The Onboarding Accelerator: Knowledge Synthesis as a Service
A complete, multi-service backend platform that transforms scattered internal documents into a centralized, intelligent Q&A engine. This project demonstrates the architecture of a sophisticated, AI-native application utilizing microservices, Knowledge Graph Augmented RAG, and asynchronous cross-document synthesis.
My Role: Product Architect & Systems Integrator
I owned the end-to-end vision, architectural design, technology selection, and integration strategy for this platform. I utilized advanced AI-assisted development tools to rapidly execute the implementation (coding, debugging, and optimization), allowing me to focus on the complex system architecture, integration challenges, and realizing the product vision.
Core Technologies Integrated:
The Challenge: Beyond Simple Search
In knowledge-based organizations, information is scattered and siloed. Standard RAG implementations and generic AI tools rely on simple semantic search, which often fails to provide the deep context required for complex questions or multi-hop reasoning.
- Lack of Explainability: Standard RAG struggles to show *how* it connected different pieces of information.
- Multi-Hop Failure: Inability to synthesize answers that require connecting concepts across multiple, disparate documents.
- Expensive Inefficiency: New hires waste time ramping up, and senior staff are constantly interrupted.
The Solution: Knowledge Graph Augmented RAG
The Onboarding Accelerator addresses these limitations by moving beyond semantic search to structured understanding. It uses a sophisticated, multi-stage AI pipeline to build a Knowledge Graph (KG), enabling capabilities that generic AI tools cannot match.
The KG Advantage: Accuracy, Context, and Synthesis
By integrating the Knowledge Graph with the RAG pipeline, the system provides verifiable ground truth, enables complex multi-hop traversal to discover hidden connections, and delivers deeply contextualized answers with precise citations.
- Agentic AI Workflows: Utilizing tiered LLMs (Flash/Pro) in specialized, asynchronous workers for extraction, normalization, and refinement.
- Proactive Synthesis: The "Global Brain" feature asynchronously connects siloed information across documents.
Demonstration Walkthrough
[CRITICAL PLACEHOLDER: Insert 3-5 minute narrated video walkthrough here. Must showcase the RAG Q&A, the Knowledge Graph visualization, and the Manifold view.]
Architectural Deep Dive
The system is designed as a resilient, scalable platform utilizing a microservices approach defined in Docker Compose. This architecture enables the integration of complex, asynchronous AI/ML pipelines and a sophisticated polyglot persistence strategy.
System Architecture Diagram
graph TD
%% Define Zones/Subgraphs
subgraph "User Interface (React Frontend)"
direction TB
User(["
User
"])
FE[React App
Vite, Tailwind, D3/Plotly]
User <--> FE
end
subgraph "Backend API (Node.js / Express)"
API[API Server
(Handles Auth, Validation, Job Dispatch)]
end
subgraph "Asynchronous Worker Fleet (BullMQ)"
direction LR
IngestW[Ingestion & Chunking]
GraphW[Graph Construction]
EmbedW[Vector Embedding]
EnrichW[Content Enrichment]
SynthW[Synthesis Workers
(Global Brain, Validation)]
ManifoldW[ML/Manifold
(Python: UMAP, Node2Vec)]
end
subgraph "Data Persistence Layer"
direction LR
PG[("PostgreSQL + pgvector
Chunks, Metadata, Vector Embeddings")]
NEO[("Neo4j + APOC
Knowledge Graph, Nodes, Edges")]
REDIS[("Redis
Job Queue & LLM Cache")]
end
subgraph "External Services"
GEMINI[
Google Gemini API
(Tiered Strategy: Flash/Pro)]
end
%% Styling - Using Dark Theme defaults and custom classes
%% Since we initialized with 'dark', we define classes to slightly adjust colors if needed
classDef default fill:#1e293b,stroke:#334155,color:#e2e8f0
classDef user fill:#06b6d4,stroke:#06b6d4,color:#0f172a,font-weight:bold
classDef db fill:#334155,stroke:#475569,color:#e2e8f0
