In the realm of data management and AI, knowledge graphs are transformative tools that organize and represent information in a structured way, offering powerful insights and enabling advanced analytics. Combining Neo4j, a leading graph database, with LangChain, a versatile tool for text processing, provides a robust framework for constructing and utilizing knowledge graphs. In this blog, we’ll explore how to integrate these technologies to build a knowledge graph from a research paper, demonstrating their practical applications and potential benefits.

The Power of Knowledge Graphs

Knowledge graphs excel in representing complex relationships between entities. They are instrumental in:

Improving Search Capabilities: By understanding and linking related concepts, knowledge graphs enhance search accuracy and relevance.

Enabling Advanced Analytics: They facilitate the discovery of patterns and insights through interconnected data.

Powering Recommendation Systems: By mapping relationships between users, products, and preferences, knowledge graphs provide personalized recommendations.

Using Neo4j and LangChain together amplifies these benefits by providing a seamless method for extracting, processing, and analyzing text data.

Example Use Case: Research Paper Analysis

Let’s walk through how to create a knowledge graph from a research paper using Neo4j and LangChain. We’ll cover the following steps:

• Extracting Text from the Research Paper

• Generating and Storing Embeddings

• Constructing Relationships

• Querying and Analyzing the Graph

Step 1: Extracting Text from the Research Paper

First, we need to extract text from the research paper. For this, we’ll use the PyPDFLoader class from LangChain, which allows us to load and split the text from a PDF document.

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load and split the PDF file
loader = PyPDFLoader("path/to/your/research_paper.pdf")
pages = loader.load_and_split()
# Split pages into manageable chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=0)
chunks = text_splitter.split_documents(pages)

Here, PyPDFLoader helps load the PDF and split it into pages, while RecursiveCharacterTextSplitter divides the text into chunks suitable for further processing.

Step 2: Generating and Storing Embeddings

Next, we’ll generate embeddings for these text chunks and store them in Neo4j. Embeddings capture the semantic meaning of the text, making it easier to analyze and retrieve relevant information.

from langchain_community.graphs import Neo4jGraph
from langchain_community.vectorstores import Neo4jVector
from langchain.embeddings import OpenAIEmbeddings
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv('.env')
neo4j_uri = os.getenv('NEO4J_URI')
neo4j_username = os.getenv('NEO4J_USERNAME')
neo4j_password = os.getenv('NEO4J_PASSWORD')
neo4j_database = 'neo4j'
# Initialize Neo4j graph and vector store
kg = Neo4jGraph(
    url=neo4j_uri, username=neo4j_username, password=neo4j_password, database=neo4j_database
)
neo4j_vector_store = Neo4jVector.from_documents(
    embedding=OpenAIEmbeddings(),
    documents=chunks,
    url=neo4j_uri,
    username=neo4j_username,
    password=neo4j_password,
    index_name='research_chunks',
    text_node_property='text',
    embedding_node_property='embedding'
)

In this step, we use OpenAIEmbeddings to create embeddings and Neo4jVector to store these embeddings in Neo4j. This setup allows us to efficiently manage and query the text data.

Step 3: Constructing Relationships

To fully leverage the knowledge graph, we need to establish relationships between nodes. For a research paper, these relationships might include linking chunks to the main document and ordering chunks sequentially.

# Create a node for the research paper
cypher = """
MERGE (p:Paper {title: $title})
RETURN p
"""
kg.query(cypher, params={'title': "Title of Your Research Paper"})

# Connect chunks to their parent paper with a PART_OF relationship
cypher = """
MATCH (c:Chunk), (p:Paper)
WHERE p.title = $title
MERGE (c)-[r:PART_OF]->(p)
RETURN count(r)
"""
kg.query(cypher, params={'title': "Title of Your Research Paper"})
# Create a NEXT relationship between sequential chunks
cypher = """
MATCH (c1:Chunk), (c2:Chunk)
WHERE c1.chunkSeqId = c2.chunkSeqId - 1
MERGE (c1)-[r:NEXT]->(c2)
RETURN count(r)
"""
kg.query(cypher)

In this stage, we establish PART_OF relationships between chunks and the main paper node. We also create NEXT relationships to capture the sequence of chunks.

Step 4: Querying and Analyzing the Graph

With the knowledge graph constructed, we can now perform sophisticated queries to extract insights and answer questions based on the research paper’s content.

from langchain.chains import RetrievalQAWithSourcesChain
from langchain.llms import OpenAI

# Create a retriever from the vector store
retriever = neo4j_vector_store.as_retriever()
# Create a question-answering chain
chain = RetrievalQAWithSourcesChain.from_chain_type(
    OpenAI(temperature=0),
    chain_type="stuff",
    retriever=retriever
)
# Ask a question
question = "What are the main findings of this research paper?"
answer = chain({"question": question}, return_only_outputs=True)
print(answer["answer"])

This final step leverages LangChain to create a question-answering system. By querying the graph, we can extract meaningful information from the research paper, providing valuable insights.

Conclusion

Integrating Neo4j with LangChain offers a powerful approach for constructing and utilizing knowledge graphs. By processing text from research papers, generating embeddings, and establishing meaningful relationships, you can build a robust knowledge graph that enhances data analysis and AI capabilities.

This example demonstrates how these technologies can be applied to academic research, but the approach is equally valuable in other domains such as business intelligence, content management, and more. The combination of Neo4j’s graph database and LangChain’s text processing capabilities opens new avenues for managing and extracting insights from complex datasets.