🌟 Cutting-edge LangChain Repo 🚀

A powerful, open-source LangChain implementation for building advanced AI workflows with ease!

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📌 Table of Contents

• 📖 Overview
• 🚀 Installation
• 📦 Repository Structure
• 🤝 Contribution
• 📜 License

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📖 Overview

Welcome to the LangChain Toolkit! 🎉 This repository provides a custom implementation of LangChain components to supercharge your AI projects. Whether you're building chatbots 🤖, embedding pipelines 📊, or structured outputs 🗂️, this toolkit has you covered. Designed for developers, AI enthusiasts, and businesses, it’s your go-to solution for leveraging large language models (LLMs) with ease.

🔑 Key Features:

• Custom-built Chains for flexible workflows.

• Advanced ChatModels for conversational AI.

• Optimized EmbeddedModels for semantic search.

• Robust OutputParser and StructureOutput for clean, structured data.

And much more! 🚀

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🚀 Installation

Let’s kick things off with a warm welcome! 😄 This toolkit is designed for developers, AI enthusiasts, and businesses ready to harness the power of LangChain. Follow these steps to dive in:

1. Clone the Repo:

   git clone https://github.com/Sunilyadav03/Langchain.git
   

2. Navigate to the directory

cd LangChain

3. Install dependencies

pip install -r requirements.txt

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📦 Repository Structure

Here’s a peek at the magic inside! Each folder is a building block for your AI dreams. 🎨 Let’s break it down with definitions and real-world use cases:

🎠 Chains

Definition: Chains in LangChain are sequences of operations that combine prompts, LLMs, and tools to perform complex tasks. Think of them as the backbone of your AI workflow!

Real-World Use Case: 🏬 E-commerce Product Descriptions Imagine you’re running an e-commerce store and need to generate product descriptions for thousands of items. With Chains, you can create a workflow that pulls product data, passes it to an LLM for description generation, and formats the output—all in one seamless chain.

Location: Chains/

Example:

from Chains import ProductDescriptionChain

chain = ProductDescriptionChain()
result = chain.run(product_name="Wireless Headphones", features=["noise-canceling", "20-hour battery"])
print(result)  # "Wireless Headphones with noise-canceling technology and a 20-hour battery life..."

💬 ChatModels

Definition: ChatModels are designed for conversational AI, enabling interactive dialogues with LLMs. They handle context, memory, and user prompts to create human-like interactions.

Real-World Use Case: 📞 Customer Support Chatbot A business wants to automate customer support on its website. Using ChatModels, you can build a chatbot that answers FAQs, handles returns, and escalates complex queries to human agents—all while maintaining a natural conversation flow.

Location: ChatModels/

Example:

from ChatModels import SupportBot

bot = SupportBot()
response = bot.chat("What’s your return policy?")
print(response)  # "Our return policy allows returns within 30 days with a receipt..."

📊 EmbeddedModels

Definition: EmbeddedModels generate embeddings (vector representations) of text, enabling semantic search, clustering, and similarity analysis.

Real-World Use Case: 🔍 Semantic Search for E-commerce An online retailer wants to improve its search engine so customers can find products using natural language (e.g., “cozy winter jacket”). EmbeddedModels can embed product descriptions and user queries, enabling accurate, context-aware search results.

Location: EmbeddedModels/

Example

from EmbeddedModels import SemanticSearch

search = SemanticSearch()
results = search.query("cozy winter jacket", products=["Winter Parka", "Summer Tee"])
print(results)  # ["Winter Parka"]

🧩 OutputParser

Definition: OutputParser extracts and formats LLM outputs into usable structures, like JSON, lists, or custom formats, making raw LLM responses actionable.

Real-World Use Case: 📈 Market Research Analysis A market research firm uses an LLM to analyze customer feedback but needs structured data (e.g., sentiment scores, themes). OutputParser can parse the LLM’s output into a structured JSON format for analysis.

Example:

from OutputParser import FeedbackParser

parser = FeedbackParser()
feedback = "I love the product but the delivery was slow."
result = parser.parse(feedback)
print(result)  # {"sentiment": "mixed", "themes": ["product", "delivery"]}

Location: OutputParser/

⚡ Runnables

Definition: Runnables are modular, executable components in LangChain that can be combined to create dynamic workflows, such as API calls, data processing, or LLM interactions.

Real-World Use Case: 🌐 Automated Content Generation A content agency needs to generate blog posts by fetching trending topics via an API, summarizing them with an LLM, and formatting the output. Runnables can orchestrate this multi-step process.

Example:

from Runnables import ContentGenerator

generator = ContentGenerator()
post = generator.run(topic="AI Trends 2025")
print(post)  # "AI Trends 2025: Generative AI dominates with..."

🗂️ StructureOutput

Definition: StructureOutput ensures LLM outputs are formatted into predefined structures (e.g., tables, schemas), ideal for applications requiring strict data formats.

Real-World Use Case: 📅 Event Planning Automation An event planner uses an LLM to generate schedules but needs them in a structured table format. StructureOutput can convert raw LLM output into a structured schedule.

Example:

from StructureOutput import ScheduleFormatter

formatter = ScheduleFormatter()
schedule = formatter.format("9 AM: Keynote, 10 AM: Break")
print(schedule)  # {"schedule": [{"time": "9 AM", "event": "Keynote"}, {"time": "10 AM", "event": "Break"}]}

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🤝 Contribution

I’d love your contributions!. Whether it’s bug fixes, new features, or docs, every bit helps make this toolkit better.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.