DataFab Graph RAG

Version: 3.0 Last Updated: February 2026

Component Overview

Graph RAG (Retrieval-Augmented Generation) represents DataFab’s advanced retrieval architecture that combines graph databases with semantic search to provide contextually rich, multi-faceted information for large language model reasoning. Unlike traditional RAG systems that rely solely on vector embeddings, Graph RAG leverages the explicit relationship structure of DataFab’s knowledge graph to improve retrieval quality, context assembly, and reasoning accuracy.

Core Capabilities:

Capability	Description
Graph-Enhanced Retrieval	Combines graph navigation with semantic search
Entity-Centric Queries	Retrieve full context around entities and relationships
Relationship Traversal	Navigate multi-hop relationships for comprehensive context
Community Detection	Identify and retrieve hierarchical clusters of related entities
Hybrid Search	Blend vector similarity with graph structure scoring
Context Assembly	Automatically structure retrieved context for LLM consumption
Multi-Turn Awareness	Build context across conversational turns using graph memory
Provenance Tracking	Maintain source lineage for all retrieved information
Access-Controlled Retrieval	Enforce security constraints during graph traversal
Performance Optimization	Query planning and caching for sub-second retrieval

Architecture Overview

┌─────────────────────────────────────────────────────────────────────────┐
│                        CLIENT LAYER                                     │
│  ┌────────────────────┐  ┌────────────────────┐  ┌────────────────────┐ │
│  │  LLM Integration   │  │  Exchange Interface│  │   Analytics UI     │ │
│  └─────────┬──────────┘  └─────────┬──────────┘  └─────────┬──────────┘ │
└────────────┼─────────────────────────┼──────────────────────┼────────────┘
             │                         │                      │
             ▼                         ▼                      ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                       GRAPH RAG API GATEWAY                             │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │  Authentication │ Rate Limiting │ Query Validation │ Logging    │    │
│  └─────────────────────────────────────────────────────────────────┘    │
└────────────┼──────────────────────────────────────────────────────────┬─┘
             │                                                          │
             ▼                                                          │
┌─────────────────────────────────────────────────────────────────────────┐
│                    GRAPH RAG RETRIEVAL ENGINE                           │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │            QUERY INTENT & SEMANTICS ANALYSIS                    │    │
│  │  • Intent Detection  • Entity Identification  • Relationship    │    │
│  │    • Scope Analysis   • Temporal Constraints   • Filters        │    │
│  └─────────────────────────────────────────────────────────────────┘    │
│                              │                                          │
│  ┌──────────────────────────┴──────────────────────────────────────┐    │
│  │           HYBRID RETRIEVAL STRATEGY SELECTOR                    │    │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌──────────────────────┐  │    │
│  │  │ Entity- │ │Relation-│ │Community│ │  Adaptive Weighting  │  │    │
│  │  │ Centric │ │  Shift  │ │ Based   │ │  (Query-dependent)   │  │    │
│  │  └─────────┘ └─────────┘ └─────────┘ └──────────────────────┘  │    │
│  └─────────────────────────┬──────────────────────────────────────┘    │
│                            │                                           │
│  ┌─────────────────────────▼──────────────────────────────────────┐    │
│  │           GRAPH TRAVERSAL & VECTOR SEARCH                      │    │
│  │  ┌─────────────────┐     ┌────────────────────────────┐        │    │
│  │  │ Graph Queries   │     │  Vector Similarity Search  │        │    │
│  │  │ • Path Finding  │     │  • Embedding Lookup        │        │    │
│  │  │ • Subgraph      │     │  • Semantic Matching       │        │    │
│  │  │ • Aggregations  │     │  • Relevance Scoring       │        │    │
│  │  └─────────────────┘     └────────────────────────────┘        │    │
│  │         │                                    │                 │    │
│  │         └────────────────┬───────────────────┘                 │    │
│  │                          ▼                                     │    │
│  │         ┌──────────────────────────────────┐                  │    │
│  │         │  RESULT FUSION & RANKING         │                  │    │
│  │         │  • Score Normalization           │                  │    │
│  │         │  • Graph-Aware Re-ranking        │                  │    │
│  │         │  • Relevance Combination         │                  │    │
│  │         │  • Diversity Optimization        │                  │    │
│  │         └──────────────────────────────────┘                  │    │
│  └─────────────────────────┬──────────────────────────────────────┘    │
│                            │                                           │
│  ┌─────────────────────────▼──────────────────────────────────────┐    │
│  │           ACCESS CONTROL & FILTERING                           │    │
│  │  • Permission Propagation  • Tenant Isolation  • Field Masking │    │
│  └─────────────────────────┬──────────────────────────────────────┘    │
│                            │                                           │
└────────────┬───────────────▼──────────────────────┬────────────────────┘
             │                                      │
             ▼                                      ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                  CONTEXT ASSEMBLY & LLM PREPARATION                     │
│  ┌─────────────────┐  ┌─────────────────┐  ┌──────────────────────┐     │
│  │ Context Builder │  │ Provenance Join │  │  Output Formatter    │     │
│  │ (Hierarchical)  │  │ (Citation Info) │  │  (Template-based)    │     │
│  └─────────────────┘  └─────────────────┘  └──────────────────────┘     │
└────────────┬────────────────────────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                     CACHING & OPTIMIZATION LAYER                        │
│  ┌────────────────┐  ┌──────────────┐  ┌─────────────────────────┐      │
│  │  Query Cache   │  │ Result Cache │  │  Graph Index Caching    │      │
│  │  (Exact/Fuzzy) │  │  (Semantic)  │  │  (Subgraph Memoization) │      │
│  └────────────────┘  └──────────────┘  └─────────────────────────┘      │
└─────────────────────────────────────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                    KNOWLEDGE GRAPH STORAGE LAYER                        │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌────────────┐   │
│  │   Entities   │  │ Relationships│  │   Properties │  │ Vector Idx │   │
│  │   (Nodes)    │  │   (Edges)    │  │  (Metadata)  │  │ (Embeddings)   │
│  └──────────────┘  └──────────────┘  └──────────────┘  └────────────┘   │
└─────────────────────────────────────────────────────────────────────────┘

