How to Develop Graph RAG Applications Without OpenAI

In recent years, Retrieval-Augmented Generation (RAG) models have gained traction for their ability to combine retrieval-based techniques with generative models to deliver contextually rich responses. While many RAG applications depend on OpenAI, building Graph RAG applications without OpenAI is entirely possible. This guide will explore how you can independently create a Graph RAG system to leverage the power of information retrieval and graph-based reasoning.

What is Graph RAG?

Graph RAG (Retrieval-Augmented Generation using Graphs) is a type of RAG model that leverages knowledge graphs instead of or in addition to vector-based embeddings. By representing relationships as graphs, a Graph RAG system can provide more context-aware responses, especially in applications where entity relationships and context play significant roles, such as in scientific research, legal data analysis, and business intelligence.

Why Build Graph RAG Without OpenAI?

Choosing to build Graph RAG without OpenAI may be beneficial for organizations focused on:

• Data Privacy: Keeping sensitive data within private infrastructures.
• Customization: Designing models tailored to unique use cases that don’t rely on OpenAI’s API or limitations.
• Cost Control: Reducing dependency on third-party APIs to minimize operational costs.

With a well-structured pipeline and the right tools, you can independently develop a Graph RAG solution.

Key Components of a Graph RAG System

To build a Graph RAG system without OpenAI, you’ll need to set up three main components:

1. Knowledge Graphs: Structured repositories of entity relationships.
2. Graph Database: A database designed for handling complex relationships.
3. Custom RAG Model Pipeline: A pipeline that integrates retrieval, augmentation, and generation functionalities.

Setting Up Knowledge Graphs

1. Define Entities and Relationships

To build an effective knowledge graph, start by defining the key entities and relationships within your domain. These might include:

• Entities: Core subjects like people, products, companies, or scientific terms.
• Relationships: Links between entities, such as “is a subsidiary of,” “develops,” or “published.”

2. Choose Graph Extraction Tools

Use tools to automatically extract entities and relationships from documents. Options include:

• SpaCy: A Python library with Named Entity Recognition (NER) for identifying entities.
• Stanford NLP: Another open-source library that can parse sentences and establish entity relationships.

Selecting a Graph Database

A graph database stores nodes (entities) and edges (relationships) effectively. Some popular open-source or self-hosted options include:

1. Neo4j

Neo4j is a highly popular graph database designed for complex queries and relationship management. Its Cypher query language is optimized for traversing graph structures.

2. Amazon Neptune

Amazon Neptune is a managed graph database service that can integrate with cloud storage, useful for scalable graph processing without heavy maintenance requirements.

3. ArangoDB

ArangoDB combines graph, document, and key-value database functionalities, providing flexibility in data storage and access.

Building the RAG Pipeline Without OpenAI

1. Retrieval Component

The retrieval component searches for relevant information within the knowledge graph. There are two primary retrieval methods:

• Vector Search (Optional): Aids in finding semantically similar information.
• Graph-Based Retrieval: Relies solely on relationships and context within the graph database, using queries to locate nodes with relevant connections.

For example, you might use Cypher queries in Neo4j to fetch all entities connected to a certain company or concept.

2. Augmentation with Knowledge Graph Context

In a Graph RAG application, augmentation uses retrieved information to add context. This stage can be handled with custom scripts or graph traversal queries that collect neighboring entities and their connections to enrich the response context.

Steps for context augmentation:

• Use queries to gather related entities within a specified range (e.g., 1 to 3 hops away).
• Apply filtering to reduce irrelevant nodes, ensuring the context remains focused on the query.

3. Generation with a Custom Language Model

Since this approach excludes OpenAI, you’ll need to choose an open-source generative model for the response generation step. Some viable options include:

• GPT-J or GPT-Neo: Open-source language models by EleutherAI that can be fine-tuned for specific use cases.
• LLaMA: Meta’s open-source model optimized for research and large language tasks.

Integration of the Language Model

Once you’ve retrieved and augmented relevant information, feed this context into your chosen model. This can be achieved using Python libraries like Hugging Face’s transformers to create a structured pipeline.

Optimizing Graph RAG Performance Without OpenAI

Performance optimization is essential to ensure your Graph RAG system is fast, efficient, and accurate. Here are some steps to consider:

1. Indexing and Caching

Use indexing to speed up retrieval from the knowledge graph and cache frequently accessed queries for faster response times. Graph databases like Neo4j offer built-in indexing tools that optimize graph traversal speed.

2. Batch Processing

Batch process large data sets to manage memory usage efficiently. This can be done by setting limits on the number of nodes and relationships retrieved in one query.

3. Response Tuning

Fine-tune your language model to produce responses in a format best suited to your domain. This may involve training the model with domain-specific data or adjusting the inference pipeline for higher relevance.

Deploying and Scaling a Graph RAG Solution

1. Deployment Options

Deploy your Graph RAG application on cloud services like AWS or GCP, or on-premises for maximum data control. Options include containerized deployment with Docker or Kubernetes to ensure scalability.

2. Scaling with Microservices

Consider a microservices approach for your RAG pipeline, splitting retrieval, augmentation, and generation into separate services. This modular design helps scale components independently based on demand.

Benefits of Developing Graph RAG Applications Without OpenAI

Creating a Graph RAG application without OpenAI brings several advantages:

• Full Data Ownership: Complete control over data without third-party dependency.
• Flexible Customization: Tailored solutions for specific business or research needs.
• Cost Efficiency: Reduced reliance on external API fees or quotas.

Conclusion: Building a Future-Proof Graph RAG System Without OpenAI

Developing a Graph RAG system without OpenAI provides enhanced control, customization, and cost efficiency. Leveraging Lettria’s GraphRAG enables enterprises to parse, process, and preserve the context of complex unstructured data. Unlike standard vector-based RAG solutions, which can lose important nuances, Lettria’s approach captures intricate relationships within the data, boosting response accuracy, explainability, and user trust—essential for high-stakes applications like scientific research, fraud detection, and internal data retrieval.

To explore how Lettria’s GraphRAG can optimize your data extraction and retrieval, you can request a demo and see firsthand how it meets your organization's unique needs for secure, contextually rich information processing.