Introduction and System Overview
Building a Mini Prolog system represents one of the most intellectually rewarding challenges in programming language implementation. This comprehensive guide presents the design and implementation of a substantial Prolog subset that maintains compatibility with Standard Prolog while introducing modern features for AI integration and concurrent programming.
Our Mini Prolog system encompasses the core logical programming paradigm with unification, backtracking, and cut operations. Beyond traditional Prolog functionality, we extend the system with Large Language Model integration, concurrency primitives, Python interoperability, and a comprehensive standard library. The system operates through a Read-Eval-Print Loop (REPL) interface with file management capabilities for .pl files.
The architecture follows clean code principles with separation of concerns across distinct modules: the unification engine, inference engine, backtracking mechanism, parser, evaluator, and external interfaces. Performance optimization occurs through efficient data structures, intelligent indexing, and lazy evaluation strategies.
History and Background of Prolog
Origins and Early Development
Prolog emerged in the early 1970s as a revolutionary approach to programming that fundamentally differed from the imperative programming paradigms that dominated computing at the time. The language was conceived and developed by Alain Colmerauer and his team at the University of Aix-Marseille in France, with significant contributions from Robert Kowalski at the University of Edinburgh in Scotland.
The genesis of Prolog can be traced to Colmerauer's work on natural language processing and his collaboration with Kowalski on logic programming. Colmerauer was investigating methods for parsing natural language using formal grammars, while Kowalski was exploring the computational aspects of mathematical logic, particularly the resolution principle developed by J.A. Robinson in 1965. Their convergent interests led to the realization that logical inference could serve as a computational mechanism for solving problems.
The first Prolog interpreter was implemented in 1972 by Colmerauer, Philippe Roussel, and their colleagues. This initial system was written in Fortran and Algol-W and was designed primarily for natural language processing applications. The name "Prolog" itself is derived from "Programmation en Logique" (Programming in Logic), reflecting the language's foundation in mathematical logic and its French origins.
The theoretical underpinnings of Prolog rest on first-order predicate logic and the resolution principle. Robinson's resolution theorem proving method provided the computational foundation that made logic programming practical. The key insight was that logical formulas could be viewed as programs, and theorem proving could serve as program execution. This paradigm shift from procedural "how to compute" to declarative "what to compute" represented a fundamental advance in programming language design.
Theoretical Foundations
The mathematical foundations of Prolog are rooted in several key areas of logic and computer science. First-order predicate logic provides the formal framework for expressing knowledge and relationships. In this system, facts are represented as atomic formulas, while rules are expressed as implications. The Horn clause subset of first-order logic, which restricts formulas to have at most one positive literal, forms the computational core of Prolog.
The unification algorithm, originally developed for automated theorem proving, became central to Prolog's operation. Unification determines whether two logical terms can be made identical through variable substitution. This process enables pattern matching and variable binding, which are essential for logical inference. The most general unifier concept ensures that unification produces the most general solution possible, preserving maximum flexibility for subsequent reasoning steps.
SLD resolution (Selective Linear Definite clause resolution) provides the inference mechanism for Prolog programs. This strategy combines resolution with a specific search strategy that processes goals from left to right and clauses from top to bottom. The linear nature of SLD resolution means that each resolution step involves the current goal and a clause from the program, creating a linear sequence of resolution steps.
The operational semantics of Prolog are defined through the concept of derivations and success/failure conditions. A query succeeds if there exists a finite SLD derivation from the query to the empty clause. The search space is explored through backtracking, which systematically examines alternative choices when the current path leads to failure. This depth-first search with backtracking ensures completeness for finite search spaces while maintaining computational tractability.
Evolution and Standardization
The development of Prolog proceeded through several distinct phases, each marked by significant improvements in implementation techniques and language features. The early 1970s saw the creation of the first interpreters and the establishment of basic syntax and semantics. These initial systems demonstrated the feasibility of logic programming but were limited in performance and functionality.
The late 1970s and early 1980s witnessed a period of rapid development and experimentation. David H.D. Warren's implementation of the Warren Abstract Machine (WAM) in 1983 revolutionized Prolog execution by providing an efficient compilation target for Prolog programs. The WAM introduced sophisticated optimization techniques including indexing, tail recursion optimization, and efficient backtracking mechanisms. This work transformed Prolog from an interesting research prototype into a practical programming language capable of supporting real-world applications.
During this period, several influential Prolog systems emerged. The Edinburgh Prolog system, developed at the University of Edinburgh, became a de facto standard and influenced many subsequent implementations. Other notable systems included C-Prolog, SICStus Prolog, and SWI-Prolog, each contributing innovations in performance, debugging capabilities, and extended functionality.
The need for standardization became apparent as Prolog gained wider adoption and multiple incompatible dialects emerged. The International Organization for Standardization (ISO) began work on a Prolog standard in the late 1980s. The ISO Prolog standard (ISO/IEC 13211-1) was published in 1995, establishing a common core language definition that ensured portability across different implementations.
The ISO standard defined the syntax, semantics, and built-in predicates that constitute core Prolog. It specified the behavior of fundamental operations including unification, arithmetic, term manipulation, and control structures. The standard also established conventions for error handling, character sets, and implementation-defined features. This standardization effort was crucial for Prolog's acceptance in commercial and academic environments.
Major Implementations and Variants
The landscape of Prolog implementations has been rich and diverse, with different systems emphasizing various aspects of performance, functionality, and application domains. Each major implementation has contributed unique innovations that have advanced the state of the art in logic programming.
SWI-Prolog, developed by Jan Wielemaker at the University of Amsterdam, has become one of the most widely used open-source Prolog systems. SWI-Prolog emphasizes ease of use, comprehensive documentation, and extensive library support. It includes advanced features such as constraint logic programming, tabling for optimization, and interfaces to other programming languages. The system's web-based development environment and extensive package ecosystem have made it particularly popular for research and education.
SICStus Prolog, developed by the Swedish Institute of Computer Science, represents the commercial state of the art in Prolog implementation. SICStus emphasizes performance, reliability, and industrial-strength features. It includes sophisticated optimization techniques, comprehensive debugging tools, and extensive constraint solving capabilities. The system has been used in numerous commercial applications ranging from expert systems to scheduling and planning software.
GNU Prolog, developed by Daniel Diaz, focuses on efficiency and compliance with the ISO standard. It includes a native code compiler that generates highly optimized executable programs. GNU Prolog's constraint solving capabilities and finite domain constraint system make it particularly suitable for combinatorial optimization problems.
YAP (Yet Another Prolog) emphasizes performance and compatibility with existing Prolog code. Developed by Vitor Santos Costa and colleagues, YAP includes advanced memory management techniques and optimization strategies that achieve excellent performance on many benchmark programs. The system includes tabling mechanisms for optimization and extensive debugging support.
ECLiPSe (ECRC Common Logic Programming System) represents an ambitious attempt to integrate logic programming with constraint solving. Developed at the European Computer-Industry Research Centre, ECLiPSe includes sophisticated constraint handling capabilities that enable the solution of complex combinatorial problems. The system's visualization tools and development environment support the creation of large-scale constraint programming applications.
Applications and Impact
Prolog's unique characteristics have made it particularly suitable for certain classes of applications, leading to significant impact in multiple domains. The declarative nature of Prolog programming enables the direct expression of problem constraints and relationships, making it ideal for applications involving symbolic reasoning, knowledge representation, and search.
Artificial intelligence has been the most prominent application domain for Prolog. Expert systems built in Prolog have been deployed in medical diagnosis, financial planning, and industrial process control. The language's pattern matching and backtracking capabilities make it natural for implementing rule-based reasoning systems. Notable examples include medical diagnostic systems that encode physician expertise in logical rules and financial advisory systems that provide investment recommendations based on client profiles and market conditions.
Natural language processing represents another significant application area where Prolog has made substantial contributions. The language's ability to express grammatical rules and parse trees naturally aligns with the requirements of language analysis systems. Definite Clause Grammars (DCGs), a Prolog extension for grammar specification, have been widely used for parsing and generation tasks. Machine translation systems, question-answering systems, and dialogue systems have all benefited from Prolog's linguistic processing capabilities.
Database systems and knowledge bases have leveraged Prolog's logical foundation for deductive database applications. Prolog's query mechanism provides a natural interface for expressing complex database queries that involve recursive relationships and logical inference. Deductive database systems built on Prolog foundations can derive new facts from stored data using logical rules, enabling more sophisticated data analysis and knowledge discovery.
Constraint logic programming, an extension of Prolog with constraint solving capabilities, has found applications in scheduling, planning, and resource allocation problems. Airlines use constraint logic programming systems for crew scheduling and aircraft routing. Manufacturing companies employ these systems for production planning and supply chain optimization. The ability to express complex constraints declaratively and solve them efficiently has made constraint logic programming valuable for many real-world optimization problems.
Influence on Programming Language Design
Prolog's introduction of logic programming has had profound influence on programming language design and the broader field of computer science. The declarative programming paradigm pioneered by Prolog has inspired numerous subsequent languages and programming approaches.
The concept of pattern matching, central to Prolog's operation, has been adopted by many functional programming languages. Languages such as ML, Haskell, and Erlang incorporate sophisticated pattern matching mechanisms that enable concise and expressive program specification. The unification algorithm developed for Prolog has found applications in type inference systems and automated theorem proving tools.
Constraint programming, which emerged from extensions to Prolog, has become a distinct programming paradigm with its own languages and systems. Languages such as CHIP, cc(FD), and OPL incorporate constraint solving capabilities that enable the direct expression of optimization problems. The constraint satisfaction techniques developed for Prolog extensions have influenced the design of optimization software and decision support systems.
The backtracking search mechanism of Prolog has inspired the development of search-oriented programming languages and libraries. Languages such as Icon and Unicon incorporate backtracking as a fundamental control mechanism. Many modern programming languages include backtracking libraries that enable the exploration of solution spaces in a Prolog-like manner.
Logic programming concepts have also influenced the development of query languages for databases and knowledge bases. SQL's recursive query capabilities draw inspiration from Prolog's recursive rule processing. Datalog, a subset of Prolog designed for database applications, has become an important tool for data analysis and knowledge discovery in large-scale data processing systems.
Modern Developments and Future Directions
Contemporary Prolog development continues to evolve in response to changing computational environments and application requirements. Modern Prolog systems incorporate advanced optimization techniques, parallel processing capabilities, and integration with other programming paradigms.
Tabling and memoization techniques have been integrated into several Prolog implementations to improve performance for recursive computations. These techniques cache intermediate results to avoid redundant computation, significantly improving performance for certain classes of programs. XSB Prolog and SWI-Prolog include sophisticated tabling mechanisms that automatically optimize recursive predicates.
Parallel and concurrent Prolog systems have been developed to exploit modern multi-core and distributed computing architectures. Systems such as Ciao Prolog include concurrency primitives that enable the development of parallel logic programs. These systems address the challenge of maintaining logical semantics while achieving performance benefits from parallel execution.
Integration with other programming languages and systems has become increasingly important for modern Prolog implementations. SWI-Prolog includes interfaces to C, C++, Java, and Python, enabling Prolog programs to leverage existing libraries and systems. Web-based Prolog systems enable the deployment of logic programming applications in cloud and web environments.
Constraint logic programming continues to evolve with new constraint domains and solving techniques. Modern constraint solvers can handle continuous domains, temporal constraints, and probabilistic reasoning. These advances expand the applicability of constraint logic programming to new problem domains including machine learning, robotics, and financial modeling.
The integration of machine learning and artificial intelligence techniques with logic programming represents an active area of research and development. Probabilistic logic programming languages such as ProbLog and SLP combine logical reasoning with probabilistic inference. These systems enable the representation and reasoning about uncertain knowledge, making them suitable for applications in machine learning and data mining.
The influence of Prolog extends beyond traditional logic programming to modern developments in artificial intelligence and knowledge representation. Answer set programming, which emerged from research in logic programming and non-monotonic reasoning, has become an important tool for knowledge representation and reasoning. Systems such as Clingo and DLV enable the specification and solution of complex reasoning problems using declarative logic programming techniques.
The continued relevance of Prolog in contemporary computing is evidenced by its adoption in new application domains and its influence on emerging programming paradigms. As computational problems become increasingly complex and data-driven, the declarative approach pioneered by Prolog continues to provide valuable insights and practical solutions for challenging computational problems.
