LangChain OpenTutorial
  • 🦜️🔗 The LangChain Open Tutorial for Everyone
  • 01-Basic
    • Getting Started on Windows
    • 02-Getting-Started-Mac
    • OpenAI API Key Generation and Testing Guide
    • LangSmith Tracking Setup
    • Using the OpenAI API (GPT-4o Multimodal)
    • Basic Example: Prompt+Model+OutputParser
    • LCEL Interface
    • Runnable
  • 02-Prompt
    • Prompt Template
    • Few-Shot Templates
    • LangChain Hub
    • Personal Prompts for LangChain
    • Prompt Caching
  • 03-OutputParser
    • PydanticOutputParser
    • PydanticOutputParser
    • CommaSeparatedListOutputParser
    • Structured Output Parser
    • JsonOutputParser
    • PandasDataFrameOutputParser
    • DatetimeOutputParser
    • EnumOutputParser
    • Output Fixing Parser
  • 04-Model
    • Using Various LLM Models
    • Chat Models
    • Caching
    • Caching VLLM
    • Model Serialization
    • Check Token Usage
    • Google Generative AI
    • Huggingface Endpoints
    • HuggingFace Local
    • HuggingFace Pipeline
    • ChatOllama
    • GPT4ALL
    • Video Q&A LLM (Gemini)
  • 05-Memory
    • ConversationBufferMemory
    • ConversationBufferWindowMemory
    • ConversationTokenBufferMemory
    • ConversationEntityMemory
    • ConversationKGMemory
    • ConversationSummaryMemory
    • VectorStoreRetrieverMemory
    • LCEL (Remembering Conversation History): Adding Memory
    • Memory Using SQLite
    • Conversation With History
  • 06-DocumentLoader
    • Document & Document Loader
    • PDF Loader
    • WebBaseLoader
    • CSV Loader
    • Excel File Loading in LangChain
    • Microsoft Word(doc, docx) With Langchain
    • Microsoft PowerPoint
    • TXT Loader
    • JSON
    • Arxiv Loader
    • UpstageDocumentParseLoader
    • LlamaParse
    • HWP (Hangeul) Loader
  • 07-TextSplitter
    • Character Text Splitter
    • 02. RecursiveCharacterTextSplitter
    • Text Splitting Methods in NLP
    • TokenTextSplitter
    • SemanticChunker
    • Split code with Langchain
    • MarkdownHeaderTextSplitter
    • HTMLHeaderTextSplitter
    • RecursiveJsonSplitter
  • 08-Embedding
    • OpenAI Embeddings
    • CacheBackedEmbeddings
    • HuggingFace Embeddings
    • Upstage
    • Ollama Embeddings With Langchain
    • LlamaCpp Embeddings With Langchain
    • GPT4ALL
    • Multimodal Embeddings With Langchain
  • 09-VectorStore
    • Vector Stores
    • Chroma
    • Faiss
    • Pinecone
    • Qdrant
    • Elasticsearch
    • MongoDB Atlas
    • PGVector
    • Neo4j
    • Weaviate
    • Faiss
    • {VectorStore Name}
  • 10-Retriever
    • VectorStore-backed Retriever
    • Contextual Compression Retriever
    • Ensemble Retriever
    • Long Context Reorder
    • Parent Document Retriever
    • MultiQueryRetriever
    • MultiVectorRetriever
    • Self-querying
    • TimeWeightedVectorStoreRetriever
    • TimeWeightedVectorStoreRetriever
    • Kiwi BM25 Retriever
    • Ensemble Retriever with Convex Combination (CC)
  • 11-Reranker
    • Cross Encoder Reranker
    • JinaReranker
    • FlashRank Reranker
  • 12-RAG
    • Understanding the basic structure of RAG
    • RAG Basic WebBaseLoader
    • Exploring RAG in LangChain
    • RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
    • Conversation-With-History
    • Translation
    • Multi Modal RAG
  • 13-LangChain-Expression-Language
    • RunnablePassthrough
    • Inspect Runnables
    • RunnableLambda
    • Routing
    • Runnable Parallel
    • Configure-Runtime-Chain-Components
    • Creating Runnable objects with chain decorator
    • RunnableWithMessageHistory
    • Generator
    • Binding
    • Fallbacks
