> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prisme.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Advanced RAG

> Implement sophisticated Retrieval Augmented Generation architectures for complex knowledge scenarios

While basic Retrieval Augmented Generation (RAG) is powerful for many use cases, complex knowledge scenarios often require more sophisticated approaches. Advanced RAG architectures address challenges such as multi-step reasoning, diverse information types, and specialized domain knowledge.

## Beyond Basic RAG

Standard RAG has limitations in certain scenarios:

<CardGroup cols={2}>
  <Card title="Complex Reasoning" icon="brain">
    Questions requiring multi-step analysis or inference
  </Card>

  <Card title="Large Document Sets" icon="database">
    Knowledge bases with millions of documents or fragments
  </Card>

  <Card title="Diverse Information Types" icon="layer-group">
    Heterogeneous data including structured and unstructured content
  </Card>

  <Card title="Domain-Specific Nuances" icon="microscope">
    Technical fields with specialized terminology and concepts
  </Card>

  <Card title="Multi-Turn Conversations" icon="comments">
    Discussions that build on previous interactions
  </Card>

  <Card title="Dynamic Information" icon="bolt">
    Content that changes frequently or requires real-time updates
  </Card>
</CardGroup>

Advanced RAG architectures address these challenges through specialized retrieval strategies, context processing techniques, and generation approaches.

## Advanced RAG Architectures

Prisme.ai supports several advanced RAG architectures that you can implement based on your specific needs:

<Tabs>
  <Tab title="Multi-Stage Retrieval">
    A sequential approach that refines retrieval results through multiple phases.

    **Key Components:**

    * Initial broad retrieval stage
    * One or more refinement stages
    * Final re-ranking and selection

    **Ideal For:**

    * Large document collections
    * Ambiguous or complex queries
    * High precision requirements

    **Implementation Complexity:** Medium

    **How It Works**:

    1. First stage performs efficient but less precise retrieval (e.g., BM25 keyword search)
    2. Second stage applies more intensive semantic filtering on first-stage results
    3. Final stage re-ranks candidates using cross-encoders or other precise methods
    4. Only the highest quality content is passed to the LLM
  </Tab>

  <Tab title="Recursive Retrieval">
    An iterative approach that breaks down complex queries and retrieves information in stages.

    **Key Components:**

    * Query decomposition
    * Sub-query execution
    * Information synthesis

    **Ideal For:**

    * Multi-part questions
    * Complex reasoning scenarios
    * Research-oriented queries

    **Implementation Complexity:** High

    **How It Works**:

    1. Complex query is broken down into simpler sub-questions
    2. Each sub-question is processed through its own retrieval cycle
    3. Results from sub-questions are collected and synthesized
    4. Final answer incorporates information from all retrieval paths
  </Tab>

  <Tab title="Hypothetical Document Embeddings">
    An approach that generates ideal "hypothetical" documents before retrieval to improve query understanding.

    **Key Components:**

    * Query-to-document expansion
    * Enhanced query representation
    * Semantic matching

    **Ideal For:**

    * Domain-specific knowledge
    * Terminology gaps
    * Intent clarification

    **Implementation Complexity:** Medium

    **How It Works**:

    1. LLM expands the user's query into a hypothetical "ideal document" that would answer it
    2. This expanded representation is embedded and used for retrieval
    3. The approach bridges terminology gaps between queries and documents
    4. Retrieved documents better match the user's actual intent
  </Tab>

  <Tab title="Knowledge Graph RAG">
    Integrates structured knowledge graphs with traditional document retrieval.

    **Key Components:**

    * Knowledge graph navigation
    * Entity and relationship linking
    * Combined structured/unstructured retrieval

    **Ideal For:**

    * Highly connected information
    * Entity-centric domains
    * Reasoning about relationships

    **Implementation Complexity:** High

    **How It Works**:

    1. Identifies entities and relationships in the user query
    2. Navigates a knowledge graph to find relevant entities and connections
    3. Retrieves both structured data (from the graph) and unstructured content (from documents)
    4. Provides context that includes both factual relationships and detailed explanations
  </Tab>

  <Tab title="Self-Reflective RAG">
    Incorporates reasoning and self-critique to improve retrieval quality.

