8/24/2024

Building Knowledge Graphs with LangChain

Creating knowledge graphs has become a vital part of developing sophisticated AI applications, notably within the domains of data retrieval & question answering frameworks like RAG (Retrieval-Augmented Generation). If you're looking to delve into this fascinating topic, you're in the right place! In this post, we'll explore how to build knowledge graphs using LangChain and its features, while highlighting the crucial role played by Neo4j as a powerful graph database for your applications.

What Are Knowledge Graphs?

Knowledge graphs are structured representations of interconnected information where entities (such as people, places, or objects) are represented as nodes & relationships between them are depicted as edges. This sophisticated data structuring enables applications to derive deeper insights, answer complex queries, & navigate vast amounts of information effortlessly. They serve as the backbone for various AI applications that require contextual understanding.

The Importance of LangChain

LangChain provides a robust framework designed to make the development of LLM-based (Large Language Model) applications easier & more efficient. One of LangChain’s compelling features is its ability to interact with various graph databases like Neo4j. This capability allows developers to extract structured insights from unstructured data, making it an invaluable tool for constructing knowledge graphs.

The Architecture of LangChain for Knowledge Graphs

The basic architecture for constructing a knowledge graph within LangChain revolves around two primary steps:

Extracting Structured Information: Using language models, we can extract structured graph information from unstructured text data.
Storing in Graph Database: Once the information is structured, it can be stored in a graph database such as Neo4j, enabling downstream applications to utilize this data effectively.

To kick off the process, you need to establish your environment:

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bash
%pip install --upgrade langchain langchain-community langchain-openai langchain-experimental neo4j

Next, you'll want to import necessary libraries into your script:

python
import getpass
import os
from langchain_community.graphs import Neo4jGraph

Once your libraries are in the house, set the Neo4j credentials to create a connection:

python
os.environ['NEO4J_URI'] = 'bolt://localhost:7687'
os.environ['NEO4J_USERNAME'] = 'neo4j'
os.environ['NEO4J_PASSWORD'] = 'password'
graph = Neo4jGraph()

The Role of
`1LLMGraphTransformer`

To extract structured data from unstructured text, we can leverage the

LLMGraphTransformer

. This transformer assists in converting text documents into structured graph documents, enabling the parsing & categorizing of entities & relationships.

Here's how to utilize

LLMGraphTransformer

: ```python from langchain_experimental.graph_transformers import LLMGraphTransformer from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0, model_name="gpt-4-turbo") llm_transformer = LLMGraphTransformer(llm=llm)

This allows you to feed text into the transformer & examine the results:

python from langchain_core.documents import Document text = "Marie Curie, born 1867, was a Polish-French physicist and chemist who conducted pioneering research on radioactivity." documents = [Document(page_content=text)] graph_documents = llm_transformer.convert_to_graph_documents(documents) print(f"Nodes:{graph_documents[0].nodes}") print(f"Relationships:{graph_documents[0].relationships}")

The output will show you the nodes & relationships identified:

Nodes:[Node(id='Marie Curie', type='Person'), ...] Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(...), type='MARRIED')] ```

Filtering Nodes & Relationships

The flexibility within LangChain allows filtering the types of nodes & relationships during the extraction process. For instance:

python
llm_transformer_filtered = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Country", "Organization"],
    allowed_relationships=["NATIONALITY", "LOCATED_IN", "WORKED_AT", "SPOUSE"],
)
graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(documents)
print(f"Filtered Nodes:{graph_documents_filtered[0].nodes}")
print(f"Filtered Relationships:{graph_documents_filtered[0].relationships}")

This enables a tighter control over what data to extract based on your application’s requirements.

Customizing Node Properties

You can enhance your knowledge graph further by extracting specific node properties. Here’s an example with the

node_properties

parameter:

python
llm_transformer_props = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Country", "Organization"],
    allowed_relationships=["NATIONALITY", "LOCATED_IN", "WORKED_AT", "SPOUSE"],
    node_properties=["born_year"],
)
graph_documents_props = llm_transformer_props.convert_to_graph_documents(documents)
print(f"Nodes with Properties:{graph_documents_props[0].nodes}")
print(f"Relationships with Properties:{graph_documents_props[0].relationships}")

When you set

node_properties=True

, the model autonomously identifies relevant properties.

Storing Data in Neo4j

Now that we have our structured graphs ready, it’s time to store them. For that, we can use the

add_graph_documents

function:

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python
graph.add_graph_documents(graph_documents_props)

This method effectively stores the generated graph documents into the Neo4j graph database, making them accessible for downstream applications.

Analyzing the Graphs

With your knowledge graph saved, you can start analyzing it! For example, check all nodes:

python
query = "MATCH (n) RETURN n"
results = graph.query(query)
print(results)

This simple Cypher query will provide you with all the nodes stored in your graph database.

Enhancing Application Accuracy with Knowledge Graphs

Another compelling use of knowledge graphs is their ability to enhance the accuracy of RAG-based applications. The structured nature of graph databases helps orchestrate data nodes & relationships more efficiently than traditional unstructured data storage methods. As noted in this blog post on LangChain’s website, leveraging knowledge graphs not only improves contextuality but also enhances data navigation across complex datasets.

Seamless Integration with Arsturn

Creating knowledge graphs can be a complex task, but it doesn’t have to be! Using tools like Arsturn, you can swiftly create tailored AI chatbots that engage your audience while managing structured data effortlessly. Arsturn allows businesses to build conversational AI solutions without the need for coding skills. Whether you’re looking to handle user inquiries or provide tailored experiences based on rich data insights, Arsturn’s chatbot creation process is simple & efficient.

Benefits of Using Arsturn:

User-Friendly Interface: No coding knowledge required!
Robust Customization: Tailor your chatbot’s functionality & appearance to suit your brand.
Instant Engagement: Enhance user interaction & provide immediate responses to queries.
Insightful Analytics: Collect valuable information on user behavior to refine your strategies.

Conclusion

Building knowledge graphs with LangChain provides a STRUCTURED WAY to navigate complex information landscapes, making data more usable, insightful, & comprehensible. The integration of Neo4j as a powerful graph database enables efficient data retrieval & contextual awareness essential for innovative applications.

As you embark on your journey to build knowledge graphs, don’t forget to explore Arsturn for user-friendly chatbot solutions that can effectively manage your data & engage your audience effortlessly. Start leveraging the power of knowledge graphs & conversational AI today, and watch your AI applications soar!

With LangChain, the possibilities are endless. Happy graphing! 💡