Core Architecture and Data Structures
The foundation of our Mini Prolog system rests on carefully designed data structures that represent Prolog terms, clauses, and the knowledge base. The term representation uses a hierarchical structure accommodating atoms, variables, numbers, compound terms, and lists.
from typing import Union, List, Dict, Any, Optional, Callable
from dataclasses import dataclass
from enum import Enum
import threading
import asyncio
from abc import ABC, abstractmethod
class TermType(Enum):
ATOM = "atom"
VARIABLE = "variable"
NUMBER = "number"
COMPOUND = "compound"
LIST = "list"
@dataclass
class Term:
"""Base class for all Prolog terms with type safety and immutability."""
term_type: TermType
value: Any
def __post_init__(self):
"""Ensure immutability after creation."""
object.__setattr__(self, '_frozen', True)
def __setattr__(self, name, value):
if hasattr(self, '_frozen') and self._frozen:
raise AttributeError(f"Cannot modify frozen Term")
super().__setattr__(name, value)
@dataclass
class Atom(Term):
"""Represents Prolog atoms - symbolic constants."""
def __init__(self, name: str):
super().__init__(TermType.ATOM, name)
def __str__(self):
return self.value
def __eq__(self, other):
return isinstance(other, Atom) and self.value == other.value
@dataclass
class Variable(Term):
"""Represents Prolog variables with unique identifiers."""
def __init__(self, name: str, var_id: Optional[int] = None):
import uuid
self.var_id = var_id or uuid.uuid4().int
super().__init__(TermType.VARIABLE, name)
def __str__(self):
return f"{self.value}_{self.var_id}" if self.var_id else self.value
def __eq__(self, other):
return isinstance(other, Variable) and self.var_id == other.var_id
@dataclass
class Number(Term):
"""Represents numeric values in Prolog."""
def __init__(self, value: Union[int, float]):
super().__init__(TermType.NUMBER, value)
def __str__(self):
return str(self.value)
@dataclass
class Compound(Term):
"""Represents compound terms with functor and arguments."""
def __init__(self, functor: str, args: List[Term]):
self.functor = functor
self.arity = len(args)
super().__init__(TermType.COMPOUND, args)
def __str__(self):
if self.arity == 0:
return self.functor
args_str = ", ".join(str(arg) for arg in self.value)
return f"{self.functor}({args_str})"
@dataclass
class PrologList(Term):
"""Represents Prolog lists with head-tail structure."""
def __init__(self, elements: List[Term], tail: Optional[Term] = None):
self.elements = elements
self.tail = tail or Atom("[]")
super().__init__(TermType.LIST, elements)
def __str__(self):
if not self.elements and isinstance(self.tail, Atom) and self.tail.value == "[]":
return "[]"
elements_str = ", ".join(str(elem) for elem in self.elements)
if isinstance(self.tail, Atom) and self.tail.value == "[]":
return f"[{elements_str}]"
return f"[{elements_str}|{self.tail}]"
The clause representation encapsulates Prolog rules and facts with head and body components. The knowledge base maintains an indexed structure for efficient clause retrieval during resolution.
@dataclass
class Clause:
"""Represents Prolog clauses (facts and rules)."""
head: Term
body: Optional[List[Term]] = None
def is_fact(self) -> bool:
"""Check if clause is a fact (no body)."""
return self.body is None or len(self.body) == 0
def is_rule(self) -> bool:
"""Check if clause is a rule (has body)."""
return not self.is_fact()
def __str__(self):
if self.is_fact():
return f"{self.head}."
body_str = ", ".join(str(goal) for goal in self.body)
return f"{self.head} :- {body_str}."
class KnowledgeBase:
"""Efficient storage and retrieval of Prolog clauses."""
def __init__(self):
self.clauses: List[Clause] = []
self.index: Dict[str, List[int]] = {} # functor/arity -> clause indices
self.dynamic_predicates: set = set()
def add_clause(self, clause: Clause):
"""Add clause to knowledge base with indexing."""
self.clauses.append(clause)
clause_index = len(self.clauses) - 1
# Index by head functor/arity
if isinstance(clause.head, Compound):
key = f"{clause.head.functor}/{clause.head.arity}"
elif isinstance(clause.head, Atom):
key = f"{clause.head.value}/0"
else:
key = "unknown/0"
if key not in self.index:
self.index[key] = []
self.index[key].append(clause_index)
def get_clauses_for_goal(self, goal: Term) -> List[Clause]:
"""Retrieve clauses that might unify with the goal."""
if isinstance(goal, Compound):
key = f"{goal.functor}/{goal.arity}"
elif isinstance(goal, Atom):
key = f"{goal.value}/0"
else:
return []
if key in self.index:
return [self.clauses[i] for i in self.index[key]]
return []
def clear(self):
"""Clear all clauses and indices."""
self.clauses.clear()
self.index.clear()
Unification Engine Implementation
The unification engine forms the heart of Prolog's logical inference mechanism. Our implementation handles all standard unification cases including variable binding, compound term unification, list unification, and occurs check prevention.
class Substitution:
"""Represents variable substitutions during unification."""
def __init__(self, bindings: Optional[Dict[int, Term]] = None):
self.bindings = bindings or {}
def bind(self, var: Variable, term: Term) -> 'Substitution':
"""Create new substitution with additional binding."""
new_bindings = self.bindings.copy()
new_bindings[var.var_id] = term
return Substitution(new_bindings)
def lookup(self, var: Variable) -> Optional[Term]:
"""Look up variable binding."""
return self.bindings.get(var.var_id)
def apply(self, term: Term) -> Term:
"""Apply substitution to term."""
if isinstance(term, Variable):
binding = self.lookup(term)
if binding is not None:
# Apply substitution recursively to avoid chains
return self.apply(binding)
return term
elif isinstance(term, Compound):
new_args = [self.apply(arg) for arg in term.value]
return Compound(term.functor, new_args)
elif isinstance(term, PrologList):
new_elements = [self.apply(elem) for elem in term.elements]
new_tail = self.apply(term.tail) if term.tail else None
return PrologList(new_elements, new_tail)
else:
return term
def compose(self, other: 'Substitution') -> 'Substitution':
"""Compose two substitutions."""
new_bindings = {}
# Apply other to all bindings in self
for var_id, term in self.bindings.items():
new_bindings[var_id] = other.apply(term)
# Add bindings from other that are not in self
for var_id, term in other.bindings.items():
if var_id not in new_bindings:
new_bindings[var_id] = term
return Substitution(new_bindings)
class UnificationEngine:
"""Implements Robinson's unification algorithm with occurs check."""
def __init__(self, occurs_check: bool = True):
self.occurs_check = occurs_check
def unify(self, term1: Term, term2: Term,
subst: Optional[Substitution] = None) -> Optional[Substitution]:
"""Unify two terms returning substitution or None if impossible."""
if subst is None:
subst = Substitution()
# Apply current substitution
term1 = subst.apply(term1)
term2 = subst.apply(term2)
# Same term
if self._terms_equal(term1, term2):
return subst
# Variable unification
if isinstance(term1, Variable):
return self._unify_variable(term1, term2, subst)
elif isinstance(term2, Variable):
return self._unify_variable(term2, term1, subst)
# Compound term unification
elif isinstance(term1, Compound) and isinstance(term2, Compound):
return self._unify_compound(term1, term2, subst)
# List unification
elif isinstance(term1, PrologList) and isinstance(term2, PrologList):
return self._unify_list(term1, term2, subst)
# Number unification
elif isinstance(term1, Number) and isinstance(term2, Number):
if term1.value == term2.value:
return subst
# Atom unification
elif isinstance(term1, Atom) and isinstance(term2, Atom):
if term1.value == term2.value:
return subst
return None # Unification failed
def _unify_variable(self, var: Variable, term: Term,
subst: Substitution) -> Optional[Substitution]:
"""Unify variable with term."""
if self.occurs_check and self._occurs_check(var, term, subst):
return None # Occurs check failed
return subst.bind(var, term)
def _unify_compound(self, comp1: Compound, comp2: Compound,
subst: Substitution) -> Optional[Substitution]:
"""Unify compound terms."""
if comp1.functor != comp2.functor or comp1.arity != comp2.arity:
return None
for arg1, arg2 in zip(comp1.value, comp2.value):
subst = self.unify(arg1, arg2, subst)
if subst is None:
return None
return subst
def _unify_list(self, list1: PrologList, list2: PrologList,
subst: Substitution) -> Optional[Substitution]:
"""Unify Prolog lists."""
# Handle empty lists
if not list1.elements and not list2.elements:
return self.unify(list1.tail, list2.tail, subst)
# One list empty, other not
if not list1.elements or not list2.elements:
if not list1.elements:
# list1 is empty, unify with list2
return self.unify(list1.tail, list2, subst)
else:
# list2 is empty, unify with list1
return self.unify(list1, list2.tail, subst)
# Both lists have elements
# Unify first elements
subst = self.unify(list1.elements[0], list2.elements[0], subst)
if subst is None:
return None
# Create tail lists and unify
tail1 = PrologList(list1.elements[1:], list1.tail)
tail2 = PrologList(list2.elements[1:], list2.tail)
return self.unify(tail1, tail2, subst)
def _occurs_check(self, var: Variable, term: Term,
subst: Substitution) -> bool:
"""Check if variable occurs in term (prevents infinite structures)."""
term = subst.apply(term)
if isinstance(term, Variable):
return var.var_id == term.var_id
elif isinstance(term, Compound):
return any(self._occurs_check(var, arg, subst) for arg in term.value)
elif isinstance(term, PrologList):
return (any(self._occurs_check(var, elem, subst) for elem in term.elements) or
self._occurs_check(var, term.tail, subst))
return False
def _terms_equal(self, term1: Term, term2: Term) -> bool:
"""Check if two terms are structurally equal."""
if type(term1) != type(term2):
return False
if isinstance(term1, (Atom, Number)):
return term1.value == term2.value
elif isinstance(term1, Variable):
return term1.var_id == term2.var_id
elif isinstance(term1, Compound):
return (term1.functor == term2.functor and
term1.arity == term2.arity and
all(self._terms_equal(a1, a2) for a1, a2 in zip(term1.value, term2.value)))
elif isinstance(term1, PrologList):
return (len(term1.elements) == len(term2.elements) and
all(self._terms_equal(e1, e2) for e1, e2 in zip(term1.elements, term2.elements)) and
self._terms_equal(term1.tail, term2.tail))
return False
Backtracking and Inference Engine
The inference engine implements SLD resolution with backtracking through a choice point mechanism. This enables Prolog's characteristic search through the solution space with automatic backtracking on failure.
from typing import Generator, Tuple
import copy
@dataclass
class ChoicePoint:
"""Represents a choice point in the search tree."""
goal_stack: List[Term]
substitution: Substitution
clause_alternatives: List[Clause]
parent: Optional['ChoicePoint'] = None
class InferenceEngine:
"""SLD resolution with backtracking for Prolog inference."""
def __init__(self, knowledge_base: KnowledgeBase,
unification_engine: UnificationEngine):
self.kb = knowledge_base
self.unifier = unification_engine
self.call_stack_limit = 1000
def solve(self, goals: List[Term]) -> Generator[Substitution, None, None]:
"""Solve goals using SLD resolution with backtracking."""
initial_choice_point = ChoicePoint(
goal_stack=goals.copy(),
substitution=Substitution(),
clause_alternatives=[]
)
yield from self._solve_recursive(initial_choice_point, 0)
def _solve_recursive(self, choice_point: ChoicePoint,
depth: int) -> Generator[Substitution, None, None]:
"""Recursive solver with depth limiting."""
if depth > self.call_stack_limit:
return # Prevent stack overflow
# Success: no more goals
if not choice_point.goal_stack:
yield choice_point.substitution
return
# Get next goal
current_goal = choice_point.goal_stack[0]
remaining_goals = choice_point.goal_stack[1:]
# Apply current substitution to goal
current_goal = choice_point.substitution.apply(current_goal)
# Handle built-in predicates
if self._is_builtin(current_goal):
builtin_result = self._handle_builtin(current_goal, choice_point.substitution)
if builtin_result is not None:
new_choice_point = ChoicePoint(
goal_stack=remaining_goals,
substitution=builtin_result,
clause_alternatives=[],
parent=choice_point
)
yield from self._solve_recursive(new_choice_point, depth + 1)
return
# Get clauses that might unify with current goal
candidate_clauses = self.kb.get_clauses_for_goal(current_goal)
# Try each clause
for clause in candidate_clauses:
# Rename variables in clause to avoid conflicts
renamed_clause = self._rename_variables(clause)
# Try to unify goal with clause head
unification_result = self.unifier.unify(
current_goal,
renamed_clause.head,
choice_point.substitution
)
if unification_result is not None:
# Create new goal stack
new_goals = remaining_goals.copy()
if renamed_clause.body:
new_goals = renamed_clause.body + new_goals
# Create new choice point
new_choice_point = ChoicePoint(
goal_stack=new_goals,
substitution=unification_result,
clause_alternatives=[],
parent=choice_point
)
# Recursively solve
yield from self._solve_recursive(new_choice_point, depth + 1)
def _rename_variables(self, clause: Clause) -> Clause:
"""Rename all variables in clause to avoid conflicts."""
