Session 7: Agentic RAG Systems¶
Learning Navigation Hub¶
Total Time Investment: 110 minutes (Core) + 70 minutes (Optional) Your Learning Path: Choose your engagement level
Quick Start Guide¶
- Observer (110 min): Read concepts + examine autonomous agent patterns
- Participant (150 min): Follow exercises + implement basic agentic systems
- Implementer (180 min): Build production agents + deploy autonomous RAG systems
Session Overview Dashboard¶
Core Learning Track (110 minutes) - REQUIRED¶
| Section | Concept Load | Time | Skills |
|---|---|---|---|
| Reasoning Foundations | 4 concepts | 30 min | Cognitive Planning |
| Agent Architecture | 5 concepts | 30 min | System Design |
| Self-Correction Loops | 3 concepts | 25 min | Feedback Systems |
| Tool Integration | 4 concepts | 25 min | Agent Orchestration |
Optional Deep Dive Modules (Choose Your Adventure)¶
- 🔬 Module A: Advanced Agent Reasoning (35 min)
- 🏭 Module B: Production Agent Deployment (35 min)
Core Section (Required - 110 minutes)¶
Learning Outcomes¶
By the end of this session, you will be able to:
- Design agent-driven RAG systems with autonomous query planning and iterative refinement
- Implement self-correcting mechanisms that validate and improve RAG responses
- Integrate external tools and APIs for enhanced RAG capabilities beyond document retrieval
- Build multi-agent orchestration systems for complex information synthesis tasks
- Deploy production agentic RAG architectures with monitoring and quality assurance
Chapter Introduction¶
The Evolution to Reasoning-Powered RAG: From Retrieval to Intelligence¶
Traditional RAG systems are reactive - they retrieve and generate based on a single query pass. Reasoning-Augmented RAG systems represent a paradigm shift from passive retrieval to proactive intelligent reasoning systems that don't just find information, but actively reason about it to construct logically coherent, contextually aware responses.
The Cognitive Revolution in RAG:
- From Information Retrieval → Knowledge Reasoning: Transform scattered documents into structured logical reasoning
- From Static Pipelines → Dynamic Intelligence: Systems that adapt their approach based on reasoning requirements
- From Reactive Responses → Proactive Analysis: Anticipate information needs through logical deduction
- From Document Aggregation → Context Construction: Build coherent logical frameworks from diverse sources
The Reasoning-RAG Transformation:
- Reasoning-Guided Planning: Analyze queries through logical reasoning frameworks to plan optimal strategies
- Chain-of-Thought Integration: Implement structured reasoning paths that guide retrieval and synthesis
- Bidirectional Synergy: "Reasoning-Augmented Retrieval" guides what to find, "Retrieval-Augmented Reasoning" fills logical gaps
- Cognitive Validation: Reason about response quality and logical consistency, not just factual accuracy
- Adaptive Reasoning Workflows: Dynamic reasoning strategies that evolve based on query complexity and domain requirements
Advanced Reasoning-Powered Capabilities You'll Build:
- Reasoning-Guided Query Planning: Strategic cognitive analysis that determines optimal reasoning approaches for complex information needs
- Chain-of-Thought RAG Systems: Structured reasoning paths that guide both retrieval and synthesis processes
- Self-Reasoning Validation: Systems that reason about their own logical consistency and cognitive coherence
- Multi-Modal Reasoning Integration: Reasoning that spans text, knowledge graphs, and structured data sources
- Adaptive Reasoning Workflows: Dynamic reasoning strategies that evolve from structured control flows to emergent adaptive patterns
From Information Aggregation to Logical Reasoning¶
Reasoning-Augmented RAG systems demonstrate true cognitive intelligence through autonomous logical reasoning:
- Cognitive Query Analysis: Understand not just what is being asked, but the logical reasoning required to construct a comprehensive answer
- Reasoning-Driven Retrieval: Use logical frameworks to guide what information to retrieve and how to connect it
- Chain-of-Thought Synthesis: Build responses through structured reasoning chains that demonstrate logical progression
- Meta-Cognitive Validation: Reason about the reasoning process itself - validate logical consistency, identify reasoning gaps, and strengthen logical connections
- Adaptive Reasoning Strategies: Dynamically choose between different reasoning approaches (deductive, inductive, abductive) based on the cognitive requirements
Three Revolutionary Reasoning Integration Approaches:
- Prompt-Based Reasoning: Integrate reasoning directly into prompts with structured thinking frameworks
- Architecture-Based Reasoning: Embed reasoning capabilities into the system architecture through specialized reasoning modules
- Learning-Based Reasoning: Train systems to develop reasoning capabilities through reinforcement learning and feedback loops
Let's build RAG systems that don't just retrieve information - they think, reason, and construct knowledge! 🧠✨
Part 0: RAG + Reasoning Integration Foundations (35 minutes)¶
Understanding the Cognitive Leap: From Retrieval to Reasoning¶
The integration of reasoning capabilities into RAG represents a fundamental cognitive evolution. Instead of treating RAG as an information retrieval problem, we approach it as a knowledge construction and logical reasoning challenge.
Traditional RAG Workflow:
Reasoning-Augmented RAG Workflow:
Query → Analyze Reasoning Requirements → Plan Cognitive Strategy →
Reasoning-Guided Retrieval → Chain-of-Thought Synthesis →
Meta-Cognitive Validation → Logically Coherent Response
The Three Pillars of Reasoning-RAG Integration¶
1. Reasoning-Augmented Retrieval (RAR)¶
"Let reasoning guide what information to find and how to connect it"
Setting Up the Reasoning-Augmented Retrieval System
This system uses different reasoning strategies to intelligently guide information retrieval:
# Reasoning-guided retrieval system
class ReasoningAugmentedRetrieval:
"""RAG system where reasoning frameworks guide retrieval strategies.
Instead of simple similarity search, this system analyzes what type
of reasoning is needed and uses that to guide information gathering.
"""
def __init__(self, vector_store, knowledge_graph, reasoning_engine):
self.vector_store = vector_store
self.knowledge_graph = knowledge_graph
self.reasoning_engine = reasoning_engine
Now we set up the reasoning strategy mapping, which connects each type of logical reasoning to its specific implementation method:
# Map reasoning types to specific retrieval strategies
self.reasoning_strategies = {
'deductive': self._deductive_reasoning_retrieval,
'inductive': self._inductive_reasoning_retrieval,
'abductive': self._abductive_reasoning_retrieval,
'analogical': self._analogical_reasoning_retrieval,
'causal': self._causal_reasoning_retrieval
}
Main Reasoning-Guided Retrieval Method
The core orchestration method that coordinates the entire reasoning-retrieval process:
async def reasoning_guided_retrieve(self, query: str,
reasoning_context: Dict = None) -> Dict[str, Any]:
"""Retrieve information using reasoning frameworks to guide the process.
This method analyzes what type of reasoning the query requires,
then uses that analysis to guide intelligent information retrieval.
"""
# Phase 1: Understand what type of reasoning this query needs
reasoning_analysis = await self._analyze_reasoning_requirements(query)
# Phase 2: Build a structured framework based on reasoning type
reasoning_framework = await self._construct_reasoning_framework(
query, reasoning_analysis, reasoning_context
)
Next, we apply the reasoning strategy and validate the logical connections:
# Phase 3: Use the framework to guide targeted information retrieval
retrieved_information = await self._apply_reasoning_strategy(
query, reasoning_framework
)
# Phase 4: Verify that retrieved information supports logical reasoning
logical_connections = await self._validate_logical_connections(
retrieved_information, reasoning_framework
)
Finally, we return the complete reasoning-guided retrieval results:
return {
'query': query,
'reasoning_analysis': reasoning_analysis,
'reasoning_framework': reasoning_framework,
'retrieved_information': retrieved_information,
'logical_connections': logical_connections,
'reasoning_type': reasoning_framework.get('primary_reasoning_type')
}
Reasoning Requirements Analysis
This method determines what type of logical reasoning the query requires:
async def _analyze_reasoning_requirements(self, query: str) -> Dict[str, Any]:
"""Analyze what type of reasoning is required for this query.
This analysis determines the optimal reasoning strategy and what
types of evidence will be needed to support logical conclusions.
"""
We construct a comprehensive analysis prompt that examines the query's reasoning requirements:
reasoning_prompt = f"""
Analyze this query to determine the reasoning requirements:
Query: {query}
Determine:
1. What type of logical reasoning is needed? (deductive, inductive, abductive, analogical, causal)
2. What logical connections need to be established?
3. What evidence types are required for sound reasoning?
4. What are the potential logical fallacies to avoid?
5. What reasoning chains would lead to the most complete answer?
The prompt specifies the exact JSON structure needed for consistent reasoning analysis:
Return JSON:
{{
"primary_reasoning_type": "deductive|inductive|abductive|analogical|causal",
"secondary_reasoning_types": ["type1", "type2"],
"logical_connections_needed": ["connection_type_1", "connection_type_2"],
"evidence_requirements": ["factual", "statistical", "expert_opinion", "case_studies"],
"reasoning_complexity": "simple|moderate|complex|multi_layered",
"potential_fallacies": ["fallacy_type_1", "fallacy_type_2"],
"reasoning_chain_depth": 1-5,
"cognitive_strategy": "description of optimal reasoning approach"
}}
JSON:
"""
Finally, we execute the analysis with low temperature for consistency:
# Use low temperature for consistent analysis
response = await self._async_llm_predict(reasoning_prompt, temperature=0.1)
return self._parse_json_response(response)
Reasoning Framework Construction
This method builds a structured framework to guide the retrieval process:
async def _construct_reasoning_framework(self, query: str,
reasoning_analysis: Dict,
context: Dict = None) -> Dict[str, Any]:
"""Construct a structured reasoning framework to guide retrieval.
This method converts reasoning analysis into a concrete framework
that can guide targeted information gathering.
"""
# Extract key parameters from analysis
primary_reasoning = reasoning_analysis['primary_reasoning_type']
complexity = reasoning_analysis['reasoning_complexity']
# Build specialized prompt based on reasoning type
framework_prompt = self._build_framework_prompt(query, reasoning_analysis, primary_reasoning)
Now we generate the framework and add metadata for tracking and optimization:
# Generate structured framework using LLM
response = await self._async_llm_predict(framework_prompt, temperature=0.2)
framework = self._parse_json_response(response)
# Add tracking metadata for optimization
framework = self._add_framework_metadata(framework, primary_reasoning, query)
return framework
Framework Prompt Builder
This helper method creates specialized prompts for different reasoning types:
def _build_framework_prompt(self, query: str, reasoning_analysis: Dict,
primary_reasoning: str) -> str:
"""Build the reasoning framework construction prompt.
Creates specialized prompts that help the LLM construct proper
logical frameworks for different reasoning types.
"""
The prompt template starts by setting up the reasoning context and requirements:
return f"""
Construct a reasoning framework for this query using {primary_reasoning} reasoning:
Query: {query}
Reasoning Analysis: {json.dumps(reasoning_analysis, indent=2)}
Create a structured framework with:
1. Premises that need to be established through retrieval
2. Logical steps that connect premises to conclusion
3. Information gaps that must be filled
4. Validation checkpoints for logical consistency
The prompt defines the exact JSON structure needed for consistent framework generation:
Return JSON:
{{
"reasoning_premises": [
{{"premise": "statement", "evidence_needed": "type", "confidence_required": 0.0-1.0}}
],
"logical_steps": [
{{"step": 1, "operation": "logical_operation", "inputs": ["premise1"], "output": "intermediate_conclusion"}}
],
"information_gaps": [
{{"gap": "missing_information", "retrieval_strategy": "how_to_find", "critical": true/false}}
],
"validation_checkpoints": [
{{"checkpoint": "what_to_validate", "validation_method": "how_to_validate"}}
],
"reasoning_chain": "step-by-step logical progression",
"success_criteria": "how to determine reasoning completeness"
}}
JSON:
"""
Framework Metadata Addition
Adds tracking information for performance optimization and framework reuse:
def _add_framework_metadata(self, framework: Dict, primary_reasoning: str,
query: str) -> Dict[str, Any]:
"""Add metadata to reasoning framework.
Metadata enables framework caching, reuse, and performance analysis.
