Introduction
In this tutorial, we'll build an AI-powered knowledge graph system inspired by IWE (Intelligent Wiki Engine), a Rust-based personal knowledge management tool. We'll create a developer knowledge base from scratch, implement markdown link navigation as a directed graph, and integrate it with OpenAI's function calling and agentic RAG (Retrieval-Augmented Generation) capabilities. This system will allow us to traverse knowledge graphs and retrieve context-aware information using AI agents.
By the end of this tutorial, you'll have a working knowledge graph system that can:
- Parse markdown files and extract wiki-links
- Build a directed graph representation of your knowledge base
- Query the graph using OpenAI's function calling
- Perform graph traversal to find related concepts
Prerequisites
To follow this tutorial, you'll need:
- Python 3.8+
- Basic understanding of graph theory and knowledge graphs
- OpenAI API key
- Installations:
networkx,openai,markdown,pyyaml
Step-by-Step Instructions
1. Initialize Project Structure
We start by creating a basic project structure to house our knowledge graph implementation.
mkdir knowledge-graph-project
cd knowledge-graph-project
mkdir data notebooks src
This structure allows us to organize markdown files in data, Jupyter notebooks for experimentation in notebooks, and our Python source code in src.
2. Create Sample Markdown Files
Let's create a small developer knowledge base with sample markdown files that contain wiki-links:
mkdir -p data/docs
Create data/docs/introduction.md:
# Introduction to Knowledge Graphs
This is an introduction to knowledge graphs. For more information, see [[Graph Traversal]].
We can also link to [[AI Agents]] and [[RAG Systems]].
Create data/docs/graph-traversal.md:
# Graph Traversal
Graph traversal is essential for navigating knowledge graphs. It's related to [[Knowledge Graphs]].
See also [[OpenAI Function Calling]].
Create data/docs/ai-agents.md:
# AI Agents
AI agents are intelligent systems that can perform tasks autonomously. They are used in [[RAG Systems]] and [[Knowledge Graphs]].
3. Parse Markdown and Extract Links
We'll create a parser to extract wiki-links from markdown files and build a graph structure:
from src.graph_parser import extract_links
import os
# Sample markdown content
markdown_content = '''
# Introduction to Knowledge Graphs
This is an introduction to knowledge graphs. For more information, see [[Graph Traversal]].
We can also link to [[AI Agents]] and [[RAG Systems]].
'''
# Extract links
links = extract_links(markdown_content)
print(links)
This step is crucial because it transforms unstructured markdown text into structured graph edges, enabling navigation and traversal.
4. Implement Graph Construction
Create a Python script to build the knowledge graph using NetworkX:
import networkx as nx
from networkx import DiGraph
class KnowledgeGraph:
def __init__(self):
self.graph = DiGraph()
def add_node(self, node_name):
self.graph.add_node(node_name)
def add_edge(self, source, target):
self.graph.add_edge(source, target)
def build_from_files(self, folder_path):
# Implementation to read markdown files and build graph
pass
def traverse(self, start_node, max_depth=2):
# Implementation for graph traversal
pass
Using a directed graph allows us to model the relationships between concepts as one-way dependencies, which is essential for knowledge navigation.
5. Integrate OpenAI Function Calling
Now, we'll integrate OpenAI's function calling to enable our system to query the knowledge graph:
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
def query_knowledge_graph(query, graph):
# Prepare function definition for OpenAI
functions = [
{
"name": "search_graph",
"description": "Search the knowledge graph for related concepts",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "The search query"}
},
"required": ["query"]
}
}
]
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an assistant that can query a knowledge graph."},
{"role": "user", "content": query}
],
functions=functions,
function_call="auto"
)
return response
This integration allows us to leverage AI's natural language understanding to query our structured knowledge graph, making it more accessible to users who don't understand graph traversal.
6. Implement Agentic RAG System
Let's build a simple RAG system that can retrieve relevant context from our knowledge graph:
def retrieve_context(query, graph, top_k=3):
# Use graph traversal to find related nodes
related_nodes = find_related_nodes(query, graph, top_k)
# Retrieve content from related nodes
context = ""
for node in related_nodes:
node_content = get_node_content(node)
context += f"{node}: {node_content}\n"
return context
# Simple function to find related nodes
def find_related_nodes(query, graph, top_k):
# Implementation using graph similarity or node attributes
pass
The RAG system enhances the AI's ability to provide relevant information by retrieving context from the knowledge graph before generating responses.
7. Run the Complete System
Finally, let's tie everything together with a main execution:
def main():
# Initialize knowledge graph
kg = KnowledgeGraph()
# Build graph from markdown files
kg.build_from_files("data/docs")
# Query the system
query = "How are AI agents related to knowledge graphs?"
response = query_knowledge_graph(query, kg.graph)
print(response.choices[0].message.content)
if __name__ == "__main__":
main()
This final step demonstrates how all components work together to create an AI-powered knowledge graph system that can understand queries and provide context-aware responses.
Summary
In this tutorial, we've built an AI-powered knowledge graph system inspired by IWE. We learned how to:
- Parse markdown files and extract wiki-links
- Construct a directed graph from these links
- Integrate OpenAI's function calling for natural language queries
- Implement a basic agentic RAG system for context retrieval
This implementation provides a foundation for more complex knowledge management systems that can scale to larger knowledge bases and support more sophisticated AI agents.
