Langchain runnables deep dive with hands on |Tutorial:128

github : https://github.com/ronidas39/LLMtutorial/tree/main/tutorial128 telegram: https://t.me/ttyoutubediscussion # Welcome to Total Technology Zone - Tutorial 128 Welcome back to **Total Technology Zone**! In **Tutorial 128**, we’re diving deep into one of the most advanced and powerful features in the LangChain framework — the **LangChain Runnable Interface**. This tutorial is crafted for **developers, ML engineers, AI enthusiasts, and enterprise practitioners** who are looking to master the **core building blocks of LangChain applications** using LCEL (LangChain Expression Language) and `Runnable` primitives. The goal is to bridge the gap between theory and **real-world, production-level implementation** using **structured, reusable, and scalable chains**. --- 🧩 **What This Tutorial Covers:** ✅ **Detailed explanation of LangChain Runnable primitives**, including: - `RunnableLambda`: For injecting custom Python logic - `RunnablePassThrough`: For value propagation through chains - `RunnableParallel`: For executing multiple chains concurrently with shared input - `RunnableBranch`: For conditional logic and dynamic flow control ✅ **Understanding LCEL (LangChain Expression Language)**: - Learn how LCEL helps declaratively combine LangChain components - Understand how LCEL simplifies chaining of LLMs, prompts, retrievers, tools, and more - How to visualize your chains using `get_graph().print_ascii()` and `.draw_io()` ✅ **How runnables work behind the scenes**: - Each component (prompt, LLM, parser, etc.) becomes a `Runnable` - Chaining them enables composability and modular design - Explanation of the `invoke`, `batch`, `stream`, and `astream` methods --- 🍽️ **Real-World Use Case: Building a Smart Restaurant Assistant** In this hands-on walkthrough, we build a practical, production-style application using the LangChain Runnable architecture. 🔹 **Step-by-step flow:** 1. **User Input Handling**: - Take free-form text from the user (e.g., “I want to eat tandoori chicken”) - Use LangChain + OpenAI to extract the *dish name* and *cuisine type* (Indian, Italian, Chinese, etc.) 2. **Dessert Suggestion (Parallel Execution)**: - Based on the cuisine type, suggest a relevant dessert - Use `RunnableParallel` to generate both dish and dessert simultaneously 3. **Recipe Generator**: - Generate recipes for both the main dish and dessert using prompt templates - Implemented using `RunnableLambda` for customized logic with OpenAI’s GPT model 4. **Smart Summarization (Conditional Branch)**: - If the final recipe exceeds a specific word count, summarize it - Applied using `RunnableBranch` to dynamically control the output flow 5. **Final Output Formatting & Visualization**: - Output structured data (JSON or string) for easy rendering in front-end or APIs - Generate visual graph of the entire chain using `.get_graph().draw_io("chain.png")` --- 🛠️ **Technologies & Frameworks Used:** - Python 🐍 - LangChain Core (`langchain-core`) - OpenAI GPT-4 via `ChatOpenAI` - LangChain Expression Language (LCEL) - PromptTemplate & ChatPromptTemplate - Output parsers: `StrOutputParser`, `JsonOutputParser` - Pydantic models for structured outputs - Visualization via `get_graph()` and PNG export --- 🚀 **Why This Tutorial is Unique:** Unlike most tutorials or documentation examples that focus on isolated code snippets or surface-level examples, this video walks you through a **complete application** using **multiple runnable primitives together**. You'll not only understand *what* each component does but also *why* it is used and *how* it fits into an enterprise-grade workflow. The solution shown here can be extended to: - Restaurant chatbots - Multilingual menu planning tools - Dynamic recommendation systems - LLM-driven customer support assistants - And much more! -- 🧠 **Extra Tips Shared:** - How to modularize your LLM chains - Using `Runnable` methods like `invoke()`, `batch()`, `stream()` - How to transform runnable outputs into tools for use in agents - Pydantic integration with LangChain JSON output parser - Handling multiple inputs and outputs gracefully with parallel execution - Realistic approach to debugging chain errors and handling exceptions - Best practices to keep your chains reusable and production-ready 📣 **Let’s Grow Together!** If you found this content helpful: ✅ **Like** this video ✅ **Subscribe** to **Total Technology Zone** ✅ **Share** it with your peers, teams, and community ✅ **Drop a comment** — even a "Thanks" or emoji helps the algorithm push this to a wider audience! Your support keeps me motivated and helps this channel reach more learners around the globe. 🌍 🔖 **#LangChain #LangChainRunnable #GPT4 #OpenAI #AIdevelopment #PythonLangchain #LCEL #PromptEngineering #AIrecipes #RunnableLambda #RunnableBranch #RunnableParallel #LangChainTutorial #TotalTechnologyZone**