AI Engineering Hub Agentic RAG Source Code Deep Dive: Agent-Based Retrieval-Augmented Generation
Overview
AI Engineering Hub is one of the most popular AI engineering learning repositories, with over 10.7k stars. Among its projects, the Agentic RAG project stands out as a reference implementation of an agent-based retrieval-augmented generation system.
This post provides a detailed, source-level analysis of the Agentic RAG project from AI Engineering Hub, and presents practical usage guidance from an AgentOps perspective.
What Is Agentic RAG?
Limitations of traditional RAG:
- Rigid single-pass retrieve-then-generate pipeline
- Insufficient support for complex multi-hop reasoning
- No dynamic adaptability
- Difficulty maintaining context across turns
What Agentic RAG addresses:
- Multi-agent collaboration: specialized agents with clearly divided responsibilities
- Dynamic workflow: adaptive process routing based on query characteristics
- Tool use: integration with external APIs and databases
- Memory management: context retention and incremental learning
AI Engineering Hub Agentic RAG Project Structure
1. Project Architecture Analysis
# AI Engineering Hub Agentic RAG core architecture
class AgenticRAGSystem:
def __init__(self):
# Initialize agents
self.router_agent = RouterAgent()
self.retriever_agent = RetrieverAgent()
self.synthesizer_agent = SynthesizerAgent()
self.evaluator_agent = EvaluatorAgent()
# Initialize tools
self.vector_store = VectorStore()
self.web_search = WebSearchTool()
self.memory = ConversationMemory()
async def process_query(self, query: str) -> str:
"""Agentic RAG main process"""
# 1. Routing step
route_decision = await self.router_agent.route(query)
# 2. Retrieval step
if route_decision == "vector_search":
context = await self.retriever_agent.vector_search(query)
elif route_decision == "web_search":
context = await self.retriever_agent.web_search(query)
else:
context = await self.retriever_agent.hybrid_search(query)
# 3. Synthesis step
response = await self.synthesizer_agent.synthesize(query, context)
# 4. Evaluation step
evaluation = await self.evaluator_agent.evaluate(query, response, context)
# 5. Update memory
await self.memory.update(query, response, evaluation)
return response
2. Router Agent Implementation
# Router agent: query classification and routing
class RouterAgent:
def __init__(self, llm_config):
self.llm = ChatOpenAI(**llm_config)
self.classifier = QueryClassifier()
async def route(self, query: str) -> str:
"""Route a query to the appropriate retrieval strategy"""
# Analyze the query
query_analysis = await self._analyze_query(query)
# Make routing decision
routing_prompt = f"""
Analyze the following query and determine the optimal retrieval strategy:
Query: {query}
Analysis: {query_analysis}
Options:
1. vector_search: when document-based retrieval is needed
2. web_search: when up-to-date information is needed
3. hybrid_search: when a combination of both is needed
Decision: (respond with one of: vector_search/web_search/hybrid_search)
"""
response = await self.llm.ainvoke(routing_prompt)
return response.content.strip().lower()
async def _analyze_query(self, query: str) -> dict:
"""Analyze query characteristics"""
analysis_prompt = f"""
Analyze the characteristics of the following query:
Query: {query}
Analysis dimensions:
1. Temporality (whether current information is needed)
2. Complexity (whether multi-hop reasoning is needed)
3. Domain (whether it belongs to a specialized field)
4. Intent (information retrieval, analysis, summarization, etc.)
Respond in JSON format.