Graph-Enhanced Retrieval

Traditional RAG systems retrieve context based primarily on semantic similarity between the query and stored vectors. Graph RAG enhances this approach by leveraging the explicit structure of the knowledge graph to understand entity relationships, navigate semantic networks, and assemble context that reflects the actual data topology.

Retrieval Advantages

Advantage	Description	Example
Relationship Awareness	Understands how entities connect across the graph	Query about Person A automatically includes related Organizations, Addresses, and Identifiers
Multi-Hop Context	Can traverse multiple relationship degrees for comprehensive context	Query about a merger includes 5-hop relationship chains showing how entities are connected
Structural Bias	Weights entities based on their role in the graph structure	Central hub entities score higher than peripheral ones
Coherence	Retrieved context naturally aligns because it comes from connected subgraphs	All retrieved information about a topic comes from a cohesive cluster
Disambiguation	Graph structure helps disambiguate entities with similar names	Different “John Smith” entities are kept separate based on relationship patterns

Retrieval Patterns

Graph RAG supports multiple retrieval strategies optimized for different query types. These patterns can be combined and weighted adaptively based on the current query.

Entity-Centric Retrieval

When users query for information about a specific entity, entity-centric retrieval returns the complete context around that entity.

Process:

Identify primary entity from query
Retrieve entity attributes and properties
Fetch all direct relationships (one-hop)
Retrieve connected entity contexts
Assemble hierarchically with primary entity at center

Example Query: “Tell me everything about Organization X”

Step	Result
Entity Lookup	Organization X (EIN, legal name, jurisdiction)
Direct Relationships	47 related entities: 12 Person, 15 Organization, 8 Address, 12 Document
Relationship Details	OWNS (5), CONTROLS (3), LOCATED_AT (2), REPRESENTS (8), EMPLOYS (29)
Connected Entities	Full context for each related entity
Assembly	Hierarchical structure with Org X at center, relationships as next level, related entities as leaves

Security Considerations:

All relationships are filtered based on user access permissions
Related entities inherit access controls from parent
Masked fields are propagated consistently

Relationship-Traversal Retrieval

For queries that ask about connections between entities, relationship-traversal retrieval finds paths and patterns that connect them.

Process:

Identify source and target entities
Find all paths connecting them (with configurable max depth)
Retrieve properties of each entity on the path
Score paths by relevance and specificity
Return top paths with full context

Example Query: “How is Person A connected to Organization B?”

