LlamaIndex Webinar: Advanced RAG with Knowledge Graphs (with Tomaz from Neo4j)
Science & Technology
Introduction
Welcome back to another episode of the Llama Index webinar series! Today, we have an exciting workshop lined up focused on property graph indexes within Llama Index, in partnership with Neo4j. This session will delve into building advanced knowledge graphs (KGs) and leveraging the new property graph abstractions for constructing and querying graphs.
Understanding Property Graphs
Many present in this discussion might be more familiar with triplestore formats, which typically use the structure of subject, predicate, and object. However, property graphs introduce a new standard as part of the GQL (Graph Query Language) standard from the ISO committee. In property graphs, nodes can have properties, and relationships can also carry properties.
For instance, if we have a node representing a person, the properties might include name, date of birth, and employee ID. Furthermore, nodes can be labeled for categorization purposes, such as employee, city, or organization. Relationships allow for a nuanced representation of how entities interact.
Introduction to Property Graph Index Integration
The integration of property graphs into Llama Index simplifies the process of constructing and querying knowledge graphs. The typical workflow starts with a collection of documents, with Llama Index providing robust support for various document types. These documents can be viewed as wrappers around the text and are the basis for building a knowledge graph.
The integration involves two main components: Graph Constructors and Graph Retrievers. Graph Constructors extract structured information from documents, which is then stored in the knowledge graph. Conversely, Graph Retrievers employ specific logic based on user queries to retrieve data from the knowledge graph.
Out-of-the-Box Graph Constructors
Llama Index offers several Graph Constructors:
Implicit Path Extractor: This constructs a lexical graph from documents, chunking the document while linking text chunks back to the original document.
Simple LLM-Prompted Extractor: This extractor requires an LLM and works through prompt engineering, where a prompt defines the desired outcomes and pairing functions extract that output to create a knowledge graph.
Schema LLM-Prompted Extractor: This advanced extractor allows users to define specific nodes, labels, and relationship types to extract. It is particularly effective with LLMs that support function calling.
Entity Disambiguation
An important consideration is entity disambiguation, which merges nodes in the knowledge graph that refer to the same real-world entity. This process enhances structural integrity and involves using text embeddings and word distance heuristics to find and merge potential duplicates.
Graph Retrievers
Once a knowledge graph is built, Graph Retrievers handle user queries. Llama Index currently supports four types of Graph Retrievers:
LLM Synonym Retriever: Generates synonyms for user input, relying on an exact keyword match to find relevant nodes.
Vector Context Retriever: Uses vector searches rather than keyword matches, making it less reliant on exact matches and more robust.
Text-to-Cypher Retriever: Transforms user queries into Cypher statements using an LLM, allowing for greater flexibility, though it may sacrifice some reliability.
Cypher Template Retriever: Executes pre-defined Cypher templates populated with parameters extracted by the LLM, allowing for efficient query execution.
Workshop Insights and Practical Implementation
The workshop illustrated how to construct a knowledge graph, extract entities, and implement custom retrieval methods. Participants learned about defining schemas for the types of nodes and relationships and using strict mode to ensure compliance to the provided schema.
Without a doubt, the discussion reinforced the power of knowledge graphs in enhancing information retrieval systems. As data complexity increases, the ability to structure information meaningfully allows users to extract valuable insights from vast datasets efficiently.
Conclusion
We wrapped up the workshop by exploring how to implement custom retrieval functionalities that prioritize identifying relevant entities within a given text. The multi-faceted approach to constructing and querying knowledge graphs can significantly enhance applications across various sectors.
Keywords
- Llama Index
- Knowledge Graphs
- Property Graphs
- Graph Constructors
- Graph Retrievers
- Entity Disambiguation
FAQ
Q1: What are property graphs?
A1: Property graphs are a type of graph data model that supports nodes and relationships, where both can have properties. This creates a more nuanced representation of entities and their connections.
Q2: How does Llama Index integrate with Neo4j?
A2: Llama Index integrates with Neo4j to provide users with tools for constructing and querying knowledge graphs using property graphs, allowing for flexible and efficient data retrieval.
Q3: What are the available Graph Constructors in Llama Index?
A3: Llama Index provides three out-of-the-box Graph Constructors: the Implicit Path Extractor, Simple LLM-Prompted Extractor, and Schema LLM-Prompted Extractor.
Q4: Why is entity disambiguation important?
A4: Entity disambiguation is crucial for merging nodes that refer to the same real-world entity to improve the structural integrity and reliability of the knowledge graph.
Q5: What types of Graph Retrievers can I use?
A5: Llama Index supports several Graph Retrievers, including the LLM Synonym Retriever, Vector Context Retriever, Text-to-Cypher Retriever, and Cypher Template Retriever.
Feel free to reach out for further inquiries or to share your experiences with knowledge graphs!