"""
response = await self.llm.ainvoke(analysis_prompt)
try:
return json.loads(response.content)
except:
return {"error": "analysis failed"}
3. Retriever Agent Implementation
# Retriever agent: multiple retrieval strategies
class RetrieverAgent:
def __init__(self, vector_store, web_search_tool):
self.vector_store = vector_store
self.web_search = web_search_tool
self.llm = ChatOpenAI(temperature=0)
async def vector_search(self, query: str, k: int = 5) -> List[Document]:
"""Perform vector search"""
# Query expansion
expanded_query = await self._expand_query(query)
# Vector search
docs = await self.vector_store.asimilarity_search(
expanded_query, k=k
)
# Post-process results
processed_docs = await self._post_process_docs(docs, query)
return processed_docs
async def web_search(self, query: str, max_results: int = 3) -> List[Document]:
"""Perform web search"""
# Optimize search query
optimized_query = await self._optimize_web_query(query)
# Run web search
search_results = await self.web_search.arun(optimized_query)
# Convert results to documents
documents = []
for result in search_results[:max_results]:
doc = Document(
page_content=result.get('content', ''),
metadata={
'source': result.get('url', ''),
'title': result.get('title', ''),
'timestamp': datetime.now().isoformat()
}
)
documents.append(doc)
return documents
async def hybrid_search(self, query: str) -> List[Document]:
"""Hybrid search: combine vector search and web search"""
# Run searches in parallel
vector_task = asyncio.create_task(self.vector_search(query, k=3))
web_task = asyncio.create_task(self.web_search(query, max_results=2))
vector_docs, web_docs = await asyncio.gather(vector_task, web_task)
# Merge results and remove duplicates
combined_docs = await self._merge_results(vector_docs, web_docs, query)
return combined_docs
async def _expand_query(self, query: str) -> str:
"""Expand the query to improve retrieval performance"""
expansion_prompt = f"""
Expand the following query semantically to get better search results:
Original query: {query}
Expanded query (include key keywords):
"""
response = await self.llm.ainvoke(expansion_prompt)
return response.content.strip()
async def _post_process_docs(self, docs: List[Document], query: str) -> List[Document]:
"""Post-process and filter retrieved documents by relevance"""
processed_docs = []
for doc in docs:
# Calculate relevance score
relevance_score = await self._calculate_relevance(doc.page_content, query)
if relevance_score > 0.7: # Threshold
doc.metadata['relevance_score'] = relevance_score
processed_docs.append(doc)
# Sort by relevance
processed_docs.sort(
key=lambda x: x.metadata.get('relevance_score', 0),
reverse=True
)
return processed_docs
async def _calculate_relevance(self, content: str, query: str) -> float:
"""Calculate relevance score between document and query"""
relevance_prompt = f"""
Score how relevant the following document is to the query on a scale from 0.0 to 1.0:
Query: {query}
Document: {content[:500]}...
Respond with only the score (e.g., 0.85):
"""
response = await self.llm.ainvoke(relevance_prompt)
try:
return float(response.content.strip())
except:
return 0.5 # Default value
4. Synthesizer Agent Implementation
# Synthesizer agent: generate response from retrieved context
class SynthesizerAgent:
def __init__(self, llm_config):
self.llm = ChatOpenAI(**llm_config)
self.response_templates = ResponseTemplates()
async def synthesize(self, query: str, context: List[Document]) -> str:
"""Synthesize a response based on retrieved context"""
# Prepare context
formatted_context = await self._format_context(context)
# Determine synthesis strategy
synthesis_strategy = await self._determine_strategy(query, context)
# Generate response according to strategy
if synthesis_strategy == "direct_answer":
response = await self._generate_direct_answer(query, formatted_context)
elif synthesis_strategy == "comparative_analysis":
response = await self._generate_comparative_analysis(query, formatted_context)
elif synthesis_strategy == "step_by_step":
response = await self._generate_step_by_step(query, formatted_context)
else:
response = await self._generate_comprehensive_response(query, formatted_context)
return response
async def _format_context(self, documents: List[Document]) -> str:
"""Format documents into a readable structure"""
formatted_sections = []
for i, doc in enumerate(documents, 1):