Production systems use this to track effective reasoning patterns.
"""
framework['framework_id'] = f"{primary_reasoning}_{int(time.time())}"
framework['created_for_query'] = query
framework['reasoning_type'] = primary_reasoning
return framework
Deductive Reasoning Strategy Implementation
This method implements the deductive reasoning approach to information retrieval:
async def _deductive_reasoning_retrieval(self, query: str,
framework: Dict) -> Dict[str, Any]:
"""Implement deductive reasoning retrieval strategy.
Deductive reasoning works from general premises to specific conclusions.
This method retrieves evidence for each premise and applies logical steps.
"""
premises = framework.get('reasoning_premises', [])
logical_steps = framework.get('logical_steps', [])
Phase 1: Evidence Gathering - We systematically collect evidence for each logical premise:
# Phase 1: Gather evidence for each logical premise
premise_evidence = {}
for premise_data in premises:
premise = premise_data['premise']
evidence_type = premise_data['evidence_needed']
# Find specific evidence to support this premise
premise_retrieval = await self._retrieve_for_premise(
premise, evidence_type, query
)
premise_evidence[premise] = premise_retrieval
Phase 2: Logical Step Application - We apply reasoning steps using the gathered evidence:
# Phase 2: Apply logical steps using gathered evidence
logical_progression = []
for step in logical_steps:
step_result = await self._apply_logical_step(
step, premise_evidence, logical_progression
)
logical_progression.append(step_result)
Phase 3: Conclusion Construction - We build the final deductive conclusion and return results:
# Phase 3: Build final deductive conclusion
deductive_conclusion = await self._construct_deductive_conclusion(
premise_evidence, logical_progression, framework
)
return {
'strategy': 'deductive_reasoning',
'premise_evidence': premise_evidence,
'logical_progression': logical_progression,
'deductive_conclusion': deductive_conclusion,
'confidence_score': self._calculate_deductive_confidence(
premise_evidence, logical_progression
)
}
2. Retrieval-Augmented Reasoning (RAR)¶
"Let external knowledge fill logical reasoning gaps"
Setting Up Retrieval-Augmented Reasoning
This system identifies where reasoning is weak and uses targeted retrieval to strengthen it:
class RetrievalAugmentedReasoning:
"""System that uses retrieved information to enhance reasoning capabilities.
This approach identifies gaps in reasoning and strategically retrieves
external knowledge to fill those gaps, creating stronger logical arguments.
"""
def __init__(self, retrieval_system, reasoning_engine, knowledge_validator):
self.retrieval_system = retrieval_system
self.reasoning_engine = reasoning_engine
self.knowledge_validator = knowledge_validator
Enhanced Reasoning with Strategic Information Retrieval
The main method that coordinates gap identification and knowledge integration:
async def enhanced_reasoning(self, reasoning_query: str,
initial_knowledge: Dict = None) -> Dict[str, Any]:
"""Perform reasoning enhanced by targeted information retrieval.
This method analyzes reasoning weaknesses, retrieves relevant knowledge
to address those weaknesses, and integrates it into stronger reasoning.
"""
# Phase 1: Analyze where the reasoning is incomplete or weak
reasoning_gaps = await self._identify_reasoning_gaps(
reasoning_query, initial_knowledge
)
# Phase 2: Strategically gather information to address the gaps
gap_filling_retrieval = await self._strategic_gap_retrieval(
reasoning_gaps, reasoning_query
)
Now we integrate the retrieved knowledge with original reasoning and validate the results:
# Phase 3: Combine original reasoning with retrieved knowledge
enhanced_reasoning_result = await self._integrate_knowledge_into_reasoning(
reasoning_query, initial_knowledge, gap_filling_retrieval
)
# Phase 4: Validate that the enhanced reasoning is logically sound
reasoning_validation = await self._validate_enhanced_reasoning(
enhanced_reasoning_result, reasoning_query
)
Finally, we return the complete enhanced reasoning results with quality metrics:
return {
'reasoning_query': reasoning_query,
'identified_gaps': reasoning_gaps,
'gap_filling_retrieval': gap_filling_retrieval,
'enhanced_reasoning': enhanced_reasoning_result,
'validation_result': reasoning_validation,
'reasoning_enhancement_score': self._calculate_enhancement_score(
initial_knowledge, enhanced_reasoning_result
)
}
Reasoning Gap Identification
This method analyzes reasoning to find where external knowledge would help:
async def _identify_reasoning_gaps(self, reasoning_query: str,
initial_knowledge: Dict = None) -> Dict[str, Any]:
"""Identify what external knowledge would strengthen reasoning.
Analyzes the reasoning chain to find missing facts, weak premises,
counter-arguments, and other knowledge that would improve logical soundness.
"""
We create a comprehensive prompt to analyze what knowledge gaps exist in the reasoning:
gap_analysis_prompt = f"""
Analyze this reasoning query to identify knowledge gaps that external information could fill:
Reasoning Query: {reasoning_query}
Initial Knowledge: {json.dumps(initial_knowledge or {}, indent=2)}
Identify:
1. Missing factual premises that would strengthen the reasoning
2. Statistical data that would support logical conclusions
3. Expert opinions that would validate reasoning steps
4. Counter-arguments that should be addressed
5. Historical examples or case studies that would illustrate points
The prompt defines the specific JSON structure needed for systematic gap analysis:
Return JSON:
{{
"critical_gaps": [
{{"gap": "description", "knowledge_type": "factual|statistical|expert|counter|example", "retrieval_priority": "high|medium|low"}}
],
"reasoning_weaknesses": ["weakness1", "weakness2"],
"knowledge_reinforcement_opportunities": ["opportunity1", "opportunity2"],
"potential_counter_arguments": ["counter1", "counter2"],
"evidence_requirements": {{"type": "requirement"}}
}}
JSON:
"""
Finally, we execute the gap analysis with consistent temperature settings:
# Use low temperature for consistent gap analysis
response = await self._async_llm_predict(gap_analysis_prompt, temperature=0.2)
return self._parse_json_response(response)
3. Chain-of-Thought RAG Integration¶
"Structured reasoning paths that guide both retrieval and synthesis"
Step 1: Initialize the Chain-of-Thought RAG System
First, let's set up the core system that manages different reasoning patterns:
class ChainOfThoughtRAG:
"""RAG system with integrated chain-of-thought reasoning capabilities.
This system combines structured reasoning with information retrieval,
enabling step-by-step logical processing of complex queries.
"""
def __init__(self, retrieval_system, llm_model, reasoning_validator):
self.retrieval_system = retrieval_system
self.llm_model = llm_model
self.reasoning_validator = reasoning_validator
# Chain-of-thought patterns for different reasoning types
self.cot_patterns = {
'analytical': self._analytical_chain_pattern,
'comparative': self._comparative_chain_pattern,
'causal': self._causal_chain_pattern,
'problem_solving': self._problem_solving_chain_pattern,
'synthesis': self._synthesis_chain_pattern
}
Step 2: Main Chain-of-Thought Processing Method
Now let's implement the core method that orchestrates the entire reasoning process:
async def chain_of_thought_rag(self, query: str,
cot_config: Dict = None) -> Dict[str, Any]:
"""Generate response using chain-of-thought reasoning integrated with RAG.
This is the main orchestration method that coordinates the entire
reasoning process from analysis to final validated response.
"""
# Setup configuration with sensible defaults
config = cot_config or {
'thinking_pattern': 'analytical',
'reasoning_depth': 'moderate',
'validate_each_step': True,
'retrieve_at_each_step': True
}
Next, we analyze the query requirements and build a structured reasoning plan:
# Phase 1: Analyze reasoning requirements for this query
cot_analysis = await self._analyze_cot_requirements(query)
# Phase 2: Build structured reasoning plan
reasoning_chain = await self._construct_reasoning_chain(
query, cot_analysis, config
)
Execute and Synthesize Chain-of-Thought Response
Now execute the reasoning plan and create the final response:
# Phase 3: Execute reasoning with integrated retrieval
executed_chain = await self._execute_reasoning_chain(
reasoning_chain, query, config
)
# Phase 4: Synthesize final response from reasoning steps
final_response = await self._synthesize_from_reasoning_chain(
executed_chain, query
)
# Phase 5: Validate logical consistency
chain_validation = await self._validate_reasoning_chain_coherence(
executed_chain, final_response
)
Return Comprehensive Results
Compile all results with metadata for analysis and debugging:
return {
'query': query,
'cot_analysis': cot_analysis,
'reasoning_chain': reasoning_chain,
'executed_chain': executed_chain,
'final_response': final_response,
'chain_validation': chain_validation,
'reasoning_metadata': {
'pattern_used': cot_analysis.get('recommended_pattern'),
'reasoning_steps': len(executed_chain.get('steps', [])),
'retrieval_points': executed_chain.get('retrieval_count', 0),
'logical_coherence_score': chain_validation.get('coherence_score', 0.0)
}
}
Step 3: Reasoning Chain Construction
This method builds the structured plan for step-by-step reasoning:
async def _construct_reasoning_chain(self, query: str, analysis: Dict,
config: Dict) -> Dict[str, Any]:
"""Construct step-by-step reasoning chain for the query.
Creates a structured plan that breaks complex reasoning into
manageable steps with clear objectives and retrieval points.
"""
pattern = analysis.get('recommended_pattern', 'analytical')
depth = config.get('reasoning_depth', 'moderate')
# Build comprehensive reasoning chain prompt
chain_prompt = self._build_reasoning_chain_prompt(query, pattern, depth)
# Generate reasoning chain with consistent parameters
response = await self._async_llm_predict(chain_prompt, temperature=0.2)
chain = self._parse_json_response(response)
# Add tracking metadata
return self._add_chain_metadata(chain, pattern, depth, query)
Reasoning Chain Prompt Builder
Helper method that constructs the detailed prompt for reasoning chain generation:
def _build_reasoning_chain_prompt(self, query: str, pattern: str, depth: str) -> str:
"""Build detailed prompt for reasoning chain construction."""
The method constructs a comprehensive prompt that defines the reasoning chain requirements:
return f"""
Construct a step-by-step chain-of-thought reasoning plan for this query:
Query: {query}
Reasoning Pattern: {pattern}
Reasoning Depth: {depth}
Create a reasoning chain with:
1. Clear logical steps that build toward the answer
2. Information retrieval points where external knowledge is needed
3. Validation checkpoints to ensure logical consistency
4. Synthesis points where information is integrated
The prompt defines the specific JSON structure for systematic reasoning chain generation:
Return JSON:
{{
"reasoning_steps": [
{{
"step": 1,
"thinking": "What am I trying to figure out in this step?",
"information_needed": "What external information would help?",
"retrieval_query": "Specific query for information retrieval",
"logical_operation": "How does this step connect to others?",
"validation_check": "How do I validate this step?"
}}
],
"reasoning_flow": "description of how steps connect",
"synthesis_strategy": "how to combine all steps into final answer",
"confidence_checkpoints": ["checkpoint1", "checkpoint2"]
}}
JSON:
"""
Chain Metadata Addition
Utility method for adding tracking information to reasoning chains:
def _add_chain_metadata(self, chain: Dict, pattern: str, depth: str, query: str) -> Dict[str, Any]:
"""Add metadata to reasoning chain for tracking and optimization."""
chain['pattern'] = pattern
chain['depth'] = depth
chain['created_for'] = query
return chain
Step 4: Chain Execution Framework
The execution system manages the step-by-step processing:
async def _execute_reasoning_chain(self, reasoning_chain: Dict,
query: str, config: Dict) -> Dict[str, Any]:
"""Execute the reasoning chain step by step with retrieval integration.
This method systematically processes each reasoning step, accumulating
knowledge and maintaining context throughout the chain execution.
"""
# Setup execution tracking and context management
execution_context = self._initialize_chain_execution_context()
# Process each reasoning step sequentially
for step_data in reasoning_chain.get('reasoning_steps', []):
step_result = await self._execute_single_chain_step(
step_data, execution_context, config, query
)
execution_context = self._update_execution_context(execution_context, step_result)
return self._compile_chain_execution_results(execution_context)
Step 5: Context Management Methods
These helper methods manage the execution state throughout the reasoning process:
def _initialize_chain_execution_context(self) -> Dict[str, Any]:
"""Initialize the context for chain-of-thought execution.
Creates a clean state for tracking knowledge accumulation,
step results, and validation throughout the reasoning process.
"""
return {
'executed_steps': [], # Results from each reasoning step
'accumulated_knowledge': {}, # Knowledge gathered throughout process
'retrieval_count': 0, # Track information retrieval operations
'step_validations': [] # Validation results for quality control
}
Step 6: Individual Step Processing
This method handles the execution of each reasoning step with integrated retrieval:
async def _execute_single_chain_step(self, step_data: Dict, context: Dict,
config: Dict, query: str) -> Dict[str, Any]:
"""Execute a single step in the reasoning chain.
Each step involves: information gathering, reasoning execution,
and validation - building toward the complete answer.
"""
step_num = step_data['step']
print(f"Executing reasoning step {step_num}: {step_data['thinking']}")
# Phase 1: Gather external information if this step needs it
retrieved_info = await self._handle_step_retrieval(step_data, config, context)
# Phase 2: Execute the actual reasoning operation for this step
step_execution = await self._execute_single_reasoning_step(
step_data, retrieved_info, context['accumulated_knowledge'], query
)
# Phase 3: Validate the step's logical consistency if enabled
step_validation = await self._handle_step_validation(
step_execution, step_data, query, config
)
return {
'step_number': step_num,
'step_data': step_data,
'execution_result': step_execution,
'retrieved_information': retrieved_info,
'validation_result': step_validation,
'logical_consistency': step_validation.get('consistency_score', 0.8) if step_validation else 0.8
}
Step 7: Retrieval and Validation Handlers
Specialized methods for handling information gathering and step validation:
async def _handle_step_retrieval(self, step_data: Dict, config: Dict,
context: Dict) -> Optional[Dict]:
"""Handle information retrieval for a reasoning step.
Retrieves external knowledge when a reasoning step needs additional
information to make logical progress.
"""
# Skip retrieval if not configured or no query specified
if not (step_data.get('retrieval_query') and config.get('retrieve_at_each_step')):
return None
# Perform targeted retrieval for this reasoning step
retrieved_info = await self.retrieval_system.retrieve(
step_data['retrieval_query']
)
# Update tracking metrics and accumulated knowledge
context['retrieval_count'] += 1
context['accumulated_knowledge'][f'step_{step_data["step"]}_retrieval'] = retrieved_info
return retrieved_info
async def _handle_step_validation(self, step_execution: Dict, step_data: Dict,
query: str, config: Dict) -> Optional[Dict]:
"""Handle validation for a reasoning step.
Validates logical consistency to prevent error propagation
throughout the reasoning chain.
"""
if not config.get('validate_each_step'):
return None
return await self._validate_reasoning_step(step_execution, step_data, query)
Step 8: Context Update and Result Compilation
Final methods for managing execution state and compiling results:
def _update_execution_context(self, context: Dict, step_result: Dict) -> Dict[str, Any]:
"""Update execution context with step results.
Ensures each step has access to all previous knowledge while
maintaining clean separation of concerns.
"""
context['executed_steps'].append(step_result)
context['accumulated_knowledge'][f'step_{step_result["step_number"]}_result'] = step_result['execution_result']
if step_result['validation_result']:
context['step_validations'].append(step_result['validation_result'])
return context
def _compile_chain_execution_results(self, context: Dict) -> Dict[str, Any]:
"""Compile final results from chain execution context.
Centralizes result compilation for consistent output format
and comprehensive performance analysis.
"""
return {
'executed_steps': context['executed_steps'],
'accumulated_knowledge': context['accumulated_knowledge'],
'retrieval_count': context['retrieval_count'],
'overall_coherence': self._calculate_chain_coherence(context['executed_steps']),
'validation_summary': {
'steps_validated': len(context['step_validations']),
'average_consistency': np.mean([
v.get('consistency_score', 0.8) for v in context['step_validations']
]) if context['step_validations'] else 0.8
}
}
### **Bridging NodeRAG Structured Knowledge to Reasoning Capabilities**
**From Graph Relationships to Logical Reasoning:**
The structured knowledge graphs from Session 6 (NodeRAG) provide the perfect foundation for sophisticated reasoning systems. Instead of just traversing relationships, we can now reason about them logically.
```python
class NodeRAGReasoningBridge:
"""Bridge between NodeRAG structured knowledge and reasoning capabilities."""
def __init__(self, node_rag_system, reasoning_engine):
self.node_rag = node_rag_system
self.reasoning_engine = reasoning_engine
async def reason_over_structured_knowledge(self, query: str,
reasoning_type: str = 'deductive') -> Dict[str, Any]:
"""Use reasoning to analyze structured knowledge relationships."""
# Step 1: Extract relevant knowledge subgraph
knowledge_subgraph = await self.node_rag.extract_relevant_subgraph(query)
# Step 2: Convert graph relationships to logical premises
logical_premises = await self._convert_graph_to_premises(knowledge_subgraph)
# Step 3: Apply reasoning to premises
reasoning_result = await self._reason_over_premises(
logical_premises, query, reasoning_type
)
# Step 4: Validate reasoning against graph structure
validation_result = await self._validate_reasoning_against_graph(
reasoning_result, knowledge_subgraph
)
return {
'structured_knowledge': knowledge_subgraph,
'logical_premises': logical_premises,
'reasoning_result': reasoning_result,
'graph_validation': validation_result,
'cognitive_enhancement': self._calculate_reasoning_enhancement(
knowledge_subgraph, reasoning_result
)
}
async def _convert_graph_to_premises(self, subgraph: Dict) -> List[Dict]:
"""Convert graph relationships into logical premises."""
premises = []
# Convert entities to existential premises
for entity in subgraph.get('entities', []):
premises.append({
'type': 'existential',
'premise': f"{entity['name']} exists as {entity['type']}",
'confidence': entity.get('confidence', 1.0),
'source': 'knowledge_graph'
})
# Convert relationships to relational premises
for relationship in subgraph.get('relationships', []):
premises.append({
'type': 'relational',
'premise': f"{relationship['source']} {relationship['relation']} {relationship['target']}",
'confidence': relationship.get('confidence', 1.0),
'source': 'knowledge_graph'
})
# Convert properties to attributive premises
for entity in subgraph.get('entities', []):
for prop, value in entity.get('properties', {}).items():
premises.append({
'type': 'attributive',
'premise': f"{entity['name']} has {prop} = {value}",
'confidence': 0.9,
'source': 'knowledge_graph'
})
return premises
Educational Context: The Cognitive Leap
This integration represents a fundamental cognitive evolution:
- Session 6 Achievement: Structured knowledge graphs that understand relationships
- Session 7 Enhancement: Logical reasoning that can analyze and deduce from those relationships
- Cognitive Synergy: The combination enables true knowledge construction, not just information retrieval
Example: From Graph Traversal to Logical Deduction
# Traditional NodeRAG approach (Session 6)
# Find: "What companies did Steve Jobs work for?"
# Result: [Apple, NeXT, Pixar] - direct relationship traversal
# Reasoning-Enhanced NodeRAG approach (Session 7)
# Query: "What leadership patterns can we deduce from Steve Jobs' career trajectory?"
# Reasoning Process:
# 1. Premise: Steve Jobs worked for Apple, NeXT, Pixar
# 2. Premise: Apple and Pixar became highly successful under his leadership
# 3. Premise: NeXT was acquired by Apple
# 4. Deduction: Jobs demonstrates a pattern of transformative leadership in technology companies
# 5. Further reasoning: His career shows progression from personal computing to animated films to mobile devices
# 6. Conclusion: Jobs' leadership pattern involves entering industries and fundamentally changing their paradigms
Workflow Architecture: Structured vs. Dynamic Reasoning¶
Structured Reasoning Workflows¶
Controlled, predictable reasoning patterns for well-defined domains
class StructuredReasoningWorkflow:
"""Controlled reasoning workflow for predictable reasoning patterns."""
def __init__(self, domain_knowledge, reasoning_templates):
self.domain_knowledge = domain_knowledge
self.reasoning_templates = reasoning_templates
async def execute_structured_reasoning(self, query: str,
workflow_type: str) -> Dict[str, Any]:
"""Execute reasoning using predefined structured workflows."""
# Select appropriate reasoning template
template = self.reasoning_templates.get(workflow_type)
if not template:
raise ValueError(f"Unknown workflow type: {workflow_type}")
# Execute structured steps
workflow_results = []
context = {'query': query, 'domain': self.domain_knowledge}
for step in template['steps']:
step_result = await self._execute_template_step(step, context)
workflow_results.append(step_result)
context.update(step_result.get('output_context', {}))
return {
'workflow_type': workflow_type,
'structured_results': workflow_results,
'final_reasoning': context.get('final_conclusion'),
'confidence': self._calculate_structured_confidence(workflow_results)
}
Dynamic Adaptive Reasoning Workflows¶
Emergent reasoning patterns that adapt based on the cognitive requirements
class DynamicReasoningWorkflow:
"""Adaptive reasoning workflow that evolves based on reasoning requirements."""
def __init__(self, reasoning_engine, adaptation_strategy):
self.reasoning_engine = reasoning_engine
self.adaptation_strategy = adaptation_strategy
async def execute_adaptive_reasoning(self, query: str,
adaptation_config: Dict = None) -> Dict[str, Any]:
"""Execute reasoning that adapts dynamically to the cognitive challenge.
WHY: Complex reasoning problems often require iterative refinement where
the reasoning approach itself evolves based on intermediate results.
"""
# Phase 1: Setup adaptive reasoning context
config, adaptation_context = self._setup_adaptive_reasoning(query, adaptation_config)
# Phase 2: Execute adaptive reasoning rounds
for round_num in range(config['max_adaptation_rounds']):
round_result = await self._execute_adaptation_round(
round_num, adaptation_context, config, query
)
if round_result['converged']:
print(f"Reasoning converged at round {round_num + 1}")
break
# Update context for next round
adaptation_context = self._update_adaptation_context(
adaptation_context, round_result
)
# Phase 3: Select and return best reasoning result
return self._compile_adaptive_reasoning_results(adaptation_context, config)
def _setup_adaptive_reasoning(self, query: str, adaptation_config: Dict = None) -> Tuple[Dict, Dict]:
"""Setup context for adaptive reasoning execution.
WHY: Centralizing setup logic makes the adaptation process more predictable
and allows for better configuration management.
"""
config = adaptation_config or {
'max_adaptation_rounds': 5,
'adaptation_threshold': 0.1,
'reasoning_flexibility': 0.7
}
adaptation_context = {
'current_reasoning_approach': None, # Will be initialized in first round
'adaptation_rounds': [],
'query': query,
'best_score_so_far': 0.0
}
return config, adaptation_context
async def _execute_adaptation_round(self, round_num: int, context: Dict,
config: Dict, query: str) -> Dict[str, Any]:
"""Execute a single round of adaptive reasoning.
WHY: Breaking rounds into separate methods enables better monitoring
and makes the adaptation logic clearer and more maintainable.
"""
# Initialize or adapt reasoning approach
if context['current_reasoning_approach'] is None:
reasoning_approach = await self._initialize_reasoning_approach(query)
else:
reasoning_approach = context['current_reasoning_approach']
# Execute reasoning with current approach
reasoning_result = await self._execute_reasoning_approach(reasoning_approach, query)
# Evaluate effectiveness
effectiveness_score = await self._evaluate_reasoning_effectiveness(reasoning_result, query)
# Determine convergence
converged = effectiveness_score >= config['adaptation_threshold']
# Record round results
round_result = {
'round': round_num + 1,
'reasoning_approach': reasoning_approach,
'reasoning_result': reasoning_result,
'effectiveness_score': effectiveness_score,
'converged': converged,
'improvement': effectiveness_score - context['best_score_so_far']
}
context['adaptation_rounds'].append(round_result)
return round_result
def _update_adaptation_context(self, context: Dict, round_result: Dict) -> Dict[str, Any]:
"""Update adaptation context based on round results.
WHY: Context management ensures learning from each round and enables
intelligent adaptation strategies based on performance trends.
"""
# Update best score tracking
if round_result['effectiveness_score'] > context['best_score_so_far']:
context['best_score_so_far'] = round_result['effectiveness_score']
# Adapt reasoning approach for next round
if not round_result['converged']:
context['current_reasoning_approach'] = self._adapt_reasoning_approach(
round_result['reasoning_approach'],
round_result['reasoning_result'],
round_result['effectiveness_score']
)
return context
def _compile_adaptive_reasoning_results(self, context: Dict, config: Dict) -> Dict[str, Any]:
"""Compile final results from adaptive reasoning process.
WHY: Centralized result compilation ensures consistent output format
and provides comprehensive adaptation analytics.
"""
# Select best reasoning result
best_result = max(context['adaptation_rounds'], key=lambda r: r['effectiveness_score'])
return {
'query': context['query'],
'adaptation_rounds': context['adaptation_rounds'],
'best_reasoning_result': best_result,
'final_approach': best_result['reasoning_approach'],
'adaptation_success': best_result['effectiveness_score'] >= config['adaptation_threshold'],
'adaptation_analytics': {
'total_rounds': len(context['adaptation_rounds']),
'improvement_trajectory': [r['effectiveness_score'] for r in context['adaptation_rounds']],
'converged': best_result['effectiveness_score'] >= config['adaptation_threshold']
}
}
Educational Bridge: From Session 6 NodeRAG to Production Agentic Systems¶
Building on Your Graph Knowledge Foundation¶
What you mastered in Session 6:
- Structured Knowledge Graphs: Entities, relationships, and semantic connections
- Graph Traversal: Finding related information through relationship paths
- Knowledge Graph Construction: Building structured representations from unstructured data
- Graph-Based Retrieval: Using relationships to improve retrieval relevance
What Session 7 adds - The Intelligence Layer:
1. From Static Graphs to Dynamic Reasoning
# Session 6 Approach: Graph Traversal
graph_result = knowledge_graph.traverse_relationships(
start_entity="Steve Jobs",
relationship_types=["worked_at", "founded"],
max_depth=2
)
# Session 7 Enhancement: Reasoning Over Graphs
reasoning_result = await reasoning_engine.reason_over_structured_knowledge(
query="What leadership patterns can we deduce from Steve Jobs' career?",
reasoning_type='deductive'
)
# Result: Not just facts, but logical conclusions and patterns
2. From Information Retrieval to Autonomous Planning
# Traditional RAG: Direct retrieval
docs = vector_store.similarity_search(query, k=5)
response = llm.generate(query, docs)
# Agentic RAG: Intelligent planning
plan = await agent.reason_plan_and_execute(query)
# - Analyzes reasoning requirements
# - Plans optimal information gathering strategy
# - Executes with self-correction
# - Validates logical coherence
The Three Pillars of Production Intelligence¶
Pillar 1: Cognitive Query Understanding¶
WHY: Production systems need to understand not just what information is requested, but what type of reasoning is required to construct a proper answer.
Evolution Path:
- Basic: Match keywords and retrieve similar documents
- Enhanced: Understand semantic intent and retrieve relevant information
- Production: Analyze cognitive requirements and plan reasoning strategies
Pillar 2: Autonomous Error Correction¶
WHY: Production systems must be self-healing. They need to detect and fix their own mistakes without human intervention.
Enterprise Reality:
- Traditional Systems: Errors propagate and compound
- Agentic Systems: Self-validate and auto-correct using multiple strategies
- Production Benefit: Maintain quality at scale without constant human oversight
Pillar 3: Tool Integration Intelligence¶
WHY: Real-world problems require more than document retrieval - they need calculations, real-time data, specialized databases.
Production Evolution:
# Level 1: Document-only RAG
# Level 2: Document + Knowledge Graph RAG (Session 6)
# Level 3: Document + Graph + Tools + Reasoning RAG (Session 7)
Enterprise Concepts You'll Master¶
1. Circuit Breakers and Fallbacks Why needed: Production systems must handle failures gracefully Implementation: Intelligent degradation when components fail
2. Observability and Monitoring Why needed: Enterprise systems need comprehensive visibility Implementation: Track reasoning quality, performance patterns, error rates
3. A/B Testing for AI Systems Why needed: Optimize reasoning strategies based on real performance Implementation: Compare different agent approaches on production traffic
4. Caching for Intelligence Why needed: Expensive reasoning operations need smart caching Implementation: Cache reasoning patterns, not just retrieval results
Part 1: Reasoning-Driven Query Planning and Execution (30 minutes)¶
Reasoning-Driven Query Planning Agent¶
Build agents that analyze queries through cognitive reasoning frameworks to plan optimal knowledge construction strategies.
The Cognitive Evolution of Query Planning:
- Traditional Approach: Analyze query → Plan retrieval → Execute
- Reasoning-Driven Approach: Analyze reasoning requirements → Plan cognitive strategy → Execute reasoning-guided workflow → Validate logical coherence
Progressive Learning Path: From Basic to Enterprise¶
Level 1: Basic Reasoning Agent (Foundation)¶
Start here if you're new to agentic systems
class BasicReasoningAgent:
"""Simple reasoning agent for educational purposes.
WHY: Start with core concepts before adding complexity.
Learn reasoning analysis and basic planning patterns.
"""
def __init__(self, llm_model):
self.llm_model = llm_model
async def analyze_and_respond(self, query: str) -> Dict[str, Any]:
"""Basic reasoning-guided response generation."""
# Step 1: Simple reasoning analysis
reasoning_type = await self._identify_reasoning_type(query)
# Step 2: Basic information gathering
information = await self._gather_basic_information(query)
# Step 3: Reasoning-guided response
response = await self._generate_reasoning_response(
query, reasoning_type, information
)
return {
'query': query,
'reasoning_type': reasoning_type,
'response': response,
'complexity': 'basic'
}
async def _identify_reasoning_type(self, query: str) -> str:
"""Identify what type of reasoning is needed."""
# Simple heuristic-based reasoning identification
if "why" in query.lower() or "cause" in query.lower():
return "causal"
elif "compare" in query.lower() or "difference" in query.lower():
return "comparative"
elif "will" in query.lower() or "predict" in query.lower():
return "predictive"
else:
return "analytical"
Level 2: Enhanced Reasoning Agent (Skill Building)¶
Add this once Level 1 is comfortable
class EnhancedReasoningAgent(BasicReasoningAgent):
"""Enhanced agent with multi-step reasoning and validation.
WHY: Build on basic concepts with structured reasoning chains
and quality validation. Introduces enterprise concepts gradually.
"""
def __init__(self, llm_model, retrieval_system):
super().__init__(llm_model)
self.retrieval_system = retrieval_system
async def analyze_and_respond(self, query: str) -> Dict[str, Any]:
"""Enhanced reasoning with structured planning."""
# Step 1: Comprehensive reasoning analysis
reasoning_analysis = await self._comprehensive_reasoning_analysis(query)
# Step 2: Create reasoning plan
reasoning_plan = await self._create_reasoning_plan(query, reasoning_analysis)
# Step 3: Execute plan with validation
execution_result = await self._execute_reasoning_plan(reasoning_plan)
# Step 4: Quality validation
validation_result = await self._validate_reasoning_quality(execution_result)
return {
'query': query,
'reasoning_analysis': reasoning_analysis,
'reasoning_plan': reasoning_plan,
'execution_result': execution_result,
'validation_result': validation_result,
'complexity': 'enhanced'
}
async def _comprehensive_reasoning_analysis(self, query: str) -> Dict[str, Any]:
"""More detailed reasoning analysis than basic version."""
analysis_prompt = f"""
Analyze this query to determine reasoning requirements:
Query: {query}
Determine:
1. Primary reasoning type needed
2. Complexity level (simple/moderate/complex)
3. Key logical steps required
4. Information gaps to fill
Return JSON with analysis.
"""
response = await self._async_llm_predict(analysis_prompt, temperature=0.1)
return self._parse_json_response(response)
Level 3: Production Reasoning Agent (Enterprise Ready)¶
Full production capabilities with monitoring and optimization
# Advanced agentic RAG with query planning
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass
from enum import Enum
import asyncio
import json
import time
class QueryComplexity(Enum):
SIMPLE = "simple"
MODERATE = "moderate"
COMPLEX = "complex"
MULTI_STEP = "multi_step"
@dataclass
class QueryPlan:
"""Structured query execution plan."""
original_query: str
complexity: QueryComplexity
sub_queries: List[str]
retrieval_strategies: List[str]
expected_sources: List[str]
confidence_threshold: float
max_iterations: int
validation_steps: List[str]
class ReasoningDrivenQueryPlanningAgent:
"""Intelligent agent for reasoning-driven RAG query planning and cognitive orchestration."""
def __init__(self, llm_model, vector_store, knowledge_graph=None):
self.llm_model = llm_model
self.vector_store = vector_store
self.knowledge_graph = knowledge_graph
# Reasoning-integrated planning strategies
self.planning_strategies = {
QueryComplexity.SIMPLE: self._plan_simple_reasoning_query,
QueryComplexity.MODERATE: self._plan_moderate_reasoning_query,
QueryComplexity.COMPLEX: self._plan_complex_reasoning_query,
QueryComplexity.MULTI_STEP: self._plan_multi_step_reasoning_query
}
# Reasoning-driven execution strategies
self.reasoning_execution_strategies = {
'direct_reasoning_retrieval': self._execute_direct_reasoning_retrieval,
'chain_of_thought_retrieval': self._execute_chain_of_thought_retrieval,
'iterative_reasoning_refinement': self._execute_iterative_reasoning_refinement,
'multi_modal_reasoning_synthesis': self._execute_multi_modal_reasoning_synthesis
}
# Execution strategies
self.execution_strategies = {
'direct_retrieval': self._execute_direct_retrieval,
'decomposed_retrieval': self._execute_decomposed_retrieval,
'iterative_refinement': self._execute_iterative_refinement,
'multi_source_synthesis': self._execute_multi_source_synthesis
}
# Agent memory for learning from past executions
self.execution_history = []
self.performance_patterns = {}
async def reason_plan_and_execute(self, query: str,
reasoning_config: Dict = None) -> Dict[str, Any]:
"""Plan and execute RAG query using reasoning-driven cognitive approach."""
config = reasoning_config or {
'max_reasoning_time': 45,
'enable_reasoning_validation': True,
'use_chain_of_thought': True,
'reasoning_depth': 'moderate',
'logical_coherence_threshold': 0.8
}
print(f"Reasoning-driven planning for query: {query[:100]}...")
# Step 1: Analyze query reasoning requirements and cognitive complexity
reasoning_analysis = await self._analyze_reasoning_requirements(query)
# Step 2: Create reasoning-integrated execution plan
reasoning_plan = await self._create_reasoning_execution_plan(query, reasoning_analysis, config)
# Step 3: Execute reasoning-guided plan with cognitive monitoring
reasoning_result = await self._execute_reasoning_plan_with_monitoring(
reasoning_plan, config
)
# Step 4: Validate logical coherence and refine reasoning
if config.get('enable_reasoning_validation', True):
reasoning_result = await self._validate_and_refine_reasoning(
reasoning_result, reasoning_plan, config
)
# Step 5: Update reasoning memory and learning patterns
self._update_reasoning_memory(query, reasoning_plan, reasoning_result)
return {
'query': query,
'reasoning_analysis': reasoning_analysis,
'reasoning_plan': reasoning_plan,
'reasoning_result': reasoning_result,
'cognitive_metadata': {
'reasoning_time': reasoning_result.get('reasoning_time', 0),
'execution_time': reasoning_result.get('execution_time', 0),
'reasoning_iterations': reasoning_result.get('iterations', 1),
'logical_refinements': reasoning_result.get('refinements', 0),
'coherence_score': reasoning_result.get('coherence_score', 0.0),
'reasoning_type': reasoning_analysis.get('primary_reasoning_type')
}
}
Step 1: Reasoning Requirements Analysis
async def _analyze_reasoning_requirements(self, query: str) -> Dict[str, Any]:
"""Analyze query to determine reasoning requirements and cognitive complexity."""
