Many people hit the fitness center with ardour and consider they’re on the suitable path to reaching their health objectives. However the outcomes aren’t there as a consequence of poor weight loss program planning and a scarcity of path. Hiring a private coach together with an costly fitness center stack isn’t at all times an possibility. That’s the reason I’ve created this weblog put up to point out you easy methods to construct your health coach utilizing the ability of LangChain. With this, now you can get exercise and weight loss program recommendation personalized to your objectives with minimal value. Let’s get began with taking some wonderful tech and turning it into your health co-pilot!
Why Use Langchain?
Langchain allows you to do far more when constructing superior AI functions by combining massive language fashions (LLMs) with instruments, knowledge sources, and reminiscence. As a substitute of invoking the LLM with a plain textual content immediate, you possibly can create brokers that invoke features, question info, and handle conversations with state. For a health coach, Langchain permits you to mix LLM intelligence with customized logic – for instance, create exercise ideas, observe progress, and get well being knowledge – so that you is usually a smarter interactive coach with out having to determine that each one out your self.
Stipulations
To create your health coach utilizing LangChain, you’ll want:
- An OpenAI API key to entry language fashions
- A key for the SerpAPI service to make use of the online search
- Primary data of Python
That’s all, you at the moment are able to get began.
The right way to Construct Your Health Coach?
On this part, I’ll show easy methods to make your health coach utilizing a Langchain agent. Guarantee you’ve gotten the whole lot ready in response to the conditions. I’ll stroll you thru the step-by-step means of constructing the answer and clarify the position every step performs in reaching the result.
FitCoach AI is a conversational health coach that collects person knowledge persistently and generates personalised exercise and weight loss program plans utilizing LangChain brokers with OpenAI.
Core Dependencies
To put in all of the libraries required for constructing the health agent, run the next command in your command line:
pip set up gradio langchain openai serper-dev python-dotenAs soon as all of the dependencies are in place, we’d begin by importing all of the related modules for the duty:
import os
import gradio as gr
import traceback
import datetime
from typing import Listing, Tuple, Non-obligatory
from langchain_openai import ChatOpenAI
from langchain.reminiscence import ConversationBufferMemory
from langchain.brokers import initialize_agent, AgentType
from langchain.instruments import BaseTool
import json
import requests
import dotenv
# Load setting variables
dotenv.load_dotenv()SerperSearchTool Class
Performance: Offers the flexibility to have real-time net search capabilities for up-to-date health/diet info.
Most important options:
- Integrates with the Serper API to get Google search outcomes
- Returns the highest 5 formatted search outcomes that embrace the title, snippet, and URL
- Has acceptable failure modes with timeout safety
- Helps each sync and async
# ----------- SERPER SEARCH TOOL ------------
class SerperSearchTool(BaseTool):
title: str = "search_web"
description: str = "Searches the online for real-time info and returns structured outcomes"
def _run(self, question: str) -> str:
"""Search the online utilizing Serper API"""
attempt:
api_key = os.getenv("SERPER_API_KEY")
if not api_key:
return "Error: SERPER_API_KEY not present in setting variables"
url = "https://google.serper.dev/search"
payload = json.dumps({"q": question})
headers = {
'X-API-KEY': api_key,
'Content material-Kind': 'utility/json'
}
response = requests.put up(url, headers=headers, knowledge=payload, timeout=10)
response.raise_for_status()
search_results = response.json()
# Extract and format natural outcomes
outcomes = []
if 'natural' in search_results:
for merchandise in search_results['organic'][:5]: # Restrict to prime 5 outcomes
outcomes.append({
"title": merchandise.get('title', ''),
"hyperlink": merchandise.get('hyperlink', ''),
"snippet": merchandise.get('snippet', '')
})
# Format ends in a readable manner
if outcomes:
formatted_results = "Search Outcomes:nn"
for i, lead to enumerate(outcomes, 1):
formatted_results += f"{i}. {outcome['title']}n"
formatted_results += f" {outcome['snippet']}n"
formatted_results += f" URL: {outcome['link']}nn"
return formatted_results
else:
return "No search outcomes discovered."
