Graph RAG is a groundbreaking system that combines knowledge graphs with Retrieval Augmented Generation (RAG) to address the limitations of traditional RAG approaches. By leveraging knowledge graphs, Graph RAG provides a more holistic understanding of the underlying corpus, enabling more accurate and contextually relevant responses.

The key aspects of Graph RAG include:

1. Indexing Phase: During this phase, the source documents are processed, and entities are extracted and used to create a knowledge graph. The knowledge graph represents the relationships between different entities, which are then used to create summaries at various levels of granularity.

2. Query Phase: When a user query is provided, Graph RAG selects the appropriate level of community (summary) based on the query and retrieves the relevant information. The responses from multiple communities are then combined to generate the final answer.

This approach addresses the limitations of traditional RAG, such as limited contextual understanding and scalability issues. By incorporating knowledge graphs, Graph RAG can provide a more comprehensive understanding of the underlying data, leading to more accurate and coherent responses.

One of the key advantages of Graph RAG is its ability to handle complex queries that require a deeper understanding of the relationships between entities. This makes it particularly useful for applications that involve question-answering, summarization, or knowledge-intensive tasks.

However, it's important to note that the cost of running Graph RAG can be higher compared to traditional RAG systems, as it involves additional processing steps, such as entity extraction and knowledge graph creation. This cost factor should be carefully considered when deciding to implement Graph RAG in your applications.

Overall, Graph RAG represents a significant advancement in the field of Retrieval Augmented Generation, and its potential to enhance the performance of language models in various applications is worth exploring.

Traditional Retrieval Augmented Generation (RAG) is a method where the language model retrieves relevant documents from a large corpus to generate more accurate and contextually relevant responses. This approach involves three main steps:

1. Document Processing and Embedding: The original documents are divided into smaller chunks using a chunking strategy. Embeddings are then computed for each chunk and stored in a vector store, which becomes the knowledge base.

2. Query Phase: When a user asks a question, embeddings are computed for the query, and a similarity search is performed on the vector store to retrieve the most relevant chunks or sub-documents.

3. Response Generation: The retrieved context is combined with the original query and fed into a large language model to generate the final response.

However, this traditional RAG approach has several limitations:

1. Limited Contextual Understanding: RAG can sometimes miss the nuances in the data due to its reliance on retrieved documents alone. It doesn't have a holistic overview of the document, so it doesn't fully understand the overall picture.

2. Scalability Issues: As the corpus grows, the retrieval process can become less efficient.

3. Complexity in Integrating External Knowledge Sources: Integrating external knowledge sources in a meaningful way can be complex and cumbersome.

To address these limitations, Microsoft has introduced GraphRAG, a system that combines knowledge graphs with Retrieval Augmented Generation. In the next section, we will dive into the technical details of how GraphRAG works.

Graph RAG is a novel approach that combines knowledge graphs with Retrieval Augmented Generation (RAG) to address the limitations of traditional RAG systems. The key steps involved in the Graph RAG process are as follows:

1. Indexing Phase:

   • The source documents are chunked into smaller sub-documents.
   • Within each chunk, entities (such as people, places, companies) are identified, and the relationships between these entities are extracted.
   • This information is used to create a knowledge graph, where the entities are represented as nodes, and the relationships between them are represented as edges.
   • The knowledge graph is then used to detect communities, which are groups of closely related entities.
   • Summaries are created for each community at different levels of detail, ranging from the global (high-level) to the local (more granular).

2. Query Phase:

   • When a user query is received, the appropriate community level (global, intermediate, or local) is selected based on the level of detail required.
   • The summaries for the relevant communities are retrieved, and these partial responses are combined to generate the final answer.

This approach offers several advantages over traditional RAG systems:

• Improved Contextual Understanding: By leveraging the knowledge graph and community-level summaries, Graph RAG can better capture the overall context and nuances of the source documents, rather than relying solely on the retrieved documents.
• Enhanced Scalability: As the corpus grows, the community-level summaries can help maintain efficient retrieval and response generation, mitigating the scalability issues of traditional RAG.
• Simplified Integration: The knowledge graph-based approach provides a more structured and meaningful way to integrate external knowledge sources, making the process less complex and cumbersome.

However, the Graph RAG approach also comes with a potential drawback: the cost associated with running the system. The indexing phase, which involves entity extraction, relationship identification, and community summarization, can be computationally intensive and incur significant costs, especially when using large language models like GPT-4.

In the provided example, the cost of running Graph RAG on a single book (A Christmas Carol) was around $7, which could be prohibitively expensive for larger corpora. This cost factor is something to consider when deciding whether to use Graph RAG or a traditional RAG system in your application.

Overall, Graph RAG represents an innovative approach to addressing the limitations of traditional RAG systems, but the cost implications need to be carefully evaluated based on the specific requirements and scale of your project.