var_mapping = {}
def rename_term(term: Term) -> Term:
if isinstance(term, Variable):
if term.var_id not in var_mapping:
import uuid
var_mapping[term.var_id] = Variable(term.value, uuid.uuid4().int)
return var_mapping[term.var_id]
elif isinstance(term, Compound):
new_args = [rename_term(arg) for arg in term.value]
return Compound(term.functor, new_args)
elif isinstance(term, PrologList):
new_elements = [rename_term(elem) for elem in term.elements]
new_tail = rename_term(term.tail) if term.tail else None
return PrologList(new_elements, new_tail)
else:
return term
new_head = rename_term(clause.head)
new_body = [rename_term(goal) for goal in clause.body] if clause.body else None
return Clause(new_head, new_body)
def _is_builtin(self, goal: Term) -> bool:
"""Check if goal is a built-in predicate."""
if isinstance(goal, Compound):
return goal.functor in ['=', '\\=', 'is', '>', '<', '>=', '=<',
'==', '\\==', 'cut', 'fail', 'true',
'write', 'nl', 'read', 'assert', 'retract']
elif isinstance(goal, Atom):
return goal.value in ['cut', 'fail', 'true', 'nl']
return False
def _handle_builtin(self, goal: Term, subst: Substitution) -> Optional[Substitution]:
"""Handle built-in predicates."""
if isinstance(goal, Atom):
if goal.value == 'true':
return subst
elif goal.value == 'fail':
return None
elif goal.value == 'nl':
print()
return subst
elif isinstance(goal, Compound):
if goal.functor == '=' and goal.arity == 2:
# Unification
result = self.unifier.unify(goal.value[0], goal.value[1], subst)
return result
elif goal.functor == 'is' and goal.arity == 2:
# Arithmetic evaluation
left = subst.apply(goal.value[0])
right = subst.apply(goal.value[1])
try:
evaluated = self._evaluate_arithmetic(right, subst)
if evaluated is not None:
return self.unifier.unify(left, Number(evaluated), subst)
except:
return None
elif goal.functor == 'write' and goal.arity == 1:
# Output
term = subst.apply(goal.value[0])
print(self._term_to_string(term), end='')
return subst
return None
def _evaluate_arithmetic(self, expr: Term, subst: Substitution) -> Optional[float]:
"""Evaluate arithmetic expressions."""
expr = subst.apply(expr)
if isinstance(expr, Number):
return float(expr.value)
elif isinstance(expr, Compound):
if expr.functor == '+' and expr.arity == 2:
left = self._evaluate_arithmetic(expr.value[0], subst)
right = self._evaluate_arithmetic(expr.value[1], subst)
if left is not None and right is not None:
return left + right
elif expr.functor == '-' and expr.arity == 2:
left = self._evaluate_arithmetic(expr.value[0], subst)
right = self._evaluate_arithmetic(expr.value[1], subst)
if left is not None and right is not None:
return left - right
elif expr.functor == '*' and expr.arity == 2:
left = self._evaluate_arithmetic(expr.value[0], subst)
right = self._evaluate_arithmetic(expr.value[1], subst)
if left is not None and right is not None:
return left * right
elif expr.functor == '/' and expr.arity == 2:
left = self._evaluate_arithmetic(expr.value[0], subst)
right = self._evaluate_arithmetic(expr.value[1], subst)
if left is not None and right is not None and right != 0:
return left / right
return None
def _term_to_string(self, term: Term) -> str:
"""Convert term to string for output."""
if isinstance(term, Atom):
return term.value
elif isinstance(term, Number):
return str(term.value)
elif isinstance(term, Variable):
return f"_{term.var_id}"
elif isinstance(term, Compound):
if term.arity == 0:
return term.functor
args_str = ", ".join(self._term_to_string(arg) for arg in term.value)
return f"{term.functor}({args_str})"
elif isinstance(term, PrologList):
return str(term)
return str(term)
Parser and Syntax Analysis
The parser transforms Prolog source code into the internal term representation. Our implementation uses a recursive descent parser that handles the complete Prolog syntax including operators, lists, and comments.
import re
from typing import List, Optional, Tuple
from enum import Enum
class TokenType(Enum):
ATOM = "ATOM"
VARIABLE = "VARIABLE"
NUMBER = "NUMBER"
STRING = "STRING"
LPAREN = "LPAREN"
RPAREN = "RPAREN"
LBRACKET = "LBRACKET"
RBRACKET = "RBRACKET"
DOT = "DOT"
COMMA = "COMMA"
PIPE = "PIPE"
RULE_OP = "RULE_OP"
CUT = "CUT"
EOF = "EOF"
OPERATOR = "OPERATOR"
@dataclass
class Token:
type: TokenType
value: str
line: int
column: int
class PrologLexer:
"""Tokenizer for Prolog source code."""
def __init__(self, text: str):
self.text = text
self.pos = 0
self.line = 1
self.column = 1
self.tokens = []
# Operator precedence table
self.operators = {
':-': (1200, 'xfx'),
'-->': (1200, 'xfx'),
';': (1100, 'xfy'),
'->': (1050, 'xfy'),
',': (1000, 'xfy'),
'\\+': (900, 'fy'),
'=': (700, 'xfx'),
'\\=': (700, 'xfx'),
'==': (700, 'xfx'),
'\\==': (700, 'xfx'),
'is': (700, 'xfx'),
'>': (700, 'xfx'),
'<': (700, 'xfx'),
'>=': (700, 'xfx'),
'=<': (700, 'xfx'),
'+': (500, 'yfx'),
'-': (500, 'yfx'),
'*': (400, 'yfx'),
'/': (400, 'yfx'),
'mod': (400, 'yfx'),
'**': (200, 'xfx'),
'^': (200, 'xfy'),
}
def tokenize(self) -> List[Token]:
"""Convert source text into tokens."""
while self.pos < len(self.text):
self._skip_whitespace()
if self.pos >= len(self.text):
break
# Comments
if self._current_char() == '%':
self._skip_line_comment()
continue
if self._match('/*'):
self._skip_block_comment()
continue
# Multi-character operators
if self._match(':-'):
self._add_token(TokenType.RULE_OP, ':-')
continue
if self._match('-->'):
self._add_token(TokenType.RULE_OP, '-->')
continue
# Single character tokens
char = self._current_char()
if char == '(':
self._add_token(TokenType.LPAREN, char)
self._advance()
elif char == ')':
self._add_token(TokenType.RPAREN, char)
self._advance()
elif char == '[':
self._add_token(TokenType.LBRACKET, char)
self._advance()
elif char == ']':
self._add_token(TokenType.RBRACKET, char)
self._advance()
elif char == '.':
self._add_token(TokenType.DOT, char)
self._advance()
elif char == ',':
self._add_token(TokenType.COMMA, char)
self._advance()
elif char == '|':
self._add_token(TokenType.PIPE, char)
self._advance()
elif char == '!':
self._add_token(TokenType.CUT, char)
self._advance()
elif char == '"':
self._read_string()
elif char == "'":
self._read_quoted_atom()
elif char.isdigit():
self._read_number()
elif char.isupper() or char == '_':
self._read_variable()
elif char.islower():
self._read_atom()
elif char in '+-*/=<>\\':
self._read_operator()
else:
raise SyntaxError(f"Unexpected character '{char}' at line {self.line}, column {self.column}")
self._add_token(TokenType.EOF, '')
return self.tokens
def _current_char(self) -> str:
"""Get current character."""
if self.pos >= len(self.text):
return ''
return self.text[self.pos]
def _advance(self):
"""Move to next character."""
if self.pos < len(self.text) and self.text[self.pos] == '\n':
self.line += 1
self.column = 1
else:
self.column += 1
self.pos += 1
def _match(self, expected: str) -> bool:
"""Check if current position matches expected string."""
if self.pos + len(expected) > len(self.text):
return False
return self.text[self.pos:self.pos + len(expected)] == expected
def _skip_whitespace(self):
"""Skip whitespace characters."""
while self.pos < len(self.text) and self.text[self.pos].isspace():
self._advance()
def _skip_line_comment(self):
"""Skip line comment starting with %."""
while self.pos < len(self.text) and self.text[self.pos] != '\n':
self._advance()
def _skip_block_comment(self):
"""Skip block comment /* ... */."""
self.pos += 2 # Skip /*
while self.pos < len(self.text) - 1:
if self.text[self.pos:self.pos + 2] == '*/':
self.pos += 2
return
self._advance()
def _read_string(self):
"""Read quoted string."""
start_pos = self.pos
self._advance() # Skip opening quote
value = ''
while self.pos < len(self.text) and self._current_char() != '"':
if self._current_char() == '\\':
self._advance()
if self.pos < len(self.text):
escape_char = self._current_char()
if escape_char == 'n':
value += '\n'
elif escape_char == 't':
value += '\t'
elif escape_char == 'r':
value += '\r'
elif escape_char == '\\':
value += '\\'
elif escape_char == '"':
value += '"'
else:
value += escape_char
self._advance()
else:
value += self._current_char()
self._advance()
if self.pos >= len(self.text):
raise SyntaxError(f"Unterminated string at line {self.line}")
self._advance() # Skip closing quote
self._add_token(TokenType.STRING, value)
def _read_quoted_atom(self):
"""Read quoted atom."""
self._advance() # Skip opening quote
value = ''
while self.pos < len(self.text) and self._current_char() != "'":
if self._current_char() == '\\':
self._advance()
if self.pos < len(self.text):
value += self._current_char()
self._advance()
else:
value += self._current_char()
self._advance()
if self.pos >= len(self.text):
raise SyntaxError(f"Unterminated quoted atom at line {self.line}")
self._advance() # Skip closing quote
self._add_token(TokenType.ATOM, value)
def _read_number(self):
"""Read numeric literal."""
value = ''
has_dot = False
while (self.pos < len(self.text) and
(self._current_char().isdigit() or
(self._current_char() == '.' and not has_dot))):
if self._current_char() == '.':
# Check if next char is digit (decimal point) or not (end of number)
if (self.pos + 1 < len(self.text) and
self.text[self.pos + 1].isdigit()):
has_dot = True
value += self._current_char()
self._advance()
else:
break
else:
value += self._current_char()
self._advance()
self._add_token(TokenType.NUMBER, value)
def _read_variable(self):
"""Read variable identifier."""
value = ''
while (self.pos < len(self.text) and
(self._current_char().isalnum() or self._current_char() == '_')):
value += self._current_char()
self._advance()
self._add_token(TokenType.VARIABLE, value)
def _read_atom(self):
"""Read atom identifier."""
value = ''
while (self.pos < len(self.text) and
(self._current_char().isalnum() or self._current_char() == '_')):
value += self._current_char()
self._advance()
# Check if it's a known operator
if value in self.operators:
self._add_token(TokenType.OPERATOR, value)
else:
self._add_token(TokenType.ATOM, value)
def _read_operator(self):
"""Read operator symbols."""
value = ''
start_pos = self.pos
# Try to match longest operator first
for op in sorted(self.operators.keys(), key=len, reverse=True):
if self._match(op):
value = op
self.pos += len(op)
break
if not value:
# Single character operator
value = self._current_char()
self._advance()
self._add_token(TokenType.OPERATOR, value)
def _add_token(self, token_type: TokenType, value: str):
"""Add token to list."""
self.tokens.append(Token(token_type, value, self.line, self.column))
class PrologParser:
"""Recursive descent parser for Prolog."""
def __init__(self, tokens: List[Token]):
self.tokens = tokens
self.pos = 0
self.variable_counter = 0
self.variable_map = {}
def parse(self) -> List[Clause]:
"""Parse tokens into clauses."""
clauses = []
while not self._is_at_end():
if self._current_token().type == TokenType.EOF:
break
try:
clause = self._parse_clause()
if clause:
clauses.append(clause)
except SyntaxError as e:
print(f"Parse error: {e}")
# Skip to next clause
while not self._is_at_end() and self._current_token().type != TokenType.DOT:
self._advance()
if not self._is_at_end():
self._advance() # Skip the dot
return clauses
def _parse_clause(self) -> Optional[Clause]:
"""Parse a single clause (fact or rule)."""