Path Length	Description	Probability
1	Direct employment relationship	A EMPLOYS B
2	Through ownership	A OWNS C, C LOCATED_AT B
3	Through representation	A REPRESENTS D, D CONTROLS B
4+	Indirect connections	Multiple intermediate entities

Ranking Factors:

Path length (shorter paths often more direct)
Relationship types (some relationships more significant)
Temporal validity (historical vs. current)
Data quality (confidence scores)

Community-Based Retrieval

Communities are hierarchically organized clusters of related entities detected through graph analysis. Community-based retrieval identifies the community containing query entities and returns the complete community context.

Community Structure:

Level	Description	Size
Level 1 (Global)	Top-level communities (major business units, jurisdictions)	5-10 entities each
Level 2 (Regional)	Sub-communities within level 1	10-50 entities each
Level 3 (Local)	Tight clusters of closely connected entities	20-100 entities each
Level 4 (Micro)	Individual clusters of related records	5-15 entities each

Example Query: “What is the complete ownership structure of Organization X?”

Locate Organization X in graph
Identify its community at appropriate level
Retrieve all entities in that community
Return full relationship structure within community
Optionally include connections to adjacent communities

Security Constraints:

Communities filtered per user permissions
Only accessible community portions returned
Cross-community connections restricted based on user access

Hybrid Retrieval Pattern

Real-world queries often benefit from combining multiple retrieval patterns. The Hybrid Retrieval Pattern intelligently selects and weights multiple strategies based on query characteristics.

Adaptive Strategy Selection:

Query Characteristic	Pattern Weight	Example
Single entity focus	Entity-Centric: 70%	“Tell me about Organization X”
Connection query	Relationship-Shift: 80%	“How are X and Y related?”
Community scope	Community-Based: 75%	“Show the ownership chain”
Complex multi-faceted	Hybrid: 40/30/30	“Analyze the corporate structure and connections”
Temporal aspects	Relationship-Traversal: 60%	“How has this relationship evolved?”

Dynamic Weight Adjustment: Weights are adjusted based on:

Query complexity (number of entities mentioned)
Result overlap (to maximize information diversity)
User interaction patterns (learned from session history)
Data sparsity (available information density in graph)

Vector + Graph Hybrid Search

Graph RAG combines traditional vector embeddings with graph structure scoring to produce superior retrieval results.

Dual Retrieval Process

┌──────────────────────────────────────────────────────────┐
│                    USER QUERY                            │
└────────────────────┬─────────────────────────────────────┘
                     │
         ┌───────────┴───────────┐
         │                       │
         ▼                       ▼
┌──────────────────┐     ┌──────────────────────┐
│ VECTOR SEARCH    │     │  GRAPH ANALYSIS      │
│                  │     │                      │
│ 1. Embed query   │     │ 1. Parse intent      │
│ 2. Find similar  │     │ 2. Identify entities │
│    embeddings    │     │ 3. Build subgraph    │
│ 3. Score by      │     │ 4. Calculate centrality
│    similarity    │     │ 5. Score structure   │
│                  │     │                      │
└────────┬─────────┘     └──────────┬───────────┘
         │ Similarity Scores        │ Structure Scores
         │                          │
         └───────────────┬──────────┘
                         │
                         ▼
         ┌──────────────────────────────┐
         │   SCORE NORMALIZATION        │
         │   • Min-Max scaling          │
         │   • Percentile mapping       │
         │   • Confidence calibration   │
         └────────────────┬─────────────┘
                          │
         ┌────────────────┴────────────────┐
         │                                 │
         ▼                                 ▼
   ┌──────────────────┐      ┌────────────────────┐
   │ FUSION WEIGHTING │      │  DIVERSITY CHECK   │
   │ α = 0.6 vector   │      │ Remove redundant   │
   │ β = 0.4 graph    │      │ high-overlap items │
   └────────┬─────────┘      └────────┬───────────┘
            │                         │
            └───────────────┬─────────┘
                            │
                            ▼
               ┌────────────────────────┐
               │  FINAL RANKING         │
               │  Top-K Results         │
               └────────────────────────┘

Score Fusion Strategy

Component	Weight	Calculation	Purpose
Vector Similarity	60%	Cosine distance in embedding space	Semantic relevance to query
Entity Centrality	20%	PageRank-style importance score	Entity prominence in graph
Relationship Score	15%	Proximity to query entities	Connection strength
Temporal Freshness	5%	Recency of last update	Data currency

