How to Build a Knowledge Graph from Documents with Large Language Models (LLMs) 2026

Introduction & Architecture

In this tutorial, we will explore how to build a knowledge graph from documents using large language models (LLMs). This approach leverages the natural language processing capabilities of LLMs to extract semantic relationships and entities from unstructured text data. The resulting knowledge graph can then be used for various applications such as information retrieval, recommendation systems, or even advanced analytics.

📺 Watch: Intro to Large Language Models

Video by Andrej Karpathy

The architecture we will implement involves several key components:

1. Document Preprocessing: Cleaning and tokenizing the input documents.
2. Entity Extraction with LLMs: Using an LLM to identify entities and their relationships within the text.
3. Graph Construction: Building a graph data structure from extracted entities and relations.
4. Optimization for Production Use: Configuring the system to handle large volumes of data efficiently.

This tutorial is based on verified research papers that highlight the importance of knowledge graphs in various domains, such as particle physics [1], high-energy astrophysics [3], and experimental physics [2]. These studies underscore the utility of LLMs in extracting meaningful information from complex scientific documents.

Prerequisites & Setup

To follow this tutorial, you will need a Python environment with specific packages installed. The following dependencies are required:

• transformers [5]: A library for state-of-the-art models and tokenizers.
• networkx: For building graph structures.
• spacy: An NLP library that can be used in conjunction with LLMs.

pip install transformers networkx spacy

Ensure you have the latest versions of these libraries. The choice of transformers is due to its extensive support for various pre-trained models, while networkx offers robust graph manipulation capabilities. spacy can be used for additional preprocessing and entity recognition tasks.

Core Implementation: Step-by-Step

Step 1: Document Preprocessing

We start by cleaning the input documents and tokenizing them to prepare for LLM processing.

import spacy
from transformers import AutoTokenizer, AutoModelForTokenClassification

# Load a pre-trained tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")

nlp = spacy.load('en_core_web_sm')

def preprocess_document(doc):
    # Tokenize and clean the document
    doc_spacy = nlp(doc)
    tokens = [token.text for token in doc_spacy if not token.is_stop]

    return ' '.join(tokens)

# Example usage
clean_doc = preprocess_document("This is a sample text.")

Step 2: Entity Extraction with LLMs

Next, we use the pre-trained model to extract entities and their relationships from the cleaned document.

def extract_entities(doc):
    inputs = tokenizer(clean_doc, return_tensors="pt")

    # Get token classification outputs
    outputs = model(**inputs)

    # Decode predictions
    predictions = torch.argmax(outputs.logits, dim=2).squeeze().tolist()
    tokens = [tokenizer.convert_ids_to_tokens(pred_id) for pred_id in inputs.input_ids.squeeze()]

    entities = []
    current_entity = None

    for token, prediction in zip(tokens[0], predictions):
        if prediction == 1:  # Assuming label 1 represents an entity start
            current_entity = token
        elif prediction == 2 and current_entity is not None:  # Label 2 represents continuation of the same entity
            current_entity += ' ' + token
        elif prediction != 0 or (current_entity is not None):  # Label 0 represents no entity, end current if any
            entities.append(current_entity)
            current_entity = None

    return entities

# Example usage
entities = extract_entities(clean_doc)

Step 3: Graph Construction

With the extracted entities and relationships, we now construct a graph.

import networkx as nx

def build_knowledge_graph(entities):
    G = nx.Graph()

    # Add nodes for each entity
    for entity in entities:
        G.add_node(entity)

    # Placeholder logic to add edges based on relations
    # In practice, this would involve more sophisticated relation extraction

    return G

# Example usage
knowledge_graph = build_knowledge_graph(entities)

Configuration & Production Optimization

To scale the system for production use, consider the following configurations:

• Batch Processing: Process documents in batches to manage memory and computational resources efficiently.
• Asynchronous Processing: Use asynchronous techniques to handle multiple document requests concurrently.

import asyncio

async def process_document(doc):
    clean_doc = preprocess_document(doc)
    entities = extract_entities(clean_doc)
    graph = build_knowledge_graph(entities)

    return graph

# Example usage with async processing
async def main():
    docs = ["Doc1", "Doc2"]
    tasks = [process_document(doc) for doc in docs]
    graphs = await asyncio.gather(*tasks)

# Run the main function
loop = asyncio.get_event_loop()
graphs = loop.run_until_complete(main())

Advanced Tips & Edge Cases (Deep Dive)

Error Handling and Security Risks

• Prompt Injection: Ensure that input documents are sanitized to prevent prompt injection attacks.
• Memory Management: Monitor memory usage, especially when processing large datasets.

import gc

def monitor_memory_usage():
    mem_used = psutil.Process().memory_info().rss / 1024**2
    print(f"Current Memory Usage: {mem_used:.2f} MB")

# Example usage
monitor_memory_usage()

Results & Next Steps

By following this tutorial, you have built a basic system for extracting knowledge graphs from documents using LLMs. The next steps could include:

• Scalability Improvements: Optimize the system to handle larger datasets and more concurrent requests.
• Advanced Relation Extraction: Enhance relation extraction capabilities beyond simple entity recognition.

This project can be scaled further by integrating with cloud services for distributed processing or leverag [2]ing GPU acceleration for faster model inference.