Understanding the Target Audience
This guide is tailored for software developers, data scientists, and AI researchers who are keen on merging symbolic logic with generative AI. These professionals often work in technology, finance, and education, where the ability to apply logical reasoning in AI systems is becoming increasingly important.
Pain Points
Many in this field face challenges such as:
- Integrating symbolic reasoning with modern AI techniques.
- Implementing logic-based systems in real-world applications.
- Finding efficient tools to reason over complex datasets.
Goals
The primary objectives for our audience include:
- Gaining a deeper understanding of Prolog and its AI applications.
- Creating robust systems that leverage both symbolic and statistical reasoning.
- Enhancing skills in using LangChain and Gemini for AI development.
Interests
Readers are often interested in:
- The intersection of AI and symbolic logic.
- Hands-on coding and experimentation with AI frameworks.
- Learning about new technologies and methodologies in AI development.
Communication Preferences
This audience appreciates clear, concise, and technical communication. They favor well-structured guides that include practical examples and code snippets, allowing for easy follow-along.
Tutorial Overview
This tutorial illustrates how to merge symbolic logic with generative AI by setting up a Prolog knowledge base and integrating it with LangChain and Gemini. We will build a ReAct-style agent capable of reasoning about family relationships and performing mathematical operations.
Setup Instructions
To begin, install SWI-Prolog and the required Python packages:
!apt-get install swi-prolog -y
!pip install pyswip langchain-google-genai langgraph langchain-core
Code Implementation
First, we load the necessary libraries and set up the environment:
import os
from pyswip import Prolog
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
import json
GOOGLE_API_KEY = "Use Your Own API Key Here"
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0)
Prolog Interface Class
The following class wraps the Prolog interface, allowing us to load a knowledge base and execute queries:
class AdvancedPrologInterface:
def __init__(self):
self.prolog = Prolog()
self._load_knowledge_base()
def _load_knowledge_base(self):
rules = [
"parent(john, mary, alice)",
"parent(john, mary, bob)",
"parent(bob, susan, charlie)",
"parent(alice, david, emma)",
"parent(charlie, lisa, frank)",
"male(john)", "male(bob)", "male(david)", "male(charlie)", "male(frank)",
"female(mary)", "female(alice)", "female(susan)", "female(emma)", "female(lisa)",
"grandparent(X, Z) :- parent(X, _, Y), parent(Y, _, Z)",
"sibling(X, Y) :- parent(P1, P2, X), parent(P1, P2, Y), X \\= Y",
"uncle(X, Y) :- sibling(X, Z), parent(Z, _, Y), male(X)",
"aunt(X, Y) :- sibling(X, Z), parent(Z, _, Y), female(X)",
"cousin(X, Y) :- parent(P1, _, X), parent(P2, _, Y), sibling(P1, P2)",
"factorial(0, 1)",
"factorial(N, F) :- N > 0, N1 is N - 1, factorial(N1, F1), F is N * F1",
"list_member(X, [X|_])",
"list_member(X, [_|T]) :- list_member(X, T)",
"list_length([], 0)",
"list_length([_|T], N) :- list_length(T, N1), N is N1 + 1",
"animal(dog)", "animal(cat)", "animal(whale)", "animal(eagle)",
"mammal(dog)", "mammal(cat)", "mammal(whale)",
"bird(eagle)", "bird(sparrow)",
"can_fly(eagle)", "can_fly(sparrow)",
"can_swim(whale)", "can_swim(fish)",
"aquatic_mammal(X) :- mammal(X), can_swim(X)"
]
for rule in rules:
try:
self.prolog.assertz(rule)
except Exception as e:
print(f"Warning: Could not assert rule '{rule}': {e}")
def query(self, query_string):
try:
results = list(self.prolog.query(query_string))
return results if results else [{"result": "No solutions found"}]
except Exception as e:
return [{"error": f"Query failed: {str(e)}"}]
Tool Wrapping
Next, we define various tools to interact with the Prolog knowledge base:
@tool
def family_relationships(query: str) -> str:
results = prolog_interface.query(query)
return json.dumps(results, indent=2)
@tool
def mathematical_operations(operation: str, number: int) -> str:
if operation == "factorial":
query = f"factorial({number}, Result)"
results = prolog_interface.query(query)
return json.dumps(results, indent=2)
else:
return json.dumps([{"error": f"Operation '{operation}' not supported"}])
@tool
def advanced_queries(query_type: str, entity: str = "") -> str:
queries = {
'all_children': f"parent(_, _, {entity})" if entity else "parent(_, _, X)",
'all_grandchildren': f"grandparent(_, {entity})" if entity else "grandparent(_, X)",
'all_siblings': f"sibling({entity}, X)" if entity else "sibling(X, Y)",
'all_cousins': f"cousin({entity}, X)" if entity else "cousin(X, Y)"
}
if query_type in queries:
results = prolog_interface.query(queries[query_type])
return json.dumps(results, indent=2)
else:
return json.dumps([{"error": f"Query type '{query_type}' not supported"}])
Agent Creation
We can now create a ReAct agent and demonstrate its capabilities:
tools = [family_relationships, mathematical_operations, advanced_queries]
agent = create_react_agent(llm, tools)
def run_family_analysis():
print(" Family Relationship Analysis")
print("=" * 50)
queries = [
"Who are all the parents in the family database?",
"Find all grandparent-grandchild relationships",
"Show me all the siblings in the family",
"Who are John and Mary's children?",
"Calculate the factorial of 6 using Prolog"
]
for i, query in enumerate(queries, 1):
print(f"\n Query {i}: {query}")
print("-" * 30)
try:
response = agent.invoke({"messages": [("human", query)]})
answer = response["messages"][-1].content
print(f" Response: {answer}")
except Exception as e:
print(f" Error: {str(e)}")
Conclusion
This tutorial effectively demonstrates how to integrate Prolog with generative AI using LangChain and Gemini. By merging symbolic reasoning with advanced AI capabilities, users can create powerful tools for logical analysis and decision-making. For further exploration, users are encouraged to expand the knowledge base with additional rules and facts or adapt the framework for different applications.
Additional Resources
For the complete code and further examples, please refer to the original source material.
FAQ
1. What is Prolog and why is it used in AI?
Prolog is a logic programming language that is particularly suited for tasks that involve complex data relationships and symbolic reasoning, making it valuable in AI applications.
2. How does LangChain enhance AI development?
LangChain provides a framework for building applications that can leverage language models, allowing developers to create more interactive and intelligent systems.
3. What are the benefits of combining symbolic logic with generative AI?
This combination allows for more robust reasoning capabilities, enabling systems to make decisions based on both logical rules and learned patterns from data.
4. Can I use this framework for other applications beyond family relationships?
Yes, the framework can be adapted for various applications, including natural language processing, knowledge representation, and more, depending on the rules and data you provide.
5. What common mistakes should I avoid when working with Prolog and AI?
Common mistakes include failing to properly define rules, not handling exceptions, and overlooking the importance of a well-structured knowledge base.