To set up Graph RAG on your local machine, follow these steps:

1. Create a Conda virtual environment:

   conda create -n graphrag python=3.9
   conda activate graphrag
   

2. Install the Graph RAG package:

   pip install graphrag
   

3. Create a directory for your test data:

   mkdir -p rag_test/input
   

4. Download a sample dataset, such as the text of "A Christmas Carol" by Charles Dickens:

   python -m graphrag.index --root_dir rag_test/input --download_sample_data
   

5. Set up your workspace variables and configurations:

   python -m graphrag.index --root_dir rag_test/input --init_config
   

   This will create a settings.yml file where you can configure your API key, model, and other settings.

6. Run the indexing process to create the knowledge graph:

   python -m graphrag.index --root_dir rag_test/input
   

   This step may take some time, as it involves entity extraction, relationship identification, and community summarization.

7. Run sample queries:

   python -m graphrag.query --root_dir rag_test --method global --prompt "What are the main themes in this story?"
   python -m graphrag.query --root_dir rag_test --method local --prompt "What is the relationship between Scrooge and Bob Cratchit?"
   

   The global method retrieves information at the highest level, while the local method focuses on specific entities and their relationships.

8. Analyze the cost implications: The indexing process can be costly, especially when using large language models like GPT-4. The example provided in the transcript shows that processing a single book can cost around $7. Consider the cost implications when applying Graph RAG to larger datasets.

That's the basic setup process for running Graph RAG on your local machine. Remember to customize the configurations and prompts to fit your specific use case.

In the example provided, the cost implications of using Graph RAG were significant. The total cost of processing the book and creating the Graph RAG came out to around $7, which could be prohibitively expensive for large corpora of data.

The key factors contributing to the high cost were:

1. API Requests: The Graph RAG approach made a total of 570 requests through the API, primarily for the GPT-4 model. In contrast, the traditional RAG approach would likely have a lower number of API requests.

2. Token Processing: The Graph RAG process involved processing well over 1 million tokens, which contributed to the overall cost.

In comparison, a traditional RAG system would likely have a lower cost, as it does not require the additional steps of entity extraction, relationship identification, and community summarization that are part of the Graph RAG approach.

It's important to consider the trade-offs between the improved contextual understanding and holistic overview provided by Graph RAG, and the higher cost associated with its implementation. Depending on the specific use case and the available budget, one approach may be more suitable than the other.

If working with a large corpus of data, the cost implications of Graph RAG should be carefully evaluated. In such cases, a traditional RAG system may be a more cost-effective solution, especially if the improved contextual understanding provided by Graph RAG is not a critical requirement.

Ultimately, the choice between Graph RAG and traditional RAG will depend on the specific needs of the project, the available resources, and the balance between performance and cost.

While Microsoft's GraphRAG is a groundbreaking system, it is not the only implementation of the Graph RAG approach. There are other notable solutions available that are worth exploring:

Lama Index's Knowledge Graph RAG Query Engine

Lama Index, a popular open-source knowledge management platform, has developed its own implementation of a Knowledge Graph RAG Query Engine. This solution leverages the power of knowledge graphs to enhance the retrieval and generation capabilities of large language models.

The Lama Index approach focuses on building a comprehensive knowledge graph from various data sources, including structured and unstructured data. This knowledge graph is then used to power the RAG system, providing a more holistic understanding of the underlying information.

Neo4j's Graph RAG Package

Neo4j, a leading graph database provider, has also introduced a Graph RAG package. This solution integrates Neo4j's graph database capabilities with the Retrieval Augmented Generation (RAG) technique.

By utilizing Neo4j's efficient graph traversal and querying capabilities, the Graph RAG package aims to address the scalability and contextual understanding limitations of traditional RAG systems. This approach allows for more effective retrieval of relevant information from the knowledge graph, leading to more accurate and coherent responses.

Comparing the Implementations

While the core principles of Graph RAG are shared across these solutions, each implementation may have its own unique features, strengths, and trade-offs. Factors such as performance, ease of integration, support for different language models, and cost implications may vary between the different Graph RAG solutions.

If there is interest, I can dive deeper into a comparative analysis of these Graph RAG implementations, highlighting their key differences and helping you determine the best fit for your specific use case.

Graph RAG works great, but there was one major issue and that is the cost. For the example provided, a total of 570 requests were sent through the API using GPT-4, and over 1 million tokens were processed, resulting in a cost of around $7. This cost could be prohibitively expensive for a large corpus of data, and it is substantially more expensive compared to building a traditional RAG system.

Despite the cost implications, Graph RAG is an innovative approach that combines knowledge graphs with Retrieval Augmented Generation (RAG). It addresses some of the limitations of traditional RAG systems, such as limited contextual understanding and scalability issues.

Microsoft is not the only company that has implemented a Graph RAG system. Other options, such as Lama Index's Knowledge Graph RAG query engine and Neo4j's Graph RAG package, are also available. If there is interest, further content comparing these different implementations can be created.

Overall, Graph RAG is a promising technology, but the cost implications need to be carefully considered when deciding whether to use it in your own applications.