    **Key Components:**

    * Initial retrieval and reasoning
    * Self-critique of results
    * Refinement iterations

    **Ideal For:**

    * Critical information needs
    * Complex reasoning tasks
    * High-stakes applications

    **Implementation Complexity:** High

    **How It Works**:

    1. Performs initial retrieval and drafts a response
    2. Evaluates its own response for gaps, contradictions, or uncertainties
    3. Identifies additional information needed to address issues
    4. Conducts focused retrieval to fill those gaps
    5. Revises the response based on complete information
  </Tab>
</Tabs>

## Advanced Context Processing

Beyond retrieval architectures, sophisticated methods for processing retrieved context can significantly improve response quality:

<AccordionGroup>
  <Accordion title="Context Compression">
    Techniques to reduce redundancy and focus on essential information.

    **Key Approaches**:

    * **LLM-Based Summarization**: Using a model to create concise summaries of retrieved documents
    * **Semantic Compression**: Removing redundant information while preserving meaning
    * **Information Distillation**: Extracting only the most relevant facts and details
    * **Token Optimization**: Maximizing information density within token constraints

    **Benefits**:

    * Makes more efficient use of context window
    * Reduces noise and distractions
    * Allows inclusion of more diverse sources
    * Improves response coherence
  </Accordion>

  <Accordion title="Contextual Fusion">
    Methods for combining information from multiple sources cohesively.

    **Key Approaches**:

    * **Hierarchical Aggregation**: Organizing information at different levels of detail
    * **Cross-Document Coreference**: Identifying when different documents refer to the same entities
    * **Information Reconciliation**: Resolving contradictions between sources
    * **Narrative Threading**: Creating a coherent flow across document fragments

    **Benefits**:

    * Creates unified context from fragmented sources
    * Reduces contradictions and inconsistencies
    * Preserves important relationships between facts
    * Presents information in logical progression
  </Accordion>

  <Accordion title="Contextual Routing">
    Directing different types of queries to specialized processing pipelines.

    **Key Approaches**:

    * **Query Classification**: Categorizing questions by type and intent
    * **Domain Detection**: Identifying the knowledge domain of the question
    * **Complexity Assessment**: Determining question difficulty and required approach
    * **Pipeline Selection**: Choosing the optimal processing strategy

    **Benefits**:

    * Applies specialized approaches for different question types
    * Optimizes resource allocation
    * Improves handling of diverse queries
    * Enables domain-specific customizations
  </Accordion>

  <Accordion title="Semantic Enrichment">
    Adding contextual metadata to improve understanding and retrieval.

    **Key Approaches**:

    * **Entity Recognition**: Identifying and tagging named entities
    * **Concept Linking**: Connecting text to knowledge base concepts
    * **Semantic Annotation**: Adding metadata about meaning and relationships
    * **Ontology Mapping**: Relating content to domain-specific knowledge structures

    **Benefits**:

    * Enhances retrieval precision
    * Enables concept-based rather than just keyword-based retrieval
    * Supports reasoning about relationships
    * Facilitates domain-specific understanding
  </Accordion>
</AccordionGroup>

## Multi-Agent RAG Systems

For particularly complex knowledge applications, multiple specialized agents can work together:

<Steps>
  <Step title="Query Analysis">
    A specialized agent analyzes the user's question to determine required knowledge and approach.

    Functions include:

    * Intent identification
    * Domain classification
    * Complexity assessment
    * Subtask identification
  </Step>

  <Step title="Knowledge Retrieval">
    Multiple specialized retrieval agents gather information from different sources.

    Examples include:

    * Document specialist for textual knowledge
    * Structured data agent for databases and tables
    * Knowledge graph navigator for entity relationships
    * Media analyzer for images and diagrams
  </Step>

  <Step title="Information Synthesis">
    An integration agent combines and reconciles information from various sources.