    • RunnableRetry
    • WithListeners
    • How to stream runnables
  • 14-Chains
    • Summarization
    • SQL
    • Structured Output Chain
    • StructuredDataChat
  • 15-Agent
    • Tools
    • Bind Tools
    • Tool Calling Agent
    • Tool Calling Agent with More LLM Models
    • Iteration-human-in-the-loop
    • Agentic RAG
    • CSV/Excel Analysis Agent
    • Agent-with-Toolkits-File-Management
    • Make Report Using RAG, Web searching, Image generation Agent
    • TwoAgentDebateWithTools
    • React Agent
  • 16-Evaluations
    • Generate synthetic test dataset (with RAGAS)
    • Evaluation using RAGAS
    • HF-Upload
    • LangSmith-Dataset
    • LLM-as-Judge
    • Embedding-based Evaluator(embedding_distance)
    • LangSmith Custom LLM Evaluation
    • Heuristic Evaluation
    • Compare experiment evaluations
    • Summary Evaluators
    • Groundedness Evaluation
    • Pairwise Evaluation
    • LangSmith Repeat Evaluation
    • LangSmith Online Evaluation
    • LangFuse Online Evaluation
  • 17-LangGraph
    • 01-Core-Features
      • Understanding Common Python Syntax Used in LangGraph
      • Title
      • Building a Basic Chatbot with LangGraph
      • Building an Agent with LangGraph
      • Agent with Memory
      • LangGraph Streaming Outputs
      • Human-in-the-loop
      • LangGraph Manual State Update
      • Asking Humans for Help: Customizing State in LangGraph
      • DeleteMessages
      • DeleteMessages
      • LangGraph ToolNode
      • LangGraph ToolNode
      • Branch Creation for Parallel Node Execution
      • Conversation Summaries with LangGraph
      • Conversation Summaries with LangGraph
      • LangGrpah Subgraph
      • How to transform the input and output of a subgraph
      • LangGraph Streaming Mode
      • Errors
      • A Long-Term Memory Agent
    • 02-Structures
      • LangGraph-Building-Graphs
      • Naive RAG
      • Add Groundedness Check
      • Adding a Web Search Module
      • LangGraph-Add-Query-Rewrite
      • Agentic RAG
      • Adaptive RAG
      • Multi-Agent Structures (1)
      • Multi Agent Structures (2)
    • 03-Use-Cases
      • LangGraph Agent Simulation
      • Meta Prompt Generator based on User Requirements
      • CRAG: Corrective RAG
      • Plan-and-Execute
      • Multi Agent Collaboration Network
      • Multi Agent Collaboration Network
      • Multi-Agent Supervisor
      • 08-LangGraph-Hierarchical-Multi-Agent-Teams
      • 08-LangGraph-Hierarchical-Multi-Agent-Teams
      • SQL-Agent
      • 10-LangGraph-Research-Assistant
      • LangGraph Code Assistant
      • Deploy on LangGraph Cloud
      • Tree of Thoughts (ToT)
      • Ollama Deep Researcher (Deepseek-R1)
      • Functional API
      • Reflection in LangGraph
  • 19-Cookbook
    • 01-SQL
      • TextToSQL
      • SpeechToSQL
    • 02-RecommendationSystem
      • ResumeRecommendationReview
    • 03-GraphDB
      • Movie QA System with Graph Database
      • 05-TitanicQASystem
      • Real-Time GraphRAG QA
    • 04-GraphRAG
      • Academic Search System
      • Academic QA System with GraphRAG
    • 05-AIMemoryManagementSystem
      • ConversationMemoryManagementSystem
    • 06-Multimodal
      • Multimodal RAG
      • Shopping QnA
    • 07-Agent
      • 14-MoARAG
      • CoT Based Smart Web Search
      • 16-MultiAgentShoppingMallSystem
      • Agent-Based Dynamic Slot Filling
      • Code Debugging System
      • New Employee Onboarding Chatbot
      • 20-LangGraphStudio-MultiAgent
      • Multi-Agent Scheduler System
    • 08-Serving
      • FastAPI Serving
      • Sending Requests to Remote Graph Server
      • