Find articles connected to query entities
result = session.run("""
MATCH (a:Article)-[r:MENTIONS]->(e:Entity)
WHERE e.name IN $entity_names
AND a.language = $language
AND a.quality_score >= 70
RETURN a.id as article_id,
count(r) as entity_matches,
avg(r.confidence) as avg_confidence
ORDER BY entity_matches DESC, avg_confidence DESC
LIMIT 10
""",
entity_names=[entity['text'] for entity in query_entities],
language=user_profile.preferred_language
)
articles = []
for record in result:
article = await self._get_article_by_id(record['article_id'])
if article:
article.confidence_score = record['avg_confidence']
articles.append(article)
return articles
except Exception as e:
self.logger.error(f"Graph search failed: {str(e)}")
return []
async def _get_personalized_recommendations(self, query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Get personalized recommendations based on user profile"""
try:
# Find articles in user's expertise areas
async with self.db_pool.acquire() as conn:
results = await conn.fetch("""
SELECT * FROM knowledge_articles
WHERE category = ANY($1)
AND language = $2
AND quality_score >= 70
ORDER BY usage_count DESC, quality_score DESC
LIMIT 5
""", user_profile.expertise_areas, user_profile.preferred_language)
articles = []
for row in results:
article = await self._convert_db_row_to_article(row)
if article:
article.confidence_score = 0.8 # Base confidence for personalized results
articles.append(article)
return articles
except Exception as e:
self.logger.error(f"Personalized recommendations failed: {str(e)}")
return []
async def _rank_and_deduplicate_results(self, results: List[KnowledgeArticle], query: KnowledgeQuery, user_profile: UserProfile) -> List[KnowledgeArticle]:
"""Rank and deduplicate search results"""
try:
# Deduplicate by article ID
unique_articles = {}
for article in results:
if article.id not in unique_articles:
unique_articles[article.id] = article
else:
# Keep the one with higher confidence score
if article.confidence_score > unique_articles[article.id].confidence_score:
unique_articles[article.id] = article
# Calculate composite ranking score
for article in unique_articles.values():
ranking_score = (
article.confidence_score * 0.4 + # Search relevance
(article.quality_score / 100) * 0.3 + # Quality
(article.usage_count / 1000) * 0.2 + # Popularity
(article.feedback_score / 5) * 0.1 # User feedback
)
article.ranking_score = ranking_score
# Sort by ranking score
sorted_articles = sorted(
unique_articles.values(),
key=lambda x: x.ranking_score,
reverse=True
)
return sorted_articles
except Exception as e:
self.logger.error(f"Result ranking failed: {str(e)}")
return list(unique_articles.values()) if 'unique_articles' in locals() else []
async def _log_search_analytics(self, query: KnowledgeQuery, results: List[KnowledgeArticle], user_profile: UserProfile):
"""Log search analytics for optimization"""
try:
analytics_data = {
'query_id': query.id,
'query_text': query.query_text,
'user_id': query.user_id,
'user_department': user_profile.department,
'user_role': user_profile.role,
'timestamp': query.timestamp.isoformat(),
'language': query.language,
'results_count': len(results),
'result_ids': [article.id for article in results],
'avg_quality_score': sum(article.quality_score for article in results) / len(results) if results else 0
}
# Store in analytics database
await self.redis_client.lpush(
'search_analytics',
json.dumps(analytics_data)
)
# Update user usage patterns
await self._update_user_usage_patterns(user_profile, query, results)
except Exception as e:
self.logger.error(f"Search analytics logging failed: {str(e)}")
async def _update_user_usage_patterns(self, user_profile: UserProfile, query: KnowledgeQuery, results: List[KnowledgeArticle]):
"""Update user usage patterns for personalization"""
try:
# Extract topics from query
query_topics = await self._extract_topics(query.query_text)
# Update user's topic interests
for topic in query_topics:
topic_key = f"user:{user_profile.user_id}:topic_interest"
await self.redis_client.zincrby(topic_key, 1, str(topic['words']))
# Update category interests based on results
for article in results:
category_key = f"user:{user_profile.user_id}:category_interest"
await self.redis_client.zincrby(category_key, 1, article.category)
except Exception as e:
self.logger.error(f"User usage pattern update failed: {str(e)}")
# Helper methods
async def _get_article_by_id(self, article_id: str) -> Optional[KnowledgeArticle]:
"""Get article by ID from cache or database"""
try:
# Try cache first
cache_key = f"article:{article_id}"
cached_data = await self.redis_client.get(cache_key)
if cached_data:
data = json.loads(cached_data)
return self._convert_dict_to_article(data)
# Fall back to database
async with self.db_pool.acquire() as conn:
row = await conn.fetchrow(
"SELECT * FROM knowledge_articles WHERE id = $1",
article_id
)
if row:
article = await self._convert_db_row_to_article(row)
await self._update_article_cache(article)
return article
return None
except Exception as e:
self.logger.error(f"Article retrieval failed: {str(e)}")
return None
def _convert_dict_to_article(self, data: Dict) -> KnowledgeArticle:
"""Convert dictionary to KnowledgeArticle object"""
return KnowledgeArticle(
id=data['id'],
title=data['title'],
content=data['content'],
tags=data['tags'],
category=data['category'],
author=data['author'],
created_at=datetime.fromisoformat(data['created_at']),
updated_at=datetime.fromisoformat(data['updated_at']),
version=data.get('version', 1),
language=data['language'],
confidence_score=data.get('confidence_score', 0.0),
quality_score=data['quality_score'],
usage_count=data.get('usage_count', 0),
feedback_score=data.get('feedback_score', 0.0)
)
async def _convert_db_row_to_article(self, row) -> KnowledgeArticle:
"""Convert database row to KnowledgeArticle object"""
return KnowledgeArticle(
id=row['id'],
title=row['title'],
content=row['content'],
tags=row['tags'],
category=row['category'],
author=row['author'],
created_at=row['created_at'],
updated_at=row['updated_at'],
version=row.get('version', 1),
language=row['language'],
confidence_score=0.0, # Will be set by search methods
quality_score=row['quality_score'],
usage_count=row.get('usage_count', 0),
feedback_score=row.get('feedback_score', 0.0)
)
async def _convert_es_hit_to_article(self, hit) -> KnowledgeArticle:
"""Convert Elasticsearch hit to KnowledgeArticle object"""
source = hit['_source']
return KnowledgeArticle(
id=source['id'],
title=source['title'],
content=source['content'],
tags=source['tags'],
category=source['category'],
author=source['author'],
created_at=datetime.fromisoformat(source['created_at']),
updated_at=datetime.fromisoformat(source['updated_at']),
version=source.get('version', 1),
language=source['language'],
confidence_score=0.0, # Will be set by search methods
quality_score=source['quality_score'],
usage_count=source.get('usage_count', 0),
feedback_score=source.get('feedback_score', 0.0)
)
# Usage example
async def main():
# Initialize knowledge management platform
km = EnterpriseKnowledgeManager('/etc/knowledge/config.json')
await km.initialize()
# Create sample article
article = KnowledgeArticle(
id=str(uuid.uuid4()),
title="Advanced Kubernetes Troubleshooting",
content="Comprehensive guide for diagnosing and resolving Kubernetes issues...",
tags=["kubernetes", "troubleshooting", "devops"],
category="Infrastructure",
author="Matthew Mattox",
created_at=datetime.now(),
updated_at=datetime.now(),
version=1,
language="en",
confidence_score=0.0,
quality_score=0.0,
usage_count=0,
feedback_score=0.0
)
# Create article
article_id = await km.create_knowledge_article(article)
print(f"Created article: {article_id}")
# Create user profile
user_profile = UserProfile(
user_id="user123",
department="Engineering",
role="DevOps Engineer",
expertise_areas=["Infrastructure", "Cloud"],
preferred_language="en",
access_level="standard"
)
# Search for knowledge
query = KnowledgeQuery(
id=str(uuid.uuid4()),
query_text="How to debug pod crashes in Kubernetes?",
user_id="user123",
timestamp=datetime.now(),
language="en",
context={}
)
results = await km.search_knowledge(query, user_profile)
print(f"Found {len(results)} articles")
if __name__ == "__main__":
asyncio.run(main())
AI-Powered Knowledge Discovery Framework
Automated Content Mining and Classification
#!/usr/bin/env python3
"""
AI-Powered Knowledge Discovery System
Automated content mining, classification, and knowledge gap detection
"""
import asyncio
import logging
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import json
import re
from datetime import datetime, timedelta
import spacy
import transformers
from transformers import pipeline, AutoTokenizer, AutoModel
import torch
import numpy as np
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import openai
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import WebBaseLoader, PDFPlumberLoader
from langchain.embeddings import OpenAIEmbeddings
@dataclass
class KnowledgeGap:
gap_id: str
topic: str
description: str
priority: str
confidence: float
related_queries: List[str]
suggested_experts: List[str]
estimated_effort: str
@dataclass
class ContentSource:
source_id: str
source_type: str # web, pdf, email, slack, confluence, etc.
url: str
last_crawled: datetime
content_hash: str
extraction_status: str
class AIKnowledgeDiscovery:
def __init__(self, config: Dict):
self.config = config
self.logger = self._setup_logging()
self.nlp_model = None
self.embedding_model = None
self.classification_pipeline = None
self.knowledge_gaps = []
def _setup_logging(self) -> logging.Logger:
"""Configure logging"""
logger = logging.getLogger('ai-knowledge-discovery')
logger.setLevel(logging.INFO)
return logger
async def initialize(self):
"""Initialize AI models and components"""
# Load NLP model
self.nlp_model = spacy.load("en_core_web_lg")
# Initialize embedding model
self.embedding_model = OpenAIEmbeddings(
openai_api_key=self.config['openai']['api_key']
)
# Initialize classification pipeline
self.classification_pipeline = pipeline(
"text-classification",
model="microsoft/DialoGPT-medium",
device=0 if torch.cuda.is_available() else -1
)
self.logger.info("AI Knowledge Discovery system initialized")
async def discover_content_from_sources(self, sources: List[ContentSource]) -> List[Dict]:
"""Discover and extract content from various sources"""
discovered_content = []
for source in sources:
try:
content = await self._extract_content_from_source(source)
if content:
# Process and classify content
processed_content = await self._process_discovered_content(content, source)
discovered_content.extend(processed_content)
except Exception as e:
self.logger.error(f"Content discovery failed for {source.url}: {str(e)}")
return discovered_content
async def _extract_content_from_source(self, source: ContentSource) -> Optional[str]:
"""Extract content from different source types"""
try:
if source.source_type == "web":
loader = WebBaseLoader(source.url)
documents = loader.load()
return "\n".join([doc.page_content for doc in documents])
elif source.source_type == "pdf":
loader = PDFPlumberLoader(source.url)
documents = loader.load()
return "\n".join([doc.page_content for doc in documents])
elif source.source_type == "confluence":
return await self._extract_from_confluence(source.url)
elif source.source_type == "slack":
return await self._extract_from_slack(source.url)
elif source.source_type == "email":
return await self._extract_from_email(source.url)
else:
self.logger.warning(f"Unsupported source type: {source.source_type}")
return None
except Exception as e:
self.logger.error(f"Content extraction failed: {str(e)}")
return None
async def _extract_from_confluence(self, url: str) -> str:
"""Extract content from Confluence pages"""
# Implementation for Confluence API
try:
import requests
# Get Confluence credentials from config
auth = (
self.config['confluence']['username'],
self.config['confluence']['api_token']
)
# Extract page ID from URL
page_id = re.search(r'pageId=(\d+)', url)
if not page_id:
return None
# Fetch page content
api_url = f"{self.config['confluence']['base_url']}/rest/api/content/{page_id.group(1)}?expand=body.storage"
response = requests.get(api_url, auth=auth)
if response.status_code == 200:
data = response.json()
# Extract text from HTML content
from bs4 import BeautifulSoup
soup = BeautifulSoup(data['body']['storage']['value'], 'html.parser')
return soup.get_text()
return None
except Exception as e:
self.logger.error(f"Confluence extraction failed: {str(e)}")
return None
async def _extract_from_slack(self, channel_id: str) -> str:
"""Extract content from Slack channels"""
try:
import slack_sdk
client = slack_sdk.WebClient(token=self.config['slack']['bot_token'])
# Get channel history
response = client.conversations_history(
channel=channel_id,
limit=1000
)
if response['ok']:
messages = []
for message in response['messages']:
if 'text' in message:
messages.append(message['text'])
return "\n".join(messages)
return None
except Exception as e:
self.logger.error(f"Slack extraction failed: {str(e)}")
return None
async def _extract_from_email(self, mailbox_path: str) -> str:
"""Extract content from email archives"""
try:
# Implementation would depend on email system (Exchange, Gmail, etc.)
# This is a placeholder for email extraction logic
self.logger.info(f"Email extraction not implemented for: {mailbox_path}")
return None
except Exception as e:
self.logger.error(f"Email extraction failed: {str(e)}")
return None
async def _process_discovered_content(self, content: str, source: ContentSource) -> List[Dict]:
"""Process and classify discovered content"""
try:
# Split content into meaningful chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = text_splitter.split_text(content)
processed_chunks = []
for i, chunk in enumerate(chunks):
# Extract entities and topics
entities = await self._extract_entities(chunk)
topics = await self._extract_topics(chunk)
# Classify content type
content_type = await self._classify_content_type(chunk)
# Calculate quality score
quality_score = await self._calculate_content_quality(chunk)
# Check for potential knowledge article
is_knowledge_worthy = await self._assess_knowledge_worthiness(chunk)
processed_chunk = {
'source_id': source.source_id,
'chunk_id': f"{source.source_id}_{i}",
'content': chunk,
'entities': entities,
'topics': topics,
'content_type': content_type,
'quality_score': quality_score,
'is_knowledge_worthy': is_knowledge_worthy,
'source_url': source.url,
'discovered_at': datetime.now().isoformat()
}
processed_chunks.append(processed_chunk)
return processed_chunks
except Exception as e:
self.logger.error(f"Content processing failed: {str(e)}")
return []
async def _extract_entities(self, text: str) -> List[Dict]:
"""Extract named entities from text"""
try:
doc = self.nlp_model(text)
entities = []
for ent in doc.ents:
entities.append({
'text': ent.text,
'label': ent.label_,
'start': ent.start_char,
'end': ent.end_char,
'confidence': float(ent._.confidence) if hasattr(ent._, 'confidence') else 1.0
})
return entities
except Exception as e:
self.logger.error(f"Entity extraction failed: {str(e)}")
return []
async def _extract_topics(self, text: str) -> List[str]:
"""Extract topics from text"""
try:
# Use transformer-based topic modeling
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
# Split into sentences
sentences = [sent.text.strip() for sent in self.nlp_model(text).sents]
if len(sentences) < 2:
return []
# Get embeddings
embeddings = model.encode(sentences)
# Cluster sentences to find topics
num_clusters = min(5, len(sentences))
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(embeddings)
# Extract representative topics
topics = []
for i in range(num_clusters):
cluster_sentences = [sentences[j] for j, label in enumerate(cluster_labels) if label == i]
if cluster_sentences:
# Use the most central sentence as topic
topics.append(cluster_sentences[0][:100]) # Truncate for brevity
return topics
except Exception as e:
self.logger.error(f"Topic extraction failed: {str(e)}")
return []
async def _classify_content_type(self, text: str) -> str:
"""Classify the type of content"""
try:
# Define classification patterns
patterns = {
'troubleshooting': [
r'error|issue|problem|fix|solve|troubleshoot',
r'steps to|how to|resolution|workaround'
],
'documentation': [
r'overview|introduction|guide|manual|documentation',
r'getting started|setup|configuration|installation'
],
'faq': [
r'frequently asked|common questions|q&a|question',
r'what is|how does|why does|when should'
],
'tutorial': [
r'tutorial|walkthrough|step by step|lesson',
r'learn|example|demo|practice'
],
'reference': [
r'api|specification|schema|reference|parameters',
r'syntax|format|structure|definition'
]
}
scores = {}
text_lower = text.lower()
for content_type, pattern_list in patterns.items():
score = 0
for pattern in pattern_list:
matches = len(re.findall(pattern, text_lower))
score += matches
scores[content_type] = score
# Return the type with highest score
if max(scores.values()) > 0:
return max(scores, key=scores.get)
else:
return 'general'
except Exception as e:
self.logger.error(f"Content classification failed: {str(e)}")
return 'unknown'
async def _calculate_content_quality(self, text: str) -> float:
"""Calculate content quality score"""
try:
score = 0.0
max_score = 100.0
# Length factor (20 points)
word_count = len(text.split())
if word_count > 200:
score += 20
elif word_count > 100:
score += 15
elif word_count > 50:
score += 10
else:
score += 5
# Structure factor (20 points)
has_headers = bool(re.search(r'#+\s+|^[A-Z][^.]*:$', text, re.MULTILINE))
has_lists = bool(re.search(r'^\s*[\-\*\+\d]\s+', text, re.MULTILINE))
has_code = bool(re.search(r'```|`[^`]+`', text))
if has_headers:
score += 8
if has_lists:
score += 6
if has_code:
score += 6
# Technical content factor (20 points)
technical_indicators = [
'configuration', 'installation', 'error', 'solution',
'command', 'parameter', 'function', 'method',
'troubleshoot', 'debug', 'fix', 'resolve'
]
technical_score = sum(1 for indicator in technical_indicators
if indicator in text.lower())
score += min(technical_score * 2, 20)
# Readability factor (20 points)
sentences = len([sent for sent in self.nlp_model(text).sents])
if sentences > 0:
avg_words_per_sentence = word_count / sentences
if 10 <= avg_words_per_sentence <= 25:
score += 20
elif 8 <= avg_words_per_sentence <= 30:
score += 15
else:
score += 10
# Completeness factor (20 points)
completeness_indicators = [
'example', 'step', 'result', 'output',
'screenshot', 'diagram', 'note', 'warning'
]
completeness_score = sum(1 for indicator in completeness_indicators
if indicator in text.lower())
score += min(completeness_score * 3, 20)
return min(score, max_score)
except Exception as e:
self.logger.error(f"Quality calculation failed: {str(e)}")
return 50.0
async def _assess_knowledge_worthiness(self, text: str) -> bool:
"""Assess if content is worthy of becoming a knowledge article"""
try:
# Check minimum quality threshold
quality_score = await self._calculate_content_quality(text)
if quality_score < 60:
return False
# Check for problem-solution pattern
has_problem = bool(re.search(r'error|issue|problem|fail|exception', text, re.IGNORECASE))
has_solution = bool(re.search(r'fix|solve|resolution|workaround|solution', text, re.IGNORECASE))
if has_problem and has_solution:
return True
# Check for instructional content
has_instructions = bool(re.search(r'step|how to|install|configure|setup', text, re.IGNORECASE))
if has_instructions and len(text.split()) > 100:
return True
# Check for technical documentation
has_technical = bool(re.search(r'api|parameter|function|method|configuration', text, re.IGNORECASE))
if has_technical and quality_score > 70:
return True
return False
except Exception as e:
self.logger.error(f"Knowledge worthiness assessment failed: {str(e)}")
return False
async def identify_knowledge_gaps(self, search_queries: List[str], existing_articles: List[Dict]) -> List[KnowledgeGap]:
"""Identify knowledge gaps based on user queries and existing content"""
try:
gaps = []
# Analyze search queries for patterns
query_analysis = await self._analyze_search_patterns(search_queries)
# Find gaps between queries and existing content
content_topics = await self._extract_existing_content_topics(existing_articles)
for query_pattern in query_analysis:
gap = await self._detect_knowledge_gap(query_pattern, content_topics)
if gap:
gaps.append(gap)
# Prioritize gaps
prioritized_gaps = await self._prioritize_knowledge_gaps(gaps)
return prioritized_gaps
except Exception as e:
self.logger.error(f"Knowledge gap identification failed: {str(e)}")
return []
async def _analyze_search_patterns(self, queries: List[str]) -> List[Dict]:
"""Analyze search query patterns"""
try:
# Group similar queries
query_embeddings = []
for query in queries:
embedding = await self.embedding_model.aembed_query(query)
query_embeddings.append(embedding)
# Cluster similar queries
if len(query_embeddings) > 1:
kmeans = KMeans(n_clusters=min(10, len(queries)), random_state=42)
cluster_labels = kmeans.fit_predict(query_embeddings)
# Group queries by cluster
clusters = {}
for i, label in enumerate(cluster_labels):
if label not in clusters:
clusters[label] = []
clusters[label].append(queries[i])
# Analyze each cluster
patterns = []
for label, cluster_queries in clusters.items():
if len(cluster_queries) >= 3: # Minimum threshold for a pattern
pattern = {
'queries': cluster_queries,
'frequency': len(cluster_queries),
'representative_query': cluster_queries[0],
'topics': await self._extract_topics(' '.join(cluster_queries))
}
patterns.append(pattern)
return patterns
return []
except Exception as e:
self.logger.error(f"Search pattern analysis failed: {str(e)}")
return []
async def _extract_existing_content_topics(self, articles: List[Dict]) -> List[str]:
"""Extract topics from existing knowledge articles"""
try:
all_content = ' '.join([article.get('content', '') for article in articles])
topics = await self._extract_topics(all_content)
return topics
except Exception as e:
self.logger.error(f"Existing content topic extraction failed: {str(e)}")
return []
async def _detect_knowledge_gap(self, query_pattern: Dict, existing_topics: List[str]) -> Optional[KnowledgeGap]:
"""Detect if a query pattern represents a knowledge gap"""
try:
pattern_topics = query_pattern['topics']
# Calculate similarity with existing topics
if existing_topics and pattern_topics:
# Use embedding similarity
pattern_text = ' '.join(pattern_topics)
existing_text = ' '.join(existing_topics)
pattern_embedding = await self.embedding_model.aembed_query(pattern_text)
existing_embedding = await self.embedding_model.aembed_query(existing_text)
similarity = cosine_similarity([pattern_embedding], [existing_embedding])[0][0]
# If similarity is low, it's likely a gap
if similarity < 0.6: # Threshold for gap detection
gap = KnowledgeGap(
gap_id=f"gap_{hash(pattern_text) % 10000}",
topic=pattern_text[:100],
description=f"Knowledge gap detected for queries: {query_pattern['representative_query']}",
priority=await self._calculate_gap_priority(query_pattern),
confidence=1 - similarity,
related_queries=query_pattern['queries'],
suggested_experts=await self._suggest_experts_for_gap(pattern_topics),
estimated_effort=await self._estimate_gap_effort(query_pattern)
)
return gap
return None
except Exception as e:
self.logger.error(f"Knowledge gap detection failed: {str(e)}")
return None
async def _calculate_gap_priority(self, query_pattern: Dict) -> str:
"""Calculate priority for a knowledge gap"""
frequency = query_pattern['frequency']
if frequency >= 20:
return "high"
elif frequency >= 10:
return "medium"
elif frequency >= 5:
return "low"
else:
return "very_low"
async def _suggest_experts_for_gap(self, topics: List[str]) -> List[str]:
"""Suggest potential experts for filling a knowledge gap"""
try:
# This would typically integrate with HR systems or expert directories
# For now, return placeholder experts based on topics
expert_mapping = {
'kubernetes': ['kubernetes-team@company.com'],
'database': ['dba-team@company.com'],
'network': ['network-team@company.com'],
'security': ['security-team@company.com'],
'api': ['api-team@company.com']
}
suggested_experts = []
for topic in topics:
topic_lower = topic.lower()
for keyword, experts in expert_mapping.items():
if keyword in topic_lower:
suggested_experts.extend(experts)
return list(set(suggested_experts)) # Remove duplicates
except Exception as e:
self.logger.error(f"Expert suggestion failed: {str(e)}")
return []
async def _estimate_gap_effort(self, query_pattern: Dict) -> str:
"""Estimate effort required to fill a knowledge gap"""
complexity_indicators = [
'complex', 'advanced', 'enterprise', 'architecture',
'integration', 'multi-step', 'comprehensive'
]
query_text = ' '.join(query_pattern['queries']).lower()
complexity_score = sum(1 for indicator in complexity_indicators
if indicator in query_text)
if complexity_score >= 3:
return "high"
elif complexity_score >= 1:
return "medium"
else:
return "low"
async def _prioritize_knowledge_gaps(self, gaps: List[KnowledgeGap]) -> List[KnowledgeGap]:
"""Prioritize knowledge gaps"""
try:
# Sort by priority and confidence
priority_order = {"high": 4, "medium": 3, "low": 2, "very_low": 1}
sorted_gaps = sorted(
gaps,
key=lambda x: (priority_order.get(x.priority, 0), x.confidence),
reverse=True
)
return sorted_gaps
except Exception as e:
self.logger.error(f"Gap prioritization failed: {str(e)}")
return gaps
async def generate_content_recommendations(self, gaps: List[KnowledgeGap]) -> List[Dict]:
"""Generate content creation recommendations"""
try:
recommendations = []
for gap in gaps:
# Generate content outline using AI
outline = await self._generate_content_outline(gap)
recommendation = {
'gap_id': gap.gap_id,
'recommended_title': await self._suggest_article_title(gap),
'content_outline': outline,
'target_audience': await self._identify_target_audience(gap),
'estimated_effort': gap.estimated_effort,
'priority': gap.priority,
'suggested_experts': gap.suggested_experts,
'related_queries': gap.related_queries
}
recommendations.append(recommendation)
return recommendations
except Exception as e:
self.logger.error(f"Content recommendation generation failed: {str(e)}")
return []
async def _generate_content_outline(self, gap: KnowledgeGap) -> List[str]:
"""Generate content outline for a knowledge gap"""
try:
prompt = f"""
Generate a detailed content outline for a knowledge article that addresses the following topic:
Topic: {gap.topic}
Related queries: {', '.join(gap.related_queries[:5])}
The outline should include:
1. Introduction/Overview
2. Prerequisites
3. Main content sections (3-5 sections)
4. Examples or use cases
5. Troubleshooting or common issues
6. Conclusion/Next steps
Provide a bulleted outline:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=500,
temperature=0.3
)
outline_text = response.choices[0].message.content.strip()
# Parse outline into list
outline_lines = [line.strip() for line in outline_text.split('\n')
if line.strip() and (line.strip().startswith('-') or line.strip().startswith('*'))]
return outline_lines
except Exception as e:
self.logger.error(f"Content outline generation failed: {str(e)}")
return [
"- Introduction and overview",
"- Prerequisites and requirements",
"- Step-by-step instructions",
"- Examples and use cases",
"- Troubleshooting common issues",
"- Conclusion and next steps"
]
async def _suggest_article_title(self, gap: KnowledgeGap) -> str:
"""Suggest a title for the knowledge article"""
try:
# Extract key terms from queries
key_terms = []
for query in gap.related_queries:
# Extract nouns and important terms
doc = self.nlp_model(query)
terms = [token.text for token in doc if token.pos_ in ['NOUN', 'PROPN'] and len(token.text) > 2]
key_terms.extend(terms)
# Get most common terms
from collections import Counter
common_terms = Counter(key_terms).most_common(3)