source = doc.metadata.get('source', 'unknown source')
relevance = doc.metadata.get('relevance_score', 'N/A')
section = f"""
[Reference {i}] (Source: {source}, Relevance: {relevance})
{doc.page_content}
"""
formatted_sections.append(section)
return "\n".join(formatted_sections)
async def _determine_strategy(self, query: str, context: List[Document]) -> str:
"""Determine the response generation strategy"""
strategy_prompt = f"""
Analyze the following query and context to determine the optimal response strategy:
Query: {query}
Number of context documents: {len(context)}
Strategy options:
1. direct_answer: a direct factual answer
2. comparative_analysis: comparative analysis
3. step_by_step: step-by-step explanation
4. comprehensive_response: comprehensive response
Selected strategy:
"""
response = await self.llm.ainvoke(strategy_prompt)
return response.content.strip().lower()
async def _generate_direct_answer(self, query: str, context: str) -> str:
"""Generate a direct answer"""
prompt = f"""
Based on the following context, provide a clear and direct answer to the question:
Question: {query}
Context:
{context}
Answer guidelines:
1. Include only the key content
2. Be clear and concise
3. Include source information
4. Note anything that is uncertain
Answer:
"""
response = await self.llm.ainvoke(prompt)
return response.content
async def _generate_comparative_analysis(self, query: str, context: str) -> str:
"""Generate a comparative analysis response"""
prompt = f"""
Compare and analyze the information from the context to answer the question:
Question: {query}
Context:
{context}
Analysis structure:
1. Identify the main viewpoints
2. Analyze similarities and differences
3. Present a synthesized conclusion
4. Assess the credibility of each source
Comparative analysis:
"""
response = await self.llm.ainvoke(prompt)
return response.content
async def _generate_step_by_step(self, query: str, context: str) -> str:
"""Generate a step-by-step explanation"""
prompt = f"""
Based on the following context, provide a step-by-step explanation for the question:
Question: {query}
Context:
{context}
Explanation structure:
1. Overview
2. Detailed step-by-step explanation
3. Importance of each step
4. Practical application
5. Notes and warnings
Step-by-step explanation:
"""
response = await self.llm.ainvoke(prompt)
return response.content
5. Evaluator Agent Implementation
# Evaluator agent: evaluate and improve response quality
class EvaluatorAgent:
def __init__(self, llm_config):
self.llm = ChatOpenAI(**llm_config)
self.metrics = ResponseMetrics()
async def evaluate(self, query: str, response: str, context: List[Document]) -> dict:
"""Comprehensive evaluation of response quality"""
# Run multiple evaluation metrics in parallel
evaluation_tasks = [
self._evaluate_relevance(query, response),
self._evaluate_accuracy(response, context),
self._evaluate_completeness(query, response),
self._evaluate_clarity(response),
self._evaluate_factuality(response, context)
]
results = await asyncio.gather(*evaluation_tasks)
evaluation = {
'relevance': results[0],
'accuracy': results[1],
'completeness': results[2],
'clarity': results[3],
'factuality': results[4],
'overall_score': sum(results) / len(results),
'timestamp': datetime.now().isoformat()
}
# Generate improvement suggestions when quality is below threshold
if evaluation['overall_score'] < 0.8:
evaluation['improvement_suggestions'] = await self._generate_improvements(
query, response, context, evaluation
)
return evaluation
async def _evaluate_relevance(self, query: str, response: str) -> float:
"""Evaluate how relevant the response is to the query"""
prompt = f"""
Score the relevance between the query and response on a scale from 0.0 to 1.0:
Query: {query}
Response: {response}
Evaluation criteria:
- How well does the response capture the core intent of the query
- Direct relevance of the response
- Whether unnecessary information is included
Score (0.0-1.0):
"""
result = await self.llm.ainvoke(prompt)
try:
return float(result.content.strip())
except:
return 0.5
async def _evaluate_accuracy(self, response: str, context: List[Document]) -> float:
"""Evaluate the accuracy of the response"""
context_text = "\n".join([doc.page_content for doc in context])
prompt = f"""
Evaluate how well the response matches the provided context:
Context:
{context_text[:2000]}...
