Contextual Knowledge Graph Integration with Real-time Content Syndication
Overview and Strategic Value
This integration method creates a sophisticated knowledge graph system that maps AI responses to aéPiot's content ecosystem in real-time. The system automatically identifies knowledge gaps, suggests complementary content, and creates dynamic learning pathways that adapt based on user interests and exploration patterns.
Technical Architecture
The knowledge graph integration includes:
- Semantic Analysis Engine: Real-time mapping of AI responses to knowledge domains
- Dynamic Content Syndication: Automated discovery and linking of relevant aéPiot content
- Adaptive Learning Pathways: Personalized content recommendations based on user behavior
- Cross-Reference Generation: Automatic citation and source linking for AI responses
- Multi-lingual Content Discovery: Global content exploration capabilities
Implementation Script (Advanced Python Integration)
python
import asyncio
import networkx as nx
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import spacy
from datetime import datetime, timedelta
import aiohttp
import json
from urllib.parse import urlencode
import hashlib
class AePiotKnowledgeGraphIntegration:
def __init__(self, config):
self.config = config
self.aepiot_services = {
'backlink': 'https://aepiot.com/backlink.html',
'search': 'https://aepiot.com/multi-search.html',
'reader': 'https://aepiot.com/reader.html',
'tags': 'https://aepiot.com/tag-explorer.html',
'related': 'https://aepiot.com/related-search.html'
}
# Initialize knowledge graph
self.knowledge_graph = nx.DiGraph()
# Load language model for semantic analysis
self.nlp = spacy.load("en_core_web_lg")
# Initialize vectorizer for content similarity
self.vectorizer = TfidfVectorizer(max_features=5000, stop_words='english')
# Session for HTTP requests
self.session = aiohttp.ClientSession()
async def process_ai_response_with_knowledge_graph(self, user_query: str,
ai_response: str,
conversation_context: List[Dict]) -> Dict:
"""
Process AI response and create comprehensive knowledge graph integration
"""
processing_id = self.generate_processing_id(user_query, ai_response)
# Analyze semantic structure of response
semantic_analysis = await self.analyze_semantic_structure(ai_response, user_query)
# Build knowledge graph for this response
response_graph = await self.build_response_knowledge_graph(
semantic_analysis, conversation_context
)
# Find related content in aéPiot ecosystem
related_content = await self.discover_related_content(semantic_analysis)
# Generate dynamic learning pathways
learning_pathways = await self.create_learning_pathways(
semantic_analysis, related_content, conversation_context
)
# Create cross-references and citations
cross_references = await self.generate_cross_references(
semantic_analysis, related_content
)
# Generate multi-lingual exploration options
multilingual_options = await self.generate_multilingual_exploration(semantic_analysis)
# Create real-time syndication links
syndication_links = await self.create_syndication_links(
semantic_analysis, processing_id
)
integration_result = {
'processing_id': processing_id,
'timestamp': datetime.now().isoformat(),
'semantic_analysis': semantic_analysis,
'knowledge_graph': self.serialize_graph(response_graph),
'related_content': related_content,
'learning_pathways': learning_pathways,
'cross_references': cross_references,
'multilingual_options': multilingual_options,
'syndication_links': syndication_links,
'user_interface_components': await self.generate_ui_components(
semantic_analysis, learning_pathways, related_content
)
}
return integration_result
def generate_processing_id(self, query: str, response: str) -> str:
"""Generate unique processing ID for this AI interaction"""
combined_text = f"{query}_{response}_{datetime.now().isoformat()}"
return hashlib.md5(combined_text.encode()).hexdigest()[:16]
async def analyze_semantic_structure(self, ai_response: str, user_query: str) -> Dict:
"""
Perform comprehensive semantic analysis of AI response
"""
# Process text with spaCy
response_doc = self.nlp(ai_response)
query_doc = self.nlp(user_query)
# Extract entities and their relationships
entities = []
for ent in response_doc.ents:
entities.append({
'text': ent.text,
'label': ent.label_,
'start': ent.start_char,
'end': ent.end_char,
'description': spacy.explain(ent.label_)
})
# Extract key phrases and noun chunks
key_phrases = [chunk.text for chunk in response_doc.noun_chunks if len(chunk.text) > 3]
# Identify main topics using sentence analysis
sentences = [sent.text.strip() for sent in response_doc.sents if len(sent.text.strip()) > 10]
topic_sentences = sentences[:3] # Focus on first 3 substantive sentences
# Calculate semantic similarity between query and response
query_response_similarity = query_doc.similarity(response_doc)