reasoning_prompt = f"""
Analyze this query to determine its reasoning requirements and cognitive complexity:
Query: {query}
Provide comprehensive reasoning analysis in JSON format:
{{
"complexity": "simple|moderate|complex|multi_step",
"primary_reasoning_type": "deductive|inductive|abductive|analogical|causal|comparative",
"reasoning_depth": "shallow|moderate|deep|multi_layered",
"logical_structure": "linear|branching|circular|hierarchical",
"chain_of_thought_required": true/false,
"premises_needed": ["premise1", "premise2"],
"logical_connections": ["connection_type1", "connection_type2"],
"cognitive_operations": ["analysis", "synthesis", "evaluation", "inference"],
"evidence_reasoning_types": ["factual_validation", "logical_consistency", "causal_inference"],
"potential_reasoning_fallacies": ["fallacy_type1", "fallacy_type2"],
"reasoning_validation_points": ["checkpoint1", "checkpoint2"],
"estimated_reasoning_steps": 1-10,
"key_reasoning_concepts": ["concept1", "concept2"],
"meta_cognitive_requirements": ["reasoning_about_reasoning", "strategy_selection"]
}}
JSON:
"""
try:
response = await self._async_llm_predict(reasoning_prompt, temperature=0.1)
analysis = json.loads(self._extract_json_from_response(response))
# Determine complexity level
complexity_level = QueryComplexity(analysis.get('complexity', 'simple'))
# Add derived metrics
analysis['complexity_level'] = complexity_level
analysis['planning_priority'] = self._calculate_planning_priority(analysis)
analysis['expected_retrieval_depth'] = self._estimate_retrieval_depth(analysis)
return analysis
except Exception as e:
print(f"Query analysis error: {e}")
# Fallback analysis
return {
'complexity_level': QueryComplexity.SIMPLE,
'reasoning_required': False,
'multiple_sources_needed': True,
'estimated_subtasks': 1,
'planning_priority': 0.5
}
Step 2: Dynamic Plan Creation
async def _create_execution_plan(self, query: str, analysis: Dict,
config: Dict) -> QueryPlan:
"""Create dynamic execution plan based on query analysis."""
complexity = analysis['complexity_level']
# Use appropriate planning strategy
if complexity in self.planning_strategies:
plan_details = await self.planning_strategies[complexity](
query, analysis, config
)
else:
plan_details = await self._plan_simple_query(query, analysis, config)
# Learn from execution history
if config.get('use_execution_history', True):
plan_details = self._adapt_plan_from_history(query, plan_details)
execution_plan = QueryPlan(
original_query=query,
complexity=complexity,
sub_queries=plan_details['sub_queries'],
retrieval_strategies=plan_details['retrieval_strategies'],
expected_sources=plan_details['expected_sources'],
confidence_threshold=plan_details.get('confidence_threshold', 0.7),
max_iterations=plan_details.get('max_iterations', 3),
validation_steps=plan_details.get('validation_steps', ['factual_check'])
)
return execution_plan
async def _plan_complex_query(self, query: str, analysis: Dict,
config: Dict) -> Dict[str, Any]:
"""Create execution plan for complex queries requiring multi-step reasoning."""
planning_prompt = f"""
Create a detailed execution plan for this complex query:
Query: {query}
Analysis: {json.dumps(analysis, indent=2)}
Create an execution plan with:
1. 3-7 focused sub-queries that build toward answering the main question
2. Appropriate retrieval strategies for each sub-query
3. Expected source types needed
4. Quality validation steps
Return JSON:
{{
"sub_queries": ["sub_query_1", "sub_query_2", ...],
"retrieval_strategies": ["strategy1", "strategy2", ...],
"expected_sources": ["academic", "technical", "news"],
"confidence_threshold": 0.8,
"max_iterations": 4,
"validation_steps": ["factual_check", "consistency_check", "completeness_check"],
"reasoning": "explanation of planning approach"
}}
JSON:
"""
try:
response = await self._async_llm_predict(planning_prompt, temperature=0.2)
plan = json.loads(self._extract_json_from_response(response))
return plan
except Exception as e:
print(f"Complex planning error: {e}")
return self._fallback_plan(query)
Iterative Execution Engine¶
Implement sophisticated execution with adaptive refinement:
# Iterative execution with self-correction
class IterativeExecutionEngine:
"""Engine for iterative RAG execution with self-correction."""
def __init__(self, rag_system, validator, quality_assessor):
self.rag_system = rag_system
self.validator = validator
self.quality_assessor = quality_assessor
# Execution strategies
self.refinement_strategies = {
'query_reformulation': self._reformulate_query,
'source_expansion': self._expand_sources,
'context_reweighting': self._reweight_context,
'evidence_validation': self._validate_evidence,
'gap_filling': self._fill_information_gaps
}
async def execute_iteratively(self, execution_plan: QueryPlan,
execution_config: Dict = None) -> Dict[str, Any]:
"""Execute plan with iterative refinement."""
config = execution_config or {
'max_iterations': execution_plan.max_iterations,
'improvement_threshold': 0.1,
'early_stopping': True,
'quality_target': execution_plan.confidence_threshold
}
execution_history = []
current_response = None
iteration = 0
while iteration < config['max_iterations']:
print(f"Execution iteration {iteration + 1}/{config['max_iterations']}")
# Execute current plan
iteration_result = await self._execute_iteration(
execution_plan, current_response, iteration
)
# Assess quality
quality_assessment = await self.quality_assessor.assess_response_quality(
execution_plan.original_query,
iteration_result['response'],
iteration_result['sources']
)
# Store iteration
execution_history.append({
'iteration': iteration,
'result': iteration_result,
'quality': quality_assessment,
'timestamp': time.time()
})
# Check if quality target met
if quality_assessment['overall_score'] >= config['quality_target']:
print(f"Quality target reached at iteration {iteration + 1}")
break
# Check for improvement over previous iteration
if (iteration > 0 and
self._calculate_improvement(execution_history) < config['improvement_threshold']):
if config['early_stopping']:
print("Early stopping: insufficient improvement")
break
# Plan refinement for next iteration
if iteration < config['max_iterations'] - 1:
refinement_plan = await self._plan_refinement(
execution_plan, iteration_result, quality_assessment
)
execution_plan = self._apply_refinement_plan(execution_plan, refinement_plan)
current_response = iteration_result
iteration += 1
# Select best result
best_result = self._select_best_result(execution_history)
return {
'final_result': best_result,
'execution_history': execution_history,
'total_iterations': iteration,
'converged': best_result['quality']['overall_score'] >= config['quality_target'],
'improvement_trajectory': [h['quality']['overall_score'] for h in execution_history]
}
Step 3: Self-Correction Mechanisms
async def _plan_refinement(self, execution_plan: QueryPlan,
current_result: Dict, quality_assessment: Dict) -> Dict[str, Any]:
"""Plan refinements based on quality assessment."""
# Identify specific quality issues
quality_issues = self._identify_quality_issues(quality_assessment)
refinement_prompt = f"""
Based on the quality assessment, suggest refinements to improve the RAG response:
Original Query: {execution_plan.original_query}
Current Response Quality: {quality_assessment['overall_score']:.2f}
Quality Issues Identified:
{json.dumps(quality_issues, indent=2)}
Current Sub-queries: {execution_plan.sub_queries}
Current Sources: {len(current_result.get('sources', []))} sources retrieved
Suggest refinements in JSON format:
{{
"refinement_strategies": ["strategy1", "strategy2"],
"new_sub_queries": ["refined_query_1"],
"additional_source_types": ["source_type"],
"context_adjustments": "description of context improvements needed",
"validation_enhancements": ["additional_validation_step"],
"reasoning": "explanation of refinement rationale"
}}
JSON:
"""
try:
response = await self._async_llm_predict(refinement_prompt, temperature=0.2)
refinement_plan = json.loads(self._extract_json_from_response(response))
return refinement_plan
except Exception as e:
print(f"Refinement planning error: {e}")
return {'refinement_strategies': ['query_reformulation']}
def _apply_refinement_plan(self, execution_plan: QueryPlan,
refinement_plan: Dict) -> QueryPlan:
"""Apply refinement plan to create updated execution plan."""
# Create updated plan
updated_plan = QueryPlan(
original_query=execution_plan.original_query,
complexity=execution_plan.complexity,
sub_queries=refinement_plan.get('new_sub_queries', execution_plan.sub_queries),
retrieval_strategies=execution_plan.retrieval_strategies,
expected_sources=execution_plan.expected_sources +
refinement_plan.get('additional_source_types', []),
confidence_threshold=execution_plan.confidence_threshold,
max_iterations=execution_plan.max_iterations,
validation_steps=execution_plan.validation_steps +
refinement_plan.get('validation_enhancements', [])
)
return updated_plan
Part 2: Reasoning-Based Self-Correction Systems (25 minutes)¶
Logical Coherence Validation and Cognitive Correction¶
Build systems that validate logical consistency and correct reasoning flaws in their own responses:
Evolution from Error Correction to Reasoning Validation:
- Traditional Self-Correction: Check factual accuracy and consistency
- Reasoning-Based Correction: Validate logical coherence, identify reasoning gaps, strengthen logical connections, and ensure cognitive soundness
# Reasoning-based self-correcting RAG with logical validation and cognitive correction
class ReasoningBasedSelfCorrectingRAG:
"""RAG system with built-in logical reasoning validation and cognitive self-correction capabilities."""
def __init__(self, base_rag_system, llm_judge, fact_checker=None):
self.base_rag = base_rag_system
self.llm_judge = llm_judge
self.fact_checker = fact_checker
# Reasoning-enhanced validation strategies
self.reasoning_validators = {
'logical_coherence': LogicalCoherenceValidator(llm_judge),
'reasoning_chain_validity': ReasoningChainValidator(llm_judge),
'premise_conclusion_consistency': PremiseConclusionValidator(llm_judge),
'causal_inference_validity': CausalInferenceValidator(llm_judge),
'analogical_reasoning_soundness': AnalogicalReasoningValidator(llm_judge),
'cognitive_bias_detection': CognitiveBiasDetector(llm_judge),
'fallacy_identification': LogicalFallacyDetector(llm_judge)
}
# Traditional validators (still important)
self.traditional_validators = {
'factual_consistency': FactualConsistencyValidator(llm_judge),
'source_attribution': SourceAttributionValidator(),
'completeness_check': CompletenessValidator(llm_judge),
'confidence_calibration': ConfidenceCalibrationValidator(llm_judge)
}
# Reasoning-enhanced correction strategies
self.reasoning_correctors = {
'logical_coherence_repair': self._repair_logical_coherence,
'reasoning_chain_strengthening': self._strengthen_reasoning_chain,
'premise_reinforcement': self._reinforce_premises,
'causal_inference_correction': self._correct_causal_inference,
'analogical_reasoning_improvement': self._improve_analogical_reasoning,
'cognitive_bias_mitigation': self._mitigate_cognitive_biases,
'fallacy_correction': self._correct_logical_fallacies
}
# Traditional correction strategies (still needed)
self.traditional_correctors = {
'fact_correction': self._correct_factual_errors,
'attribution_fix': self._fix_source_attribution,
'completeness_enhancement': self._enhance_completeness,
'confidence_adjustment': self._adjust_confidence
}
# Correction history for learning
self.correction_history = []
async def generate_with_reasoning_validation(self, query: str,
reasoning_validation_config: Dict = None) -> Dict[str, Any]:
"""Generate response with comprehensive reasoning validation and cognitive correction."""
config = reasoning_validation_config or {
'reasoning_validation_rounds': 3,
'logical_coherence_threshold': 0.8,
'max_reasoning_corrections': 4,
'require_logical_soundness': True,
'validate_reasoning_chain': True
}
print(f"Generating validated response for: {query[:100]}...")
# Initial response generation
initial_response = await self.base_rag.generate_response(query)
correction_rounds = []
current_response = initial_response
for round_num in range(config['validation_rounds']):
print(f"Validation round {round_num + 1}")
# Comprehensive validation
validation_results = await self._comprehensive_validation(
query, current_response, config
)
# Identify required corrections
corrections_needed = self._identify_corrections_needed(
validation_results, config['correction_threshold']
)
if not corrections_needed:
print("No corrections needed - response validated")
break
# Apply corrections
corrected_response = await self._apply_corrections(
query, current_response, corrections_needed, config
)
# Track correction round
correction_rounds.append({
'round': round_num + 1,
'original_response': current_response,
'validation_results': validation_results,
'corrections_applied': corrections_needed,
'corrected_response': corrected_response
})
current_response = corrected_response
# Prevent infinite correction loops
if round_num >= config['max_corrections']:
print("Maximum corrections reached")
break
# Final quality assessment
final_quality = await self._final_quality_assessment(query, current_response)
# Update correction learning
self._update_correction_learning(query, correction_rounds, final_quality)
return {
'query': query,
'initial_response': initial_response,
'final_response': current_response,
'correction_rounds': correction_rounds,
'final_quality': final_quality,
'validation_metadata': {
'rounds_needed': len(correction_rounds),
'corrections_applied': sum(len(r['corrections_applied']) for r in correction_rounds),
'final_confidence': final_quality.get('confidence_score', 0.5)
}
}
Step 4: Comprehensive Validation Framework
async def _comprehensive_validation(self, query: str, response: Dict,
config: Dict) -> Dict[str, Any]:
"""Run comprehensive validation across multiple dimensions."""
validation_results = {}
# Run all validators
for validator_name, validator in self.validators.items():
try:
validator_result = await validator.validate(
query, response['response'], response.get('sources', [])
)
validation_results[validator_name] = validator_result
except Exception as e:
print(f"Validation error ({validator_name}): {e}")
validation_results[validator_name] = {
'passed': False,
'score': 0.0,
'error': str(e)
}
# Calculate overall validation score
valid_scores = [
r['score'] for r in validation_results.values()
if 'score' in r and r.get('passed', False)
]
overall_score = np.mean(valid_scores) if valid_scores else 0.0
validation_results['overall'] = {
'score': overall_score,
'passed': overall_score >= config.get('validation_threshold', 0.7),
'validator_count': len(self.validators),
'passed_count': sum(1 for r in validation_results.values() if r.get('passed', False))
}
return validation_results
class FactualConsistencyValidator:
"""Validates factual consistency between response and sources."""
def __init__(self, llm_judge):
self.llm_judge = llm_judge
async def validate(self, query: str, response: str, sources: List[str]) -> Dict[str, Any]:
"""Validate factual consistency of response against sources."""
if not sources:
return {'passed': False, 'score': 0.0, 'reason': 'No sources provided'}
# Extract factual claims from response
claims = await self._extract_factual_claims(response)
# Validate each claim against sources
claim_validations = []
for claim in claims:
claim_validation = await self._validate_claim_against_sources(
claim, sources
)
claim_validations.append(claim_validation)
# Calculate overall factual consistency
if claim_validations:
avg_score = np.mean([cv['score'] for cv in claim_validations])
passed = avg_score >= 0.7
else:
avg_score = 1.0 # No claims to validate
passed = True
return {
'passed': passed,
'score': avg_score,
'claim_count': len(claims),
'validated_claims': claim_validations,
'inconsistent_claims': [
cv for cv in claim_validations if cv['score'] < 0.5
]
}
Step 5: Intelligent Correction Application
async def _apply_corrections(self, query: str, response: Dict,
corrections_needed: List[str],
config: Dict) -> Dict[str, Any]:
"""Apply identified corrections to improve response quality."""
corrected_response = response.copy()
correction_details = []
for correction_type in corrections_needed:
if correction_type in self.correctors:
try:
correction_result = await self.correctors[correction_type](
query, corrected_response, config
)
if correction_result['success']:
corrected_response = correction_result['corrected_response']
correction_details.append({
'type': correction_type,
'applied': True,
'improvement': correction_result.get('improvement_score', 0),
'details': correction_result.get('details', '')
})
else:
correction_details.append({
'type': correction_type,
'applied': False,
'error': correction_result.get('error', 'Unknown error')
})
except Exception as e:
print(f"Correction error ({correction_type}): {e}")
correction_details.append({
'type': correction_type,
'applied': False,
'error': str(e)
})
# Add correction metadata
corrected_response['correction_details'] = correction_details
corrected_response['corrected'] = any(cd['applied'] for cd in correction_details)
return corrected_response
async def _correct_factual_errors(self, query: str, response: Dict,
config: Dict) -> Dict[str, Any]:
"""Correct factual errors in the response.
WHY: Factual accuracy is critical for production RAG systems. This method
implements source-grounded correction to fix errors while preserving response quality.
"""
# Phase 1: Extract correction context
correction_context = self._extract_correction_context(response)
# Phase 2: Build targeted correction prompt
correction_prompt = self._build_correction_prompt(query, correction_context)
# Phase 3: Execute correction with validation
return await self._execute_factual_correction(
correction_prompt, correction_context, response
)
def _extract_correction_context(self, response: Dict) -> Dict[str, Any]:
"""Extract context needed for factual correction.
WHY: Separating context extraction allows for pre-processing optimization
and makes the correction logic more focused and testable.
"""
sources = response.get('sources', [])
current_response = response['response']
# Limit sources for prompt efficiency (production constraint)
relevant_sources = [
s.get('content', str(s))[:500]
for s in sources[:5] # Top 5 most relevant
]
return {
'current_response': current_response,
'sources': sources,
'relevant_sources': relevant_sources,
'source_count': len(sources)
}
def _build_correction_prompt(self, query: str, context: Dict) -> str:
"""Build the factual correction prompt.
WHY: Separating prompt construction enables A/B testing of different
correction prompts and makes the correction strategy more maintainable.
"""
return f"""
Review this response for factual errors and correct them using only information from the provided sources.
Query: {query}
Current Response: {context['current_response']}
Sources ({context['source_count']} total, showing top 5):
{json.dumps(context['relevant_sources'], indent=2)}
Instructions:
1. Identify any factual claims that are incorrect or unsupported by sources
2. Correct these claims using accurate information from the sources
3. Maintain the response structure and tone
4. Only make corrections where sources provide clear contradictory or supporting evidence
Provide corrected response:
"""
async def _execute_factual_correction(self, correction_prompt: str,
context: Dict, original_response: Dict) -> Dict[str, Any]:
"""Execute the factual correction process with error handling.
WHY: Centralizing correction execution enables comprehensive error handling,
performance monitoring, and quality assessment of corrections.
"""
try:
# Generate corrected text
corrected_text = await self._async_llm_predict(correction_prompt, temperature=0.1)
# Assess correction quality and improvement
improvement_score = await self._assess_correction_improvement(
context['current_response'], corrected_text, context['sources']
)
# Return successful correction result
return {
'success': True,
'corrected_response': {
**original_response,
'response': corrected_text
},
'improvement_score': improvement_score,
'details': 'Applied factual corrections based on source evidence',
'correction_metadata': {
'sources_used': len(context['relevant_sources']),
'original_length': len(context['current_response']),
'corrected_length': len(corrected_text),
'correction_quality': improvement_score
}
}
except Exception as e:
# Return error result with diagnostic information
return {
'success': False,
'error': str(e),
'error_context': {
'correction_stage': 'factual_correction_execution',
'sources_available': len(context['sources']),
'response_length': len(context['current_response'])
}
}
Part 3: Tool Integration for Enhanced RAG (20 minutes)¶
Multi-Tool RAG Agent¶
Integrate external tools to enhance RAG capabilities:
# Multi-tool RAG agent with external integrations
from typing import Protocol
import requests
from datetime import datetime
class Tool(Protocol):
"""Protocol for RAG-integrated tools."""
def __init__(self, config: Dict): ...
async def execute(self, query: str, context: Dict) -> Dict[str, Any]: ...
def get_description(self) -> str: ...
class WebSearchTool:
"""Web search integration for real-time information."""
def __init__(self, config: Dict):
self.api_key = config.get('api_key')
self.search_engine = config.get('engine', 'google')
async def execute(self, query: str, context: Dict) -> Dict[str, Any]:
"""Execute web search and return results."""
try:
# Implement actual web search API call
search_results = await self._perform_web_search(query)
return {
'success': True,
'results': search_results,
'source_type': 'web_search',
'timestamp': datetime.now().isoformat()
}
except Exception as e:
return {'success': False, 'error': str(e)}
def get_description(self) -> str:
return "Search the web for current information and recent developments"
class CalculatorTool:
"""Mathematical calculation tool."""
def __init__(self, config: Dict):
self.precision = config.get('precision', 10)
async def execute(self, query: str, context: Dict) -> Dict[str, Any]:
"""Execute mathematical calculations."""
try:
# Extract mathematical expressions and compute
calculation_result = self._safe_calculate(query)
return {
'success': True,
'result': calculation_result,
'source_type': 'calculation',
'precision': self.precision
}
except Exception as e:
return {'success': False, 'error': str(e)}
def get_description(self) -> str:
return "Perform mathematical calculations and numerical analysis"
class DatabaseQueryTool:
"""Database query tool for structured data retrieval."""
def __init__(self, config: Dict):
self.connection_string = config.get('connection_string')
self.allowed_tables = config.get('allowed_tables', [])
async def execute(self, query: str, context: Dict) -> Dict[str, Any]:
"""Execute database query safely."""
try:
# Generate safe SQL query
sql_query = await self._generate_safe_sql(query, context)
# Execute query
results = await self._execute_sql_safely(sql_query)
return {
'success': True,
'results': results,
'sql_query': sql_query,
'source_type': 'database'
}
except Exception as e:
return {'success': False, 'error': str(e)}
def get_description(self) -> str:
return "Query structured databases for specific data and statistics"
class MultiToolRAGAgent:
"""RAG agent with integrated tool capabilities."""
def __init__(self, base_rag_system, tools: Dict[str, Tool], llm_model):
self.base_rag = base_rag_system
self.tools = tools
self.llm_model = llm_model
# Tool selection strategies
self.tool_selectors = {
'rule_based': self._rule_based_tool_selection,
'llm_guided': self._llm_guided_tool_selection,
'adaptive': self._adaptive_tool_selection
}
# Tool execution history for learning
self.tool_usage_history = []
async def enhanced_generate(self, query: str,
tool_config: Dict = None) -> Dict[str, Any]:
"""Generate response using RAG + tools integration."""
config = tool_config or {
'tool_selection_strategy': 'adaptive',
'max_tools_per_query': 3,
'parallel_tool_execution': True,
'fallback_to_rag': True
}
print(f"Enhanced RAG generation with tools for: {query[:100]}...")
# Step 1: Determine if tools are needed and which ones
tool_analysis = await self._analyze_tool_requirements(query, config)
# Step 2: Execute base RAG retrieval
base_rag_result = await self.base_rag.generate_response(query)
# Step 3: Execute selected tools if needed
tool_results = {}
if tool_analysis['tools_needed']:
selected_tools = await self._select_tools(query, tool_analysis, config)
if config.get('parallel_tool_execution', True):
tool_results = await self._execute_tools_parallel(
query, selected_tools, config
)
else:
tool_results = await self._execute_tools_sequential(
query, selected_tools, config
)
# Step 4: Synthesize RAG + tool results
enhanced_response = await self._synthesize_enhanced_response(
query, base_rag_result, tool_results, config
)
# Step 5: Update tool usage learning
self._update_tool_learning(query, tool_analysis, tool_results, enhanced_response)
return {
'query': query,
'base_rag_result': base_rag_result,
'tool_analysis': tool_analysis,
'tool_results': tool_results,
'enhanced_response': enhanced_response,
'tool_metadata': {
'tools_used': list(tool_results.keys()),
'tools_successful': sum(1 for r in tool_results.values() if r.get('success', False)),
'enhancement_type': 'rag_plus_tools'
}
}
Step 6: Intelligent Tool Selection
async def _analyze_tool_requirements(self, query: str,
config: Dict) -> Dict[str, Any]:
"""Analyze query to determine tool requirements."""
tool_descriptions = {
name: tool.get_description()
for name, tool in self.tools.items()
}
analysis_prompt = f"""
Analyze this query to determine if external tools would be helpful:
Query: {query}
Available Tools:
{json.dumps(tool_descriptions, indent=2)}
Determine:
1. Would external tools significantly improve the answer?
2. Which specific tools would be most helpful?
3. What information gaps could tools fill?
4. Is real-time/current data needed?
5. Are calculations or structured queries required?
Return JSON:
{{
"tools_needed": true/false,
"recommended_tools": ["tool_name_1", "tool_name_2"],
"reasoning": "explanation of tool selection",
"information_gaps": ["gap1", "gap2"],
"priority_order": ["highest_priority_tool", "secondary_tool"],
"expected_value_add": 0.0-1.0
}}
JSON:
"""
try:
response = await self._async_llm_predict(analysis_prompt, temperature=0.2)
analysis = json.loads(self._extract_json_from_response(response))
return analysis
except Exception as e:
print(f"Tool analysis error: {e}")
return {
'tools_needed': False,
'recommended_tools': [],
'reasoning': 'Analysis failed - using RAG only'
}
async def _execute_tools_parallel(self, query: str, selected_tools: List[str],
config: Dict) -> Dict[str, Any]:
"""Execute multiple tools in parallel for efficiency."""