besides requests.exceptions.RequestException as e:
return f"Error performing search - Community subject: {str(e)}"
besides Exception as e:
return f"Error performing search: {str(e)}"
async def _arun(self, question: str) -> str:
"""Async model of search"""
return self._run(question)UserDataTracker Class
Performance: Get all obligatory info earlier than creating any health plans
Required Information Fields (so as):Health objective (weight reduction, muscle achieve, and so on.)
Age (in vary 10-100 validation)
Gender (male/feminine/different)
Weight (in items, - kg/lbs)
Top (in cm or toes/inches)
Exercise Degree (5 predefined ranges)
Weight loss program Preferences (vegetarian, vegan, and so on.)
Weight loss program Restrictions/allergy
Exercise-Preferencing & limitations
Most important Options:
- Discipline Validation: Every enter will likely be validated with customized validation features.
- Sequential Move: Nobody can skip forward.
- Error Dealing with: Present particular error messages for invalid inputs.
# ----------- USER DATA TRACKER CLASS ------------
class UserDataTracker:
def __init__(self):
self.knowledge = {}
# Outline required fields with their validation features and query prompts
self.required_fields = {
'fitness_goal': {
'query': "What's your main health objective? (e.g., weight reduction, muscle achieve, normal health)",
'validate': self._validate_fitness_goal
},
'age': {
'query': "How previous are you? (Should be between 10-100)",
'validate': self._validate_age
},
'gender': {
'query': "What's your gender? (male/feminine/different)",
'validate': self._validate_gender
},
'weight': {
'query': "What's your present weight? (e.g., 150 lbs or 68 kg)",
'validate': self._validate_weight
},
'top': {
'query': "What's your top? (e.g., 5'10" or 178 cm)",
'validate': self._validate_height
},
'activity_level': {
'query': "What's your exercise degree? (sedentary, frivolously lively, reasonably lively, very lively, extraordinarily lively)",
'validate': self._validate_activity_level
},
'dietary_preferences': {
'query': "Do you comply with any particular weight loss program? (e.g., vegetarian, vegan, keto, none)",
'validate': self._validate_dietary_preferences
},
'dietary_restrictions': {
'query': "Any meals allergic reactions or dietary restrictions? (e.g., nuts, dairy, gluten, none)",
'validate': self._validate_dietary_restrictions
},
'workout_preferences': {
'query': "What are your exercise preferences? (e.g., fitness center, house exercises, gear obtainable, any accidents?)",
'validate': self._validate_workout_preferences
},
}
self.current_step = 0Langchain Agent Configuration
Agent Initialization:
- Mannequin: GPT-4o-mini with temperature 0.3 for consistency.
- Reminiscence: ConversationBufferMemory for context consistency.
- Instruments: Internet search to let the agent lookup real-time info.
The initialize_fitcoach_agent operate configures FitCoach, a Langchain conversational agent that serves as a digital health and diet coach. It connects to the language mannequin GPT-4o-mini, is probably augmented by net search instruments, and retains observe of dialog reminiscence for context. The agent follows a stringent, rule-based dialogue continuity: it asks customers particular questions separately to extract all necessary info concerning health objectives, age, physique metrics, meals habits, and medical historical past, amongst others. Solely in spite of everything you wanted to know has been gathered and confirmed, the agent will decide to not producing any health or weight loss program plans. This manner, the agent permits for the protected, correct, and personalised directions that customers need in an agent. As soon as all the required info has been gathered, FitCoach generates complete exercise routines and meal plans based mostly on the person, whereas providing an interactive and fascinating teaching plan.