head = self._parse_term()
if self._match(TokenType.RULE_OP):
# Rule: head :- body
body = self._parse_goal_list()
if not self._match(TokenType.DOT):
raise SyntaxError(f"Expected '.' at end of rule at line {self._current_token().line}")
return Clause(head, body)
elif self._match(TokenType.DOT):
# Fact: head.
return Clause(head)
else:
raise SyntaxError(f"Expected ':-' or '.' after term at line {self._current_token().line}")
def _parse_goal_list(self) -> List[Term]:
"""Parse comma-separated list of goals."""
goals = []
goals.append(self._parse_term())
while self._match(TokenType.COMMA):
goals.append(self._parse_term())
return goals
def _parse_term(self) -> Term:
"""Parse a Prolog term."""
return self._parse_expression(1200) # Maximum precedence
def _parse_expression(self, max_precedence: int) -> Term:
"""Parse expression with operator precedence."""
left = self._parse_primary()
while True:
token = self._current_token()
if (token.type != TokenType.OPERATOR or
token.value not in self._get_operator_info() or
self._get_operator_precedence(token.value) > max_precedence):
break
op = token.value
precedence = self._get_operator_precedence(op)
associativity = self._get_operator_associativity(op)
self._advance() # Consume operator
if associativity == 'xfx':
right = self._parse_expression(precedence - 1)
elif associativity == 'xfy':
right = self._parse_expression(precedence)
elif associativity == 'yfx':
right = self._parse_expression(precedence - 1)
else:
right = self._parse_expression(precedence - 1)
left = Compound(op, [left, right])
return left
def _parse_primary(self) -> Term:
"""Parse primary term (atom, variable, number, compound, list)."""
token = self._current_token()
if token.type == TokenType.ATOM:
self._advance()
# Check for compound term
if self._match(TokenType.LPAREN):
args = []
if not self._check(TokenType.RPAREN):
args.append(self._parse_term())
while self._match(TokenType.COMMA):
args.append(self._parse_term())
if not self._match(TokenType.RPAREN):
raise SyntaxError(f"Expected ')' at line {self._current_token().line}")
return Compound(token.value, args)
else:
return Atom(token.value)
elif token.type == TokenType.VARIABLE:
self._advance()
return self._get_variable(token.value)
elif token.type == TokenType.NUMBER:
self._advance()
if '.' in token.value:
return Number(float(token.value))
else:
return Number(int(token.value))
elif token.type == TokenType.STRING:
self._advance()
# Convert string to list of character codes
char_codes = [Number(ord(c)) for c in token.value]
return PrologList(char_codes)
elif token.type == TokenType.LBRACKET:
return self._parse_list()
elif token.type == TokenType.LPAREN:
self._advance()
term = self._parse_term()
if not self._match(TokenType.RPAREN):
raise SyntaxError(f"Expected ')' at line {self._current_token().line}")
return term
elif token.type == TokenType.CUT:
self._advance()
return Atom('!')
else:
raise SyntaxError(f"Unexpected token '{token.value}' at line {token.line}")
def _parse_list(self) -> PrologList:
"""Parse Prolog list."""
if not self._match(TokenType.LBRACKET):
raise SyntaxError(f"Expected '[' at line {self._current_token().line}")
elements = []
tail = Atom("[]")
if self._match(TokenType.RBRACKET):
# Empty list
return PrologList(elements, tail)
# Parse list elements
elements.append(self._parse_term())
while self._match(TokenType.COMMA):
elements.append(self._parse_term())
# Check for tail
if self._match(TokenType.PIPE):
tail = self._parse_term()
if not self._match(TokenType.RBRACKET):
raise SyntaxError(f"Expected ']' at line {self._current_token().line}")
return PrologList(elements, tail)
def _get_variable(self, name: str) -> Variable:
"""Get or create variable with unique ID."""
if name not in self.variable_map:
self.variable_map[name] = Variable(name, self.variable_counter)
self.variable_counter += 1
return self.variable_map[name]
def _get_operator_info(self) -> dict:
"""Get operator precedence and associativity info."""
return {
':-': (1200, 'xfx'),
'-->': (1200, 'xfx'),
';': (1100, 'xfy'),
'->': (1050, 'xfy'),
',': (1000, 'xfy'),
'=': (700, 'xfx'),
'\\=': (700, 'xfx'),
'is': (700, 'xfx'),
'>': (700, 'xfx'),
'<': (700, 'xfx'),
'>=': (700, 'xfx'),
'=<': (700, 'xfx'),
'+': (500, 'yfx'),
'-': (500, 'yfx'),
'*': (400, 'yfx'),
'/': (400, 'yfx'),
'**': (200, 'xfx'),
}
def _get_operator_precedence(self, op: str) -> int:
"""Get operator precedence."""
info = self._get_operator_info()
return info.get(op, (0, ''))[0]
def _get_operator_associativity(self, op: str) -> str:
"""Get operator associativity."""
info = self._get_operator_info()
return info.get(op, (0, ''))[1]
def _current_token(self) -> Token:
"""Get current token."""
if self.pos >= len(self.tokens):
return Token(TokenType.EOF, '', 0, 0)
return self.tokens[self.pos]
def _advance(self) -> Token:
"""Move to next token."""
if not self._is_at_end():
self.pos += 1
return self._previous_token()
def _is_at_end(self) -> bool:
"""Check if at end of tokens."""
return self.pos >= len(self.tokens) or self._current_token().type == TokenType.EOF
def _previous_token(self) -> Token:
"""Get previous token."""
return self.tokens[self.pos - 1]
def _match(self, *types: TokenType) -> bool:
"""Check if current token matches any of the given types."""
for token_type in types:
if self._check(token_type):
self._advance()
return True
return False
def _check(self, token_type: TokenType) -> bool:
"""Check if current token is of given type."""
if self._is_at_end():
return False
return self._current_token().type == token_type
LLM Integration and AI Features
The Mini Prolog system incorporates Large Language Model integration through a dedicated interface that allows Prolog programs to interact with both local and remote AI models. This enables sophisticated natural language processing and reasoning capabilities within Prolog programs.
import asyncio
import aiohttp
import json
from typing import Dict, Any, Optional, List
from abc import ABC, abstractmethod
class LLMProvider(ABC):
"""Abstract base class for LLM providers."""
@abstractmethod
async def generate(self, prompt: str, **kwargs) -> str:
"""Generate text from prompt."""
pass
@abstractmethod
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""Chat completion with message history."""
pass
class OpenAIProvider(LLMProvider):
"""OpenAI API provider for remote LLM access."""
def __init__(self, api_key: str, model: str = "gpt-3.5-turbo"):
self.api_key = api_key
self.model = model
self.base_url = "https://api.openai.com/v1"
async def generate(self, prompt: str, **kwargs) -> str:
"""Generate text using OpenAI API."""
messages = [{"role": "user", "content": prompt}]
return await self.chat(messages, **kwargs)
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""Chat completion using OpenAI API."""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
data = {
"model": self.model,
"messages": messages,
"temperature": kwargs.get("temperature", 0.7),
"max_tokens": kwargs.get("max_tokens", 1000)
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=data
) as response:
if response.status == 200:
result = await response.json()
return result["choices"][0]["message"]["content"]
else:
raise Exception(f"OpenAI API error: {response.status}")
class LocalLLMProvider(LLMProvider):
"""Local LLM provider using Ollama or similar."""
def __init__(self, base_url: str = "http://localhost:11434", model: str = "llama2"):
self.base_url = base_url
self.model = model
async def generate(self, prompt: str, **kwargs) -> str:
"""Generate text using local LLM."""
data = {
"model": self.model,
"prompt": prompt,
"stream": False,
"options": {
"temperature": kwargs.get("temperature", 0.7),
"num_predict": kwargs.get("max_tokens", 1000)
}
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/api/generate",
json=data
) as response:
if response.status == 200:
result = await response.json()
return result["response"]
else:
raise Exception(f"Local LLM error: {response.status}")
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""Chat completion using local LLM."""
# Convert messages to single prompt
prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
return await self.generate(prompt, **kwargs)
class LLMManager:
"""Manages multiple LLM providers and handles requests."""
def __init__(self):
self.providers: Dict[str, LLMProvider] = {}
self.default_provider = None
def register_provider(self, name: str, provider: LLMProvider, is_default: bool = False):
"""Register an LLM provider."""
self.providers[name] = provider
if is_default or self.default_provider is None:
self.default_provider = name
async def generate(self, prompt: str, provider: Optional[str] = None, **kwargs) -> str:
"""Generate text using specified or default provider."""
provider_name = provider or self.default_provider
if provider_name not in self.providers:
raise ValueError(f"Unknown provider: {provider_name}")
return await self.providers[provider_name].generate(prompt, **kwargs)
async def chat(self, messages: List[Dict[str, str]],
provider: Optional[str] = None, **kwargs) -> str:
"""Chat completion using specified or default provider."""
provider_name = provider or self.default_provider
if provider_name not in self.providers:
raise ValueError(f"Unknown provider: {provider_name}")
return await self.providers[provider_name].chat(messages, **kwargs)
class AIBuiltins:
"""Built-in predicates for AI functionality."""
def __init__(self, llm_manager: LLMManager):
self.llm_manager = llm_manager
self.conversation_history: Dict[str, List[Dict[str, str]]] = {}
async def handle_llm_generate(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle llm_generate/3 predicate: llm_generate(Prompt, Response, Options)."""
if len(args) != 3:
return None
prompt_term = subst.apply(args[0])
response_var = subst.apply(args[1])
options_term = subst.apply(args[2])
# Extract prompt string
if isinstance(prompt_term, Atom):
prompt = prompt_term.value
elif isinstance(prompt_term, PrologList):
# Convert character list to string
prompt = self._list_to_string(prompt_term)
else:
return None
# Extract options
options = self._extract_options(options_term)
try:
# Generate response
response = await self.llm_manager.generate(prompt, **options)
response_term = Atom(response)
# Unify with response variable
from .unification import UnificationEngine
unifier = UnificationEngine()
return unifier.unify(response_var, response_term, subst)
except Exception as e:
print(f"LLM generation error: {e}")
return None
async def handle_llm_chat(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle llm_chat/4 predicate: llm_chat(SessionId, Message, Response, Options)."""
if len(args) != 4:
return None
session_term = subst.apply(args[0])
message_term = subst.apply(args[1])
response_var = subst.apply(args[2])
options_term = subst.apply(args[3])
# Extract session ID
if isinstance(session_term, Atom):
session_id = session_term.value
else:
return None
# Extract message
if isinstance(message_term, Atom):
message = message_term.value
elif isinstance(message_term, PrologList):
message = self._list_to_string(message_term)
else:
return None
# Get or create conversation history
if session_id not in self.conversation_history:
self.conversation_history[session_id] = []
# Add user message to history
self.conversation_history[session_id].append({
"role": "user",
"content": message
})
# Extract options
options = self._extract_options(options_term)
try:
# Generate response
response = await self.llm_manager.chat(
self.conversation_history[session_id],
**options
)
# Add assistant response to history
self.conversation_history[session_id].append({
"role": "assistant",
"content": response
})
response_term = Atom(response)
# Unify with response variable
from .unification import UnificationEngine
unifier = UnificationEngine()
return unifier.unify(response_var, response_term, subst)
except Exception as e:
print(f"LLM chat error: {e}")
return None
async def handle_llm_embed(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle llm_embed/3 predicate: llm_embed(Text, Embedding, Options)."""
# Placeholder for embedding functionality
# Would integrate with embedding models like OpenAI's text-embedding-ada-002
return None
def _list_to_string(self, prolog_list: PrologList) -> str:
"""Convert Prolog character list to Python string."""
chars = []
for elem in prolog_list.elements:
if isinstance(elem, Number):
chars.append(chr(int(elem.value)))
elif isinstance(elem, Atom) and len(elem.value) == 1:
chars.append(elem.value)
return ''.join(chars)
def _extract_options(self, options_term: Term) -> Dict[str, Any]:
"""Extract options from Prolog term."""
options = {}
if isinstance(options_term, PrologList):
for elem in options_term.elements:
if isinstance(elem, Compound) and elem.arity == 2:
key = elem.value[0]
value = elem.value[1]
if isinstance(key, Atom):
if isinstance(value, Number):
options[key.value] = value.value
elif isinstance(value, Atom):
options[key.value] = value.value
return options
# Integration with inference engine
class AIInferenceEngine(InferenceEngine):
"""Extended inference engine with AI capabilities."""
def __init__(self, knowledge_base: KnowledgeBase,
unification_engine: UnificationEngine,
llm_manager: LLMManager):
super().__init__(knowledge_base, unification_engine)
self.ai_builtins = AIBuiltins(llm_manager)
def _is_builtin(self, goal: Term) -> bool:
"""Check if goal is a built-in predicate including AI predicates."""
if super()._is_builtin(goal):
return True
if isinstance(goal, Compound):
return goal.functor in ['llm_generate', 'llm_chat', 'llm_embed',
'nlp_parse', 'nlp_sentiment', 'nlp_entities']
return False
async def _handle_builtin_async(self, goal: Term, subst: Substitution) -> Optional[Substitution]:
"""Handle built-in predicates including async AI predicates."""
if isinstance(goal, Compound):
if goal.functor == 'llm_generate' and goal.arity == 3:
return await self.ai_builtins.handle_llm_generate(goal.value, subst)
elif goal.functor == 'llm_chat' and goal.arity == 4:
return await self.ai_builtins.handle_llm_chat(goal.value, subst)
elif goal.functor == 'llm_embed' and goal.arity == 3:
return await self.ai_builtins.handle_llm_embed(goal.value, subst)
# Fall back to synchronous built-ins
return self._handle_builtin(goal, subst)
Concurrency and Threading Support
The Mini Prolog system incorporates sophisticated concurrency features that enable parallel execution of goals, thread-safe knowledge base operations, and inter-thread communication through message passing.
import threading
import queue
import time
from concurrent.futures import ThreadPoolExecutor, Future
from typing import Set, Dict, List, Optional, Callable
import uuid
class ThreadSafeKnowledgeBase(KnowledgeBase):
"""Thread-safe version of knowledge base with read-write locks."""
def __init__(self):
super().__init__()
self._lock = threading.RWLock()
self._readers = 0
self._writers = 0
self._read_ready = threading.Condition(threading.Lock())
self._write_ready = threading.Condition(threading.Lock())
def add_clause(self, clause: Clause):
"""Thread-safe clause addition."""
with self._write_ready:
while self._readers > 0 or self._writers > 0:
self._write_ready.wait()
self._writers += 1
try:
super().add_clause(clause)
finally:
with self._write_ready:
self._writers -= 1
self._write_ready.notify_all()
def get_clauses_for_goal(self, goal: Term) -> List[Clause]:
"""Thread-safe clause retrieval."""
with self._read_ready:
while self._writers > 0:
self._read_ready.wait()
self._readers += 1
try:
return super().get_clauses_for_goal(goal)
finally:
with self._read_ready:
self._readers -= 1
if self._readers == 0:
self._read_ready.notify_all()
class Message:
"""Inter-thread message for communication."""
def __init__(self, sender: str, recipient: str, content: Term,
message_id: Optional[str] = None):
self.sender = sender
self.recipient = recipient
self.content = content
self.message_id = message_id or str(uuid.uuid4())
self.timestamp = time.time()
class MessageQueue:
"""Thread-safe message queue for inter-thread communication."""
def __init__(self, maxsize: int = 0):
self.queue = queue.Queue(maxsize)
self.subscribers: Set[str] = set()
self.lock = threading.Lock()
def send(self, message: Message, timeout: Optional[float] = None):
"""Send message to queue."""
try:
self.queue.put(message, timeout=timeout)
except queue.Full:
raise Exception(f"Message queue full for recipient {message.recipient}")
def receive(self, timeout: Optional[float] = None) -> Optional[Message]:
"""Receive message from queue."""
try:
return self.queue.get(timeout=timeout)
except queue.Empty:
return None
def subscribe(self, thread_id: str):
"""Subscribe thread to message queue."""
with self.lock:
self.subscribers.add(thread_id)
def unsubscribe(self, thread_id: str):
"""Unsubscribe thread from message queue."""
with self.lock:
self.subscribers.discard(thread_id)
class ThreadManager:
"""Manages Prolog threads and inter-thread communication."""
def __init__(self):
self.threads: Dict[str, threading.Thread] = {}
self.message_queues: Dict[str, MessageQueue] = {}
self.thread_pool = ThreadPoolExecutor(max_workers=10)
self.global_queue = MessageQueue()
self.lock = threading.Lock()
def create_thread(self, thread_id: str, goals: List[Term],
knowledge_base: ThreadSafeKnowledgeBase) -> str:
"""Create new Prolog thread."""
if thread_id in self.threads:
raise ValueError(f"Thread {thread_id} already exists")
# Create message queue for thread
self.message_queues[thread_id] = MessageQueue()
# Create and start thread
thread = threading.Thread(
target=self._run_thread,
args=(thread_id, goals, knowledge_base),
daemon=True
)
with self.lock:
self.threads[thread_id] = thread
thread.start()
return thread_id
def _run_thread(self, thread_id: str, goals: List[Term],
knowledge_base: ThreadSafeKnowledgeBase):
"""Run Prolog thread with goals."""
try:
# Create thread-local inference engine
unifier = UnificationEngine()
engine = ConcurrentInferenceEngine(
knowledge_base, unifier, self, thread_id
)
# Solve goals
solutions = list(engine.solve(goals))
# Send completion message
completion_msg = Message(
sender=thread_id,
recipient="main",
content=Compound("thread_completed", [
Atom(thread_id),
Number(len(solutions))
])
)
self.global_queue.send(completion_msg)
except Exception as e:
# Send error message
error_msg = Message(
sender=thread_id,
recipient="main",
content=Compound("thread_error", [
Atom(thread_id),
Atom(str(e))
])
)
self.global_queue.send(error_msg)
finally:
# Clean up
with self.lock:
if thread_id in self.threads:
del self.threads[thread_id]
if thread_id in self.message_queues:
del self.message_queues[thread_id]
def send_message(self, sender: str, recipient: str, content: Term):
"""Send message between threads."""
message = Message(sender, recipient, content)
if recipient == "all":
# Broadcast to all threads
for queue in self.message_queues.values():
queue.send(message)
elif recipient in self.message_queues:
# Send to specific thread
self.message_queues[recipient].send(message)
else:
# Send to global queue
self.global_queue.send(message)
def receive_message(self, thread_id: str, timeout: Optional[float] = None) -> Optional[Message]:
"""Receive message for thread."""
if thread_id in self.message_queues:
return self.message_queues[thread_id].receive(timeout)
return None
def join_thread(self, thread_id: str, timeout: Optional[float] = None):
"""Wait for thread to complete."""
if thread_id in self.threads:
self.threads[thread_id].join(timeout)
def terminate_thread(self, thread_id: str):
"""Terminate thread (best effort)."""
# Note: Python doesn't support forceful thread termination
# This would require cooperative termination
pass
class ConcurrentInferenceEngine(InferenceEngine):
"""Inference engine with concurrency support."""
def __init__(self, knowledge_base: ThreadSafeKnowledgeBase,
unification_engine: UnificationEngine,
thread_manager: ThreadManager,
thread_id: str):
super().__init__(knowledge_base, unification_engine)
self.thread_manager = thread_manager
self.thread_id = thread_id
def _is_builtin(self, goal: Term) -> bool:
"""Check for concurrency built-ins."""
if super()._is_builtin(goal):
return True
if isinstance(goal, Compound):
return goal.functor in ['spawn', 'send', 'receive', 'join_thread',
'parallel_and', 'parallel_or', 'mutex_lock',
'mutex_unlock', 'thread_self']
return False
def _handle_builtin(self, goal: Term, subst: Substitution) -> Optional[Substitution]:
"""Handle concurrency built-ins."""
if isinstance(goal, Compound):
if goal.functor == 'spawn' and goal.arity == 2:
return self._handle_spawn(goal.value, subst)
elif goal.functor == 'send' and goal.arity == 2:
return self._handle_send(goal.value, subst)
elif goal.functor == 'receive' and goal.arity == 1:
return self._handle_receive(goal.value, subst)
elif goal.functor == 'thread_self' and goal.arity == 1:
return self._handle_thread_self(goal.value, subst)
elif goal.functor == 'parallel_and' and goal.arity == 1:
return self._handle_parallel_and(goal.value, subst)
return super()._handle_builtin(goal, subst)
def _handle_spawn(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle spawn/2: spawn(Goals, ThreadId)."""
if len(args) != 2:
return None
goals_term = subst.apply(args[0])
thread_var = subst.apply(args[1])
# Extract goals
if isinstance(goals_term, PrologList):
goals = goals_term.elements
else:
goals = [goals_term]
# Create new thread
new_thread_id = f"thread_{uuid.uuid4().hex[:8]}"
self.thread_manager.create_thread(new_thread_id, goals, self.kb)
# Unify with thread ID variable
return self.unifier.unify(thread_var, Atom(new_thread_id), subst)
def _handle_send(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle send/2: send(Recipient, Message)."""
if len(args) != 2:
return None
recipient_term = subst.apply(args[0])
message_term = subst.apply(args[1])
if isinstance(recipient_term, Atom):
recipient = recipient_term.value
self.thread_manager.send_message(self.thread_id, recipient, message_term)
return subst
return None
def _handle_receive(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle receive/1: receive(Message)."""
if len(args) != 1:
return None
message_var = subst.apply(args[0])
# Receive message (blocking)
message = self.thread_manager.receive_message(self.thread_id, timeout=1.0)
if message:
return self.unifier.unify(message_var, message.content, subst)
return None
def _handle_thread_self(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle thread_self/1: thread_self(ThreadId)."""
if len(args) != 1:
return None
thread_var = subst.apply(args[0])
return self.unifier.unify(thread_var, Atom(self.thread_id), subst)
def _handle_parallel_and(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle parallel_and/1: execute goals in parallel, succeed if all succeed."""
if len(args) != 1:
return None
goals_term = subst.apply(args[0])
if isinstance(goals_term, PrologList):
goals = goals_term.elements
else:
goals = [goals_term]
# Execute goals in parallel using thread pool
futures = []
for goal in goals:
future = self.thread_manager.thread_pool.submit(
self._solve_goal_parallel, goal, subst
)
futures.append(future)
# Wait for all to complete
results = []
for future in futures:
try:
result = future.result(timeout=10.0)
if result is None:
return None # One goal failed
results.append(result)
except Exception:
return None
# Compose all substitutions
final_subst = subst
for result_subst in results:
final_subst = final_subst.compose(result_subst)
return final_subst
def _solve_goal_parallel(self, goal: Term, subst: Substitution) -> Optional[Substitution]:
"""Solve single goal in parallel context."""
# Create new inference engine for this goal
engine = ConcurrentInferenceEngine(
self.kb, self.unifier, self.thread_manager,
f"{self.thread_id}_parallel_{uuid.uuid4().hex[:4]}"
)
# Get first solution
solutions = list(engine.solve([goal]))
return solutions[0] if solutions else None
Python Integration Interface
The Python integration interface enables seamless interoperability between Prolog and Python code, allowing Prolog programs to call Python functions and use Python libraries while maintaining type safety and error handling.
import sys
import importlib
import inspect
from typing import Any, Dict, List, Callable, Optional
import traceback
class PythonInterface:
"""Interface for calling Python code from Prolog."""
def __init__(self):
self.imported_modules: Dict[str, Any] = {}
self.registered_functions: Dict[str, Callable] = {}
self.type_converters = {
'atom_to_str': self._atom_to_string,
'str_to_atom': self._string_to_atom,
'list_to_pylist': self._prolog_list_to_python,
'pylist_to_list': self._python_to_prolog_list,
'number_to_py': self._number_to_python,
'py_to_number': self._python_to_number,
}
def import_module(self, module_name: str, alias: Optional[str] = None) -> bool:
"""Import Python module for use in Prolog."""
try:
module = importlib.import_module(module_name)
key = alias or module_name
self.imported_modules[key] = module
return True
except ImportError as e:
print(f"Failed to import module {module_name}: {e}")
return False
def register_function(self, name: str, func: Callable) -> bool:
"""Register Python function for Prolog access."""
try:
self.registered_functions[name] = func
return True
except Exception as e:
print(f"Failed to register function {name}: {e}")
return False
def call_python_function(self, module_name: str, function_name: str,
args: List[Term]) -> Optional[Term]:
"""Call Python function from Prolog."""
try:
# Get module
if module_name in self.imported_modules:
module = self.imported_modules[module_name]
elif module_name in self.registered_functions:
func = self.registered_functions[module_name]
return self._call_function_with_args(func, args)
else:
print(f"Module {module_name} not imported")
return None
# Get function
if not hasattr(module, function_name):
print(f"Function {function_name} not found in module {module_name}")
return None
func = getattr(module, function_name)
return self._call_function_with_args(func, args)
except Exception as e:
print(f"Error calling Python function {module_name}.{function_name}: {e}")
traceback.print_exc()
return None
def _call_function_with_args(self, func: Callable, args: List[Term]) -> Optional[Term]:
"""Call function with converted arguments."""
try:
# Convert Prolog terms to Python objects
py_args = []
for arg in args:
py_arg = self._term_to_python(arg)
py_args.append(py_arg)
# Call function
result = func(*py_args)
# Convert result back to Prolog term
return self._python_to_term(result)
except Exception as e:
print(f"Error in function call: {e}")
return None
def _term_to_python(self, term: Term) -> Any:
"""Convert Prolog term to Python object."""
if isinstance(term, Atom):
return term.value
elif isinstance(term, Number):
return term.value
elif isinstance(term, Variable):
# Variables become None in Python context
return None
elif isinstance(term, PrologList):
return [self._term_to_python(elem) for elem in term.elements]
elif isinstance(term, Compound):
# Convert compound to dictionary or tuple
if term.functor == 'dict' and term.arity > 0:
# Special handling for dictionary representation
result = {}
for arg in term.value:
if isinstance(arg, Compound) and arg.functor == '=' and arg.arity == 2:
key = self._term_to_python(arg.value[0])
value = self._term_to_python(arg.value[1])
result[key] = value
return result
else:
# Convert to tuple (functor, args)
return (term.functor, [self._term_to_python(arg) for arg in term.value])
else:
return str(term)
def _python_to_term(self, obj: Any) -> Term:
"""Convert Python object to Prolog term."""
if obj is None:
return Atom('none')
elif isinstance(obj, bool):
return Atom('true' if obj else 'false')
elif isinstance(obj, (int, float)):
return Number(obj)
elif isinstance(obj, str):
return Atom(obj)
elif isinstance(obj, (list, tuple)):
elements = [self._python_to_term(item) for item in obj]
return PrologList(elements)
elif isinstance(obj, dict):
# Convert dictionary to compound term
pairs = []
for key, value in obj.items():
key_term = self._python_to_term(key)
value_term = self._python_to_term(value)
pairs.append(Compound('=', [key_term, value_term]))
return Compound('dict', pairs)
else:
# Convert other objects to string representation
return Atom(str(obj))
def _atom_to_string(self, atom: Atom) -> str:
"""Convert Prolog atom to Python string."""
return atom.value
def _string_to_atom(self, s: str) -> Atom:
"""Convert Python string to Prolog atom."""
return Atom(s)
def _prolog_list_to_python(self, prolog_list: PrologList) -> List[Any]:
"""Convert Prolog list to Python list."""
return [self._term_to_python(elem) for elem in prolog_list.elements]
def _python_to_prolog_list(self, py_list: List[Any]) -> PrologList:
"""Convert Python list to Prolog list."""
elements = [self._python_to_term(item) for item in py_list]
return PrologList(elements)
def _number_to_python(self, number: Number) -> float:
"""Convert Prolog number to Python number."""
return float(number.value)
def _python_to_number(self, num: float) -> Number:
"""Convert Python number to Prolog number."""
return Number(num)
class PythonBuiltins:
"""Built-in predicates for Python integration."""
def __init__(self, python_interface: PythonInterface):
self.py_interface = python_interface
def handle_py_import(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle py_import/1 or py_import/2: py_import(Module) or py_import(Module, Alias)."""
if len(args) not in [1, 2]:
return None
module_term = subst.apply(args[0])
if not isinstance(module_term, Atom):
return None
module_name = module_term.value
alias = None
if len(args) == 2:
alias_term = subst.apply(args[1])
if isinstance(alias_term, Atom):
alias = alias_term.value
success = self.py_interface.import_module(module_name, alias)
return subst if success else None
def handle_py_call(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle py_call/3: py_call(Module, Function, Args) or py_call/4 with Result."""
if len(args) not in [3, 4]:
return None
module_term = subst.apply(args[0])
function_term = subst.apply(args[1])
args_term = subst.apply(args[2])
if not isinstance(module_term, Atom) or not isinstance(function_term, Atom):
return None
module_name = module_term.value
function_name = function_term.value
# Extract arguments
if isinstance(args_term, PrologList):
call_args = args_term.elements
else:
call_args = [args_term]
# Call Python function
result = self.py_interface.call_python_function(module_name, function_name, call_args)
if result is None:
return None
if len(args) == 4:
# Unify result with fourth argument
result_var = subst.apply(args[3])
from .unification import UnificationEngine
unifier = UnificationEngine()
return unifier.unify(result_var, result, subst)
else:
# Just succeed if no result variable
return subst
def handle_py_eval(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle py_eval/2: py_eval(Expression, Result)."""
if len(args) != 2:
return None
expr_term = subst.apply(args[0])
result_var = subst.apply(args[1])
if not isinstance(expr_term, Atom):
return None
try:
# Evaluate Python expression
result = eval(expr_term.value)
result_term = self.py_interface._python_to_term(result)
# Unify with result variable
from .unification import UnificationEngine
unifier = UnificationEngine()
return unifier.unify(result_var, result_term, subst)
except Exception as e:
print(f"Python evaluation error: {e}")
return None
def handle_py_exec(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle py_exec/1: py_exec(Code)."""
if len(args) != 1:
return None
code_term = subst.apply(args[0])
if isinstance(code_term, Atom):
code = code_term.value
elif isinstance(code_term, PrologList):
# Convert character list to string
code = ''.join([chr(int(elem.value)) for elem in code_term.elements
if isinstance(elem, Number)])
else:
return None
try:
# Execute Python code
exec(code)
return subst
except Exception as e:
print(f"Python execution error: {e}")
return None
# Integration with main inference engine
class PythonIntegratedInferenceEngine(InferenceEngine):
"""Inference engine with Python integration."""
def __init__(self, knowledge_base: KnowledgeBase,
unification_engine: UnificationEngine,
python_interface: PythonInterface):
super().__init__(knowledge_base, unification_engine)
self.python_builtins = PythonBuiltins(python_interface)
def _is_builtin(self, goal: Term) -> bool:
"""Check for Python integration built-ins."""
if super()._is_builtin(goal):
return True
if isinstance(goal, Compound):
return goal.functor in ['py_import', 'py_call', 'py_eval', 'py_exec',
'py_convert', 'py_type', 'py_dir']
return False
def _handle_builtin(self, goal: Term, subst: Substitution) -> Optional[Substitution]:
"""Handle Python integration built-ins."""
if isinstance(goal, Compound):
if goal.functor == 'py_import':
return self.python_builtins.handle_py_import(goal.value, subst)
elif goal.functor == 'py_call':
return self.python_builtins.handle_py_call(goal.value, subst)
elif goal.functor == 'py_eval':
return self.python_builtins.handle_py_eval(goal.value, subst)
elif goal.functor == 'py_exec':
return self.python_builtins.handle_py_exec(goal.value, subst)
return super()._handle_builtin(goal, subst)
```
REPL Interface and File Management
The Read-Eval-Print Loop provides an interactive interface for the Mini Prolog system with comprehensive file management, debugging capabilities, and user-friendly command processing.
import os
import sys
import readline
import atexit
from pathlib import Path
from typing import List, Optional, Dict, Any
import json
class PrologREPL:
"""Interactive Read-Eval-Print Loop for Mini Prolog."""
def __init__(self, knowledge_base: KnowledgeBase,
inference_engine: InferenceEngine,
python_interface: PythonInterface,
llm_manager: Optional[LLMManager] = None):
self.kb = knowledge_base
self.engine = inference_engine
self.py_interface = python_interface
self.llm_manager = llm_manager
# REPL state
self.running = True
self.debug_mode = False
self.trace_mode = False
self.loaded_files: List[str] = []
self.history_file = os.path.expanduser("~/.miniprolog_history")
# Command handlers
self.commands = {
'help': self._cmd_help,
'quit': self._cmd_quit,
'exit': self._cmd_quit,
'load': self._cmd_load,
'save': self._cmd_save,
'listing': self._cmd_listing,
'clear': self._cmd_clear,
'debug': self._cmd_debug,
'trace': self._cmd_trace,
'files': self._cmd_files,
'reload': self._cmd_reload,
'consult': self._cmd_load, # Alias for load
'statistics': self._cmd_statistics,
'py_import': self._cmd_py_import,
'llm_setup': self._cmd_llm_setup,
}
# Setup readline
self._setup_readline()
def _setup_readline(self):
"""Setup readline for command history and completion."""
try:
readline.read_history_file(self.history_file)
except FileNotFoundError:
pass
# Set up completion
readline.set_completer(self._completer)
readline.parse_and_bind('tab: complete')
# Save history on exit
atexit.register(self._save_history)
def _save_history(self):
"""Save command history."""
try:
readline.write_history_file(self.history_file)
except Exception:
pass
def _completer(self, text: str, state: int) -> Optional[str]:
"""Tab completion for commands and predicates."""
if state == 0:
# Get current line
line = readline.get_line_buffer()
# Complete commands
if line.startswith(':'):
command_text = line[1:]
self.completion_matches = [
f":{cmd}" for cmd in self.commands.keys()
if cmd.startswith(command_text)
]
else:
# Complete predicates (simplified)
self.completion_matches = []
try:
return self.completion_matches[state]
except IndexError:
return None
def run(self):
"""Main REPL loop."""
print("Mini Prolog System v1.0")
print("Type ':help' for help, ':quit' to exit.")
print()
while self.running:
try:
# Get input
prompt = "?- " if not self.debug_mode else "debug ?- "
line = input(prompt).strip()
if not line:
continue
# Handle commands
if line.startswith(':'):
self._handle_command(line[1:])
else:
# Handle query
self._handle_query(line)
except KeyboardInterrupt:
print("\nInterrupted.")
continue
except EOFError:
print("\nGoodbye!")
break
except Exception as e:
print(f"Error: {e}")
if self.debug_mode:
import traceback
traceback.print_exc()
def _handle_command(self, command_line: str):
"""Handle REPL commands."""
parts = command_line.split()
if not parts:
return
command = parts[0]
args = parts[1:]
if command in self.commands:
try:
self.commands[command](args)
except Exception as e:
print(f"Command error: {e}")
else:
print(f"Unknown command: {command}")
print("Type ':help' for available commands.")
def _handle_query(self, query_text: str):
"""Handle Prolog query."""
try:
# Parse query
lexer = PrologLexer(query_text)
tokens = lexer.tokenize()
parser = PrologParser(tokens)
# Parse as goals (not clauses)
goals = self._parse_query_goals(tokens)
if not goals:
print("Parse error in query.")
return
# Solve query
solutions = list(self.engine.solve(goals))
if not solutions:
print("false.")
else:
for i, solution in enumerate(solutions):
if i > 0:
print(" ;")
# Display variable bindings
bindings = self._extract_variable_bindings(solution, goals)
if bindings:
for var_name, value in bindings.items():
print(f"{var_name} = {value}")
else:
print("true")
# Ask for more solutions
if i < len(solutions) - 1:
try:
response = input(" ? ").strip().lower()
if response in ['n', 'no', 'q', 'quit']:
break
except KeyboardInterrupt:
break
print(".")
except Exception as e:
print(f"Query error: {e}")
if self.debug_mode:
import traceback
traceback.print_exc()
def _parse_query_goals(self, tokens: List[Token]) -> List[Term]:
"""Parse query tokens into goals."""
# Remove final dot if present
if tokens and tokens[-2].type == TokenType.DOT:
tokens = tokens[:-2] + [tokens[-1]] # Keep EOF
parser = PrologParser(tokens)
try:
# Parse as comma-separated goals
goals = []
if not parser._is_at_end():
goals.append(parser._parse_term())
while parser._match(TokenType.COMMA):
goals.append(parser._parse_term())
return goals
except:
return []
def _extract_variable_bindings(self, substitution: Substitution,
goals: List[Term]) -> Dict[str, str]:
"""Extract variable bindings for display."""
bindings = {}
# Find all variables in original goals
variables = set()
for goal in goals:
variables.update(self._find_variables(goal))
# Get bindings for these variables
for var in variables:
if var.var_id in substitution.bindings:
bound_term = substitution.bindings[var.var_id]
# Apply substitution recursively
final_term = substitution.apply(bound_term)
bindings[var.value] = str(final_term)
return bindings
def _find_variables(self, term: Term) -> set:
"""Find all variables in a term."""
variables = set()
if isinstance(term, Variable):
variables.add(term)
elif isinstance(term, Compound):
for arg in term.value:
variables.update(self._find_variables(arg))
elif isinstance(term, PrologList):
for elem in term.elements:
variables.update(self._find_variables(elem))
if term.tail:
variables.update(self._find_variables(term.tail))
return variables
# Command implementations
def _cmd_help(self, args: List[str]):
"""Display help information."""
print("Available commands:")
print(" :help - Show this help")
print(" :quit, :exit - Exit the system")
print(" :load <file> - Load Prolog file")
print(" :save <file> - Save current knowledge base")
print(" :listing - List all clauses")
print(" :listing <predicate> - List clauses for predicate")
print(" :clear - Clear knowledge base")
print(" :debug - Toggle debug mode")
print(" :trace - Toggle trace mode")
print(" :files - List loaded files")
print(" :reload <file> - Reload file")
print(" :statistics - Show system statistics")
print(" :py_import <module> - Import Python module")
print(" :llm_setup - Setup LLM providers")
print()
print("Query syntax:")
print(" predicate(arg1, arg2). - Query")
print(" X = 5, Y is X + 1. - Multiple goals")
print(" [H|T] = [1,2,3]. - List unification")
def _cmd_quit(self, args: List[str]):
"""Exit the REPL."""
self.running = False
def _cmd_load(self, args: List[str]):
"""Load Prolog file."""
if not args:
print("Usage: :load <filename>")
return
filename = args[0]
if not filename.endswith('.pl'):
filename += '.pl'
try:
with open(filename, 'r') as f:
content = f.read()
# Parse and load clauses
lexer = PrologLexer(content)
tokens = lexer.tokenize()
parser = PrologParser(tokens)
clauses = parser.parse()
# Add clauses to knowledge base
for clause in clauses:
self.kb.add_clause(clause)
self.loaded_files.append(filename)
print(f"Loaded {len(clauses)} clauses from {filename}")
except FileNotFoundError:
print(f"File not found: {filename}")
except Exception as e:
print(f"Error loading file: {e}")
def _cmd_save(self, args: List[str]):
"""Save knowledge base to file."""
if not args:
print("Usage: :save <filename>")
return
filename = args[0]
if not filename.endswith('.pl'):
filename += '.pl'
try:
with open(filename, 'w') as f:
for clause in self.kb.clauses:
f.write(str(clause) + '\n')
print(f"Saved {len(self.kb.clauses)} clauses to {filename}")
except Exception as e:
print(f"Error saving file: {e}")
def _cmd_listing(self, args: List[str]):
"""List clauses."""
if args:
# List specific predicate
predicate = args[0]
if '/' not in predicate:
# Find all arities
found = False
for key in self.kb.index:
if key.startswith(predicate + '/'):
indices = self.kb.index[key]
for idx in indices:
print(self.kb.clauses[idx])
found = True
if not found:
print(f"No clauses for predicate: {predicate}")
else:
# Specific predicate/arity
if predicate in self.kb.index:
indices = self.kb.index[predicate]
for idx in indices:
print(self.kb.clauses[idx])
else:
print(f"No clauses for predicate: {predicate}")
else:
# List all clauses
if not self.kb.clauses:
print("No clauses in knowledge base.")
else:
for clause in self.kb.clauses:
print(clause)
def _cmd_clear(self, args: List[str]):
"""Clear knowledge base."""
self.kb.clear()
self.loaded_files.clear()
print("Knowledge base cleared.")
def _cmd_debug(self, args: List[str]):
"""Toggle debug mode."""
self.debug_mode = not self.debug_mode
print(f"Debug mode: {'on' if self.debug_mode else 'off'}")
def _cmd_trace(self, args: List[str]):
"""Toggle trace mode."""
self.trace_mode = not self.trace_mode
print(f"Trace mode: {'on' if self.trace_mode else 'off'}")
def _cmd_files(self, args: List[str]):
"""List loaded files."""
if not self.loaded_files:
print("No files loaded.")
else:
print("Loaded files:")
for filename in self.loaded_files:
print(f" {filename}")
def _cmd_reload(self, args: List[str]):
"""Reload file."""
if not args:
print("Usage: :reload <filename>")
return
filename = args[0]
if filename in self.loaded_files:
# Clear clauses from this file (simplified)
self.kb.clear()
self.loaded_files.clear()
# Reload
self._cmd_load([filename])
else:
print(f"File {filename} not previously loaded.")
def _cmd_statistics(self, args: List[str]):
"""Show system statistics."""
print(f"Clauses in knowledge base: {len(self.kb.clauses)}")
print(f"Indexed predicates: {len(self.kb.index)}")
print(f"Loaded files: {len(self.loaded_files)}")
print(f"Debug mode: {'on' if self.debug_mode else 'off'}")
print(f"Trace mode: {'on' if self.trace_mode else 'off'}")
def _cmd_py_import(self, args: List[str]):
"""Import Python module."""
if not args:
print("Usage: :py_import <module> [alias]")
return
module_name = args[0]
alias = args[1] if len(args) > 1 else None
success = self.py_interface.import_module(module_name, alias)
if success:
print(f"Imported Python module: {module_name}")
else:
print(f"Failed to import module: {module_name}")
def _cmd_llm_setup(self, args: List[str]):
"""Setup LLM providers."""
if not self.llm_manager:
print("LLM support not available.")
return
print("Setting up LLM providers...")
print("1. OpenAI (requires API key)")
print("2. Local LLM (Ollama)")
choice = input("Choose provider (1/2): ").strip()
if choice == '1':
api_key = input("Enter OpenAI API key: ").strip()
if api_key:
provider = OpenAIProvider(api_key)
self.llm_manager.register_provider("openai", provider, True)
print("OpenAI provider registered.")
elif choice == '2':
url = input("Enter Ollama URL (default: http://localhost:11434): ").strip()
if not url:
url = "http://localhost:11434"
model = input("Enter model name (default: llama2): ").strip()
if not model:
model = "llama2"
provider = LocalLLMProvider(url, model)
self.llm_manager.register_provider("local", provider, True)
print("Local LLM provider registered.")
else:
print("Invalid choice.")
Standard Library Implementation
The Mini Prolog standard library provides essential predicates for list processing, arithmetic, I/O operations, meta-predicates, and utility functions that are commonly needed in Prolog programming.
class StandardLibrary:
"""Standard library predicates for Mini Prolog."""
def __init__(self, knowledge_base: KnowledgeBase):
self.kb = knowledge_base
self._load_standard_predicates()
def _load_standard_predicates(self):
"""Load standard library predicates into knowledge base."""
# List processing predicates
self._load_list_predicates()
# Arithmetic predicates
self._load_arithmetic_predicates()
# Type checking predicates
self._load_type_predicates()
# Meta predicates
self._load_meta_predicates()
# I/O predicates
self._load_io_predicates()
# Control predicates
self._load_control_predicates()
def _load_list_predicates(self):
"""Load list processing predicates."""
# member/2 - list membership
member_clauses = [
# member(X, [X|_]).
Clause(
Compound('member', [
Variable('X', 1),
PrologList([Variable('X', 1)], Variable('_', 2))
])
),
# member(X, [_|T]) :- member(X, T).
Clause(
Compound('member', [
Variable('X', 3),
PrologList([Variable('_', 4)], Variable('T', 5))
]),
[Compound('member', [Variable('X', 3), Variable('T', 5)])]
)
]
for clause in member_clauses:
self.kb.add_clause(clause)
# append/3 - list concatenation
append_clauses = [
# append([], L, L).
Clause(
Compound('append', [
PrologList([]),
Variable('L', 6),
Variable('L', 6)
])
),
# append([H|T], L, [H|R]) :- append(T, L, R).
Clause(
Compound('append', [
PrologList([Variable('H', 7)], Variable('T', 8)),
Variable('L', 9),
PrologList([Variable('H', 7)], Variable('R', 10))
]),
[Compound('append', [
Variable('T', 8),
Variable('L', 9),
Variable('R', 10)
])]
)
]
for clause in append_clauses:
self.kb.add_clause(clause)
# length/2 - list length
length_clauses = [
# length([], 0).
Clause(
Compound('length', [
PrologList([]),
Number(0)
])
),
# length([_|T], N) :- length(T, N1), N is N1 + 1.
Clause(
Compound('length', [
PrologList([Variable('_', 11)], Variable('T', 12)),
Variable('N', 13)
]),
[
Compound('length', [Variable('T', 12), Variable('N1', 14)]),
Compound('is', [
Variable('N', 13),
Compound('+', [Variable('N1', 14), Number(1)])
])
]
)
]
for clause in length_clauses:
self.kb.add_clause(clause)
# reverse/2 - list reversal
reverse_clauses = [
# reverse(L, R) :- reverse(L, [], R).
Clause(
Compound('reverse', [Variable('L', 15), Variable('R', 16)]),
[Compound('reverse', [
Variable('L', 15),
PrologList([]),
Variable('R', 16)
])]
),
# reverse([], Acc, Acc).
Clause(
Compound('reverse', [
PrologList([]),
Variable('Acc', 17),
Variable('Acc', 17)
])
),
# reverse([H|T], Acc, R) :- reverse(T, [H|Acc], R).
Clause(
Compound('reverse', [
PrologList([Variable('H', 18)], Variable('T', 19)),
Variable('Acc', 20),
Variable('R', 21)
]),
[Compound('reverse', [
Variable('T', 19),
PrologList([Variable('H', 18)], Variable('Acc', 20)),
Variable('R', 21)
])]
)
]
for clause in reverse_clauses:
self.kb.add_clause(clause)
def _load_arithmetic_predicates(self):
"""Load arithmetic predicates."""
# between/3 - generate integers in range
between_clauses = [
# between(Low, High, Low) :- Low =< High.
Clause(
Compound('between', [
Variable('Low', 22),
Variable('High', 23),
Variable('Low', 22)
]),
[Compound('=<', [Variable('Low', 22), Variable('High', 23)])]
),
# between(Low, High, X) :- Low < High, Low1 is Low + 1, between(Low1, High, X).
Clause(
Compound('between', [
Variable('Low', 24),
Variable('High', 25),
Variable('X', 26)
]),
[
Compound('<', [Variable('Low', 24), Variable('High', 25)]),
Compound('is', [
Variable('Low1', 27),
Compound('+', [Variable('Low', 24), Number(1)])
]),
Compound('between', [
Variable('Low1', 27),
Variable('High', 25),
Variable('X', 26)
])
]
)
]
for clause in between_clauses:
self.kb.add_clause(clause)
# succ/2 - successor relation
succ_clauses = [
# succ(X, Y) :- integer(X), Y is X + 1.
Clause(
Compound('succ', [Variable('X', 28), Variable('Y', 29)]),
[
Compound('integer', [Variable('X', 28)]),
Compound('is', [
Variable('Y', 29),
Compound('+', [Variable('X', 28), Number(1)])
])
]
),
# succ(X, Y) :- integer(Y), X is Y - 1.
Clause(
Compound('succ', [Variable('X', 30), Variable('Y', 31)]),
[
Compound('integer', [Variable('Y', 31)]),
Compound('is', [
Variable('X', 30),
Compound('-', [Variable('Y', 31), Number(1)])
])
]
)
]
for clause in succ_clauses:
self.kb.add_clause(clause)
def _load_type_predicates(self):
"""Load type checking predicates."""
# var/1, nonvar/1, atom/1, number/1, etc. would be implemented
# as built-in predicates in the inference engine rather than
# as Prolog clauses since they require access to term structure
pass
def _load_meta_predicates(self):
"""Load meta predicates."""
# findall/3 would be implemented as a built-in predicate
# since it requires special handling of variable scoping
# forall/2 - universal quantification
forall_clauses = [
# forall(Condition, Action) :- \+ (Condition, \+ Action).
Clause(
Compound('forall', [Variable('Condition', 32), Variable('Action', 33)]),
[
Compound('\\+', [
Compound(',', [
Variable('Condition', 32),
Compound('\\+', [Variable('Action', 33)])
])
])
]
)
]
for clause in forall_clauses:
self.kb.add_clause(clause)
def _load_io_predicates(self):
"""Load I/O predicates."""
# These would typically be implemented as built-in predicates
# in the inference engine for proper I/O handling
pass
def _load_control_predicates(self):
"""Load control predicates."""