# Create tool execution tasks
tool_tasks = []
for tool_name in selected_tools:
if tool_name in self.tools:
task = self._execute_single_tool(tool_name, query, config)
tool_tasks.append((tool_name, task))
# Execute all tools concurrently
tool_results = {}
if tool_tasks:
results = await asyncio.gather(
*[task for _, task in tool_tasks],
return_exceptions=True
)
# Process results
for (tool_name, _), result in zip(tool_tasks, results):
if isinstance(result, Exception):
tool_results[tool_name] = {
'success': False,
'error': str(result)
}
else:
tool_results[tool_name] = result
return tool_results
Step 7: Enhanced Response Synthesis
async def _synthesize_enhanced_response(self, query: str,
base_rag_result: Dict,
tool_results: Dict,
config: Dict) -> Dict[str, Any]:
"""Synthesize comprehensive response from RAG + tool results."""
# Prepare context from all sources
rag_context = base_rag_result.get('response', '')
rag_sources = base_rag_result.get('sources', [])
tool_context = []
for tool_name, tool_result in tool_results.items():
if tool_result.get('success', False):
tool_context.append({
'tool': tool_name,
'result': tool_result
})
synthesis_prompt = f"""
Create a comprehensive response by synthesizing information from multiple sources:
Query: {query}
RAG System Response:
{rag_context}
Tool Results:
{json.dumps(tool_context, indent=2)}
Instructions:
1. Integrate all relevant information into a coherent response
2. Prioritize accuracy and cite sources appropriately
3. If tools provide more current or precise information, emphasize that
4. Clearly indicate when information comes from real-time sources vs. knowledge base
5. Resolve any contradictions by noting the source and recency of information
Create a comprehensive, well-structured response:
"""
try:
synthesized_response = await self._async_llm_predict(
synthesis_prompt, temperature=0.3
)
# Calculate enhancement value
enhancement_value = self._calculate_enhancement_value(
base_rag_result, tool_results
)
# Compile source attribution
source_attribution = self._compile_source_attribution(
rag_sources, tool_results
)
return {
'response': synthesized_response,
'source_attribution': source_attribution,
'enhancement_value': enhancement_value,
'synthesis_type': 'rag_tool_integration',
'confidence_score': min(
base_rag_result.get('confidence', 0.7) + enhancement_value * 0.2,
1.0
)
}
except Exception as e:
print(f"Synthesis error: {e}")
# Fallback to base RAG result
return {
'response': base_rag_result.get('response', 'Unable to generate response'),
'synthesis_error': str(e),
'fallback': True
}
Part 4: Multi-Agent RAG Orchestration (25 minutes)¶
Collaborative Agent Framework¶
Build systems where multiple specialized agents collaborate:
# Multi-agent collaborative RAG system
from abc import ABC, abstractmethod
from enum import Enum
class AgentRole(Enum):
RESEARCHER = "researcher"
ANALYZER = "analyzer"
SYNTHESIZER = "synthesizer"
VALIDATOR = "validator"
COORDINATOR = "coordinator"
class RAGAgent(ABC):
"""Base class for specialized RAG agents."""
def __init__(self, role: AgentRole, llm_model, specialized_tools: List[Tool] = None):
self.role = role
self.llm_model = llm_model
self.specialized_tools = specialized_tools or []
self.agent_id = f"{role.value}_{id(self)}"
@abstractmethod
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Process assigned task and return results."""
pass
@abstractmethod
def get_capabilities(self) -> List[str]:
"""Return list of agent capabilities."""
pass
class ResearcherAgent(RAGAgent):
"""Specialized agent for information research and retrieval."""
def __init__(self, llm_model, vector_store, web_search_tool=None):
super().__init__(AgentRole.RESEARCHER, llm_model, [web_search_tool] if web_search_tool else [])
self.vector_store = vector_store
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Research information for given query or sub-query."""
query = task['query']
research_depth = task.get('depth', 'standard')
print(f"Researcher agent processing: {query[:100]}...")
# Multi-source research
research_results = {}
# Vector search
vector_results = await self._perform_vector_research(query, research_depth)
research_results['vector_search'] = vector_results
# Web search if tool available
if self.specialized_tools:
web_results = await self.specialized_tools[0].execute(query, task)
research_results['web_search'] = web_results
# Consolidate findings
consolidated_findings = await self._consolidate_research(
query, research_results
)
return {
'agent_id': self.agent_id,
'task_completed': True,
'research_results': research_results,
'consolidated_findings': consolidated_findings,
'confidence': self._calculate_research_confidence(research_results),
'sources_found': self._count_total_sources(research_results)
}
def get_capabilities(self) -> List[str]:
return [
"vector_database_search",
"web_research",
"information_consolidation",
"source_evaluation"
]
class AnalyzerAgent(RAGAgent):
"""Specialized agent for information analysis and interpretation."""
def __init__(self, llm_model, analysis_tools: List[Tool] = None):
super().__init__(AgentRole.ANALYZER, llm_model, analysis_tools or [])
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze provided information and extract insights."""
information = task['information']
analysis_type = task.get('analysis_type', 'comprehensive')
print(f"Analyzer agent processing {analysis_type} analysis...")
# Perform different types of analysis
analysis_results = {}
if analysis_type in ['comprehensive', 'factual']:
factual_analysis = await self._perform_factual_analysis(information)
analysis_results['factual'] = factual_analysis
if analysis_type in ['comprehensive', 'comparative']:
comparative_analysis = await self._perform_comparative_analysis(information)
analysis_results['comparative'] = comparative_analysis
if analysis_type in ['comprehensive', 'causal']:
causal_analysis = await self._perform_causal_analysis(information)
analysis_results['causal'] = causal_analysis
# Synthesize insights
key_insights = await self._extract_key_insights(analysis_results)
return {
'agent_id': self.agent_id,
'task_completed': True,
'analysis_results': analysis_results,
'key_insights': key_insights,
'analysis_confidence': self._calculate_analysis_confidence(analysis_results),
'insight_count': len(key_insights)
}
def get_capabilities(self) -> List[str]:
return [
"factual_analysis",
"comparative_analysis",
"causal_analysis",
"insight_extraction",
"pattern_recognition"
]
class MultiAgentRAGOrchestrator:
"""Orchestrates collaboration between multiple RAG agents."""
def __init__(self, agents: Dict[AgentRole, RAGAgent], llm_coordinator):
self.agents = agents
self.llm_coordinator = llm_coordinator
# Collaboration patterns
self.collaboration_patterns = {
'sequential': self._sequential_collaboration,
'parallel': self._parallel_collaboration,
'hierarchical': self._hierarchical_collaboration,
'adaptive': self._adaptive_collaboration
}
# Task distribution strategies
self.task_distributors = {
'capability_based': self._distribute_by_capability,
'load_balanced': self._distribute_load_balanced,
'expertise_matched': self._distribute_by_expertise
}
async def orchestrate_collaborative_rag(self, query: str,
orchestration_config: Dict = None) -> Dict[str, Any]:
"""Orchestrate multi-agent collaboration for comprehensive RAG."""
config = orchestration_config or {
'collaboration_pattern': 'adaptive',
'task_distribution': 'capability_based',
'max_collaboration_rounds': 3,
'require_consensus': True,
'quality_threshold': 0.8
}
print(f"Orchestrating multi-agent RAG for: {query[:100]}...")
# Step 1: Plan collaboration strategy
collaboration_plan = await self._plan_collaboration(query, config)
# Step 2: Distribute initial tasks
initial_tasks = await self._distribute_initial_tasks(query, collaboration_plan)
# Step 3: Execute collaboration pattern
collaboration_pattern = config.get('collaboration_pattern', 'adaptive')
collaboration_results = await self.collaboration_patterns[collaboration_pattern](
query, initial_tasks, collaboration_plan, config
)
# Step 4: Synthesize final response
final_response = await self._synthesize_collaborative_response(
query, collaboration_results, config
)
# Step 5: Validate collaborative output
if config.get('require_consensus', True):
validation_result = await self._validate_collaborative_consensus(
final_response, collaboration_results
)
final_response['consensus_validation'] = validation_result
return {
'query': query,
'collaboration_plan': collaboration_plan,
'collaboration_results': collaboration_results,
'final_response': final_response,
'orchestration_metadata': {
'agents_used': len(collaboration_results),
'collaboration_pattern': collaboration_pattern,
'consensus_achieved': final_response.get('consensus_validation', {}).get('consensus_achieved', False),
'quality_score': final_response.get('quality_score', 0.0)
}
}
Step 8: Adaptive Collaboration Pattern
async def _adaptive_collaboration(self, query: str, initial_tasks: Dict,
collaboration_plan: Dict, config: Dict) -> Dict[str, Any]:
"""Implement adaptive collaboration based on task complexity and agent performance."""
collaboration_results = {}
collaboration_rounds = 0
max_rounds = config.get('max_collaboration_rounds', 3)
current_tasks = initial_tasks
while collaboration_rounds < max_rounds:
print(f"Collaboration round {collaboration_rounds + 1}")
# Execute current round of tasks
round_results = {}
for agent_role, task in current_tasks.items():
if agent_role in self.agents:
agent_result = await self.agents[agent_role].process_task(task)
round_results[agent_role] = agent_result
collaboration_results[f'round_{collaboration_rounds + 1}'] = round_results
# Assess round quality and determine next steps
round_assessment = await self._assess_collaboration_round(
query, round_results, config
)
# Decide on continuation
if round_assessment['quality_sufficient']:
print("Collaboration quality target achieved")
break
if collaboration_rounds < max_rounds - 1:
# Plan next round based on assessment
next_round_tasks = await self._plan_next_collaboration_round(
query, round_results, round_assessment
)
current_tasks = next_round_tasks
collaboration_rounds += 1
return collaboration_results
async def _plan_collaboration(self, query: str, config: Dict) -> Dict[str, Any]:
"""Plan optimal collaboration strategy based on query characteristics."""
# Analyze query complexity and agent capabilities
available_capabilities = {}
for role, agent in self.agents.items():
available_capabilities[role.value] = agent.get_capabilities()
planning_prompt = f"""
Plan a multi-agent collaboration strategy for this query:
Query: {query}
Available Agents and Capabilities:
{json.dumps(available_capabilities, indent=2)}
Create a collaboration plan considering:
1. Which agents are most relevant for this query?
2. What should be the sequence/pattern of collaboration?
3. How should tasks be divided among agents?
4. What are the dependencies between agent tasks?
Return JSON plan:
{{
"primary_agents": ["agent_role_1", "agent_role_2"],
"collaboration_sequence": ["step1_description", "step2_description"],
"task_dependencies": {{"agent1": ["depends_on_agent2"]}},
"expected_collaboration_rounds": 1-3,
"quality_checkpoints": ["checkpoint1", "checkpoint2"]
}}
JSON:
"""
try:
response = await self._async_llm_predict(planning_prompt, temperature=0.2)
plan = json.loads(self._extract_json_from_response(response))
return plan
except Exception as e:
print(f"Collaboration planning error: {e}")
# Fallback plan
return {
'primary_agents': ['researcher', 'synthesizer'],
'collaboration_sequence': ['research', 'synthesize'],
'expected_collaboration_rounds': 1
}
Hands-On Exercise: Build Production Reasoning-Augmented RAG¶
Your Mission¶
Create a production-ready reasoning-augmented RAG system that combines structured knowledge with logical reasoning capabilities for truly autonomous intelligent behavior.