# ----------- LANGCHAIN AGENT SETUP ------------
def initialize_fitcoach_agent():
"""Initialize the FitCoach agent with error dealing with"""
attempt:
# Examine for OpenAI API key
openai_key = os.getenv("OPENAI_API_KEY")
if not openai_key:
increase ValueError("OPENAI_API_KEY not present in setting variables")
# Initialize the language mannequin with appropriate mannequin title
llm = ChatOpenAI(
mannequin="gpt-4o-mini",
temperature=0.3,
openai_api_key=openai_key
)
# Initialize instruments
instruments = []
attempt:
if os.getenv("SERPER_API_KEY"):
search_tool = SerperSearchTool()
instruments.append(search_tool)
print("✅ Search software initialized efficiently")
else:
print("⚠️ SERPER_API_KEY not discovered - search performance will likely be restricted")
besides Exception as e:
print(f"⚠️ Couldn't initialize search software: {e}")
# Initialize reminiscence
reminiscence = ConversationBufferMemory(memory_key="chat_history", return_messages=True)Gradio Chatbot Logic
- is_plan_content: Determines if a given textual content has an in depth health or diet plan by checking for a number of key phrases, akin to days of the week, meal names, and exercise comparisons. Helps to separate plans from casual conversations round health.
- format_plan_for_text: Codecs uncooked health plan texts into cleaner sections whereas retaining headings, lists, and paragraphs, to enhance readability and suitability for sharing in chat or e-mail.
- chat_function: Manages the FitCoach chat circulation. Collects info from the person in steps (person health objective, meal preferences), calls the AI agent to supply a customized exercise & meal plan, and safely handles errors to maintain chat circulation uninterrupted.
----------- GRADIO CHATBOT LOGIC ------------
def is_plan_content(textual content: str) -> bool:
"""Examine if the textual content accommodates a health plan with detailed content material"""
if not textual content or len(textual content.strip()) = 3Be aware: I’ve proven solely elements of the code within the article. My full code is out there right here.
Consumer Interface
On the subject of the person interface, you might use options like Streamlit or Gradio to maintain it easy. I used Gradio because it permits me to create a sophisticated net app with a customized design, computerized updates, and a fast, responsive interface that fits well being and health functions. Click on right here to view the supply code.
Use Instances for Langchain
- Buyer Assist Bots: Create an assistant that may search buyer assist data bases to seek out solutions to buyer questions.
- Search-Aided Chatbots: Curse maps to sources of real-time data akin to Google and Wikipedia.
- Doc Q&A: Permit the person to add a PDF and mechanically retrieve correct solutions with citations.
- Information Manipulation Assistants: Permit customers to add and discover knowledge in a spreadsheet whereas asking questions associated to the information.
- Content material Technology Instruments: Generate content material, together with blogs, emails, or social media posts.
- Multi-agent Programs: Create programs through which AI Brokers can collaborate or specialize within the process.
Conclusion
When it’s all stated and carried out, AI isn’t all about tech; it’s in regards to the internal workings of easy methods to leverage know-how as an influence to enhance our on a regular basis lives! Whether or not it’s to get in form, eat nicely, or keep motivated, designing your individual distinctive private health coach is an ideal instance of how AI can assist and inspire, but nonetheless preserve us accountable for our actions to satisfy our objectives. And one of the best half is you don’t should be a tech wizard to start out constructing your utility! There are a selection of instruments like LangChain for growth, OpenAI for AI capabilities, and Gradio for deploying your good utility, simply to say a number of, that may assist anybody construct good and distinctive functions for themselves. The way forward for health, in addition to many different areas of life, is out there to us!
Information Scientist | AWS Licensed Options Architect | AI & ML Innovator
As a Information Scientist at Analytics Vidhya, I specialise in Machine Studying, Deep Studying, and AI-driven options, leveraging NLP, pc imaginative and prescient, and cloud applied sciences to construct scalable functions.
With a B.Tech in Laptop Science (Information Science) from VIT and certifications like AWS Licensed Options Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Pretend Information Detection, and Emotion Recognition. Keen about innovation, I attempt to develop clever programs that form the way forward for AI.
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