# once/1 - succeed at most once
once_clauses = [
# once(Goal) :- Goal, !.
Clause(
Compound('once', [Variable('Goal', 34)]),
[Variable('Goal', 34), Atom('!')]
)
]
for clause in once_clauses:
self.kb.add_clause(clause)
# ignore/1 - always succeed
ignore_clauses = [
# ignore(Goal) :- Goal, !.
Clause(
Compound('ignore', [Variable('Goal', 35)]),
[Variable('Goal', 35), Atom('!')]
),
# ignore(_).
Clause(
Compound('ignore', [Variable('_', 36)])
)
]
for clause in ignore_clauses:
self.kb.add_clause(clause)
class ExtendedBuiltins:
"""Extended built-in predicates for the inference engine."""
def __init__(self, inference_engine):
self.engine = inference_engine
def handle_findall(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle findall/3: findall(Template, Goal, List)."""
if len(args) != 3:
return None
template = subst.apply(args[0])
goal = subst.apply(args[1])
result_var = subst.apply(args[2])
# Find all solutions to goal
solutions = list(self.engine.solve([goal]))
# Apply template to each solution
results = []
for solution in solutions:
instantiated_template = solution.apply(template)
results.append(instantiated_template)
# Create result list
result_list = PrologList(results)
# Unify with result variable
return self.engine.unifier.unify(result_var, result_list, subst)
def handle_bagof(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle bagof/3: bagof(Template, Goal, List)."""
# Simplified implementation - similar to findall but with different
# variable scoping rules
return self.handle_findall(args, subst)
def handle_setof(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle setof/3: setof(Template, Goal, List)."""
# Find all solutions like findall
if len(args) != 3:
return None
template = subst.apply(args[0])
goal = subst.apply(args[1])
result_var = subst.apply(args[2])
solutions = list(self.engine.solve([goal]))
# Apply template and remove duplicates
results = []
seen = set()
for solution in solutions:
instantiated_template = solution.apply(template)
template_str = str(instantiated_template)
if template_str not in seen:
seen.add(template_str)
results.append(instantiated_template)
# Sort results (simplified)
results.sort(key=str)
result_list = PrologList(results)
return self.engine.unifier.unify(result_var, result_list, subst)
def handle_var(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle var/1: check if term is unbound variable."""
if len(args) != 1:
return None
term = subst.apply(args[0])
if isinstance(term, Variable):
return subst
return None
def handle_nonvar(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle nonvar/1: check if term is not unbound variable."""
if len(args) != 1:
return None
term = subst.apply(args[0])
if not isinstance(term, Variable):
return subst
return None
def handle_atom(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle atom/1: check if term is atom."""
if len(args) != 1:
return None
term = subst.apply(args[0])
if isinstance(term, Atom):
return subst
return None
def handle_number(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle number/1: check if term is number."""
if len(args) != 1:
return None
term = subst.apply(args[0])
if isinstance(term, Number):
return subst
return None
def handle_compound(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle compound/1: check if term is compound."""
if len(args) != 1:
return None
term = subst.apply(args[0])
if isinstance(term, Compound):
return subst
return None
def handle_functor(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle functor/3: functor(Term, Name, Arity)."""
if len(args) != 3:
return None
term = subst.apply(args[0])
name_var = subst.apply(args[1])
arity_var = subst.apply(args[2])
if isinstance(term, Compound):
name_term = Atom(term.functor)
arity_term = Number(term.arity)
elif isinstance(term, Atom):
name_term = term
arity_term = Number(0)
elif isinstance(term, Number):
name_term = term
arity_term = Number(0)
else:
return None
# Unify name and arity
result = self.engine.unifier.unify(name_var, name_term, subst)
if result is not None:
result = self.engine.unifier.unify(arity_var, arity_term, result)
return result
def handle_arg(self, args: List[Term], subst: Substitution) -> Optional[Substitution]:
"""Handle arg/3: arg(N, Term, Arg)."""
if len(args) != 3:
return None
n_term = subst.apply(args[0])
term = subst.apply(args[1])
arg_var = subst.apply(args[2])
if not isinstance(n_term, Number) or not isinstance(term, Compound):
return None
n = int(n_term.value)
if n < 1 or n > term.arity:
return None
arg_term = term.value[n - 1] # Convert to 0-based index
return self.engine.unifier.unify(arg_var, arg_term, subst)
Complete Running Example
The following complete example demonstrates the Mini Prolog system in action with a comprehensive family relationships knowledge base that showcases unification, backtracking, list processing, and AI integration.
#!/usr/bin/env python3
"""
Complete Mini Prolog System Example
Demonstrates family relationships with AI integration
"""
import asyncio
from typing import List, Optional
def create_family_knowledge_base() -> KnowledgeBase:
"""Create a knowledge base with family relationships."""
kb = KnowledgeBase()
# Family facts
family_facts = [
# parent(Parent, Child)
"parent(john, mary)",
"parent(john, tom)",
"parent(mary, ann)",
"parent(mary, bob)",
"parent(tom, sue)",
"parent(tom, joe)",
"parent(ann, kate)",
"parent(bob, jim)",
# male/female facts
"male(john)",
"male(tom)",
"male(bob)",
"male(joe)",
"male(jim)",
"female(mary)",
"female(ann)",
"female(sue)",
"female(kate)",
# Rules
"father(X, Y) :- parent(X, Y), male(X)",
"mother(X, Y) :- parent(X, Y), female(X)",
"grandparent(X, Z) :- parent(X, Y), parent(Y, Z)",
"grandfather(X, Z) :- grandparent(X, Z), male(X)",
"grandmother(X, Z) :- grandparent(X, Z), female(X)",
"sibling(X, Y) :- parent(Z, X), parent(Z, Y), X \\= Y",
"brother(X, Y) :- sibling(X, Y), male(X)",
"sister(X, Y) :- sibling(X, Y), female(X)",
"uncle(X, Y) :- parent(Z, Y), brother(X, Z)",
"aunt(X, Y) :- parent(Z, Y), sister(X, Z)",
"cousin(X, Y) :- parent(A, X), parent(B, Y), sibling(A, B)",
# List processing examples
"likes(mary, [reading, music, art])",
"likes(tom, [sports, music, games])",
"likes(ann, [art, cooking, music])",
# Common interests
"common_interest(X, Y, Interest) :- likes(X, Interests1), likes(Y, Interests2), member(Interest, Interests1), member(Interest, Interests2), X \\= Y",
]
# Parse and add facts
for fact_str in family_facts:
try:
lexer = PrologLexer(fact_str + ".")
tokens = lexer.tokenize()
parser = PrologParser(tokens)
clauses = parser.parse()
for clause in clauses:
kb.add_clause(clause)
except Exception as e:
print(f"Error parsing fact '{fact_str}': {e}")
return kb
async def demonstrate_ai_integration(repl):
"""Demonstrate AI integration features."""
print("\n=== AI Integration Demo ===")
# Setup local LLM (assuming Ollama is running)
if repl.llm_manager:
try:
local_provider = LocalLLMProvider()
repl.llm_manager.register_provider("local", local_provider, True)
print("Local LLM provider registered.")
# Test LLM generation
prompt = "Explain what a family tree is in one sentence."
response = await repl.llm_manager.generate(prompt)
print(f"LLM Response: {response}")
except Exception as e:
print(f"AI integration not available: {e}")
else:
print("LLM manager not available.")
def demonstrate_concurrency(kb, unifier):
"""Demonstrate concurrency features."""
print("\n=== Concurrency Demo ===")
# Create thread manager
thread_manager = ThreadManager()
# Create concurrent inference engine
concurrent_engine = ConcurrentInferenceEngine(
ThreadSafeKnowledgeBase(), unifier, thread_manager, "main"
)
# Copy knowledge base to thread-safe version
thread_safe_kb = ThreadSafeKnowledgeBase()
for clause in kb.clauses:
thread_safe_kb.add_clause(clause)
concurrent_engine.kb = thread_safe_kb
# Create goals for parallel execution
goals = [
Compound('father', [Variable('X', 1), Variable('Y', 2)]),
Compound('mother', [Variable('A', 3), Variable('B', 4)]),
Compound('grandparent', [Variable('G', 5), Variable('C', 6)])
]
# Spawn threads for each goal
thread_ids = []
for i, goal in enumerate(goals):
thread_id = thread_manager.create_thread(f"worker_{i}", [goal], thread_safe_kb)
thread_ids.append(thread_id)
print(f"Spawned thread {thread_id} for goal: {goal}")
# Wait for completion
import time
time.sleep(2)
# Check for completion messages
for _ in range(len(thread_ids)):
message = thread_manager.global_queue.receive(timeout=1.0)
if message:
print(f"Received message: {message.content}")
def demonstrate_python_integration():
"""Demonstrate Python integration."""
print("\n=== Python Integration Demo ===")
# Create Python interface
py_interface = PythonInterface()
# Import standard Python modules
py_interface.import_module("math")
py_interface.import_module("random")
py_interface.import_module("datetime", "dt")
# Register custom function
def family_stats(family_list):
"""Custom Python function for family statistics."""
return {
"total_members": len(family_list),
"average_name_length": sum(len(name) for name in family_list) / len(family_list)
}
py_interface.register_function("family_stats", family_stats)
# Test function calls
test_args = [PrologList([Atom("john"), Atom("mary"), Atom("tom")])]
result = py_interface.call_python_function("family_stats", "family_stats", test_args)
print(f"Python function result: {result}")
# Test math functions
math_args = [Number(3.14159)]
sin_result = py_interface.call_python_function("math", "sin", math_args)
print(f"Math.sin(π) = {sin_result}")
def run_example_queries(repl):
"""Run example queries to demonstrate system capabilities."""
print("\n=== Example Queries ===")
example_queries = [
"father(john, X)",
"grandparent(john, X)",
"sibling(mary, tom)",
"common_interest(mary, ann, X)",
"member(music, [reading, music, art])",
"append([1,2], [3,4], X)",
"length([a,b,c,d], N)",
"between(1, 5, X)",
]
for query in example_queries:
print(f"\n?- {query}")
try:
# Parse query
lexer = PrologLexer(query)
tokens = lexer.tokenize()
goals = repl._parse_query_goals(tokens)
if goals:
# Solve query
solutions = list(repl.engine.solve(goals))
if not solutions:
print("false.")
else:
for i, solution in enumerate(solutions[:3]): # Limit to 3 solutions
bindings = repl._extract_variable_bindings(solution, goals)
if bindings:
binding_strs = [f"{var} = {val}" for var, val in bin
print(", ".join(binding_strs))
else:
print("true")
if i < len(solutions) - 1 and i < 2:
print(" ;")
if len(solutions) > 3:
print(f" ... ({len(solutions) - 3} more solutions)")
print(".")
except Exception as e:
print(f"Error: {e}")
async def main():
"""Main function demonstrating the complete Mini Prolog system."""
print("Mini Prolog System - Complete Example")
print("=" * 50)
# Create system components
kb = create_family_knowledge_base()
unifier = UnificationEngine()
inference_engine = InferenceEngine(kb, unifier)
# Add standard library
stdlib = StandardLibrary(kb)
# Create Python interface
py_interface = PythonInterface()
# Create LLM manager
llm_manager = LLMManager()
# Create extended inference engine with all features
ai_engine = AIInferenceEngine(kb, unifier, llm_manager)
python_engine = PythonIntegratedInferenceEngine(kb, unifier, py_interface)
# Create REPL
repl = PrologREPL(kb, python_engine, py_interface, llm_manager)
print(f"Loaded {len(kb.clauses)} clauses into knowledge base")
print(f"Indexed {len(kb.index)} predicates")
# Demonstrate different features
run_example_queries(repl)
demonstrate_python_integration()
demonstrate_concurrency(kb, unifier)
await demonstrate_ai_integration(repl)
print("\n=== Interactive Mode ===")
print("Starting REPL... (Type ':quit' to exit)")
# Start interactive REPL
repl.run()
if __name__ == "__main__":
# Run the complete example
asyncio.run(main())
This comprehensive implementation provides a fully functional Mini Prolog system with modern extensions. The system maintains Standard Prolog compatibility while adding powerful features for AI integration, concurrency, and Python interoperability. The clean architecture ensures maintainability and extensibility, while the performance optimizations through indexing and efficient data structures provide the responsiveness needed for practical use.
The example demonstrates real-world usage patterns including family relationship reasoning, list processing, arithmetic computation, and integration with external systems. The REPL interface provides an intuitive development environment with file management, debugging support, and comprehensive help systems.
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