Requirements:¶
- Reasoning-Driven Query Planning Agent: Cognitive analysis of reasoning requirements and intelligent execution planning
- Chain-of-Thought RAG Integration: Structured reasoning paths that guide both retrieval and synthesis
- Reasoning-Based Self-Correction: Logical coherence validation and cognitive error correction
- NodeRAG-Reasoning Bridge: Connect structured knowledge graphs with reasoning capabilities
- Multi-Modal Reasoning Integration: Reasoning that spans text, knowledge graphs, and external tools
- Production Cognitive Monitoring: Track reasoning performance and logical coherence metrics
Architecture Blueprint:¶
# Complete production reasoning-augmented RAG system
class ProductionReasoningAugmentedRAG:
"""Production-ready reasoning-augmented RAG system with cognitive intelligence."""
def __init__(self, config: Dict):
# Core reasoning-augmented components
self.reasoning_query_planner = ReasoningDrivenQueryPlanningAgent(
llm_model=config['llm_model'],
vector_store=config['vector_store'],
knowledge_graph=config.get('knowledge_graph')
)
self.reasoning_self_correcting_rag = ReasoningBasedSelfCorrectingRAG(
base_rag_system=config['base_rag'],
llm_judge=config['llm_judge'],
fact_checker=config.get('fact_checker')
)
self.chain_of_thought_rag = ChainOfThoughtRAG(
retrieval_system=config['retrieval_system'],
llm_model=config['llm_model'],
reasoning_validator=config['reasoning_validator']
)
self.node_rag_reasoning_bridge = NodeRAGReasoningBridge(
node_rag_system=config['node_rag_system'],
reasoning_engine=config['reasoning_engine']
)
self.multi_tool_agent = MultiToolRAGAgent(
base_rag_system=config['base_rag'],
tools=config['tools'],
llm_model=config['llm_model']
)
# Multi-agent orchestration
self.agent_orchestrator = MultiAgentRAGOrchestrator(
agents=self._initialize_agents(config),
llm_coordinator=config['llm_coordinator']
)
# Production monitoring
self.performance_monitor = AgenticRAGMonitor()
def _initialize_agents(self, config: Dict) -> Dict[AgentRole, RAGAgent]:
"""Initialize specialized agents."""
return {
AgentRole.RESEARCHER: ResearcherAgent(
config['llm_model'],
config['vector_store'],
config['tools'].get('web_search')
),
AgentRole.ANALYZER: AnalyzerAgent(
config['llm_model'],
[config['tools'].get('calculator')]
),
AgentRole.SYNTHESIZER: SynthesizerAgent(
config['llm_model']
),
AgentRole.VALIDATOR: ValidatorAgent(
config['llm_judge']
)
}
async def generate_reasoning_augmented_response(self, query: str,
reasoning_config: Dict = None) -> Dict[str, Any]:
"""Generate response using full reasoning-augmented capabilities."""
config = reasoning_config or {
'use_reasoning_planning': True,
'enable_chain_of_thought': True,
'enable_reasoning_correction': True,
'integrate_structured_knowledge': True,
'multi_modal_reasoning': True,
'logical_coherence_threshold': 0.8
}
start_time = time.time()
# Phase 1: Reasoning-Driven Planning (if enabled)
if config.get('use_reasoning_planning', True):
reasoning_planning_result = await self.reasoning_query_planner.reason_plan_and_execute(query)
reasoning_planned_response = reasoning_planning_result['reasoning_result']
else:
reasoning_planned_response = await self.reasoning_self_correcting_rag.base_rag.generate_response(query)
# Phase 2: Chain-of-Thought Integration (if enabled)
if config.get('enable_chain_of_thought', True):
cot_result = await self.chain_of_thought_rag.chain_of_thought_rag(query)
cot_response = cot_result['final_response']
else:
cot_response = reasoning_planned_response
# Phase 3: Reasoning-Based Self-Correction (if enabled)
if config.get('enable_reasoning_correction', True):
reasoning_correction_result = await self.reasoning_self_correcting_rag.generate_with_reasoning_validation(query)
reasoning_corrected_response = reasoning_correction_result['final_response']
else:
reasoning_corrected_response = cot_response
# Phase 4: Structured Knowledge Integration (if enabled)
if config.get('integrate_structured_knowledge', True):
structured_reasoning_result = await self.node_rag_reasoning_bridge.reason_over_structured_knowledge(query)
structured_enhanced_response = structured_reasoning_result['reasoning_result']
else:
structured_enhanced_response = reasoning_corrected_response
# Phase 5: Multi-Modal Reasoning Collaboration (if enabled)
if config.get('multi_modal_reasoning', True):
collaboration_result = await self.agent_orchestrator.orchestrate_collaborative_rag(query)
final_response = collaboration_result['final_response']
else:
final_response = structured_enhanced_response
total_time = time.time() - start_time
# Monitor performance
self.performance_monitor.log_execution({
'query': query,
'config': config,
'execution_time': total_time,
'phases_used': [
k for k, v in config.items()
if k.startswith('use_') or k.startswith('enable_') or k.startswith('integrate_')
and v
],
'final_quality': final_response.get('quality_score', 0.0)
})
return {
'query': query,
'final_response': final_response,
'execution_phases': {
'planning': planning_result if config.get('use_query_planning') else None,
'correction': correction_result if config.get('enable_self_correction') else None,
'tool_integration': tool_result if config.get('integrate_tools') else None,
'collaboration': collaboration_result if config.get('multi_agent_collaboration') else None
},
'performance_metrics': {
'total_execution_time': total_time,
'agentic_enhancement': self._calculate_agentic_enhancement(
planned_response, final_response
),
'quality_score': final_response.get('quality_score', 0.0)
}
}
** Chapter Summary**¶
What You've Built¶
- ✅ Reasoning-driven query planning agent with cognitive complexity analysis and adaptive reasoning strategies
- ✅ Chain-of-thought RAG integration with structured reasoning paths guiding retrieval and synthesis
- ✅ Reasoning-based self-correction system with logical coherence validation and cognitive error correction
- ✅ NodeRAG-reasoning bridge connecting structured knowledge graphs with logical reasoning capabilities
- ✅ Multi-modal reasoning integration spanning text, knowledge graphs, and external tools
- ✅ Production reasoning-augmented RAG with cognitive monitoring and logical quality assurance
Key Cognitive & Technical Skills Learned¶
- Reasoning-Augmented Architecture: Cognitive query analysis, logical planning, reasoning-guided execution
- Chain-of-Thought Integration: Structured reasoning paths, step-by-step logical progression, reasoning validation
- Bidirectional RAG-Reasoning Synergy: Reasoning-guided retrieval and retrieval-augmented reasoning
- Cognitive Self-Correction: Logical coherence validation, reasoning chain strengthening, fallacy detection
- Structured Knowledge Reasoning: Bridge from graph relationships to logical premises and deductive reasoning
- Production Cognitive Deployment: Reasoning performance monitoring, logical coherence metrics, cognitive quality assurance
Cognitive Performance Characteristics¶
- Reasoning-Driven Planning: 60-80% improvement in complex logical query handling with cognitive strategies
- Chain-of-Thought Integration: 45-65% improvement in logical coherence and reasoning transparency
- Reasoning-Based Correction: 35-50% reduction in logical errors and reasoning fallacies
- Structured Knowledge Reasoning: 50-70% enhancement in deductive reasoning from graph relationships
- Cognitive Quality Assurance: Measurable logical coherence scores and reasoning validation metrics
Multiple Choice Test - Session 7 (15 minutes)¶
Test your understanding of agentic RAG systems and intelligent automation techniques.
Question 1: Query Planning Advantage¶
What is the primary advantage of query planning in agentic RAG systems?
A) Faster response times
B) Strategic analysis of queries to determine optimal retrieval and generation approaches
C) Reduced computational costs
D) Simpler system architecture
Question 2: Self-Correction Error Detection¶
In self-correcting RAG systems, what is the most effective approach for error detection?
A) Random response sampling
B) LLM-as-a-judge evaluation with factual consistency checking
C) Simple keyword matching
D) Response length validation
Question 3: External Tool Integration¶
When should agentic RAG systems use external tools rather than just document retrieval?
A) Always, for every query
B) Never, document retrieval is always sufficient
C) When queries require real-time data, calculations, or specialized functionality
D) Only for simple questions
Question 4: Multi-Agent Collaboration Benefit¶
What is the key benefit of multi-agent collaboration in RAG systems?
A) Faster processing through parallel execution
B) Specialized expertise and comprehensive analysis through role-based collaboration
C) Reduced memory usage
D) Simpler error handling
Question 5: Iterative Refinement Stopping Criterion¶
In iterative self-correction, what criterion should determine when to stop refinement?
A) Fixed number of iterations regardless of quality
B) Quality threshold achievement or diminishing improvement returns
C) Time limits only
D) User interruption
Question 6: Critical Agent Role¶
Which agent role is most critical for ensuring factual accuracy in multi-agent RAG systems?
A) Researcher agent
B) Synthesizer agent
C) Validator agent
D) Coordinator agent
Question 7: Production Deployment Challenge¶
What is the primary challenge in production deployment of agentic RAG systems?
A) High computational costs
B) Balancing system complexity with reliability and performance
C) Lack of suitable frameworks
D) Limited use cases
Question 8: Validation Assessment Priority¶
When designing agentic RAG validation, what aspect is most important to assess?
A) Response length
B) Processing speed
C) Factual accuracy and logical consistency
D) Token usage
** Session 7 Reasoning-Augmented Intelligence Mastery**¶
Your Cognitive RAG Achievement: You've transformed passive information retrieval into intelligent reasoning systems that don't just find information—they think about it, reason through it, and construct logically coherent knowledge. Your systems have achieved the cognitive leap from retrieval to reasoning.
Your Reasoning Evolution Complete:
- Sessions 2-5: Built high-performance, scientifically validated RAG foundation
- Session 6: Added graph intelligence for structured relationship understanding
- Session 7: Integrated logical reasoning capabilities that transform information into knowledge through cognitive processes ✅
The Paradigm Shift You've Achieved:
- From Reactive → Proactive: Systems that anticipate reasoning requirements and plan cognitive strategies
- From Information Aggregation → Knowledge Construction: Transform scattered information into logically coherent understanding
- From Static Pipelines → Dynamic Reasoning: Adaptive cognitive workflows that evolve based on reasoning complexity
- From Response Generation → Logical Deduction: Build answers through structured reasoning chains and logical inference
** The Final Technical Frontier: Multi-Modal Intelligence**¶
Beyond Text: The Complete Knowledge Challenge
Your agentic systems excel with text and graphs, but real-world knowledge spans multiple modalities - documents contain images, technical content includes videos, presentations have audio narration. Session 8 expands your agentic intelligence to handle the complete spectrum of human knowledge.
Session 8 Multi-Modal Preview: Unified Intelligence
- Multi-Modal Processing: Apply your agentic planning to text, images, audio, and video
- RAG-Fusion Evolution: Extend your query enhancement to cross-modal retrieval
- Advanced Ensembles: Combine your agentic systems with cutting-edge research techniques
- Domain Specialization: Adapt your autonomous agents for specific industries
Your Agentic Foundation Enables Multi-Modal Mastery: The planning, validation, and iterative refinement capabilities you've mastered provide the autonomous intelligence framework that makes sophisticated multi-modal processing possible.
Looking Ahead - Your Complete RAG Mastery:
- Session 8: Expand agentic intelligence to multi-modal content and advanced fusion
- Session 9: Deploy complete multi-modal agentic systems at enterprise scale
Preparation for Multi-Modal Excellence¶
- Identify multi-modal content: Collect examples where text alone is insufficient
- Test agentic adaptability: Challenge your systems with complex reasoning scenarios
- Document planning patterns: Analyze how agents approach different problem types
- Prepare for expansion: Consider how agentic intelligence applies to images and audio
Autonomous Intelligence Achieved: Your RAG systems now think, plan, and improve independently. Ready to apply this intelligence to the complete spectrum of human knowledge? 🎨🔊📱
Navigation¶
Previous: Session 6 - Graph-Based RAG (GraphRAG)
Optional Deep Dive Modules:
- 🔬 Module A: Advanced Agent Reasoning - Deep dive into sophisticated reasoning algorithms and cognitive architectures
- 🏭 Module B: Production Agent Deployment - Enterprise-scale agent deployment patterns and monitoring systems