🤖 Agent Binod v1.0.0

A Comprehensive LangChain AI Agent for Learning and Practical Applications

Agent Binod is a powerful, feature-rich AI assistant built with LangChain that demonstrates modern AI agent capabilities. Named after the legendary "Binod" meme, this agent brings intelligence, versatility, and a touch of humor to your AI learning journey.

✨ What is Agent Binod?

Agent Binod is a comprehensive LangChain-powered AI agent that showcases the full spectrum of modern AI capabilities:

• 🌤️ Real-time Weather Information - Get current weather for any city worldwide
• 🧮 Smart Calculator - Perform complex mathematical calculations
• 🔍 Web Search - Search the internet for current information
• 📚 Wikipedia Integration - Access detailed encyclopedia information
• 🧠 Knowledge Base Search - Semantic search through LangChain documentation
• 🎯 Expert Analysis - Get specialized responses from science, history, and technology experts
• 📖 Learning Plans - Generate comprehensive study plans for any topic
• 💡 Topic Analysis - Get summaries and thought-provoking questions simultaneously
• 📊 CSV Data Analysis - Load, analyze, and get insights from CSV files

🛠️ Setup Instructions

Prerequisites

• Python 3.8 or higher
• OpenAI API key
• OpenWeatherMap API key (optional for weather features)

1. Clone or Download

git clone https://github.com/farhapartex/agent-binod-008
cd agent-binod

2. Install Dependencies (without docker)

pip install -r requirements.txt
source venv/bin/activate

3. Set Up Environment Variables

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key_here
WEATHER_API_KEY=your_openweathermap_api_key_here

Getting API Keys:

OpenAI API Key:

1. Visit OpenAI Platform

OpenWeatherMap API Key (Optional):

1. Visit OpenWeatherMap
2. Note: Without this key, weather tool will use mock data

4. Project Structure

agent-binod/
├── agent
│   ├── __init__.py
|   ├── core.py           # Main file which have agent details
├── agent_libs/
│   ├── tools.py          # Calculator and custom output parser
│   └── weather.py        # Weather tool implementation
├── .dockerignore         # docker ignore items file
├── .env                  # Environment variables
├── .gitignore            # git ignore items file
├── Dockerfile            # Main dockerfile file
├── requirements.txt      # Dependencies
├── main.py
└── README.md            # This file

🎮 How to Use Agent Binod

Quick Start (without docker)

python main.py

Quick Start (with docker)

docker build -t agent-binod:latest . # one time build
docker run -it --rm --env-file .env agent-binod:latest

Interactive Commands

Once running, you can use these special commands:

• quit - Exit the agent
• demo - Run comprehensive feature demonstration
• memory - View conversation history

Example Conversations

Vector search

If you want to feed agent with your own data, you can mention any pdf file in .env file. The pdf file will be takes automatically during first loading. That's it. Next time you will ask anything, Agent Binod will first look at vector db, if not find any resource then will go for other tools. 

📊 CSV Data Analysis

You: Load the CSV file at /home/user/data/sales.csv

Agent: ✅ CSV Loaded Successfully!
📁 File: sales.csv
📊 Shape: 1,000 rows × 8 columns
📋 Columns: date, product, sales, region, price...
🔧 Delimiter: ',' | Encoding: utf-8

You: Do a general analysis of the data

Agent: 📈 General Data Analysis
- Total Records: 1,000
- Numeric Columns: 3
- Categorical Columns: 4
- Missing Data: 25 cells
- Complete Rows: 975 (97.5%)
...

You: Show correlation analysis

Agent: 🔗 Correlation Analysis
Strong Correlations (|r| > 0.7):
- sales ↔ price: 0.823 (positive)
- region ↔ sales: -0.745 (negative)
...

🌤️ Weather Queries

You: What's the weather in London?
Agent: 🌤️ Weather for London
Current: 15.3°C (feels like 14.1°C)
Condition: Light Rain
Humidity: 78% | Wind: 4.2 m/s
...

🧮 Calculations

You: Calculate the area of a circle with radius 7.5
Agent: ✅ Result: 3.14159 * 7.5 * 7.5 = 176.71

🎯 Expert Analysis

You: Explain photosynthesis scientifically
Agent: *Uses science expert chain*
As a science expert, photosynthesis is the complex biochemical process...

📖 Learning Plans

You: I want to learn machine learning completely
Agent: 📚 Learning Plan for Machine Learning

📝 Summary:
Machine learning is a subset of artificial intelligence...

❓ Key Questions to Explore:
1. What are the different types of ML algorithms?
2. How do neural networks learn from data?
3. What are common real-world applications?

🎯 Next Steps:
1. Start with Python and key libraries (NumPy, Pandas, Scikit-learn)
2. Take an online course covering supervised/unsupervised learning
3. Practice with real datasets on Kaggle

Advanced Features

Chain Demonstrations

# Run individual chain demos
agent.run_simple_chain("artificial intelligence")
agent.run_parallel_chain("quantum computing")
agent.run_sequential_chain("blockchain")
agent.run_router_chain("How does DNA work?")

Vector Search

# Search internal knowledge base
agent.vector_search_demo("What are LangChain agents?")

🔧 Technical Features

Modern LangChain Implementation

• LCEL (LangChain Expression Language) with pipe operators
• Tool-calling agents with automatic tool selection
• Vector stores with semantic search capabilities
• Memory management for conversation context
• Multiple chain types for different use cases

Chain Types Demonstrated

1. Simple Chain - Basic prompt → LLM → response
2. Structured Chain - Parsed output with metadata
3. Parallel Chain - Simultaneous processing of multiple tasks
4. Sequential Chain - Multi-step workflows
5. Routing Chain - Intelligent expert selection

Tools Available

Tool	Description	Example Usage
Weather	Real-time weather data	"Weather in Tokyo?"
Calculator	Mathematical calculations	"Calculate 15 * 23 + 100"
Search	Internet search	"Recent news about AI"
Wikipedia	Encyclopedia lookup	"Tell me about Python language"
Knowledge Search	LangChain documentation	"What are agents in LangChain?"
Expert Analysis	Specialized expert responses	"Explain quantum physics scientifically"
Learning Plan	Complete study plans	"I want to learn blockchain"
Topic Analysis	Summary + questions	"Analyze machine learning"

🎯 Use Cases

Educational

• Learn LangChain concepts through hands-on experience
• Understand modern AI agent architecture
• Study prompt engineering and chain composition

Development

• Template for building production AI agents
• Reference implementation for LangChain best practices
• Starting point for custom AI applications

Daily Assistant

• Get weather updates for travel planning
• Perform quick calculations and research
• Generate learning plans for new topics
• Get expert-level explanations on various subjects

📝 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Built with LangChain
• Weather data from OpenWeatherMap
• Search capabilities powered by DuckDuckGo
• Encyclopedia data from Wikipedia

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Happy Learning with Agent Binod! 🤖✨

"Just like the legendary Binod comment that took the internet by storm, Agent Binod is here to make AI learning simple, powerful, and memorable!"