Adaptive Weight Tuning: Weights automatically adjust based on:

Query type (single entity vs. relationship queries)
Graph sparsity (adjust entity centrality if few connections)
Domain-specific importance (allow customer override per entity type)

Vector Index Integration

Component	Purpose	Update Frequency
Entity Embeddings	Vector representation of entity attributes	On entity update
Relationship Embeddings	Vector representation of connection types	On relationship schema change
Context Embeddings	Vector representation of surrounding context	Weekly
Query Embeddings	Runtime representation of incoming queries	Per query

Embedding Models:

Primary: Sentence transformers (general domain understanding)
Domain-Specific: Fine-tuned models per customer (when available)
Fallback: BM25 lexical matching (when embeddings unavailable)

Context Assembly

Once Graph RAG has retrieved relevant entities and relationships, the Context Assembly stage structures this information for consumption by language models.

Context Hierarchy

Retrieved context is organized hierarchically to maximize LLM token efficiency while preserving information relationships:

┌─────────────────────────────────────────────┐
│  LEVEL 0: PRIMARY ENTITY                    │
│  (1-3 sentences, key attributes)            │
│  • Name, ID, type                           │
│  • Most important status/classification     │
└─────────────────────────────────────────────┘
              ▼
┌─────────────────────────────────────────────┐
│  LEVEL 1: DIRECT RELATIONSHIPS              │
│  (1-2 sentences per relationship)           │
│  • Related entities and connection types    │
│  • Key attributes of related entities      │
└─────────────────────────────────────────────┘
              ▼
┌─────────────────────────────────────────────┐
│  LEVEL 2: RELATIONSHIP DETAILS              │
│  (1 sentence per significant connection)    │
│  • Temporal validity                        │
│  • Relationship properties                  │
│  • Data quality/confidence                  │
└─────────────────────────────────────────────┘
              ▼
┌─────────────────────────────────────────────┐
│  LEVEL 3: SUPPORTING CONTEXT (OPTIONAL)     │
│  (Only if space available in LLM context)   │
│  • Historical relationships                 │
│  • Statistical properties                   │
│  • External enrichment                      │
└─────────────────────────────────────────────┘

Smart Context Truncation

Context is sized dynamically based on:

Factor	Impact	Example
LLM Context Window	Absolute limit	Limit to 4K tokens for 8K context window
Query Complexity	Allocation	10 entities: use ~2K tokens; 100 entities: use ~7K tokens
Result Quality	Adjustment	High-confidence results: fewer tokens needed; ambiguous: more context
Conversational State	Carryover	Multi-turn query: reserve tokens for conversation history

Provenance Integration

Each piece of retrieved context includes provenance information—the source system and document reference from which it originated:

Provenance Fields:

Field	Content	Purpose
Source System	Connector ID or source name	Identifies originating system
Document ID	Document, record, or asset ID	Allows user to locate source
Document Title	Human-readable reference	Quick identification
Extraction Reference	Page, paragraph, cell range, or section	Precise location within document
Confidence Score	0-1 confidence of extraction	Indicates data quality
Last Updated	Timestamp of last sync	Shows data currency

Citation Generation: The system automatically generates citations for retrieved information, enabling users to verify facts and audit data lineage.

Ranking and Relevance

Graph RAG employs multiple ranking strategies to ensure the most relevant entities and relationships surface in retrieval results.

Ranking Algorithm Architecture

┌────────────────────────────────────────────┐
│         INITIAL CANDIDATES                 │
│  (From graph traversal + vector search)    │
└───────────────┬────────────────────────────┘
                │
                ▼
┌────────────────────────────────────────────┐
│      SCORE CALCULATION                     │
│  • Semantic similarity (vector)            │
│  • Graph centrality                        │
│  • Relationship strength                   │
│  • Type-specific scoring                   │
│  • Temporal factors                        │
└───────────────┬────────────────────────────┘
                │
                ▼
┌────────────────────────────────────────────┐
│    GRAPH-AWARE RE-RANKING                  │
│  • Coherence clustering (group related)    │
│  • Diversity scoring (reduce redundancy)   │
│  • Path-based importance                   │
│  • Community structure alignment           │
└───────────────┬────────────────────────────┘
                │
                ▼
┌────────────────────────────────────────────┐
│    PERSONALIZATION & CONTEXT               │
│  • User interaction history                │
│  • Session context                         │
│  • Access control filtering                │
│  • Data quality thresholds                 │
└───────────────┬────────────────────────────┘
                │
                ▼
┌────────────────────────────────────────────┐
│        FINAL RESULT SET                    │
│  (Top-K ranked entities with scores)       │
└────────────────────────────────────────────┘