classDef external fill:#334155,stroke:#06b6d4,color:#e2e8f0
class User user
class PG,NEO,REDIS db
class GEMINI external
%% FLOW 1: Document Ingestion Pipeline (Asynchronous)
FE -- "1. POST /documents" --> API
API -- "2. Enqueues Jobs" --> REDIS
REDIS -.-> IngestW
REDIS -.-> GraphW
REDIS -.-> EmbedW
REDIS -.-> EnrichW
IngestW -- "Writes Chunks" --> PG
GraphW -- "Reads Chunks" --> PG
GraphW -- "Extraction/Refinement" --> GEMINI
GraphW -- "Writes KG" --> NEO
EmbedW -- "Reads Chunks" --> PG
EmbedW -- "Generates Embeddings" --> GEMINI
EmbedW -- "Writes Vectors" --> PG
EnrichW -- "Reads Graph/Text" --> NEO
EnrichW -- "Reads Graph/Text" --> PG
EnrichW -- "Generates Definitions" --> GEMINI
%% FLOW 2: Corpus-Wide Synthesis (Asynchronous)
API -- "Enqueues Analysis Jobs" --> REDIS
REDIS -.-> SynthW
REDIS -.-> ManifoldW
SynthW -- "Reads/Writes/Merges (Global Brain)" --> NEO
SynthW -- "Inference & Validation" --> GEMINI
ManifoldW -- "Reads Graph/Vectors" --> NEO
ManifoldW -- "Reads Graph/Vectors" --> PG
ManifoldW -- "Labels Clusters" --> GEMINI
ManifoldW -- "Writes Manifold Data" --> PG
%% FLOW 3: Q&A Interaction (Synchronous RAG)
FE -- "A. POST /qa" --> API
API -- "B. Query Analysis" --> GEMINI
API -- "C. Vector Search" --> PG
API -- "D. Graph Traversal (Multi-hop)" --> NEO
API -- "E. Synthesis & Citation" --> GEMINI
API -- "F. Streams Answer" --> FE
Architectural Rationale
The architecture is designed to handle the intensive demands of AI-driven data processing while remaining responsive to the user. This is achieved through three core design principles:
1. Decoupled & Asynchronous Processing (The Engine Room)
Computationally expensive tasks (LLM calls, ML processing, database writes) are strictly offloaded from the main API server.
- The Backbone (Redis/BullMQ): A robust job queue system manages the entire pipeline. The API server simply dispatches jobs and returns immediately.
- Specialized Worker Fleet: Dedicated, containerized workers (Node.js and Python) subscribe to specific queues (e.g., Graph, Embedding, Synthesis). This allows for independent scaling and ensures that a failure in one worker does not crash the entire system. This design utilizes AI in a structured, agentic workflow rather than just simple request-response calls.
2. Polyglot Persistence (The Right Tool for the Job)
A single database cannot efficiently handle both semantic search and complex relationship modeling. A dual-database strategy is employed:
- Vector Search (PostgreSQL + pgvector): Stores raw text chunks and their high-dimensional vector embeddings.
pgvectorenables the high-speed similarity search required for the RAG pipeline. - Structured Knowledge (Neo4j): A native graph database is essential for modeling the complex, interconnected relationships extracted by the AI pipeline. It enables the crucial multi-hop traversal and the "Global Brain" synthesis features.
3. Scalability and Resilience (DevOps Foundation)
The entire system is orchestrated using Docker Compose, treating the infrastructure as code.
- Containerization: Ensures consistency across development and production environments.
- Resilience: Includes automated database migrations (
node-pg-migrate), healthchecks for all stateful services, and meticulous resource management (e.g., tuning Postgres shared_buffers and Neo4j JVM heap size). Transaction safety is enforced for all critical database operations.
Key Competencies Demonstrated in Architecture
Microservices & Decoupling
Expertise in decomposing a complex application into specialized services (API, specialized workers) to ensure scalability and maintainability. The architecture clearly separates concerns between coordination, computation, and data storage.
Asynchronous Systems Design
Mastery of asynchronous task management using BullMQ/Redis. This demonstrates the ability to design high-throughput, resilient systems capable of handling intensive background AI/ML workloads without impacting user experience.