if common_terms:
main_terms = [term[0] for term in common_terms]
title = f"Complete Guide to {' and '.join(main_terms)}"
else:
title = f"Guide to {gap.topic[:50]}"
return title
except Exception as e:
self.logger.error(f"Title suggestion failed: {str(e)}")
return f"Knowledge Article: {gap.topic[:50]}"
async def _identify_target_audience(self, gap: KnowledgeGap) -> List[str]:
"""Identify target audience for the knowledge article"""
try:
audience_indicators = {
'developer': ['code', 'api', 'programming', 'development', 'debug'],
'admin': ['configuration', 'setup', 'installation', 'server', 'system'],
'user': ['how to', 'guide', 'tutorial', 'help', 'using'],
'support': ['troubleshoot', 'error', 'issue', 'problem', 'fix']
}
query_text = ' '.join(gap.related_queries).lower()
audiences = []
for audience, indicators in audience_indicators.items():
if any(indicator in query_text for indicator in indicators):
audiences.append(audience)
return audiences if audiences else ['general']
except Exception as e:
self.logger.error(f"Target audience identification failed: {str(e)}")
return ['general']
# Usage example
async def main():
config = {
'openai': {'api_key': 'your-openai-key'},
'confluence': {
'username': 'user@company.com',
'api_token': 'token',
'base_url': 'https://company.atlassian.net'
},
'slack': {'bot_token': 'xoxb-token'}
}
discovery = AIKnowledgeDiscovery(config)
await discovery.initialize()
# Example content sources
sources = [
ContentSource(
source_id="conf_001",
source_type="confluence",
url="https://company.atlassian.net/wiki/spaces/ENG/pages/123456",
last_crawled=datetime.now(),
content_hash="",
extraction_status="pending"
)
]
# Discover content
discovered_content = await discovery.discover_content_from_sources(sources)
print(f"Discovered {len(discovered_content)} content pieces")
# Example search queries for gap analysis
search_queries = [
"How to debug Kubernetes pod crashes",
"Kubernetes troubleshooting pod issues",
"Pod not starting kubernetes",
"How to configure SSL certificates",
"SSL certificate installation",
"Certificate expired error"
]
# Identify knowledge gaps
gaps = await discovery.identify_knowledge_gaps(search_queries, [])
print(f"Identified {len(gaps)} knowledge gaps")
# Generate recommendations
recommendations = await discovery.generate_content_recommendations(gaps)
print(f"Generated {len(recommendations)} content recommendations")
if __name__ == "__main__":
asyncio.run(main())
Automated Content Generation and Curation
AI-Powered Content Creation Pipeline
#!/usr/bin/env python3
"""
Automated Content Generation and Curation System
AI-powered knowledge article creation and quality management
"""
import asyncio
import logging
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import json
import re
from datetime import datetime
import hashlib
import openai
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
import spacy
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
@dataclass
class ContentTemplate:
template_id: str
name: str
description: str
content_type: str
structure: List[str]
required_sections: List[str]
optional_sections: List[str]
target_length: int
quality_criteria: Dict[str, float]
@dataclass
class GeneratedContent:
content_id: str
title: str
content: str
template_used: str
quality_score: float
confidence_score: float
generation_method: str
source_materials: List[str]
review_status: str
created_at: datetime
class AutomatedContentGenerator:
def __init__(self, config: Dict):
self.config = config
self.logger = self._setup_logging()
self.nlp_model = None
self.llm = None
self.embeddings = None
self.quality_classifier = None
self.content_templates = {}
def _setup_logging(self) -> logging.Logger:
"""Configure logging"""
logger = logging.getLogger('content-generator')
logger.setLevel(logging.INFO)
return logger
async def initialize(self):
"""Initialize AI models and templates"""
# Initialize OpenAI
openai.api_key = self.config['openai']['api_key']
self.llm = OpenAI(temperature=0.3, openai_api_key=self.config['openai']['api_key'])
self.embeddings = OpenAIEmbeddings(openai_api_key=self.config['openai']['api_key'])
# Initialize NLP model
self.nlp_model = spacy.load("en_core_web_lg")
# Initialize quality classifier
self.quality_classifier = pipeline(
"text-classification",
model="microsoft/DialoGPT-medium"
)
# Load content templates
await self._load_content_templates()
self.logger.info("Automated Content Generator initialized")
async def _load_content_templates(self):
"""Load predefined content templates"""
templates = {
'troubleshooting': ContentTemplate(
template_id='troubleshooting',
name='Troubleshooting Guide',
description='Template for troubleshooting and problem-solving articles',
content_type='troubleshooting',
structure=[
'Problem Description',
'Symptoms',
'Root Cause Analysis',
'Solution Steps',
'Verification',
'Prevention'
],
required_sections=['Problem Description', 'Solution Steps', 'Verification'],
optional_sections=['Root Cause Analysis', 'Prevention'],
target_length=1500,
quality_criteria={
'clarity': 0.8,
'completeness': 0.9,
'accuracy': 0.95,
'usefulness': 0.85
}
),
'howto': ContentTemplate(
template_id='howto',
name='How-To Guide',
description='Template for instructional and how-to articles',
content_type='howto',
structure=[
'Overview',
'Prerequisites',
'Step-by-Step Instructions',
'Examples',
'Common Issues',
'Next Steps'
],
required_sections=['Overview', 'Step-by-Step Instructions'],
optional_sections=['Prerequisites', 'Examples', 'Common Issues'],
target_length=2000,
quality_criteria={
'clarity': 0.9,
'completeness': 0.8,
'accuracy': 0.9,
'usefulness': 0.9
}
),
'reference': ContentTemplate(
template_id='reference',
name='Reference Documentation',
description='Template for reference and API documentation',
content_type='reference',
structure=[
'Introduction',
'Syntax/Parameters',
'Description',
'Examples',
'Return Values',
'Notes/Limitations'
],
required_sections=['Introduction', 'Syntax/Parameters', 'Description'],
optional_sections=['Examples', 'Return Values', 'Notes/Limitations'],
target_length=1000,
quality_criteria={
'clarity': 0.85,
'completeness': 0.95,
'accuracy': 0.95,
'usefulness': 0.8
}
),
'faq': ContentTemplate(
template_id='faq',
name='FAQ Article',
description='Template for frequently asked questions',
content_type='faq',
structure=[
'Question',
'Short Answer',
'Detailed Explanation',
'Related Questions',
'Additional Resources'
],
required_sections=['Question', 'Short Answer'],
optional_sections=['Detailed Explanation', 'Related Questions'],
target_length=800,
quality_criteria={
'clarity': 0.9,
'completeness': 0.8,
'accuracy': 0.9,
'usefulness': 0.85
}
)
}
self.content_templates = templates
async def generate_article_from_gap(self, knowledge_gap: Dict, source_materials: List[str] = None) -> GeneratedContent:
"""Generate a knowledge article from an identified knowledge gap"""
try:
# Determine best template for the gap
template = await self._select_template_for_gap(knowledge_gap)
# Generate content using the template
generated_content = await self._generate_content_with_template(
knowledge_gap, template, source_materials
)
# Enhance content with AI
enhanced_content = await self._enhance_generated_content(generated_content)
# Calculate quality scores
quality_score = await self._calculate_generated_quality(enhanced_content)
confidence_score = await self._calculate_confidence_score(enhanced_content, source_materials)