Ranking Factors

Factor	Weight	Description	Tuning
Semantic Relevance	35%	Vector similarity to query	Per-model tuning
Entity Importance	25%	PageRank/centrality in graph	Per-domain calibration
Relationship Specificity	20%	How directly connected to query	Auto-calculated
Data Freshness	10%	Recency of last update	Configurable decay
Quality Score	5%	Source confidence/accuracy	Per-source calibration
Access Score	5%	User permission level	Dynamic per user

Type-Specific Scoring

Different entity and relationship types can have custom scoring functions:

Entity Type Scoring:

Type	Priority	Special Considerations
Person	High	Include role/title context
Organization	High	Include jurisdiction/status
Relationship	Medium	Weight by relationship type
Document	Medium	Weight by document type/classification
Address	Low	Weight by address type (primary vs. alternate)

Relationship Type Scoring:

Relationship	Base Score	Context Impact
OWNS	0.95	Very high relevance in ownership queries
CONTROLS	0.90	High relevance in governance queries
EMPLOYS	0.85	High relevance in organizational queries
LOCATED_AT	0.70	Medium relevance, used for disambiguation
RELATED_TO	0.60	Lower relevance, suggests weaker connection

Session-Based Retrieval

Graph RAG maintains session context across multiple query turns, allowing the system to incrementally build understanding and refer back to previously discovered information.

Session Graph Construction

┌──────────────────────────────────────────────────┐
│         SESSION INITIALIZATION                   │
│         (User starts exploration)                │
└────────────────┬─────────────────────────────────┘
                 │
                 ▼
    ┌────────────────────────────┐
    │  SESSION GRAPH (Empty)     │
    │                            │
    │  Entities: {}              │
    │  Relationships: {}         │
    │  History: []               │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  USER QUERY 1              │
    │  "Find Person X"           │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  RETRIEVAL                 │
    │  • Query graph for Person X│
    │  • Add to Session Graph    │
    │  • Score and rank          │
    │  • Assemble context        │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  SESSION UPDATE            │
    │                            │
    │  Entities: {Person X, ...} │
    │  Relationships: {...}      │
    │  History: [Query 1]        │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  USER QUERY 2              │
    │  "Who does Person X work  │
    │   with?"                   │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  CONTEXT-AWARE RETRIEVAL   │
    │  • Use Person X from       │
    │    session as base         │
    │  • Find EMPLOYS rels       │
    │  • Add new entities        │
    │  • Reference existing      │
    │    session data            │
    └────────────────┬───────────┘
                     │
                     ▼
    ┌────────────────────────────┐
    │  SESSION UPDATE            │
    │                            │
    │  Entities: {+new people}   │
    │  Relationships: {+new rels}│
    │  History: [Query 1, Query2]│
    └────────────────────────────┘

Session Features

Feature	Description	Benefit
Entity Memory	Discovered entities available for future queries	No need to re-discover; queries build on each other
Relationship Memory	Discovered relationships cached in session	Faster traversal for related queries
Query History	Previous queries and results preserved	User can reference earlier findings
Conversation Context	LLM has access to session context	Answers remain coherent across turns
Implicit References	User can refer to “it” or “this” from previous query	More natural conversation flow

Session Timeout and Persistence

Parameter	Value	Rationale
Active Session TTL	24 hours	Long enough for typical investigation sessions
Idle Session Timeout	30 minutes	Clear memory of idle sessions to prevent confusion
Session Persistence	Persistent until timeout	Users can continue sessions across applications
Session Archive	90-day retention	Historical session data available for audit

Session Security

Control	Implementation
User Isolation	Sessions are user-specific and tenant-scoped
Permission Propagation	Session context inherits user access controls
Incremental Filtering	As permissions change, session graph is dynamically filtered
Audit Logging	All session queries and discoveries logged for compliance

Security Controls

Graph RAG enforces comprehensive security controls to ensure data is retrieved only by authorized users and that access constraints are maintained throughout the retrieval and context assembly process.