    Key responsibilities:

    * Resolving contradictions
    * Organizing information logically
    * Identifying information gaps
    * Creating unified context
  </Step>

  <Step title="Response Generation">
    A specialized generation agent creates the final response based on synthesized information.

    Focus areas:

    * Appropriate format and style
    * Clear explanation logic
    * Accurate source attribution
    * Addressing all aspects of the query
  </Step>

  <Step title="Self-Reflection">
    A critic agent reviews the response for quality and improvement opportunities.

    Assessment criteria:

    * Factual accuracy
    * Comprehensiveness
    * Clarity and coherence
    * Appropriate detail level
  </Step>
</Steps>

Each agent focuses on its specialized task, creating a more robust system than any single agent could provide.

## Advanced RAG Implementation with Prisme.ai

Implementing advanced RAG architectures in Prisme.ai follows a structured approach:

<Tabs>
  <Tab title="Configuration Approach">
    Using Prisme.ai's built-in advanced configuration options.

    * **Implementation Complexity**: Low to Medium
    * **Technical Expertise Required**: Minimal
    * **Customization Level**: Moderate

    Available advanced options include:

    * Multi-stage retrieval configuration
    * Query preprocessing settings
    * Context handling parameters
    * Response generation strategies

    This approach is ideal for implementing moderately advanced RAG architectures without requiring coding expertise.
  </Tab>

  <Tab title="Builder Approach">
    Creating custom RAG workflows using the Builder's product.

    * **Implementation Complexity**: Medium
    * **Technical Expertise Required**: Moderate
    * **Customization Level**: High

    Builder enables:

    * Visual workflow construction
    * Custom processing steps
    * Integration with other systems
    * Complex decision logic

    This approach is ideal for implementing sophisticated RAG architectures without extensive coding while maintaining high customization flexibility.
  </Tab>

  <Tab title="Custom Development">
    Building highly specialized RAG systems using code and Prisme.ai's APIs.

    * **Implementation Complexity**: High
    * **Technical Expertise Required**: Substantial
    * **Customization Level**: Maximum

    Custom development allows:

    * Implementing cutting-edge architectures
    * Integrating proprietary algorithms
    * Creating highly specialized workflows
    * Maximum control over the entire process

    This approach is ideal for organizations with unique requirements and technical resources to implement highly specialized RAG systems.
  </Tab>
</Tabs>

## Webhook Integration for Advanced RAG

<Warning>
  **Important**: The webhook functionality described below requires Builder and subscription to specific events. This represents a more technical implementation approach for advanced users who need complete control over the RAG process.
</Warning>

Prisme.ai allows you to build advanced RAG architectures by integrating external services through webhooks. This powerful feature extends the capabilities of Knowledges by allowing you to:

* Implement custom processing logic
* Integrate with specialized AI systems
* Override various stages of the RAG pipeline
* Create sophisticated multi-step workflows

### Webhook Subscription Events

You can subscribe to different events in the Knowledges lifecycle:

<AccordionGroup>
  <Accordion title="Document Management Events">
    Monitor and control document processing in your knowledge base.

    **Available Events**:

    * `documents_created`: Triggered when new documents are added
    * `documents_updated`: Triggered when existing documents are modified
    * `documents_deleted`: Triggered when documents are removed

    **Common Uses**:

    * Custom document processing pipelines
    * Content moderation and validation
    * Metadata enrichment
    * Document transformation
  </Accordion>

  <Accordion title="Query Events">
    Intercept and process user questions and the RAG pipeline.

    **Available Events**:

    * `queries`: Triggered when users ask questions

    **Common Uses**:

    * Custom context retrieval
    * Specialized prompt engineering
    * Complete answer generation
    * Parameter customization
  </Accordion>

  <Accordion title="Test Events">
    Monitor and influence the agent testing process.