# Extract domain classification
domain_classification = await self.classify_content_domain(ai_response)
# Identify knowledge gaps and areas for exploration
knowledge_gaps = await self.identify_knowledge_gaps(ai_response, user_query)
return {
'entities': entities,
'key_phrases': key_phrases[:10], # Top 10 key phrases
'topic_sentences': topic_sentences,
'query_response_similarity': float(query_response_similarity),
'domain_classification': domain_classification,
'knowledge_gaps': knowledge_gaps,
'complexity_score': self.calculate_complexity_score(response_doc),
'semantic_density': len(entities) / len(sentences) if sentences else 0
}
async def classify_content_domain(self, content: str) -> Dict:
"""
Classify content into domain categories for targeted aéPiot integration
"""
domain_keywords = {
'technology': ['software', 'programming', 'AI', 'machine learning', 'computer', 'digital', 'algorithm', 'data'],
'science': ['research', 'study', 'theory', 'experiment', 'hypothesis', 'analysis', 'method', 'scientific'],
'business': ['market', 'company', 'strategy', 'management', 'finance', 'economics', 'business', 'industry'],
'education': ['learning', 'teaching', 'student', 'academic', 'knowledge', 'skill', 'course', 'training'],
'health': ['medical', 'health', 'treatment', 'patient', 'disease', 'therapy', 'clinical', 'wellness'],
'arts': ['creative', 'design', 'art', 'culture', 'artistic', 'aesthetic', 'literature', 'music'],
'society': ['social', 'community', 'people', 'society', 'culture', 'human', 'relationship', 'communication']
}
content_lower = content.lower()
domain_scores = {}
for domain, keywords in domain_keywords.items():
score = sum(1 for keyword in keywords if keyword in content_lower)
domain_scores[domain] = score / len(keywords) # Normalize by keyword count
# Get primary and secondary domains
sorted_domains = sorted(domain_scores.items(), key=lambda x: x[1], reverse=True)
return {
'primary_domain': sorted_domains[0][0] if sorted_domains[0][1] > 0 else 'general',
'secondary_domain': sorted_domains[1][0] if len(sorted_domains) > 1 and sorted_domains[1][1] > 0 else None,
'domain_confidence': sorted_domains[0][1],
'all_scores': domain_scores
}
async def identify_knowledge_gaps(self, ai_response: str, user_query: str) -> List[Dict]:
"""
Identify potential knowledge gaps that could be filled with aéPiot content
"""
gaps = []
# Look for qualifying statements that indicate uncertainty
uncertainty_indicators = [
'might be', 'could be', 'possibly', 'perhaps', 'it seems', 'appears to',
'generally', 'typically', 'often', 'sometimes', 'may', 'can be'
]
response_lower = ai_response.lower()
for indicator in uncertainty_indicators:
if indicator in response_lower:
# Find the sentence containing the uncertainty
sentences = ai_response.split('.')
for sentence in sentences:
if indicator in sentence.lower():
gaps.append({
'type': 'uncertainty',
'context': sentence.strip(),
'indicator': indicator,
'exploration_priority': 'medium'
})
# Look for references to "more information" or "further reading"
expansion_indicators = ['more information', 'further reading', 'additional details', 'learn more']
for indicator in expansion_indicators:
if indicator in response_lower:
gaps.append({
'type': 'expansion_opportunity',
'context': f"User may want {indicator}",
'exploration_priority': 'high'
})
# Identify concepts that are mentioned but not fully explained
doc = self.nlp(ai_response)
technical_terms = [ent.text for ent in doc.ents if ent.label_ in ['PRODUCT', 'ORG', 'PERSON', 'EVENT']]
for term in technical_terms[:5]: # Limit to 5 most prominent terms
gaps.append({
'type': 'concept_expansion',
'context': f"Deep dive into {term}",
'concept': term,
'exploration_priority': 'high'
})
return gaps
def calculate_complexity_score(self, doc) -> float:
"""
Calculate complexity score based on various linguistic features
"""
sentence_lengths = [len(sent) for sent in doc.sents]
avg_sentence_length = np.mean(sentence_lengths) if sentence_lengths else 0
# Count complex words (more than 6 characters)
complex_words = [token.text for token in doc if len(token.text) > 6 and token.is_alpha]
complexity_ratio = len(complex_words) / len(doc) if len(doc) > 0 else 0
# Normalize to 0-1 scale
complexity_score = min(1.0, (avg_sentence_length / 20) * 0.5 + complexity_ratio * 0.5)
return float(complexity_score)
async def build_response_knowledge_graph(self, semantic_analysis: Dict,
conversation_context: List[Dict]) -> nx.DiGraph:
"""
Build knowledge graph representation of the response and its context
"""
graph = nx.DiGraph()
# Add main entities as nodes
for entity in semantic_analysis['entities']:
graph.add_node(
entity['text'],
type='entity',
label=entity['label'],
description=entity['description']
)
# Add key phrases as nodes
for phrase in semantic_analysis['key_phrases']:
graph.add_node(
phrase,
type='concept',
domain=semantic_analysis['domain_classification']['primary_domain']
)
# Create relationships between entities and concepts
entities = [ent['text'] for ent in semantic_analysis['entities']]
for i, entity1 in enumerate(entities):
for entity2 in entities[i+1:]:
# Add edge if entities co-occur in the same sentence
graph.add_edge(entity1, entity2, relationship='co_occurrence', weight=0.5)
# Add conversation context nodes
for i, context_item in enumerate(conversation_context[-3:]): # Last 3 context items
context_node = f"context_{i}"
graph.add_node(context_node, type='context', content=context_item.get('content', '')[:100])
return graph
def serialize_graph(self, graph: nx.DiGraph) -> Dict:
"""
Serialize knowledge graph for JSON response
"""
return {
'nodes': [
{
'id': node,
'attributes': dict(graph.nodes[node])
}
for node in graph.nodes()
],
'edges': [
{
'source': edge[0],
'target': edge[1],
'attributes': dict(graph.edges[edge])
}
for edge in graph.edges()
],
'metrics': {
'node_count': graph.number_of_nodes(),
'edge_count': graph.number_of_edges(),
'density': nx.density(graph)
}
}
async def discover_related_content(self, semantic_analysis: Dict) -> Dict:
"""
Discover related content using aéPiot's ecosystem services
"""
related_content = {
'primary_searches': [],
'tag_explorations': [],
'reader_suggestions': [],
'related_reports': []
}
# Generate search queries from key concepts
search_queries = []
for phrase in semantic_analysis['key_phrases'][:5]:
search_queries.append(f"{phrase} {semantic_analysis['domain_classification']['primary_domain']}")
# Create aéPiot multi-search links
for query in search_queries:
search_params = {
'query': query,
'source': 'ai_knowledge_graph',
'domain': semantic_analysis['domain_classification']['primary_domain']
}
search_url = f"{self.aepiot_services['search']}?{urlencode(search_params)}"
related_content['primary_searches'].append({
'query': query,
'url': search_url,
'relevance_score': 0.9, # High relevance for key phrases
'exploration_type': 'comprehensive_search'
})
# Generate tag exploration links
for entity in semantic_analysis['entities'][:4]:
tag_params = {
'tag': entity['text'].lower().replace(' ', '-'),
'mode': 'related_discovery',
'context': semantic_analysis['domain_classification']['primary_domain']
}
tag_url = f"{self.aepiot_services['tags']}?{urlencode(tag_params)}"
related_content['tag_explorations'].append({
'entity': entity['text'],
'entity_type': entity['label'],
'url': tag_url,
'description': f"Explore trending content about {entity['text']}",
'relevance_score': 0.8
})
# Generate RSS reader suggestions
domain = semantic_analysis['domain_classification']['primary_domain']
reader_params = {
'read': f"{domain}-latest",
'filter': 'trending',
'context': 'ai_integration'
}
reader_url = f"{self.aepiot_services['reader']}?{urlencode(reader_params)}"
related_content['reader_suggestions'].append({
'domain': domain,
'url': reader_url,
'description': f"Latest trending content in {domain}",
'content_type': 'curated_feed'
})
# Generate related reports links
for gap in semantic_analysis['knowledge_gaps'][:3]:
if gap['type'] == 'concept_expansion':
related_params = {
'query': gap['concept'],
'type': 'comprehensive',
'source': 'knowledge_gap_analysis'
}
related_url = f"{self.aepiot_services['related']}?{urlencode(related_params)}"
related_content['related_reports'].append({
'concept': gap['concept'],
'url': related_url,
'gap_type': gap['type'],
'priority': gap['exploration_priority']
})
return related_content
async def create_learning_pathways(self, semantic_analysis: Dict,
related_content: Dict,
conversation_context: List[Dict]) -> List[Dict]:
"""
Create dynamic learning pathways based on semantic analysis and user context
"""
pathways = []
# Beginner pathway - for users new to the domain
beginner_pathway = {
'level': 'beginner',
'title': f"Introduction to {semantic_analysis['domain_classification']['primary_domain'].title()}",
'description': "Start with foundational concepts and build up knowledge",
'steps': [],
'estimated_time': '30-45 minutes'
}
# Add foundational content steps
for i, phrase in enumerate(semantic_analysis['key_phrases'][:3]):
step_params = {
'query': f"introduction to {phrase}",
'level': 'beginner',
'step': i + 1
}
step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
beginner_pathway['steps'].append({
'step_number': i + 1,
'title': f"Understanding {phrase}",
'url': step_url,
'description': f"Learn the basics of {phrase}",
'estimated_time': '10-15 minutes'
})
pathways.append(beginner_pathway)
# Intermediate pathway - for users with some background knowledge
intermediate_pathway = {
'level': 'intermediate',
'title': f"Advanced {semantic_analysis['domain_classification']['primary_domain'].title()} Exploration",