Response:
{response}
Evaluation criteria:
- Factual accuracy
- Consistency with the context
- Incorrect reasoning
Score (0.0-1.0):
"""
result = await self.llm.ainvoke(prompt)
try:
return float(result.content.strip())
except:
return 0.5
async def _evaluate_completeness(self, query: str, response: str) -> float:
"""Evaluate how completely the response addresses the query"""
prompt = f"""
Evaluate how completely the response addresses the query:
Query: {query}
Response: {response}
Evaluation criteria:
- Coverage of all aspects of the query
- Sufficient level of detail
- Logical structure and flow
Score (0.0-1.0):
"""
result = await self.llm.ainvoke(prompt)
try:
return float(result.content.strip())
except:
return 0.5
async def _generate_improvements(
self,
query: str,
response: str,
context: List[Document],
evaluation: dict
) -> List[str]:
"""Generate improvement suggestions"""
weak_areas = [k for k, v in evaluation.items() if isinstance(v, float) and v < 0.7]
prompt = f"""
Generate specific suggestions to improve the weak areas of the following response:
Query: {query}
Response: {response}
Weak areas: {weak_areas}
Improvement suggestions (one per item):
"""
result = await self.llm.ainvoke(prompt)
suggestions = result.content.split('\n')
return [s.strip() for s in suggestions if s.strip()]
6. Memory Management System
# Conversation memory: context retention and learning
class ConversationMemory:
def __init__(self, vector_store):
self.vector_store = vector_store
self.session_memory = {}
self.long_term_memory = []
async def update(self, query: str, response: str, evaluation: dict):
"""Update memory"""
# Update session memory
session_id = self._get_session_id()
if session_id not in self.session_memory:
self.session_memory[session_id] = []
interaction = {
'query': query,
'response': response,
'evaluation': evaluation,
'timestamp': datetime.now().isoformat()
}
self.session_memory[session_id].append(interaction)
# Store high-quality interactions in long-term memory
if evaluation.get('overall_score', 0) > 0.8:
await self._store_in_long_term_memory(interaction)
async def get_relevant_context(self, query: str, k: int = 3) -> List[dict]:
"""Retrieve relevant past interactions"""
# Find similar past queries via vector search
similar_interactions = await self.vector_store.asimilarity_search(
query, k=k, filter={'type': 'interaction'}
)
return [json.loads(doc.page_content) for doc in similar_interactions]
async def _store_in_long_term_memory(self, interaction: dict):
"""Store in long-term memory"""
# Convert interaction to document
doc = Document(
page_content=json.dumps(interaction),
metadata={
'type': 'interaction',
'timestamp': interaction['timestamp'],
'quality_score': interaction['evaluation']['overall_score']
}
)
# Save to vector store
await self.vector_store.aadd_documents([doc])
# Also append to local list
self.long_term_memory.append(interaction)
def _get_session_id(self) -> str:
"""Generate session ID (manage per user in a real implementation)"""
return "default_session"
AgentOps Integration and Monitoring
1. AgentOps Integration
import agentops
from typing import Dict, Any
class AgenticRAGWithAgentOps:
def __init__(self):
# Initialize AgentOps
agentops.init(api_key=os.getenv('AGENTOPS_API_KEY'))
# Initialize system components
self.router_agent = RouterAgent()
self.retriever_agent = RetrieverAgent()
self.synthesizer_agent = SynthesizerAgent()
self.evaluator_agent = EvaluatorAgent()
self.memory = ConversationMemory()
@agentops.record_function('agentic_rag_pipeline')
async def process_query_with_monitoring(self, query: str) -> Dict[str, Any]:
"""Agentic RAG pipeline with AgentOps monitoring"""
start_time = time.time()
pipeline_metrics = {
'query_length': len(query),
'start_time': start_time
}
try:
# 1. Routing step with monitoring
route_decision = await self._route_with_monitoring(query)
pipeline_metrics['route_decision'] = route_decision
# 2. Retrieval step with monitoring
context = await self._retrieve_with_monitoring(query, route_decision)
