Langchain runnables deep dive with hands on |Tutorial:128 скачать в хорошем качестве

Langchain runnables deep dive with hands on |Tutorial:128

github : https://github.com/ronidas39/LLMtutor... 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*