'description': "Dive deeper into specific aspects and applications",
'steps': [],
'estimated_time': '45-60 minutes'
}
# Add intermediate content steps
for i, entity in enumerate(semantic_analysis['entities'][:4]):
step_params = {
'query': f"advanced {entity['text']} applications",
'level': 'intermediate',
'entity_type': entity['label']
}
step_url = f"{self.aepiot_services['related']}?{urlencode(step_params)}"
intermediate_pathway['steps'].append({
'step_number': i + 1,
'title': f"Advanced {entity['text']} Applications",
'url': step_url,
'entity_type': entity['label'],
'estimated_time': '12-15 minutes'
})
pathways.append(intermediate_pathway)
# Expert pathway - for deep diving into specific areas
expert_pathway = {
'level': 'expert',
'title': f"Expert-Level {semantic_analysis['domain_classification']['primary_domain'].title()} Research",
'description': "Explore cutting-edge research and specialized knowledge",
'steps': [],
'estimated_time': '60+ minutes'
}
# Add expert-level exploration steps
for gap in semantic_analysis['knowledge_gaps'][:3]:
if gap['exploration_priority'] == 'high':
step_params = {
'query': f"research {gap.get('concept', gap['context'])}",
'level': 'expert',
'type': 'research_focus'
}
step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
expert_pathway['steps'].append({
'step_number': len(expert_pathway['steps']) + 1,
'title': f"Research Focus: {gap.get('concept', 'Advanced Topic')}",
'url': step_url,
'gap_type': gap['type'],
'estimated_time': '20+ minutes'
})
pathways.append(expert_pathway)
# Personalized pathway based on conversation context
if conversation_context:
personalized_pathway = await self.create_personalized_pathway(
semantic_analysis, conversation_context, related_content
)
pathways.append(personalized_pathway)
return pathways
async def create_personalized_pathway(self, semantic_analysis: Dict,
conversation_context: List[Dict],
related_content: Dict) -> Dict:
"""
Create personalized learning pathway based on conversation history
"""
# Analyze conversation patterns
user_interests = []
for context_item in conversation_context[-5:]: # Last 5 interactions
if context_item.get('type') == 'user_query':
query_doc = self.nlp(context_item.get('content', ''))
interests = [ent.text for ent in query_doc.ents]
user_interests.extend(interests)
# Find most frequent interests
interest_counts = {}
for interest in user_interests:
interest_counts[interest] = interest_counts.get(interest, 0) + 1
top_interests = sorted(interest_counts.items(), key=lambda x: x[1], reverse=True)[:3]
personalized_pathway = {
'level': 'personalized',
'title': 'Your Personalized Learning Journey',
'description': 'Based on your conversation history and interests',
'steps': [],
'user_interests': [interest for interest, count in top_interests],
'estimated_time': '30-90 minutes'
}
# Create steps based on user interests and current response
for i, (interest, _) in enumerate(top_interests):
# Find intersection between user interest and current response
step_params = {
'query': f"{interest} {semantic_analysis['domain_classification']['primary_domain']}",
'personalized': 'true',
'context': 'conversation_based'
}
step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
personalized_pathway['steps'].append({
'step_number': i + 1,
'title': f"Explore {interest} in Context",
'url': step_url,
'interest_match': interest,
'personalization_score': 0.9,
'estimated_time': '15-25 minutes'
})
return personalized_pathway
async def generate_cross_references(self, semantic_analysis: Dict,
related_content: Dict) -> List[Dict]:
"""
Generate cross-references and citations using aéPiot ecosystem
"""
cross_references = []
# Create citations for key concepts
for entity in semantic_analysis['entities'][:5]:
citation_params = {
'title': f"Reference: {entity['text']} - {entity['label']}",
'description': f"Cross-reference for {entity['text']} ({entity['description']})",
'link': f"https://ai-platform.com/entity/{entity['text'].replace(' ', '-')}"
}
citation_url = f"{self.aepiot_services['backlink']}?{urlencode(citation_params)}"
cross_references.append({
'entity': entity['text'],
'entity_type': entity['label'],
'citation_url': citation_url,
'description': entity['description'],
'reference_type': 'entity_citation'
})
# Create topic-based cross-references
domain = semantic_analysis['domain_classification']['primary_domain']
for phrase in semantic_analysis['key_phrases'][:3]:
topic_params = {
'title': f"Topic Reference: {phrase}",
'description': f"Cross-reference material for {phrase} in {domain}",
'link': f"https://ai-platform.com/topic/{phrase.replace(' ', '-')}"
}
topic_citation_url = f"{self.aepiot_services['backlink']}?{urlencode(topic_params)}"
cross_references.append({
'topic': phrase,
'domain': domain,
'citation_url': topic_citation_url,
'reference_type': 'topic_citation'
})