pipeline_metrics['context_docs_count'] = len(context)
# 3. Synthesis step with monitoring
response = await self._synthesize_with_monitoring(query, context)
pipeline_metrics['response_length'] = len(response)
# 4. Evaluation step with monitoring
evaluation = await self._evaluate_with_monitoring(query, response, context)
pipeline_metrics.update(evaluation)
# 5. Update memory
await self.memory.update(query, response, evaluation)
# Record success metrics
pipeline_metrics.update({
'success': True,
'total_time': time.time() - start_time
})
agentops.record_action({
'action_type': 'agentic_rag_success',
'metrics': pipeline_metrics
})
return {
'response': response,
'evaluation': evaluation,
'metrics': pipeline_metrics
}
except Exception as e:
# Record error metrics
pipeline_metrics.update({
'success': False,
'error': str(e),
'total_time': time.time() - start_time
})
agentops.record_action({
'action_type': 'agentic_rag_error',
'metrics': pipeline_metrics
})
raise
@agentops.record_function('routing_decision')
async def _route_with_monitoring(self, query: str) -> str:
"""Monitor routing decision"""
start_time = time.time()
route_decision = await self.router_agent.route(query)
agentops.record_action({
'action_type': 'routing_decision',
'metrics': {
'query': query,
'decision': route_decision,
'routing_time': time.time() - start_time
}
})
return route_decision
@agentops.record_function('context_retrieval')
async def _retrieve_with_monitoring(self, query: str, route_decision: str) -> List[Document]:
"""Monitor retrieval process"""
start_time = time.time()
if route_decision == "vector_search":
context = await self.retriever_agent.vector_search(query)
search_type = "vector"
elif route_decision == "web_search":
context = await self.retriever_agent.web_search(query)
search_type = "web"
else:
context = await self.retriever_agent.hybrid_search(query)
search_type = "hybrid"
agentops.record_action({
'action_type': 'context_retrieval',
'metrics': {
'search_type': search_type,
'documents_retrieved': len(context),
'retrieval_time': time.time() - start_time,
'avg_relevance': sum(doc.metadata.get('relevance_score', 0) for doc in context) / len(context) if context else 0
}
})
return context
@agentops.record_function('response_synthesis')
async def _synthesize_with_monitoring(self, query: str, context: List[Document]) -> str:
"""Monitor response synthesis"""
start_time = time.time()
response = await self.synthesizer_agent.synthesize(query, context)
agentops.record_action({
'action_type': 'response_synthesis',
'metrics': {
'synthesis_time': time.time() - start_time,
'response_length': len(response),
'context_utilization': len(context)
}
})
return response
@agentops.record_function('response_evaluation')
async def _evaluate_with_monitoring(self, query: str, response: str, context: List[Document]) -> dict:
"""Monitor response evaluation"""
start_time = time.time()
evaluation = await self.evaluator_agent.evaluate(query, response, context)
agentops.record_action({
'action_type': 'response_evaluation',
'metrics': {
'evaluation_time': time.time() - start_time,
**evaluation
}
})
return evaluation
2. Performance Optimization and A/B Testing
class AgenticRAGOptimizer:
def __init__(self):
self.performance_tracker = PerformanceTracker()
self.ab_tester = ABTester()
@agentops.record_function('agentic_rag_ab_test')
async def run_ab_test(self, test_queries: List[str], configurations: Dict[str, Dict]) -> Dict:
"""Run A/B test for the Agentic RAG system"""
results = {}
for config_name, config in configurations.items():
print(f"Running test configuration: {config_name}")
config_results = []
for query in test_queries:
# Create system with this configuration
rag_system = self._create_system_with_config(config)
start_time = time.time()
try:
result = await rag_system.process_query_with_monitoring(query)
metrics = {
'query': query,
'response': result['response'],
'response_time': time.time() - start_time,
'success': True,
