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LangChain tools that use Prolog rules to generate answers.

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Overview

The PrologTool class allows the generation of langchain tools that use Prolog rules to generate answers.

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Setup

Let’s use the following Prolog rules in the file family.pl: parent(john, bianca, mary).
parent(john, bianca, michael).
parent(peter, patricia, jennifer).
partner(X, Y) :- parent(X, Y, _). 
#!pip install langchain-prolog

from langchain_prolog import PrologConfig, PrologRunnable, PrologTool

TEST_SCRIPT = "family.pl"

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Instantiation

First create the Prolog tool: 
schema = PrologRunnable.create_schema("parent", ["men", "women", "child"])
config = PrologConfig(
    rules_file=TEST_SCRIPT,
    query_schema=schema,
)
prolog_tool = PrologTool(
    prolog_config=config,
    name="family_query",
    description="""
        Query family relationships using Prolog.
        parent(X, Y, Z) implies only that Z is a child of X and Y.
        Input can be a query string like 'parent(john, X, Y)' or 'john, X, Y'"
        You have to specify 3 parameters: men, woman, child. Do not use quotes.
    """,
)

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Invocation

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Using a Prolog tool with an LLM and function calling

#!pip install python-dotenv

from dotenv import find_dotenv, load_dotenv

load_dotenv(find_dotenv(), override=True)

#!pip install langchain-openai

from langchain.messages import HumanMessage
from langchain_openai import ChatOpenAI
To use the tool, bind it to the LLM model: 
model = ChatOpenAI(model="gpt-4o-mini")
model_with_tools = model.bind_tools([prolog_tool])
and then query the model: 
query = "Who are John's children?"
messages = [HumanMessage(query)]
response = model_with_tools.invoke(messages)
The LLM will respond with a tool call request: 
messages.append(response)
response.tool_calls[0]

{'name': 'family_query',
 'args': {'men': 'john', 'women': None, 'child': None},
 'id': 'call_gH8rWamYXITrkfvRP2s5pkbF',
 'type': 'tool_call'}
The tool takes this request and queries the Prolog database: 
tool_msg = prolog_tool.invoke(response.tool_calls[0])
The tool returns a list with all the solutions for the query: 
messages.append(tool_msg)
tool_msg

ToolMessage(content='[{"Women": "bianca", "Child": "mary"}, {"Women": "bianca", "Child": "michael"}]', name='family_query', tool_call_id='call_gH8rWamYXITrkfvRP2s5pkbF')
That we then pass to the LLM, and the LLM answers the original query using the tool response: 
answer = model_with_tools.invoke(messages)
print(answer.content)

John has two children: Mary and Michael, with Bianca as their mother.

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Chaining

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Using a Prolog Tool with an agent

To use the prolog tool with an agent, pass it to the agent’s constructor: 
#!pip install langgraph

from langchain.agents import create_agent


agent_executor = create_agent(model, [prolog_tool])
The agent takes the query and use the Prolog tool if needed: 
messages = agent_executor.invoke({"messages": [("human", query)]})
Then the agent receives the tool response and generates the answer: 
messages["messages"][-1].pretty_print()

================================== Ai Message ==================================

John has two children: Mary and Michael, with Bianca as their mother.

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API reference

See langchain-prolog.readthedocs.io/en/latest/modules.html for detail.
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