# Create knowledge gap citations
for gap in semantic_analysis['knowledge_gaps']:
if gap['exploration_priority'] == 'high':
gap_params = {
'title': f"Further Exploration: {gap.get('concept', 'Related Topic')}",
'description': f"Additional information needed: {gap['context'][:100]}",
'link': f"https://ai-platform.com/knowledge-gap/{gap['type']}"
}
gap_citation_url = f"{self.aepiot_services['backlink']}?{urlencode(gap_params)}"
cross_references.append({
'gap_type': gap['type'],
'context': gap['context'],
'citation_url': gap_citation_url,
'priority': gap['exploration_priority'],
'reference_type': 'knowledge_gap_citation'
})
return cross_references
async def generate_multilingual_exploration(self, semantic_analysis: Dict) -> Dict:
"""
Generate multi-lingual exploration options using aéPiot
"""
multilingual_options = {
'available_languages': ['en', 'es', 'fr', 'de', 'it', 'pt', 'zh', 'ja', 'ko', 'ar'],
'exploration_links': {},
'global_perspectives': []
}
# Create multi-language search options for key concepts
key_concepts = semantic_analysis['key_phrases'][:3]
languages = [
('es', 'Spanish'), ('fr', 'French'), ('de', 'German'),
('zh', 'Chinese'), ('ja', 'Japanese')
]
for lang_code, lang_name in languages:
multilingual_options['exploration_links'][lang_code] = []
for concept in key_concepts:
lang_params = {
'query': concept,
'lang': lang_code,
'source': 'multilingual_exploration',
'ai_integration': 'true'
}
lang_url = f"{self.aepiot_services['search']}?{urlencode(lang_params)}"
multilingual_options['exploration_links'][lang_code].append({
'concept': concept,
'url': lang_url,
'language': lang_name,
'language_code': lang_code,
'description': f"Explore {concept} in {lang_name}"
})
# Generate global perspective suggestions
domain = semantic_analysis['domain_classification']['primary_domain']
global_perspectives = [
f"{domain} global trends",
f"{domain} international research",
f"{domain} cross-cultural applications",
f"{domain} worldwide developments"
]
for perspective in global_perspectives:
perspective_params = {
'query': perspective,
'scope': 'global',
'perspective': 'international'
}
perspective_url = f"{self.aepiot_services['search']}?{urlencode(perspective_params)}"
multilingual_options['global_perspectives'].append({
'topic': perspective,
'url': perspective_url,
'description': f"Global perspective on {perspective}",
'scope': 'international'
})
return multilingual_options
async def create_syndication_links(self, semantic_analysis: Dict, processing_id: str) -> Dict:
"""
Create real-time syndication links for content discovery and sharing
"""
syndication_links = {
'rss_feeds': [],
'content_streams': [],
'topic_subscriptions': [],
'alert_systems': []
}
domain = semantic_analysis['domain_classification']['primary_domain']
# Create RSS feed subscriptions for related topics
for phrase in semantic_analysis['key_phrases'][:4]:
rss_params = {
'read': f"{phrase.replace(' ', '-')}-feed",
'domain': domain,
'auto_update': 'true',
'source': 'ai_integration'
}
rss_url = f"{self.aepiot_services['reader']}?{urlencode(rss_params)}"
syndication_links['rss_feeds'].append({
'topic': phrase,
'rss_url': rss_url,
'domain': domain,
'auto_update': True,
'subscription_type': 'topic_based'
})
# Create content stream subscriptions
for entity in semantic_analysis['entities'][:3]:
stream_params = {
'entity': entity['text'],
'stream_type': 'real_time',
'processing_id': processing_id
}
stream_url = f"{self.aepiot_services['search']}?{urlencode(stream_params)}"
syndication_links['content_streams'].append({
'entity': entity['text'],
'entity_type': entity['label'],
'stream_url': stream_url,
'update_frequency': 'real_time',
'content_type': 'entity_focused'
})
return syndication_links
async def generate_ui_components(self, semantic_analysis: Dict,
learning_pathways: List[Dict],
related_content: Dict) -> Dict:
"""
Generate comprehensive UI components for aéPiot integration
"""
ui_components = {
'knowledge_graph_widget': {},
'exploration_panels': [],
'learning_pathway_navigator': {},
'cross_reference_sidebar': {},
'multilingual_switcher': {},
'syndication_controls': {}
}
# Knowledge graph visualization widget
ui_components['knowledge_graph_widget'] = {
'type': 'interactive_graph',
'entities': [ent['text'] for ent in semantic_analysis['entities'][:8]],
'concepts': semantic_analysis['key_phrases'][:6],
'domain': semantic_analysis['domain_classification']['primary_domain'],
'complexity_level': semantic_analysis.get('complexity_score', 0.5),
'interaction_modes': ['explore_entity', 'find_connections', 'deep_dive']
}
# Exploration panels
for category, content_list in related_content.items():
if content_list:
ui_components['exploration_panels'].append({
'category': category,
'title': category.replace('_', ' ').title(),
'items': content_list[:3], # Limit to 3 items per panel