'config': config_name,
**result['evaluation']
}
config_results.append(metrics)
except Exception as e:
config_results.append({
'query': query,
'error': str(e),
'response_time': time.time() - start_time,
'success': False,
'config': config_name
})
results[config_name] = config_results
# Analyze A/B test results
analysis = self._analyze_ab_results(results)
agentops.record_action({
'action_type': 'ab_test_completed',
'metrics': {
'test_configurations': list(configurations.keys()),
'total_queries': len(test_queries),
'analysis': analysis
}
})
return analysis
def _create_system_with_config(self, config: Dict) -> AgenticRAGWithAgentOps:
"""Create a system with the given configuration"""
system = AgenticRAGWithAgentOps()
# Router configuration
if 'router_config' in config:
system.router_agent.configure(config['router_config'])
# Retrieval configuration
if 'retrieval_config' in config:
system.retriever_agent.configure(config['retrieval_config'])
# Synthesis configuration
if 'synthesis_config' in config:
system.synthesizer_agent.configure(config['synthesis_config'])
return system
def _analyze_ab_results(self, results: Dict) -> Dict:
"""Analyze A/B test results"""
analysis = {}
for config_name, config_results in results.items():
successful_results = [r for r in config_results if r.get('success', False)]
if successful_results:
analysis[config_name] = {
'success_rate': len(successful_results) / len(config_results),
'avg_response_time': sum(r['response_time'] for r in successful_results) / len(successful_results),
'avg_overall_score': sum(r.get('overall_score', 0) for r in successful_results) / len(successful_results),
'avg_relevance': sum(r.get('relevance', 0) for r in successful_results) / len(successful_results),
'avg_accuracy': sum(r.get('accuracy', 0) for r in successful_results) / len(successful_results)
}
else:
analysis[config_name] = {
'success_rate': 0,
'error': 'No successful results'
}
# Identify the best-performing configuration
best_config = max(
analysis.keys(),
key=lambda k: analysis[k].get('avg_overall_score', 0)
)
analysis['best_configuration'] = best_config
return analysis
Practical Use Cases
1. Customer Support System
class CustomerSupportAgenticRAG:
def __init__(self):
self.agentic_rag = AgenticRAGWithAgentOps()
self.knowledge_base = CustomerKnowledgeBase()
self.ticket_system = TicketSystem()
@agentops.record_function('customer_support_query')
async def handle_customer_inquiry(self, inquiry: str, customer_context: Dict) -> Dict:
"""Handle a customer inquiry"""
# Enrich query with customer context
enriched_query = self._enrich_query_with_context(inquiry, customer_context)
# Generate answer with Agentic RAG
result = await self.agentic_rag.process_query_with_monitoring(enriched_query)
# Personalize response
personalized_response = await self._personalize_response(
result['response'], customer_context
)
# Predict satisfaction
satisfaction_prediction = await self._predict_satisfaction(
inquiry, personalized_response
)
agentops.record_action({
'action_type': 'customer_support_completed',
'metrics': {
'customer_id': customer_context.get('customer_id'),
'inquiry_category': self._classify_inquiry(inquiry),
'predicted_satisfaction': satisfaction_prediction,
'response_quality': result['evaluation']['overall_score']
}
})
return {
'response': personalized_response,
'satisfaction_prediction': satisfaction_prediction,
'evaluation': result['evaluation']
}
2. Research Assistant System
class ResearchAssistantAgenticRAG:
def __init__(self):
self.agentic_rag = AgenticRAGWithAgentOps()
self.paper_database = AcademicPaperDatabase()
self.citation_manager = CitationManager()
@agentops.record_function('research_query')
async def research_query(self, question: str, domain: str) -> Dict:
"""Process a research question"""
# Domain-specific search
domain_context = await self.paper_database.search_by_domain(question, domain)
# Comprehensive analysis with Agentic RAG
result = await self.agentic_rag.process_query_with_monitoring(question)
