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docs: Add langchain-prolog documentation (#29788)
I want to add documentation for a new integration with SWI-Prolog. @hwchase17 check this out: https://github.com/apisani1/langchain-prolog/tree/main/examples/travel_agent --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
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Antonio Pisani
GitHub
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23
docs/docs/integrations/providers/prolog.md
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docs/docs/integrations/providers/prolog.md
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# SWI-Prolog
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SWI-Prolog offers a comprehensive free Prolog environment.
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## Installation and Setup
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Install lanchain-prolog using pip:
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```bash
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pip install langchain-prolog
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```
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## Tools
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The `PrologTool` class allows the generation of langchain tools that use Prolog rules to generate answers.
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```python
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from langchain_prolog import PrologConfig, PrologTool
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```
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See a [usage example](/docs/integrations/tools/prolog_tool).
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See the same guide for usage examples of `PrologRunnable`, which allows the generation
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of LangChain runnables that use Prolog rules to generate answers.
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376
docs/docs/integrations/tools/prolog_tool.ipynb
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376
docs/docs/integrations/tools/prolog_tool.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bf0c1ab2",
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"metadata": {},
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"source": [
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"# Prolog\n",
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"\n",
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"LangChain tools that use Prolog rules to generate answers.\n",
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"\n",
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"## Overview\n",
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"\n",
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"The PrologTool class allows the generation of langchain tools that use Prolog rules to generate answers.\n",
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"\n",
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"## Setup\n",
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"\n",
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"Let's use the following Prolog rules in the file family.pl:\n",
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"\n",
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"parent(john, bianca, mary).\\\n",
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"parent(john, bianca, michael).\\\n",
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"parent(peter, patricia, jennifer).\\\n",
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"partner(X, Y) :- parent(X, Y, _)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "60f0e3d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install langchain-prolog\n",
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"\n",
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"from langchain_prolog import PrologConfig, PrologRunnable, PrologTool\n",
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"\n",
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"TEST_SCRIPT = \"family.pl\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "b50700d4",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"First create the Prolog tool:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9b9a2015",
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"metadata": {},
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"outputs": [],
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"source": [
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"schema = PrologRunnable.create_schema(\"parent\", [\"men\", \"women\", \"child\"])\n",
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"config = PrologConfig(\n",
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" rules_file=TEST_SCRIPT,\n",
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" query_schema=schema,\n",
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")\n",
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"prolog_tool = PrologTool(\n",
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" prolog_config=config,\n",
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" name=\"family_query\",\n",
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" description=\"\"\"\n",
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" Query family relationships using Prolog.\n",
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" parent(X, Y, Z) implies only that Z is a child of X and Y.\n",
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" Input can be a query string like 'parent(john, X, Y)' or 'john, X, Y'\"\n",
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" You have to specify 3 parameters: men, woman, child. Do not use quotes.\n",
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" \"\"\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3363f7d",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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"### Using a Prolog tool with an LLM and function calling"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "68709aa2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#!pip install python-dotenv\n",
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"\n",
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"from dotenv import find_dotenv, load_dotenv\n",
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"\n",
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"load_dotenv(find_dotenv(), override=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "c171c1ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install langchain-openai\n",
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"\n",
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"from langchain_core.messages import HumanMessage\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9d23b2c5",
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"metadata": {},
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"source": [
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"To use the tool, bind it to the LLM model and query the model:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3ce4479d",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
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"llm_with_tools = llm.bind_tools([prolog_tool])\n",
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"messages = [HumanMessage(\"Who are John's children?\")]\n",
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"response = llm_with_tools.invoke(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "50154355",
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"metadata": {},
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"source": [
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"The LLM will respond with a tool call request:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "5676fe7e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'name': 'family_query',\n",
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" 'args': {'men': 'john', 'women': None, 'child': None},\n",
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" 'id': 'call_3VazWUstCGlY8zczi05TaduU',\n",
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" 'type': 'tool_call'}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages.append(response)\n",
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"response.tool_calls[0]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f774d582",
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"metadata": {},
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"source": [
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"The tool takes this request and queries the Prolog database:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "66516c4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"tool_msg = prolog_tool.invoke(response.tool_calls[0])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fca8ec2",
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"metadata": {},
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"source": [
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"The tool returns a list with all the solutions for the query:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "33f779bc",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ToolMessage(content='[{\"Women\": \"bianca\", \"Child\": \"mary\"}, {\"Women\": \"bianca\", \"Child\": \"michael\"}]', name='family_query', tool_call_id='call_3VazWUstCGlY8zczi05TaduU')"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages.append(tool_msg)\n",
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"tool_msg"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b520745e",
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"metadata": {},
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"source": [
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"That we then pass to the LLM, and the LLM answers the original query using the tool response:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "f997186a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"John has two children: Mary and Michael. Their mother is Bianca.\n"
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]
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}
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],
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"source": [
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"answer = llm_with_tools.invoke(messages)\n",
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"print(answer.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f7ce937",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"### Using a Prolog Tool with an agent"
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]
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},
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{
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"cell_type": "markdown",
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"id": "37cd7ef8",
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"metadata": {},
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"source": [
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"To use the prolog tool with an agent, pass it to the agent's constructor:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "b7bbfd79",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", \"You are a helpful assistant\"),\n",
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" (\"human\", \"{input}\"),\n",
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" (\"placeholder\", \"{agent_scratchpad}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"tools = [prolog_tool]\n",
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"\n",
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"agent = create_tool_calling_agent(llm, tools, prompt)\n",
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"\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3e866895",
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"metadata": {},
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"source": [
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"The agent takes the query and use the Prolog tool if needed:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "acad8d33",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"answer = agent_executor.invoke({\"input\": \"Who are John's children?\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "92e71ffa",
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"metadata": {},
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"source": [
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"Then the agent recieves the tool response as part of the `\"agent_scratchpad`\" placeholder and generates the answer:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "36a464f4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"John has two children: Mary and Michael, with Bianca as their mother.\n"
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]
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}
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],
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"source": [
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"print(answer[\"output\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "baee74e3",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"(TODO: update with API reference once built.)\n",
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"\n",
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"See https://github.com/apisani1/langchain-prolog/tree/main for detail."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9 (test-env)",
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"language": "python",
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"name": "test-env"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.21"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -445,3 +445,8 @@ packages:
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repo: Shikenso-Analytics/langchain-discord
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downloads: 1
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downloads_updated_at: '2025-02-15T16:00:00.000000+00:00'
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- name: langchain-prolog
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path: .
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repo: apisani1/langchain-prolog
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downloads: 0
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downloads_updated_at: '2025-02-15T16:00:00.000000+00:00'
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