    **Available Events**:

    * `tests_results`: Triggered for each test case execution

    **Common Uses**:

    * Custom evaluation criteria
    * Specialized test analytics
    * Integration with quality systems
    * Performance benchmarking
  </Accordion>
</AccordionGroup>

### Webhook Response Options

Depending on the event type, your webhook can return different responses to influence the RAG process:

<Tabs>
  <Tab title="Context Retrieval">
    Provide custom-retrieved context chunks while letting Knowledges handle prompt generation and LLM interaction.

    **Response Format**:

    ```json theme={null}
    {
      "chunks": [
        {
          "value": {
            "content": "First chunk content that will be injected within LLM prompt",
            "knowledgeId": "Corresponding AIK document id"
          }
        },
        {
          "value": {
            "content": "Second chunk content...",
            "knowledgeId": "Another document id"
          }
        }
      ]
    }
    ```

    **Ideal For**:

    * Custom retrieval strategies
    * External knowledge sources
    * Specialized context processing
    * Dynamic information integration
  </Tab>

  <Tab title="Prompt Generation">
    Take control of the entire prompt while letting Knowledges handle the LLM interaction.

    **Response Format**:

    ```json theme={null}
    {
      "prompt": [
        {
          "role": "system",
          "content": "You are an assistant ... \n Context: ... \n"
        },
        {
          "role": "user",
          "content": "How ... "
        }
      ]
    }
    ```

    **Ideal For**:

    * Specialized prompt engineering
    * Custom context formatting
    * Chain-of-thought implementation
    * Domain-specific instruction tuning
  </Tab>

  <Tab title="Complete Answer">
    Bypass the entire RAG and LLM process by providing the final answer directly.

    **Response Format**:

    ```json theme={null}
    {
      "answer": "This is the complete answer that will be shown to the user..."
    }
    ```

    **Ideal For**:

    * Integration with specialized AI systems
    * Pre-computed responses
    * Multi-agent architectures
    * Advanced processing pipelines
  </Tab>

  <Tab title="Parameter Override">
    Customize AI parameters while letting Knowledges handle the rest of the process.

    **Response Format**:

    ```json theme={null}
    {
      "aiParameters": {
        "model": "gpt-4",
        "prompt": "...",
        "max_tokens": 5000,
        "temperature": 0.9,
        "history": false
      }
    }
    ```

    **Ideal For**:

    * Dynamic model selection
    * Context-aware parameter tuning
    * Adaptive temperature setting
    * Query-specific customization
  </Tab>

  <Tab title="Test Evaluation">
    Provide custom evaluation scores for test results.

    **Response Format**:

    ```json theme={null}
    {
      "analysis": "Evaluation text summary",
      "score": "2",
      "context": "1.5"
    }
    ```

    **Ideal For**:

    * Specialized evaluation criteria
    * Domain-specific quality assessment
    * Custom benchmarking
    * Comparative analysis
  </Tab>
</Tabs>

### Setting Up Webhook Integration

To implement webhook integration for advanced RAG:

<Steps>
  <Step title="Create External Service">
    Develop your external service with the required logic to handle webhook events.

    Requirements:

    * HTTPS endpoint
    * Ability to process webhook requests
    * Business logic implementation
    * Response generation
  </Step>

  <Step title="Configure Builder">
    Set up Builder to enable webhook functionality.

    Key steps:

    * Create a new automation in Builder
    * Configure event subscriptions on Knowledges
    * Connect to your webhook endpoint
    * Set up authentication
  </Step>

  <Step title="Subscribe to Events">
    Choose which events your webhook should receive.

    Options include:

    * Document management events
    * Query processing events
    * Test evaluation events
  </Step>

  <Step title="Test Integration">
    Verify that your webhook receives events and responds correctly.