AI-Native Data Strategy
Advanced capability in designing data storage for AI applications. The integration of pgvector for efficient semantic search and Neo4j for structured knowledge representation is a sophisticated approach to modern RAG.
DevOps and IaC
Proficiency in Docker and Docker Compose to define the entire multi-service application declaratively. This includes optimized multi-stage builds, robust migration strategies, and meticulous resource management.
Polyglot Integration
Demonstrated ability to integrate disparate technology stacks seamlessly. The system bridges the gap between the Node.js backend environment and the specialized Python scientific computing ecosystem for advanced ML tasks.
Tiered AI Optimization
Strategic implementation of a Tiered LLM strategy (Gemini Flash vs. Pro) to intelligently optimize for cost, performance, and quality across different stages of the data pipeline.
The Q&A Engine: Knowledge Graph Augmented RAG
The platform's Q&A capability is a dynamic, multi-strategy RAG engine designed for multi-hop retrieval and complex synthesis. It combines graph-based reasoning with vector search to provide answers that are accurate, deeply contextual, and fully cited.
(System evaluated at 70% accuracy on the Musique multi-hop QA dataset, demonstrating strong performance in complex reasoning tasks).
The Live Query Engine: Multi-Strategy Retrieval
Strategy 1:
Graph-Based Neighborhood Search (Multi-Hop)
This is the system's primary method for complex synthesis. It "connects the dots" structurally before retrieving text.
- Entity Identification: The system extracts the main entities from the user's question.
- Node Discovery (Neo4j): It finds the corresponding nodes in the Knowledge Graph.
- Graph Traversal (Multi-Hop): The system performs a graph traversal (1-2 hops) from the initial nodes, discovering a neighborhood of related concepts, including inferred relationships from the "Global Brain".
- Context Retrieval (pgvector): The system retrieves all text chunks relevant to any concept in this discovered neighborhood, creating a rich, multi-faceted context.
- Synthesis (Gemini): This interconnected context is sent to a powerful LLM to synthesize an answer that explains the complex relationships.
Strategy 2:
Global Vector Similarity Search
This method is used for broader questions or when exploring the user's entire library of documents.
- Query Embedding: The user's question is converted into a vector embedding.
- Global Vector Search (pgvector): The system performs a similarity search against all document chunks for that user, asking, "Find the most semantically relevant paragraphs from across all documents."
- Synthesis with Citations (Gemini): The retrieved chunks are compiled. The LLM is specifically prompted to synthesize an answer and to meticulously cite which document each piece of information originated from.
Innovation Spotlight: The "Global Brain"
Asynchronous Cross-Document Synthesis
This is the system's most innovative feature, designed to overcome the limitations of standard RAG. It is a background process that runs after documents are processed to proactively build connections between them, turning a collection of siloed documents into a true, integrated knowledge base.
1. Identify "Bridge Concepts"
The service identifies concepts that appear in multiple, otherwise unrelated documents. These act as pivot points between topics.
2. Infer Cross-Document Relationships
The system takes context about a shared concept from different documents and asks a powerful LLM (Gemini Pro) to infer a new, higher-level relationship.
3. Create "Bridge Edges"
If the LLM identifies a plausible relationship, a new "bridge" edge is created in the knowledge graph (Neo4j), stored with metadata like { "inferred": true, "confidence": 0.85 }.
Example Prompt: "Given that Document A discusses 'Attractor Dynamics' in the context of 'Neural Coordination' and Document B discusses 'Attractor Dynamics' in the context of 'AI Safety,' is there an inferred relationship between 'Neural Coordination' and 'AI Safety'?"
The Impact
This proactive synthesis dramatically enhances the Q&A engine. When performing a graph-based query (Strategy 1), the traversal can now hop across these inferred "bridge" edges. This automatically pulls context from multiple documents, providing incredibly insightful answers that synthesize knowledge from the entire library.
Interested in learning more?
This project demonstrates a capacity for architecting complex, data-intensive AI applications from the ground up. If you have questions about the architecture or would like to discuss potential opportunities, please reach out.