# Recommend related papers
related_papers = await self._recommend_papers(question, domain)
# Suggest research directions
research_directions = await self._suggest_research_directions(
question, result['response']
)
# Generate citations
citations = await self.citation_manager.generate_citations(
result['response'], domain_context
)
return {
'analysis': result['response'],
'related_papers': related_papers,
'research_directions': research_directions,
'citations': citations,
'confidence_score': result['evaluation']['overall_score']
}
Performance Benchmarking and Optimization
1. Performance Metrics
class AgenticRAGBenchmark:
def __init__(self):
self.metrics = {
'response_time': [],
'accuracy_scores': [],
'relevance_scores': [],
'user_satisfaction': [],
'cost_per_query': []
}
async def run_benchmark(self, test_dataset: List[Dict]) -> Dict:
"""Run benchmark"""
system = AgenticRAGWithAgentOps()
results = []
for test_case in test_dataset:
query = test_case['query']
expected_answer = test_case.get('expected_answer')
start_time = time.time()
try:
result = await system.process_query_with_monitoring(query)
response_time = time.time() - start_time
# Calculate performance metrics
metrics = {
'query': query,
'response': result['response'],
'response_time': response_time,
'accuracy': self._calculate_accuracy(result['response'], expected_answer),
'relevance': result['evaluation']['relevance'],
'overall_score': result['evaluation']['overall_score'],
'cost': self._estimate_cost(result['metrics'])
}
results.append(metrics)
except Exception as e:
results.append({
'query': query,
'error': str(e),
'response_time': time.time() - start_time,
'success': False
})
# Overall performance analysis
performance_summary = self._analyze_performance(results)
return {
'individual_results': results,
'performance_summary': performance_summary,
'recommendations': self._generate_optimization_recommendations(performance_summary)
}
def _analyze_performance(self, results: List[Dict]) -> Dict:
"""Analyze performance"""
successful_results = [r for r in results if r.get('success', True)]
if not successful_results:
return {'error': 'No successful results'}
return {
'total_queries': len(results),
'success_rate': len(successful_results) / len(results),
'avg_response_time': sum(r['response_time'] for r in successful_results) / len(successful_results),
'avg_accuracy': sum(r.get('accuracy', 0) for r in successful_results) / len(successful_results),
'avg_relevance': sum(r.get('relevance', 0) for r in successful_results) / len(successful_results),
'avg_overall_score': sum(r.get('overall_score', 0) for r in successful_results) / len(successful_results),
'total_cost': sum(r.get('cost', 0) for r in successful_results)
}
Conclusion
The Agentic RAG project from AI Engineering Hub demonstrates a practical implementation of a next-generation RAG system built on multi-agent collaboration.
Key Advantages
- Modular architecture: each agent has a specialized, well-defined role
- Dynamic adaptability: strategy adjusts based on the query type
- Quality assurance: multi-stage evaluation and improvement mechanisms
- Extensibility: straightforward to add new agents and tools
Effects of AgentOps Integration
- Real-time monitoring: per-agent performance tracking
- A/B testing: comparing the effectiveness of different configurations
- Performance optimization: data-driven system improvements
- Operational stability: error tracking and recovery
Practical Adoption Guide
- Gradual introduction: incremental upgrade from an existing RAG system
- Domain customization: adapt agents to fit the target business domain
- Continuous improvement: optimize based on AgentOps data
- User feedback: incorporate real-world usage patterns
Agentic RAG goes beyond simple retrieval and generation, showing the potential of next-generation AI systems built on intelligent reasoning and collaboration. Use the source code from AI Engineering Hub as a foundation to build your own Agentic RAG system.