return GeneratedContent(
content_id=hashlib.md5(enhanced_content.encode()).hexdigest()[:16],
title=await self._generate_title(knowledge_gap, enhanced_content),
content=enhanced_content,
template_used=template.template_id,
quality_score=quality_score,
confidence_score=confidence_score,
generation_method='ai_template',
source_materials=source_materials or [],
review_status='pending',
created_at=datetime.now()
)
except Exception as e:
self.logger.error(f"Article generation failed: {str(e)}")
raise
async def _select_template_for_gap(self, knowledge_gap: Dict) -> ContentTemplate:
"""Select the most appropriate template for a knowledge gap"""
try:
gap_text = knowledge_gap.get('description', '') + ' ' + ' '.join(knowledge_gap.get('related_queries', []))
gap_text_lower = gap_text.lower()
# Template selection logic based on keywords
if any(keyword in gap_text_lower for keyword in ['error', 'issue', 'problem', 'troubleshoot', 'fix']):
return self.content_templates['troubleshooting']
elif any(keyword in gap_text_lower for keyword in ['how to', 'guide', 'tutorial', 'setup', 'install']):
return self.content_templates['howto']
elif any(keyword in gap_text_lower for keyword in ['api', 'reference', 'specification', 'parameter']):
return self.content_templates['reference']
elif any(keyword in gap_text_lower for keyword in ['question', 'what is', 'why', 'when']):
return self.content_templates['faq']
else:
# Default to how-to guide
return self.content_templates['howto']
except Exception as e:
self.logger.error(f"Template selection failed: {str(e)}")
return self.content_templates['howto'] # Default template
async def _generate_content_with_template(self, knowledge_gap: Dict, template: ContentTemplate, source_materials: List[str]) -> str:
"""Generate content using a specific template"""
try:
# Create sections based on template structure
sections = {}
for section_name in template.structure:
section_content = await self._generate_section_content(
section_name, knowledge_gap, template, source_materials
)
sections[section_name] = section_content
# Combine sections into full article
article_content = ""
for section_name in template.structure:
if section_name in sections and sections[section_name]:
article_content += f"## {section_name}\n\n"
article_content += sections[section_name] + "\n\n"
return article_content.strip()
except Exception as e:
self.logger.error(f"Content generation with template failed: {str(e)}")
raise
async def _generate_section_content(self, section_name: str, knowledge_gap: Dict, template: ContentTemplate, source_materials: List[str]) -> str:
"""Generate content for a specific section"""
try:
# Create section-specific prompts
prompts = {
'Problem Description': self._create_problem_description_prompt,
'Symptoms': self._create_symptoms_prompt,
'Root Cause Analysis': self._create_root_cause_prompt,
'Solution Steps': self._create_solution_steps_prompt,
'Verification': self._create_verification_prompt,
'Prevention': self._create_prevention_prompt,
'Overview': self._create_overview_prompt,
'Prerequisites': self._create_prerequisites_prompt,
'Step-by-Step Instructions': self._create_instructions_prompt,
'Examples': self._create_examples_prompt,
'Common Issues': self._create_common_issues_prompt,
'Next Steps': self._create_next_steps_prompt,
'Introduction': self._create_introduction_prompt,
'Syntax/Parameters': self._create_syntax_prompt,
'Description': self._create_description_prompt,
'Return Values': self._create_return_values_prompt,
'Notes/Limitations': self._create_notes_prompt,
'Question': self._create_question_prompt,
'Short Answer': self._create_short_answer_prompt,
'Detailed Explanation': self._create_detailed_explanation_prompt,
'Related Questions': self._create_related_questions_prompt,
'Additional Resources': self._create_additional_resources_prompt
}
prompt_function = prompts.get(section_name, self._create_generic_prompt)
prompt = prompt_function(knowledge_gap, source_materials)
# Generate content using OpenAI
response = await openai.ChatCompletion.acreate(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=800,
temperature=0.3
)
return response.choices[0].message.content.strip()
except Exception as e:
self.logger.error(f"Section content generation failed for {section_name}: {str(e)}")
return f"Content for {section_name} section needs to be developed."
def _create_problem_description_prompt(self, knowledge_gap: Dict, source_materials: List[str]) -> str:
"""Create prompt for problem description section"""
return f"""
Write a clear problem description section for a troubleshooting article.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related User Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
The problem description should:
1. Clearly state what issue users are experiencing
2. Describe the context where this problem occurs
3. Mention any error messages or symptoms
4. Be concise but comprehensive
Write the problem description:
"""
def _create_solution_steps_prompt(self, knowledge_gap: Dict, source_materials: List[str]) -> str:
"""Create prompt for solution steps section"""
source_context = '\n'.join(source_materials[:2]) if source_materials else "No specific source materials provided."
return f"""
Write a detailed solution steps section for a troubleshooting article.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related User Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
Source Context:
{source_context}
The solution steps should:
1. Be numbered and easy to follow
2. Include specific commands or actions where applicable
3. Explain what each step accomplishes
4. Include verification steps
5. Be technically accurate and complete
Write the solution steps:
"""
def _create_overview_prompt(self, knowledge_gap: Dict, source_materials: List[str]) -> str:
"""Create prompt for overview section"""
return f"""
Write an overview section for a how-to guide.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related User Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
The overview should:
1. Explain what the guide will teach
2. Mention the benefits of following the guide
3. Give a high-level summary of the process
4. Set appropriate expectations
Write the overview:
"""
def _create_instructions_prompt(self, knowledge_gap: Dict, source_materials: List[str]) -> str:
"""Create prompt for step-by-step instructions"""
source_context = '\n'.join(source_materials[:2]) if source_materials else "No specific source materials provided."
return f"""
Write detailed step-by-step instructions for a how-to guide.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related User Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
Source Context:
{source_context}
The instructions should:
1. Be numbered and sequential
2. Include specific commands, screenshots references, or actions
3. Explain the purpose of each step
4. Include expected outcomes
5. Be complete and actionable
Write the step-by-step instructions:
"""
def _create_generic_prompt(self, knowledge_gap: Dict, source_materials: List[str]) -> str:
"""Create generic prompt for any section"""
return f"""
Write content for a knowledge article section.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related User Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
Create informative, accurate, and helpful content that addresses the user's needs.