Access-Filtered Retrieval

Every graph traversal respects user access permissions:

Retrieval Security Model:

Query Authentication: User identity verified via OAuth/JWT
Permission Lookup: User roles and permissions loaded from identity store
Graph Filtering: Traversal algorithm only visits permitted entities/relationships
Result Filtering: Final results include only data user is authorized to see
Field Masking: Sensitive fields masked based on field-level access controls
Audit Logging: All retrievals logged with user ID, query, and results

Permission Enforcement:

Check	Timing	Action
Entity Access	Graph traversal	Skip unauthorized entities
Relationship Access	Traversal	Skip relationships user cannot access
Field Access	Result assembly	Mask restricted fields
Bulk Operations	Query planning	Reject if > threshold of results masked

Tenant Isolation

In multi-tenant deployments, Graph RAG ensures complete isolation between tenants:

Isolation Layer	Implementation
Graph Partitioning	Separate graph partitions per tenant
Query Scoping	All queries automatically scoped to tenant
Index Isolation	Separate vector indices per tenant
Cache Isolation	Tenant-specific cache keys
Credential Vault	Per-tenant credential isolation

Field-Level Masking

Sensitive fields are masked based on user permissions:

Masking Strategies:

Strategy	Example	Use Case
Full Masking	SSN shown as “*--****”	Highly sensitive fields
Partial Masking	Email shown as “j***@example.com”	PII fields
Hash Masking	Phone shown as SHA hash	For matching without disclosure
Redaction	Entire field omitted	When user has no access
Tokenization	Consistent token for same value	For analytics without disclosure

Injection Prevention

Graph RAG prevents multiple attack vectors:

Attack Prevention:

Attack Type	Prevention	Implementation
Query Injection	Parameterized graph queries	All user input treated as data, not code
Traversal Injection	Graph shape validation	Unauthorized relationships cannot be created
Prompt Injection	Context escaping	Retrieved text escaped before LLM consumption
Path Injection	Path analysis	Invalid paths rejected during traversal
Filtering Bypass	Multi-layer filtering	Checks applied at retrieval, assembly, and output

Performance and Caching

Graph RAG includes comprehensive caching and optimization strategies to deliver sub-second retrieval even for complex queries across large graphs.

Query Execution Planning

┌──────────────────────────────┐
│    INCOMING QUERY            │
│  "Show complete ownership"   │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  QUERY ANALYSIS              │
│  • Complexity: High          │
│  • Expected results: 50-100  │
│  • Depth: 5 hops             │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  EXECUTION PLAN GENERATION   │
│  1. Cache lookup             │
│  2. Vector search (parallel) │
│  3. Graph traversal (batch)  │
│  4. Result fusion            │
│  5. Context assembly         │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  PLAN OPTIMIZATION           │
│  • Reorder steps for speed   │
│  • Identify precalc data     │
│  • Set resource budgets      │
│  • Estimate latency          │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  EXECUTION                   │
│  (Target: < 500ms)           │
└──────────────────────────────┘

Cache Architecture

Cache Layer	Type	Purpose	TTL
Query Result Cache	Semantic	Identical/similar queries	24 hours
Entity Cache	Attribute	Entity attribute lookups	1 hour
Relationship Cache	Relationship	Relationship queries	1 hour
Subgraph Cache	Computation	Frequently traversed subgraphs	12 hours
Vector Index Cache	Search	Vector similarity results	6 hours
Embedding Cache	Computation	Query embeddings	Session

Cache Invalidation

Trigger	Scope	Latency
Entity Update	Entity + neighbors	Immediate
Relationship Change	Both entities	Immediate
Access Control Change	All caches for user	5 minutes
External Enrichment	Affected entities	30 minutes
Batch Import	Affected subgraphs	1 hour
TTL Expiration	Individual entry	Per TTL

Graph Index Optimization

Index Types:

Index	Purpose	Lookup Time
Entity ID Index	Direct entity lookup	< 1ms
Entity Type Index	All entities of type	< 10ms
Relationship Type Index	All relationships of type	< 10ms
Full-Text Index	Natural language search	< 50ms
Vector Index	Semantic similarity	< 100ms
Temporal Index	Time-range queries	< 5ms

Query Optimization Techniques:

Early Filtering: Apply access controls before traversal
Index-First: Use indices before graph traversal
Batch Operations: Group similar queries
Approximate Answers: Use statistical summaries when exact not needed
Progressive Disclosure: Return top results immediately, refine as time allows

Monitoring

Graph RAG includes comprehensive monitoring to track retrieval quality, performance, and system health.