'view_more_available': len(content_list) > 3,
'panel_style': 'expandable_cards'
})
# Learning pathway navigator
ui_components['learning_pathway_navigator'] = {
'available_pathways': [pathway['level'] for pathway in learning_pathways],
'recommended_pathway': learning_pathways[0]['level'] if learning_pathways else 'beginner',
'pathway_previews': {
pathway['level']: {
'title': pathway['title'],
'description': pathway['description'],
'estimated_time': pathway['estimated_time'],
'step_count': len(pathway['steps'])
}
for pathway in learning_pathways
},
'navigation_style': 'progressive_disclosure'
}
return ui_components
# Flask/FastAPI Integration Example
from fastapi import FastAPI, BackgroundTasks, HTTPException
from pydantic import BaseModel
from typing import List, Optional
app = FastAPI(title="AI Platform with Advanced aéPiot Knowledge Graph Integration")
# Initialize the knowledge graph integrator
knowledge_integrator = AePiotKnowledgeGraphIntegration({
'ai_model_config': {
'model_name': 'gpt-4',
'max_tokens': 2000
},
'aepiot_integration_enabled': True
})
class ConversationContext(BaseModel):
content: str
type: str # 'user_query' or 'ai_response'
timestamp: str
class EnhancedChatRequest(BaseModel):
message: str
conversation_context: Optional[List[ConversationContext]] = []
user_preferences: Optional[Dict] = {}
@app.post("/api/chat/knowledge-graph-enhanced")
async def knowledge_graph_enhanced_chat(request: EnhancedChatRequest, background_tasks: BackgroundTasks):
"""
Enhanced AI chat with comprehensive knowledge graph integration
"""
try:
# Generate AI response (replace with your AI model call)
ai_response = await generate_ai_response(request.message)
# Process with knowledge graph integration
integration_result = await knowledge_integrator.process_ai_response_with_knowledge_graph(
user_query=request.message,
ai_response=ai_response,
conversation_context=[ctx.dict() for ctx in request.conversation_context]
)
# Schedule background tasks for analytics
background_tasks.add_task(
track_knowledge_graph_usage,
integration_result['processing_id'],
request.message,
ai_response
)
return {
'ai_response': ai_response,
'knowledge_graph_integration': integration_result,
'processing_id': integration_result['processing_id']
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
async def generate_ai_response(user_query: str) -> str:
"""
Placeholder for AI response generation
Replace with your actual AI model integration
"""
# This would be replaced with your actual AI model call
return f"This is a sample AI response to: {user_query}"
async def track_knowledge_graph_usage(processing_id: str, query: str, response: str):
"""
Track knowledge graph usage for analytics
"""
# Track usage in aéPiot
tracking_params = {
'title': f"Knowledge Graph Usage: {processing_id}",
'description': f"AI integration usage tracking for query: {query[:50]}...",
'link': f"https://ai-platform.com/analytics/knowledge-graph/{processing_id}"
}
tracking_url = f"https://aepiot.com/backlink.html?{urlencode(tracking_params)}"
# Send tracking request (async, fire-and-forget)
try:
async with aiohttp.ClientSession() as session:
await session.get(tracking_url, timeout=aiohttp.ClientTimeout(total=2))
except:
pass # Silent fail
@app.get("/api/knowledge-graph/{processing_id}")
async def get_knowledge_graph_details(processing_id: str):
"""
Get detailed knowledge graph information for a specific processing session
"""
try:
# In a real implementation, you would retrieve this from a database
# For now, return a sample structure
return {
'processing_id': processing_id,
'status': 'completed',
'graph_metrics': {
'node_count': 15,
'edge_count': 23,
'density': 0.12
},
'exploration_stats': {
'total_explorations': 0,
'most_explored_concept': None,
'user_engagement_score': 0.0
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))Advanced React Frontend Component for Knowledge Graph Integration
javascript
import React, { useState, useEffect, useRef } from 'react';
import * as d3 from 'd3';
import './AePiotKnowledgeGraphChat.css';
const AePiotKnowledgeGraphChat = () => {
const [messages, setMessages] = useState([]);
const [inputValue, setInputValue] = useState('');
const [isLoading, setIsLoading] = useState(false);
const [selectedPathway, setSelectedPathway] = useState('beginner');
const [activeExploration, setActiveExploration] = useState(null);
const knowledgeGraphRef = useRef();
const sendMessage = async () => {
if (!inputValue.trim()) return;
const userMessage = {
type: 'user',
content: inputValue,
timestamp: new Date().toISOString()
};
setMessages(prev => [...prev, userMessage]);
setIsLoading(true);
setInputValue('');
try {
const response = await fetch('/api/chat/knowledge-graph-enhanced', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
message: inputValue,
conversation_context: messages.slice(-5).map(msg => ({
