Langchain example. Here is an attempt to keep track of the initiatives around LangChain. 📄️ Comparing Chain Outputs Open In Colab LangChain Expression Language is a way to create arbitrary custom chains. Don’t delay; start leveraging LangChain to build innovative applications today. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. sql_database. This repository contains a collection of apps powered by LangChain. js - langchain-ai/langgraphjs-gen-ui-examples This gives the language model concrete examples of how it should behave. We will build a small ReactJs app … Continue reading "Example app with LangChain and ReactJs" The pipe operator: | To show off how this works, let's go through an example. We'll illustrate both methods using a two step sequence where the first step classifies an input question as being about LangChain, Anthropic, or Other, then routes to a corresponding prompt chain. Please refer to the acknowledgments section for the source tutorials where most of the code examples originated and were inspired from. How to use few shot examples in chat models This guide covers how to prompt a chat model with example inputs and outputs. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. Still, this is a great way to get started with LangChain - a lot of This notebook provides a quick overview for getting started with OpenAI chat models. Example Selectors are classes responsible for selecting and then formatting examples into prompts. A few-shot prompt template can be constructed Nov 15, 2023 · A Complete LangChain tutorial to understand how to create LLM applications and RAG workflows using the LangChain framework. How to use reference examples when doing extraction The quality of extractions can often be improved by providing reference examples to the LLM. Tool-calling LLM features are often used in this context. Example Setup First, let's create a chain that will identify incoming questions as being about LangChain, Anthropic, or Other: If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported integrations. These applications use a technique known as Retrieval Augmented Generation, or RAG. Nov 17, 2023 · In this tutorial, we cover a simple example of how to interact with GPT using LangChain and query a document for semantic meaning using LangChain with a vector store. LangChain has a few different types of example selectors. Sep 12, 2024 · The examples and scenarios provided offer a comprehensive overview of how to invoke LangChain chains effectively, demonstrating their versatility and potential in AI applications. How to add memory to chatbots A key feature of chatbots is their ability to use the content of previous conversational turns as context. There are several other related concepts that you may be looking for: Conversational RAG: Enable a chatbot from langchain_core. Oct 10, 2023 · Learn about the essential components of LangChain — agents, models, chunks and chains — and how to harness the power of LangChain in Python. Below is a detailed walkthrough of LangChain’s main modules, their roles, and code examples, following the latest In this quickstart we'll show you how to build a simple LLM application with LangChain. This project contains example usage and documentation around using the LangChain library to work with language models. Nov 21, 2024 · This article gives practical examples of how to develop a fast application using LangChain, which you can use as a cheat sheet. In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. Contribute to langchain-ai/langchain-mcp-adapters development by creating an account on GitHub. Note that this chatbot that we build will only use the language model to have a conversation. Examples 🚧 Docs under construction 🚧 Below are some examples for inspecting and checking different chains. This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. Jun 16, 2025 · Learn how to use LangChain for AI and LLM application development, with best practices, prompt and chain tools, and integration with Apache Spark and Kafka. Data extraction attempts to generate structured representations of information found in text and other unstructured or semi-structured formats. SQLDatabase object at 0x10d5f9120>), ListSQLDatabaseTool(db=<langchain_community. Productionization Aug 17, 2023 · LangChain makes LLM-powered application development more accessible by providing modular abstractions and implementations for essential components like models, prompts, and memory management. This is a comprehensive implementation that uses several key libraries to create a question-answering system based on the content of uploaded PDFs. How to: chain runnables How to: stream runnables How to: invoke runnables in parallel How to: add default invocation args to runnables How Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. There are many things LangChain can help us with, but in this tutorial we will focus just on getting the first ReactJs and LangChain example up and running. While this tutorial focuses how to use examples with a tool calling model, this technique is generally applicable, and will work also with JSON more or prompt based techniques. There does not appear to be solid consensus on how best to do few-shot prompting, and the Introduction LangChain is a framework for developing applications powered by large language models (LLMs). utilities. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. You can order the results by a relevant column to return the most Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. This repository provides implementations of various tutorials found online. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Jun 4, 2025 · Build intelligent RAG applications in Java using LangChain and MongoDB for real-time, context-aware AI experiences. This tutorial demonstrates text summarization using built-in chains and LangGraph. LangChain is a framework for building LLM-powered applications. This is often the best starting point for individual developers. LangChain is an open-source framework created to aid the development of applications leveraging the power of large language models (LLMs). In this blog post, we’ll explore the core components of LangChain, specifically focusing on its powerful tools and agents that make it a game-changer for developers and businesses alike. OpenAI offers a spectrum of models with different levels of power suitable for different tasks. How to: chain runnables How to: stream runnables How to: invoke runnables in parallel Curated list of tools and projects using LangChain. ) This framework Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them. Click any example below to run it instantly or find templates that can be used as a pre-built solution! May 22, 2023 · In this tutorial, we cover a simple example of how to interact with GPT using LangChain and query a document for semantic meaning using LangChain with a vector store. LangChain is a framework for developing applications powered by language models. Explore chat models, semantic search, classification, extraction, orchestration, and more. For an overview of all these types, see the below table. A collection of working code examples using LangChain for natural language processing tasks. Click any example below to run it instantly or find templates that can be used as a pre-built solution! OpenAI offers a spectrum of models with different levels of power suitable for different tasks. Learn how to build various applications with LangChain, a framework for building language models (LLMs) and other components. Quickstart In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe Use the most basic and common components of LangChain: prompt templates, models, and output parsers Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining Build a simple application with LangChain Trace your application with Mar 17, 2025 · LangChain—a revolutionary framework designed to simplify and enhance the development of language-based AI applications. More complex modifications LangChain 🔌 MCP. 🦜通过演示 LangChain 最具有代表性的应用范例，带你快速上手 LangChain 各个使用场景。（包含完整代码和数据集） - larkwins/langchain-examples The only method it needs to define is a select_examples method. py. This example goes over how to use LangChain to interact with OpenAI models Overview Integration details How to: debug your LLM apps LangChain Expression Language (LCEL) LangChain Expression Language is a way to create arbitrary custom chains. Chains enable you to go beyond simple C# implementation of LangChain. LangChain for RAG – Final Coding Example For our example, we have implemented a local Retrieval-Augmented Generation (RAG) system for PDF documents. Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet Apr 4, 2024 · What are Chains in LangChain? In simple words, a chain is a sequence of calls, whether those calls are to LLMs, external tools, or data preprocessing steps. ) Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc. Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them. The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. A few-shot prompt template can be constructed from either a set of examples, or LangChain includes a utility function tool_example_to_messages that will generate a valid sequence for most model providers. It is built on the Runnable protocol. It was built with these and other factors in mind, and provides a wide range of integrations with closed-source model providers (like OpenAI, Anthropic, and Google), open source models, and other third-party components like vectorstores. Migration guide: For migrating legacy chain abstractions to LCEL. Jan 26, 2025 · LangChain is a versatile framework for building LLM-powered applications. Its architecture allows developers to integrate LLMs with external data, prompt engineering, retrieval-augmented generation (RAG), semantic search, and agent workflows. Click any example below to run it instantly or find templates that can be used as a pre-built solution!. Jun 17, 2025 · LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. The langchain-google-genai package provides the LangChain integration for these models. LCEL cheatsheet: For a quick overview of how to use the main LCEL primitives. These are applications that can answer questions about specific source information. Feb 22, 2024 · The Langchain. Each project is presented in a Jupyter notebook and showcases various functionalities such as creating simple chains, using tools, querying CSV files, and interacting with SQL databases. Subscribe to the newsletter to stay informed about the Awesome LangChain. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves. A collection of generative UI agents written with LangGraph. Jun 4, 2025 · Setting Up a Langchain Agent with a Local LLM Getting a Langchain agent to work with a local LLM may sound daunting, but with recent tools like Ollama, llama. js framework makes it easy to integrate LLMs (Large Language Models) such as OpenAi's GTP with our JavaScript-based apps. It enables applications that: Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. prompts import ChatPromptTemplate system_message = """ Given an input question, create a syntactically correct {dialect} query to run to help find the answer. What Is LangChain? Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. Oct 13, 2023 · With easy-to-follow instructions and lucid examples, I’ll guide you through the intricate world of LangChain, unlocking its immense potential. Providing the model with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. Related resources Example selector how-to Use cases This section contains walkthroughs and techniques for common end-to-end use tasks. This takes in the input variables and then returns a list of examples. cpp, and Langchain integrations, it’s now easier than ever. This guide demonstrates how to build few-shot LangChain Examples LangChain is a framework for developing applications powered by language models. New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. Prompt Templates Prompt templates help to translate user input and parameters into instructions for a language model. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. Apr 11, 2024 · LangChain is a popular framework for creating LLM-powered apps. If you're looking to build something specific or are more of a hands-on learner, try one out! While they reference building blocks that are explained in greater detail in other sections, we absolutely encourage folks to get started by going through them and picking apart the code in a real-world Apr 9, 2023 · In this LangChain Crash Course you will learn how to build applications powered by large language models. Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in. We send a couple of emails per month about the articles, videos, projects, and May 7, 2025 · Learn how to create an MCP client server using LangChain for improved AI interactions with external tools and data. Click any example below to run it instantly or find templates that can be used as a pre-built solution! If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported integrations. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. We'll walk through a common pattern in LangChain: using a prompt template to format input into a chat model, and finally converting the chat message output into a string with an output parser. Overview We'll go over an example of how to design and implement an LLM-powered chatbot. It simplifies the generation of structured few-shot examples by just requiring Pydantic representations of the corresponding tool calls. This chatbot will be able to have a conversation and remember previous interactions with a chat model. Apr 6, 2025 · In this series of LangChain, we are looking into building AI-powered applications using the LangChain framework. Jul 23, 2025 · LangChain is a modular framework designed to build applications powered by large language models (LLMs). It is up to each specific implementation as to how those examples are selected. Jul 17, 2024 · In this post, I will run through a basic example of how to set GraphRAG using LangChain and use it to improve your RAG systems (using any LLM model or API) My debut book: LangChain in your Pocket How to use few shot examples In this guide, we’ll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. By leveraging components like prompt templates, chains, agents, tools, and memory, you can create sophisticated workflows tailored to various use cases. Example Input: table1, table2, table3', db=<langchain_community. SQLDatabase object at 0x10d5f9120>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. Jan 31, 2025 · Learn how to build a Retrieval-Augmented Generation (RAG) application using LangChain with step-by-step instructions and example code The quality of extractions can often be improved by providing reference examples to the LLM. May 17, 2024 · Build your AI application using LLMs with LangChain. This example goes over how to use LangChain to interact with OpenAI models Overview Integration details Use reference examples The quality of extractions can often be improved by providing reference examples to the LLM. This is a relatively simple LLM application - it’s just a single LLM call plus some prompting. Jan 2, 2025 · An example of a LangChain application is a language model assisting in code review processes by analyzing code submissions, offering feedback, and suggesting improvements. Chat models and prompts: Build a simple LLM application with prompt templates and chat models. For detailed documentation of all ChatOpenAI features and configurations head to the API reference. js, TypeScript and Azure OpenAI. This application will translate text from English into another language. We would like to show you a description here but the site won’t allow us. If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported integrations. API keys and default language models for OpenAI & HuggingFace are set up in config. Prompt Templates output The only method it needs to define is a select_examples method. - tryAGI/LangChain Find Langchain Examples and Templates Use this online langchain playground to view and fork langchain example apps and templates on CodeSandbox. Use this online langchain playground to view and fork langchain example apps and templates on CodeSandbox. Build a simple LLM application with chat models and prompt templates In this quickstart we’ll show you how to build a simple LLM application with LangChain. vwczum nit wqmy wqj ivkh tqrhdrq jgnorg zyytnmz tnrga bwkhfbw