    Testing steps:

    * Monitor webhook requests
    * Validate response formats
    * Check integration behavior
    * Troubleshoot any issues
  </Step>
</Steps>

## Use Case Examples

<CardGroup cols={2}>
  <Card title="Medical Knowledge Advisor" icon="stethoscope">
    <p><strong>Challenge:</strong> Providing accurate medical information from diverse sources including research papers, clinical guidelines, and drug databases.</p>

    <p><strong>Advanced RAG Solution:</strong> Multi-stage retrieval with knowledge graph integration</p>

    <p><strong>Key Features:</strong></p>

    <ul>
      <li>Entity recognition for medical terms</li>
      <li>Relationship tracking between conditions, treatments, and medications</li>
      <li>Source prioritization based on evidence quality</li>
      <li>Self-reflective validation for factual accuracy</li>
    </ul>
  </Card>

  <Card title="Legal Research Assistant" icon="gavel">
    <p><strong>Challenge:</strong> Navigating complex legal documents, precedents, and statutes with precise citation and reasoning.</p>

    <p><strong>Advanced RAG Solution:</strong> Recursive retrieval with contextual routing</p>

    <p><strong>Key Features:</strong></p>

    <ul>
      <li>Hierarchical decomposition of legal questions</li>
      <li>Jurisdiction-aware retrieval pathways</li>
      <li>Citation tracking and verification</li>
      <li>Temporal reasoning about law changes</li>
    </ul>
  </Card>

  <Card title="Technical Support Advisor" icon="wrench">
    <p><strong>Challenge:</strong> Troubleshooting complex technical issues spanning multiple products, versions, and systems.</p>

    <p><strong>Advanced RAG Solution:</strong> Multi-agent RAG with self-reflection</p>

    <p><strong>Key Features:</strong></p>

    <ul>
      <li>Problem classification and decomposition</li>
      <li>Product-specific knowledge agents</li>
      <li>Step-by-step solution synthesis</li>
      <li>Verification against known issues database</li>
    </ul>
  </Card>

  <Card title="Financial Analyst" icon="chart-line">
    <p><strong>Challenge:</strong> Analyzing financial data from reports, market trends, and news to provide investment insights.</p>

    <p><strong>Advanced RAG Solution:</strong> Hypothetical document embeddings with structured data integration</p>

    <p><strong>Key Features:</strong></p>

    <ul>
      <li>Financial query expansion and reformulation</li>
      <li>Integration of numerical data analysis</li>
      <li>Time-sensitive information prioritization</li>
      <li>Data visualization for complex insights</li>
    </ul>
  </Card>
</CardGroup>

## Advanced RAG Best Practices

<AccordionGroup>
  <Accordion title="Architecture Selection">
    * Match architecture complexity to actual needs
    * Consider maintenance requirements and technical expertise
    * Start with simpler approaches and add complexity as needed
    * Validate architecture choices with realistic test scenarios
    * Document architecture decisions and rationales
  </Accordion>

  <Accordion title="Implementation Strategy">
    * Use configuration options for moderate customization needs
    * Leverage Builder for complex but codeless implementations
    * Reserve custom development for highly specialized requirements
    * Implement iteratively with continuous testing
    * Create reusable components for common patterns
  </Accordion>

  <Accordion title="Performance Optimization">
    * Monitor and optimize retrieval precision and recall
    * Balance response quality with latency requirements
    * Consider resource usage for production-scale deployments
    * Implement caching strategies where appropriate
    * Profile and optimize bottlenecks in the pipeline
  </Accordion>

  <Accordion title="Webhook Integration">
    * Ensure webhook endpoints are reliable and performant
    * Implement proper error handling and fallback mechanisms
    * Use appropriate authentication and security measures
    * Monitor webhook performance and reliability
    * Document webhook interfaces and expected behaviors
  </Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="RAG Settings" icon="sliders" href="./rag-settings">
    Learn about the core configuration options for RAG
  </Card>

  <Card title="Agent Capabilities" icon="screwdriver-wrench" href="/products/agent-factory/capabilities">
    Extend your agents with specialized capabilities
  </Card>

  <Card title="Agent Evaluations" icon="vial" href="/products/agent-factory/evaluations">
    Validate advanced RAG implementations
  </Card>

  <Card title="Builder Documentation" icon="code" href="/products/ai-builder/overview">
    Learn more about Builder for advanced implementations
  </Card>
</CardGroup>