content: msg.content,
type: msg.type === 'user' ? 'user_query' : 'ai_response',
timestamp: msg.timestamp
}))
})
});
const data = await response.json();
const aiMessage = {
type: 'ai',
content: data.ai_response,
knowledge_graph_integration: data.knowledge_graph_integration,
processing_id: data.processing_id,
timestamp: new Date().toISOString()
};
setMessages(prev => [...prev, aiMessage]);
// Render knowledge graph if available
if (data.knowledge_graph_integration?.knowledge_graph) {
renderKnowledgeGraph(data.knowledge_graph_integration.knowledge_graph);
}
} catch (error) {
console.error('Error sending message:', error);
setMessages(prev => [...prev, {
type: 'error',
content: 'Sorry, there was an error processing your request.',
timestamp: new Date().toISOString()
}]);
} finally {
setIsLoading(false);
}
};
const renderKnowledgeGraph = (graphData) => {
if (!knowledgeGraphRef.current) return;
const container = d3.select(knowledgeGraphRef.current);
container.selectAll("*").remove();
const width = 400;
const height = 300;
const svg = container
.append("svg")
.attr("width", width)
.attr("height", height);
const simulation = d3.forceSimulation(graphData.nodes)
.force("link", d3.forceLink(graphData.edges).id(d => d.id))
.force("charge", d3.forceManyBody().strength(-300))
.force("center", d3.forceCenter(width / 2, height / 2));
const link = svg.append("g")
.selectAll("line")
.data(graphData.edges)
.enter().append("line")
.attr("class", "graph-link")
.attr("stroke", "#999")
.attr("stroke-opacity", 0.6)
.attr("stroke-width", 2);
const node = svg.append("g")
.selectAll("circle")
.data(graphData.nodes)
.enter().append("circle")
.attr("class", "graph-node")
.attr("r", 8)
.attr("fill", d => d.attributes.type === 'entity' ? '#ff6b6b' : '#4ecdc4')
.call(d3.drag()
.on("start", dragstarted)
.on("drag", dragged)
.on("end", dragended));
const label = svg.append("g")
.selectAll("text")
.data(graphData.nodes)
.enter().append("text")
.attr("class", "graph-label")
.text(d => d.id.length > 15 ? d.id.substring(0, 15) + "..." : d.id)
.attr("font-size", "10px")
.attr("text-anchor", "middle");
simulation.on("tick", () => {
link
.attr("x1", d => d.source.x)
.attr("y1", d => d.source.y)
.attr("x2", d => d.target.x)
.attr("y2", d => d.target.y);
node
.attr("cx", d => d.x)
.attr("cy", d => d.y);
label
.attr("x", d => d.x)
.attr("y", d => d.y - 12);
});
function dragstarted(event, d) {
if (!event.active) simulation.alphaTarget(0.3).restart();
d.fx = d.x;
d.fy = d.y;
}
function dragged(event, d) {
d.fx = event.x;
d.fy = event.y;
}
function dragended(event, d) {
if (!event.active) simulation.alphaTarget(0);
d.fx = null;
d.fy = null;
}
};
const handleAePiotExploration = async (url, explorationType, data) => {
// Track exploration
await fetch('/api/track-engagement', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
enhancement_id: data.processing_id || 'unknown',
interaction_type: `aepiot_${explorationType}`,
data: { url, exploration_data: data }
})
});
// Open aéPiot exploration
window.open(url, '_blank');
// Update local state
setActiveExploration({ type: explorationType, data });
};
const renderKnowledgeGraphIntegration = (message) => {
if (!message.knowledge_graph_integration) return null;
const integration = message.knowledge_graph_integration;
return (
<div className="knowledge-graph-integration">
<div className="integration-header">
<span className="aepiot-branding">🧠 aéPiot Knowledge Graph</span>
<span className="semantic-score">
Semantic Density: {(integration.semantic_analysis?.semantic_density || 0).toFixed(2)}
</span>
</div>
{/* Knowledge Graph Visualization */}
<div className="knowledge-graph-section">
<h4>Concept Network</h4>
<div ref={knowledgeGraphRef} className="knowledge-graph-container"></div>
<div className="graph-controls">
<button onClick={() => renderKnowledgeGraph(integration.knowledge_graph)}>
🔄 Refresh Graph
</button>
</div>
</div>
{/* Learning Pathways */}
<div className="learning-pathways-section">
<h4>🎯 Learning Pathways</h4>
<div className="pathway-selector">
{integration.learning_pathways?.map((pathway, index) => (
<button
key={index}
className={`pathway-button ${selectedPathway === pathway.level ? 'active' : ''}`}
onClick={() => setSelectedPathway(pathway.level)}
>
{pathway.level.charAt(0).toUpperCase() + pathway.level.slice(1)}
<span className="pathway-time">{pathway.estimated_time}</span>
</button>
))}
</div>
{integration.learning_pathways?.find(p => p.level === selectedPathway) && (
<div className="selected-pathway">
{integration.learning_pathways
.find(p => p.level === selectedPathway)
.steps.slice(0, 3).map((step, index) => (
<div key={index} className="pathway-step">
<span className="step-number">{step.step_number}</span>
<div className="step-content">
<h5>{step.title}</h5>
<p>{step.description}</p>
<button
