LangChain’s Graph Constructor: Turning Text into Knowledge Graphs with Large Language Models (LLMs)
Graphs are everywhere! Whether you’re using social media, browsing the web, or even researching history projects, graphs help organize information and show how different things are related. But here’s something even cooler: What if we could use computer programs to build these graphs automatically from large chunks of text? This is exactly what LangChain is doing with the help of LLMs or Large Language Models. Let’s dive into how this technology works!
Why Build a Knowledge Graph from Text?
Think about reading a complicated story where lots of people, places, and events are mentioned. It can get pretty confusing! Imagine you could take that unstructured text (messy sentences) and turn it into structured data (something more organized). That’s what a knowledge graph does!
A knowledge graph maps out entities (like people or places) and relationships (how these entities are related). This makes it easier to answer tough questions or find connections across different pieces of text. Instead of searching through tons of paragraphs, the graph quickly shows you how things link together.
How LangChain is Making it Happen
About a year ago, LangChain started trying to build these graphs using modern LLMs like GPT-4. They even created their very own “LLM Graph Transformer” to help with this task. Today, the tool can handle different modes of graph building, making it easier for developers to extract useful information from text. Let’s explore how it works!
Building the Neo4j Environment
One of the best ways to store and visualize graphs is by using the Neo4j database. If you’ve never heard of it, don’t worry—it’s just a fancy tool for creating and browsing graphs. To get started, you can either use:
- A free cloud instance of Neo4j (called Neo4j Aura).
- A local instance by downloading Neo4j Desktop to your computer.
LLM Graph Transformer: Two Different Modes
The LLM Graph Transformer can work in two ways depending on the situation:
- Tool-Based Mode: This is the default mode used when the LLM can process structured data effectively. Think of it like using a specialized tool to dig out all the important bits from a pile of text. The tool knows exactly what format to expect, making it extra accurate.
- Prompt-Based Mode: When the LLM can’t use tools (or the tools are unavailable), the transformer switches to “prompting.” In this case, the program asks the LLM a few guided questions, and the LLM responds by pulling out information in a text format. It’s a fallback option if the first mode isn’t available.
You can even switch between these modes depending on your needs!
Tool-Based Mode: How It Works
The tool-based mode uses predefined classes like “Node” and “Relationship” in the code. These classes help the system make sense of entities (people, objects, or places) and their relationships (like who works where or how two people know each other).
- Node Class: Each node (like a person or organization) has an ID, a label (what kind of entity it is), and sometimes extra properties like names or dates.
- Relationship Class: This handles connections between two nodes. For example, it can show who works for which company or who is married to whom. Both the source and target nodes have specific labels and IDs to keep the information clear.
Prompt-Based Mode: Another Way to Extract Information
Prompt-based mode is the runner-up to tool-based mode. It’s used when the LLM doesn’t have special tools to extract information. Here, the transformer guides the LLM by providing a specific system prompt. The LLM then identifies the right entities and relationships from the text to create a simple, easy-to-read JSON file (JavaScript Object Notation file, a format to organize data).
While it’s not quite as detailed or accurate as the tool-based approach, the prompt-based mode is helpful in a lot of situations.
Defining the Graph Schema
A graph schema is like a blueprint for what the graph should look like. It tells the system what types of nodes and relationships it should extract from the text. This structure increases accuracy and ensures important information fits neatly into the graph.
For example, if we were building a graph about Marie Curie, a famous scientist, our schema might look for node types like:
- Person
- Organization (like the University of Paris)
- Award (such as the Nobel Prize)
This helps the system stay focused on the most relevant information.
Let’s Talk About Relationship Types
Once we define what nodes we want to extract, we need to think about relationship types. Do we want to know who won an award? Who works at a university? These relationships help enrich the graph, making it much more meaningful!
For instance, we can say:
- A Person can “WIN” an Award.
- A Person can WORK_FOR an Organization.
This standardization makes the model more consistent.
Adding Properties to the Graph
Sometimes nodes and relationships can have extra details or “properties.” For example, a person’s birth date or a relationship’s start date provides additional context, making the graph more informative. In LangChain’s tool-based mode, these properties can be added to both nodes and relationships.
Strict Mode: Keeping Things Clean
As smart as the LLM is, it doesn’t always follow instructions perfectly. That’s why LangChain added a strict mode to clean up any mistakes after the information is extracted. This mode ensures that the graph follows the rules of the schema and removes any stray or incorrect information.
Using Neo4j to Visualize the Graph
After constructing the knowledge graph in code, we can use Neo4j’s visual tools to make it come alive. This lets us see how different entities (nodes) and relationships fit together, helping us better understand the extracted information.
Importing Graph Documents into Neo4j
Want to explore your graph further? You can easily import the extracted data into your Neo4j database. Just use simple commands to bring in both nodes and relationships. You can even include the original text, allowing you to see where the extracted information came from!
Conclusion
LangChain’s LLM Graph Transformer is a powerful tool for creating knowledge graphs from text. Thanks to its two modes—tool-based and prompt-based—it can work with a variety of LLMs to extract entities, relationships, and properties. By defining a clear graph schema, you can improve accuracy and make sure the resulting graph is both useful and structured.
Now it’s easier than ever to make sense of complex information and find connections between different parts of a story or dataset.