Retrieval Quality Metrics

Metric	Description	Target
Precision	Fraction of retrieved results relevant to query	> 85%
Recall	Fraction of relevant results actually retrieved	> 80%
NDCG@10	Normalized discounted cumulative gain at rank 10	> 0.75
Mean Reciprocal Rank	Rank of first relevant result	< 3
Serendipity	Fraction of results user found surprising but valuable	> 20%

Performance Monitoring

Metric	Description	Alert Threshold
Query Latency (P95)	95th percentile execution time	> 500ms
Cache Hit Rate	Fraction of queries served from cache	< 40%
Traversal Depth	Average graph traversal depth	> 6 hops
Vector Search Latency	Time to perform similarity search	> 100ms
Context Assembly Time	Time to build LLM context	> 200ms

System Health Monitoring

Monitor	Description	Normal Range
Index Freshness	Age of indices vs. source data	< 5 minutes
Cache Memory Usage	Memory consumed by caches	< 60%
Vector Index Size	Bytes consumed by embedding indices	Monitored
Query Queue Depth	Pending queries awaiting execution	< 100
API Response Errors	Failed retrieval requests	< 1%

Logging

Log Type	Content	Retention
Query Logs	Query intent, results, timing, user	1 year
Performance Logs	Latency, cache hits, execution plans	90 days
Error Logs	Failures, timeouts, exceptions	2 years
Audit Logs	User queries, entities accessed, timing	2 years
Security Logs	Permission denials, anomalies	2 years

Audit Logging

Complete audit trails are maintained for all Graph RAG operations to support compliance and security investigations.

Logged Events

Event	Data Logged	Retention
Query Executed	User ID, query text, entities searched, results returned	2 years
Entity Retrieved	Entity ID, type, fields accessed, user, timestamp	1 year
Relationship Traversed	From/to entities, relationship type, user, timestamp	1 year
Access Denied	User, entity, reason, timestamp	2 years
Cache Hit/Miss	Query, cache type, result, timestamp	90 days
Performance Event	Query latency, execution time, resource usage	90 days

Audit Log Security

Control	Implementation
Integrity	Cryptographic hash chain prevents tampering
Encryption	Logs encrypted at rest with AES-256
Access Control	Auditor role required; no delete capability
PII Handling	Sensitive fields hashed or redacted
Retention	Configurable per regulatory requirement
Export	Auditors can export logs for external analysis

Integration with Platform Components

Graph RAG is deeply integrated with other DataFab components to provide a cohesive experience.

Knowledge Fabric Integration

Integration	Purpose	Data Flow
Entity Lookup	Resolve entities mentioned in queries	Query → KF → Entity ID
Relationship Discovery	Find connections across sources	Query → KF Graph → Relationships
Source Attribution	Trace context back to source systems	Context → KF Lineage → Source
Schema Validation	Ensure retrieved data matches schema	Results → KF Schema → Validated

Exchange Integration

Integration	Purpose
Context Provision	Supply retrieved context to conversational interface
Query Interpretation	Translate natural language to graph queries
Session Management	Maintain session graph across conversation turns
Result Explanation	Explain retrieval decisions to users

Studio Integration

Integration	Purpose
Agent Context	Provide agents with relevant context from graph
Workflow Enrichment	Enrich pipeline data with graph relationships
Pattern Detection	Identify patterns in retrieved context

Summary

Graph RAG represents a significant advancement over traditional vector-only RAG systems by:

Leveraging Explicit Structure: Uses the knowledge graph’s relationship structure to improve context quality
Multi-Pattern Retrieval: Supports entity-centric, relationship-traversal, and community-based patterns
Hybrid Scoring: Combines vector similarity with graph structure for superior relevance
Coherent Context Assembly: Automatically organizes retrieved information hierarchically for LLM consumption
Session-Aware Retrieval: Maintains context across conversational turns for cumulative understanding
Comprehensive Security: Enforces access controls throughout retrieval and assembly
High Performance: Achieves sub-500ms latency through intelligent caching and query optimization
Complete Auditability: Maintains full audit trails for compliance and security investigations

Graph RAG enables DataFab to deliver AI-augmented intelligence that is both contextually rich and verifiable, bridging the gap between operational systems and generative AI applications.