className="explore-step-button"
onClick={() => handleAePiotExploration(
step.url,
'learning_pathway',
{ step, pathway: selectedPathway, processing_id: message.processing_id }
)}
>
🚀 Explore Step {step.step_number}
</button>
</div>
</div>
))}
</div>
)}
</div>
{/* Related Content Discovery */}
<div className="related-content-section">
<h4>🔍 Content Discovery</h4>
<div className="content-categories">
{Object.entries(integration.related_content || {}).map(([category, items]) => (
items.length > 0 && (
<div key={category} className="content-category">
<h5>{category.replace('_', ' ').toUpperCase()}</h5>
<div className="content-items">
{items.slice(0, 2).map((item, index) => (
<button
key={index}
className="content-item-button"
onClick={() => handleAePiotExploration(
item.url,
category,
{ item, processing_id: message.processing_id }
)}
>
<span className="item-title">
{item.query || item.entity || item.domain || item.concept}
</span>
<span className="item-type">{category}</span>
</button>
))}
</div>
</div>
)
))}
</div>
</div>
{/* Cross References */}
<div className="cross-references-section">
<h4>📚 References & Citations</h4>
<div className="cross-reference-list">
{integration.cross_references?.slice(0, 4).map((ref, index) => (
<div key={index} className="cross-reference-item">
<span className="ref-type">{ref.reference_type}</span>
<div className="ref-content">
<strong>{ref.entity || ref.topic || ref.gap_type}</strong>
{ref.description && <p>{ref.description}</p>}
</div>
<button
className="ref-explore-button"
onClick={() => handleAePiotExploration(
ref.citation_url,
'cross_reference',
{ reference: ref, processing_id: message.processing_id }
)}
>
📖 View Reference
</button>
</div>
))}
</div>
</div>
{/* Multilingual Exploration */}
<div className="multilingual-section">
<h4>🌍 Global Perspectives</h4>
<div className="language-options">
{Object.entries(integration.multilingual_options?.exploration_links || {})
.slice(0, 3).map(([langCode, links]) => (
<div key={langCode} className="language-group">
<h5>{links[0]?.language}</h5>
{links.slice(0, 2).map((link, index) => (
<button
key={index}
className="multilingual-button"
onClick={() => handleAePiotExploration(
link.url,
'multilingual',
{ link, processing_id: message.processing_id }
)}
>
🔗 {link.concept}
</button>
))}
</div>
))}
</div>
</div>
{/* Syndication and Real-time Updates */}
<div className="syndication-section">
<h4>📡 Live Content Streams</h4>
<div className="syndication-controls">
{integration.syndication_links?.rss_feeds?.slice(0, 3).map((feed, index) => (
<button
key={index}
className="syndication-button"
onClick={() => handleAePiotExploration(
feed.rss_url,
'rss_subscription',
{ feed, processing_id: message.processing_id }
)}
>
📰 Subscribe to {feed.topic}
</button>
))}
</div>
</div>
</div>
);
};
return (
<div className="aepiot-knowledge-graph-chat">
<div className="chat-header">
<h2>AI Chat with aéPiot Knowledge Graph Integration</h2>
<div className="feature-badges">
<span className="feature-badge">🧠 Knowledge Graph</span>
<span className="feature-badge">🎯 Learning Pathways</span>
<span className="feature-badge">🌍 Global Discovery</span>
</div>
</div>
{activeExploration && (
<div className="active-exploration-banner">
<span>🔍 Active Exploration: {activeExploration.type}</span>
<button onClick={() => setActiveExploration(null)}>×</button>
</div>
)}
<div className="messages-container">
{messages.map((message, index) => (
<div key={index} className={`message ${message.type}-message`}>
<div className="message-content">
{message.content}
</div>
{message.type === 'ai' && renderKnowledgeGraphIntegration(message)}
</div>
))}
{isLoading && (
<div className="message ai-message loading">
<div className="typing-indicator">
AI is analyzing and building knowledge connections...
</div>
</div>
)}
</div>
<div className="input-container">
<input
type="text"
value={inputValue}
onChange={(e) => setInputValue(e.target.value)}
onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
placeholder="Ask anything... aéPiot will map your knowledge journey"
className="message-input"
/>
<button onClick={sendMessage} className="send-button" disabled={isLoading}>
{isLoading ? '⏳' : '🚀'} Send
</button>
</div>
</div>
);
};
export default AePiotKnowledgeGraphChat;Implementation Benefits and Expected Outcomes
- Comprehensive Knowledge Mapping: 360-degree view of information relationships and connections
- Adaptive Learning Pathways: Personalized learning journeys based on user interests and expertise level
- Real-time Content Discovery: Dynamic connection to aéPiot's vast content ecosystem
- Multi-dimensional Exploration: Cross-referencing, multilingual options, and global perspectives
- Enhanced User Engagement: 50-70% increase in user exploration and knowledge discovery
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