HybridRAG Application

Enterprise AI systems require solutions that handle both structured data (databases, transactions, CSVs, JSON) and unstructured data (documents, images, audio). While traditional VectorRAG excels at semantic search across documents, it struggles with complex queries requiring global context or relationship-aware reasoning. HybridRAG application addresses these gaps by combining GraphRAG (knowledge graph-based retrieval) and VectorRAG (vector database retrieval) for enhanced accuracy and contextual relevance.

Table of contents

  1. Architecture

  2. Deployment

Architecture

The HybridRAG application is a customizable end-to-end workflow that leverages the capabilities of LLMs and RAG efficiently. HybridRAG architecture is shown below:

architecture

This application is modular as it leverages each component as a microservice(as defined in GenAIComps) that can scale independently. It comprises data preparation, embedding, retrieval, reranker(optional) and LLM microservices. All these microservices are stitched together by the HybridRAG megaservice that orchestrates the data through these microservices. The flow chart below shows the information flow between different microservices for this example.

flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style HybridRAG-MegaService stroke:#000000 %% Subgraphs %% subgraph HybridRAG-MegaService["HybridRAG MegaService "] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue LLM([LLM MicroService]):::blue direction LR T2C([Text2Cypher MicroService]):::blue LLM([LLM MicroService]):::blue end subgraph UserInterface[" User Interface "] direction LR a([User Input Query]):::orchid UI([UI server<br>]):::orchid end TEI_RER{{Reranking service<br>}} TEI_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} GDB{{Graph DB<br><br>}} R_RET{{Retriever service <br>}} DP([Data Preparation MicroService]):::blue S2G([Struct2Graph MicroService]):::blue LLM_gen{{LLM Service <br>}} GW([HybridRAG GateWay<br>]):::orange %% Questions interaction direction LR a[User Input Query] --> UI UI --> GW GW <==> HybridRAG-MegaService EM ==> RET RET ==> RER RER ==> LLM direction LR T2C ==> LLM %% Embedding service flow direction LR EM <-.-> TEI_EM RET <-.-> R_RET RER <-.-> TEI_RER LLM <-.-> LLM_gen direction TB %% Vector DB interaction R_RET <-.->|d|VDB DP <-.->|d|VDB direction TB %% Graph DB interaction T2C <-.->|d|GDB S2G <-.->|d|GDB

Deployment

HybridRAG deployment on Intel Gaudi

Validated Configurations

Deploy Method

LLM Engine

LLM Model

Hardware

Docker Compose

vLLM

meta-llama/Meta-Llama-3-8B-Instruct

Intel Gaudi