Write the content:
"""
async def _enhance_generated_content(self, content: str) -> str:
"""Enhance generated content with additional AI processing"""
try:
enhancement_prompt = f"""
Enhance the following technical content by:
1. Improving clarity and readability
2. Adding relevant technical details where appropriate
3. Ensuring proper formatting and structure
4. Adding helpful notes or warnings where relevant
5. Maintaining technical accuracy
Original content:
{content}
Enhanced content:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-4",
messages=[{"role": "user", "content": enhancement_prompt}],
max_tokens=2000,
temperature=0.2
)
enhanced = response.choices[0].message.content.strip()
# Add formatting improvements
enhanced = await self._improve_formatting(enhanced)
return enhanced
except Exception as e:
self.logger.error(f"Content enhancement failed: {str(e)}")
return content # Return original if enhancement fails
async def _improve_formatting(self, content: str) -> str:
"""Improve content formatting"""
try:
# Add code block formatting for commands
content = re.sub(
r'`([^`]+)`',
r'```\n\1\n```',
content
)
# Ensure proper spacing around headers
content = re.sub(r'(#+\s+.*)\n([^\n])', r'\1\n\n\2', content)
# Ensure proper bullet point formatting
content = re.sub(r'^(\s*)[\-\*]\s+', r'\1- ', content, flags=re.MULTILINE)
# Ensure proper numbered list formatting
content = re.sub(r'^(\s*)(\d+)\.\s+', r'\1\2. ', content, flags=re.MULTILINE)
return content
except Exception as e:
self.logger.error(f"Formatting improvement failed: {str(e)}")
return content
async def _generate_title(self, knowledge_gap: Dict, content: str) -> str:
"""Generate an appropriate title for the article"""
try:
title_prompt = f"""
Generate a clear, descriptive title for a knowledge article.
Knowledge Gap Topic: {knowledge_gap.get('topic', 'Unknown')}
Related Queries: {', '.join(knowledge_gap.get('related_queries', [])[:3])}
Content Preview: {content[:300]}...
The title should:
1. Be descriptive and specific
2. Include key technical terms
3. Be under 80 characters
4. Appeal to the target audience
5. Follow best practices for knowledge article titles
Generate the title:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": title_prompt}],
max_tokens=100,
temperature=0.3
)
title = response.choices[0].message.content.strip()
# Clean up title
title = title.strip('"\'')
title = re.sub(r'^Title:\s*', '', title, flags=re.IGNORECASE)
return title
except Exception as e:
self.logger.error(f"Title generation failed: {str(e)}")
return f"Guide to {knowledge_gap.get('topic', 'Technical Topic')[:50]}"
async def _calculate_generated_quality(self, content: str) -> float:
"""Calculate quality score for generated content"""
try:
quality_factors = {}
# Length factor
word_count = len(content.split())
if word_count >= 500:
quality_factors['length'] = 1.0
elif word_count >= 300:
quality_factors['length'] = 0.8
elif word_count >= 150:
quality_factors['length'] = 0.6
else:
quality_factors['length'] = 0.4
# Structure factor
has_headers = len(re.findall(r'^#+\s+', content, re.MULTILINE)) >= 2
has_lists = bool(re.search(r'^\s*[\-\*\+\d]\s+', content, re.MULTILINE))
has_code = bool(re.search(r'```|`[^`]+`', content))
structure_score = 0
if has_headers:
structure_score += 0.4
if has_lists:
structure_score += 0.3
if has_code:
structure_score += 0.3
quality_factors['structure'] = structure_score
# Technical content factor
technical_keywords = [
'configure', 'install', 'error', 'solution', 'command',
'parameter', 'troubleshoot', 'debug', 'step', 'example'
]
keyword_count = sum(1 for keyword in technical_keywords
if keyword.lower() in content.lower())
quality_factors['technical'] = min(keyword_count / 5, 1.0)
# Completeness factor (based on section variety)
section_keywords = [
'overview', 'prerequisite', 'step', 'example',
'troubleshoot', 'conclusion', 'note', 'warning'
]
section_count = sum(1 for keyword in section_keywords
if keyword.lower() in content.lower())
quality_factors['completeness'] = min(section_count / 4, 1.0)
# Calculate weighted average
weights = {
'length': 0.2,
'structure': 0.3,
'technical': 0.3,
'completeness': 0.2
}
total_score = sum(quality_factors[factor] * weights[factor]
for factor in quality_factors)
return total_score * 100 # Convert to 0-100 scale
except Exception as e:
self.logger.error(f"Quality calculation failed: {str(e)}")
return 70.0 # Default quality score
async def _calculate_confidence_score(self, content: str, source_materials: List[str]) -> float:
"""Calculate confidence score for generated content"""
try:
confidence_factors = {}
# Source material factor
if source_materials:
# Calculate how much of the content is supported by sources
source_text = ' '.join(source_materials).lower()
content_words = content.lower().split()
supported_words = sum(1 for word in content_words
if len(word) > 3 and word in source_text)
confidence_factors['source_support'] = min(supported_words / len(content_words), 1.0)
else:
confidence_factors['source_support'] = 0.3 # Lower confidence without sources
# Technical accuracy indicators
accuracy_indicators = [
'specific', 'example', 'command', 'code', 'screenshot',
'verify', 'test', 'result', 'output'
]
accuracy_count = sum(1 for indicator in accuracy_indicators
if indicator.lower() in content.lower())
confidence_factors['accuracy'] = min(accuracy_count / 5, 1.0)
# Completeness indicators
completeness_indicators = [
'step', 'instruction', 'prerequisite', 'requirement',
'note', 'warning', 'tip', 'important'
]
completeness_count = sum(1 for indicator in completeness_indicators
if indicator.lower() in content.lower())
confidence_factors['completeness'] = min(completeness_count / 4, 1.0)
# Calculate weighted average
weights = {
'source_support': 0.5,
'accuracy': 0.3,
'completeness': 0.2
}
total_confidence = sum(confidence_factors[factor] * weights[factor]
for factor in confidence_factors)
return total_confidence
except Exception as e:
self.logger.error(f"Confidence calculation failed: {str(e)}")
return 0.6 # Default confidence score
async def curate_existing_content(self, articles: List[Dict]) -> List[Dict]:
"""Curate and improve existing content"""
try:
curated_articles = []
for article in articles:
# Analyze content quality
quality_analysis = await self._analyze_content_quality(article)
# Generate improvement suggestions
improvements = await self._suggest_content_improvements(article, quality_analysis)
# Apply automatic improvements if applicable
if quality_analysis['auto_improvable']:
improved_content = await self._apply_automatic_improvements(article, improvements)
article['content'] = improved_content
article['improved'] = True
# Add curation metadata
article['curation_analysis'] = quality_analysis
article['improvement_suggestions'] = improvements
article['curation_date'] = datetime.now().isoformat()
curated_articles.append(article)
return curated_articles
except Exception as e:
self.logger.error(f"Content curation failed: {str(e)}")
return articles
async def _analyze_content_quality(self, article: Dict) -> Dict:
"""Analyze the quality of existing content"""
try:
content = article.get('content', '')
analysis = {
'quality_score': 0,
'issues': [],
'strengths': [],
'auto_improvable': False
}
# Length analysis
word_count = len(content.split())
if word_count < 100:
analysis['issues'].append('Content too short')
elif word_count > 200:
analysis['strengths'].append('Adequate length')
# Structure analysis
header_count = len(re.findall(r'^#+\s+', content, re.MULTILINE))
if header_count < 2:
analysis['issues'].append('Poor structure - needs more headers')
analysis['auto_improvable'] = True
else:
analysis['strengths'].append('Good structure')
# Technical content analysis
has_code = bool(re.search(r'```|`[^`]+`', content))
has_examples = 'example' in content.lower()
if not has_code and not has_examples:
analysis['issues'].append('Lacks code examples')
else:
analysis['strengths'].append('Contains technical examples')
# Readability analysis
sentences = content.split('.')
