Retrieval Augmented Generation (RAG) has revolutionized how we fetch relevant and recent facts from vector databases. However, RAG’s capabilities fall short when it comes to connecting facts and understanding the connection between sentences and their context.
GraphRAG has emerged to help understand text datasets better by unifying text extraction, analysis over graph networks, and summarization within a single cohesive system.
How GraphRAG Maintains Data and Handles Queries
The efficiency of graphs is tied to their hierarchical nature. Graphs connect information via edges and enable traversal across nodes to reach the point of truth while understanding the dependencies.
These connections help improve query latency and enhance relevance at scale. RAGs rely on vector databases, while GraphRAG is a new paradigm that requires a graph-based database.
These graph databases are hybrid versions of vector databases. Graph database complements the hierarchical approach over semantic search which is common in vector databases. This switch in search preference is the driving factor of GraphRAG efficiency and performance.
The GraphRAG process generally extracts a knowledge graph from the raw data. This knowledge graph is then transformed into a community hierarchy where data is connected and grouped to generate summaries.
These groups and metadata of the grouped summaries make the GraphRAG outperform RAG-based tasks. At a granular level, GraphRAG contains multiple levels for graphs and text. Graph entities are embedded at the graph vector space level while text chunks are embedded at textual vector space.
GraphRAG Components
Querying information from a database at a scale with low latency requires manual optimizations that are not part of the database’s functionality. In relational databases performance tuning is achieved via indexing and partitioning.
Data is indexed to enhance query and fetch at scale and partitioned to speed up the read times. Structured CTEs and joins are curated while enabling inbuilt database functionalities to avoid data shuffle and network IO. GraphRAG operates differently compared to relational and vector databases. They have graph-centric inbuilt capabilities, which we will explore below:
1. Indexing Packages
Inbuilt indexing and query retrieval logic make a huge difference when working with graphs. GraphRAG databases withhold an indexing package that can extract relevant and meaningful information from structured and unstructured content. Generally, these indexing packages can extract graph entities and relationships from raw text. Additionally, the community hierarchy of GraphRAG helps perform entity detection, summarization, and report generation at multiple granular levels.
2. Retrieval Modules
In addition to the indexing package, graph databases have a retrieval module as part of the query engine. The module offers querying capabilities through indexes and delivers global and local search results. Local search responses are similar to RAG operations performed on documents where we get what we ask for based on the available text.
In GraphRAG the local search will first combine relevant data with LLM generated knowledge graphs. These graphs are then used to generate suitable responses for questions that require a deeper understanding of entities. The global search forms community hierarchies using map-reduce logic to generate responses at scale. It is resource and time-intensive but it offers accurate and relevant information retrieval capabilities.
GraphRAG Capabilities and Use Cases
GraphRAG can convert natural language into a knowledge graph where the model can traverse through the graph and query for information. Knowledge graph to natural language conversion is also possible with a few GraphRAG solutions.
GraphRAGs are very good at knowledge extraction, completion, and refinement. GraphRAG solutions can be applied to various domains and problems to tackle modern challenges with LLMs.
Use Case 1: With Indexing Packages and Retrieval Modules
By leveraging the graph hierarchy and indexing capabilities, LLMs can generate responses more efficiently. End-to-end custom LLM generation can be scripted using GraphRAG.
The availability of information without the need for joins makes the usability more interesting. We can set up an ETL pipeline that uses indexing packages and leverage retrieval module functionalities to insert and map the data.
Let’s look at a bridge parent node with a connection to multiple nested child nodes containing domain-specific information along the hierarchy. When a custom LLM creation is required we can route the LLM to fetch and train based on the domain-specific information.
We can separate training and live graph databases containing relevant information with metadata. By doing this, we can automate the entire flow and LLM generation which is production-ready.
Use Case 2: Real-World Scenarios
GraphRAG sends a structured response that contains entity information along with text chunks. This combination is necessary to make the LLM understand the terminologies and domain-specific details to deliver accurate and relevant responses.
This is done by applying GraphRAG to multi-modal LLMs where the graph nodes are interconnected with text and media. When queried, LLM can traverse across nodes to fetch information tagged with metadata based on similarity and relevance.
Advantages of GraphRAG Over RAG
GraphRAG is a transformative solution that exhibits many upsides in comparison to RAG, especially when managing and handling LLMs that are performing under intensive workloads. Where GraphRAG shines is:
- Better understanding of the context and relationship among queries and factual response extraction.
- Quicker response retrieval time with inbuilt indexing and query optimization capabilities.
- Scalable and responsive capabilities to handle varying loads without compromising accuracy or speed.
Conclusion
Relevance and accuracy are the driving factors of the AI paradigm. With the rise of LLMs and generative AI, content generation and process automation have become easy and efficient. Although magical, generative AI is scrutinized for slowness, delivering non-factual information and hallucinations. RAG methodologies have tried to overcome many of the limitations. However, the factuality of the response and the speed at which the responses are generated has been stagnant.
Organizations are handling the speed factor by horizontally scaling cloud computes for faster processing and delivery of results. Overcoming relevance and factual inconsistencies has been a theory until GraphGAG.
Now, with GraphRAG, we can efficiently and scalably generate and retrieve information that is accurate and relevant at scale.
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