avg_sentence_length = sum(len(s.split()) for s in sentences) / len(sentences) if sentences else 0
if avg_sentence_length > 30:
analysis['issues'].append('Sentences too long')
analysis['auto_improvable'] = True
elif 10 <= avg_sentence_length <= 25:
analysis['strengths'].append('Good readability')
# Calculate overall quality score
quality_score = max(0, 100 - len(analysis['issues']) * 15 + len(analysis['strengths']) * 10)
analysis['quality_score'] = min(quality_score, 100)
return analysis
except Exception as e:
self.logger.error(f"Content quality analysis failed: {str(e)}")
return {'quality_score': 50, 'issues': [], 'strengths': [], 'auto_improvable': False}
async def _suggest_content_improvements(self, article: Dict, quality_analysis: Dict) -> List[Dict]:
"""Generate improvement suggestions for content"""
try:
suggestions = []
for issue in quality_analysis['issues']:
if 'too short' in issue:
suggestions.append({
'type': 'expand_content',
'priority': 'high',
'description': 'Add more detailed explanations and examples',
'auto_applicable': True
})
elif 'structure' in issue:
suggestions.append({
'type': 'improve_structure',
'priority': 'medium',
'description': 'Add more section headers and organize content better',
'auto_applicable': True
})
elif 'code examples' in issue:
suggestions.append({
'type': 'add_examples',
'priority': 'high',
'description': 'Include practical code examples and use cases',
'auto_applicable': False
})
elif 'too long' in issue:
suggestions.append({
'type': 'improve_readability',
'priority': 'medium',
'description': 'Break down long sentences and improve clarity',
'auto_applicable': True
})
return suggestions
except Exception as e:
self.logger.error(f"Improvement suggestion generation failed: {str(e)}")
return []
async def _apply_automatic_improvements(self, article: Dict, improvements: List[Dict]) -> str:
"""Apply automatic improvements to content"""
try:
content = article.get('content', '')
for improvement in improvements:
if improvement['auto_applicable']:
if improvement['type'] == 'improve_structure':
content = await self._add_structure_improvements(content)
elif improvement['type'] == 'improve_readability':
content = await self._improve_readability(content)
elif improvement['type'] == 'expand_content':
content = await self._expand_content(content, article)
return content
except Exception as e:
self.logger.error(f"Automatic improvement application failed: {str(e)}")
return article.get('content', '')
async def _add_structure_improvements(self, content: str) -> str:
"""Add structural improvements to content"""
try:
# Add headers based on content patterns
lines = content.split('\n')
improved_lines = []
for line in lines:
line = line.strip()
if not line:
improved_lines.append('')
continue
# Detect potential section headers
if line.endswith(':') and len(line.split()) <= 5:
improved_lines.append(f"## {line[:-1]}")
elif line.startswith('Step') and ':' in line:
improved_lines.append(f"### {line}")
else:
improved_lines.append(line)
return '\n'.join(improved_lines)
except Exception as e:
self.logger.error(f"Structure improvement failed: {str(e)}")
return content
async def _improve_readability(self, content: str) -> str:
"""Improve content readability"""
try:
improvement_prompt = f"""
Improve the readability of the following content by:
1. Breaking down long sentences
2. Using clearer language
3. Adding bullet points where appropriate
4. Maintaining technical accuracy
Original content:
{content}
Improved content:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": improvement_prompt}],
max_tokens=1500,
temperature=0.3
)
return response.choices[0].message.content.strip()
except Exception as e:
self.logger.error(f"Readability improvement failed: {str(e)}")
return content
async def _expand_content(self, content: str, article: Dict) -> str:
"""Expand content with additional relevant information"""
try:
expansion_prompt = f"""
Expand the following technical content by adding:
1. More detailed explanations
2. Additional context
3. Helpful tips or notes
4. Common variations or alternatives
Article Title: {article.get('title', 'Unknown')}
Current Content:
{content}
Expanded content:
"""
response = await openai.ChatCompletion.acreate(
model="gpt-4",
messages=[{"role": "user", "content": expansion_prompt}],
max_tokens=2000,
temperature=0.3
)
return response.choices[0].message.content.strip()
except Exception as e:
self.logger.error(f"Content expansion failed: {str(e)}")
return content
# Usage example
async def main():
config = {
'openai': {'api_key': 'your-openai-key'}
}
generator = AutomatedContentGenerator(config)
await generator.initialize()
# Example knowledge gap
knowledge_gap = {
'topic': 'Kubernetes Pod Troubleshooting',
'description': 'Users need help debugging pod crashes and startup issues',
'related_queries': [
'How to debug pod crashes',
'Pod not starting kubernetes',
'Troubleshoot kubernetes pods'
]
}
# Generate article
generated_article = await generator.generate_article_from_gap(knowledge_gap)
print(f"Generated article: {generated_article.title}")
print(f"Quality score: {generated_article.quality_score}")
print(f"Confidence score: {generated_article.confidence_score}")
if __name__ == "__main__":
asyncio.run(main())
Best Practices and Strategic Implementation
Enterprise Knowledge Management Implementation Guidelines
Strategic Planning and Architecture
- Conduct comprehensive knowledge audit before implementation
- Design scalable architecture supporting 100M+ documents
- Plan for multi-language and global deployment requirements
- Establish clear governance and ownership models
AI and Automation Integration
- Implement gradual AI adoption with human oversight
- Use machine learning for content classification and discovery
- Automate quality assurance and content curation processes
- Deploy intelligent search with semantic understanding
Security and Compliance Framework
- Implement zero-trust security model
- Classify all content according to sensitivity levels
- Ensure GDPR, SOX, and industry-specific compliance
- Regular security audits and penetration testing
User Experience and Adoption
- Design intuitive interfaces with personalization
- Implement comprehensive onboarding programs
- Provide advanced search capabilities with faceted filtering
- Enable collaborative features and social knowledge sharing
Performance and Scalability
- Design for horizontal scaling across multiple regions
- Implement comprehensive caching strategies
- Use content delivery networks for global performance
- Monitor and optimize query performance continuously
Content Quality and Lifecycle Management
- Establish quality scoring and automated improvement systems
- Implement content freshness monitoring and updates
- Create expert review and validation processes
- Design automated content retirement and archival
Analytics and Continuous Improvement
- Implement comprehensive usage analytics and reporting
- Use AI for knowledge gap identification and filling
- Monitor user satisfaction and content effectiveness
- Establish feedback loops for continuous optimization
Integration and Ecosystem
- Integrate with existing enterprise systems (CRM, ERP, ITSM)
- Provide APIs for third-party system integration
- Implement single sign-on and identity management
- Support various content import and export formats
This comprehensive enterprise knowledge management guide provides the framework for implementing AI-powered, secure, and scalable knowledge systems that can transform organizational learning and efficiency at enterprise scale. The combination of advanced automation, security controls, analytics, and strategic implementation guidelines ensures successful deployment and adoption across global enterprise environments.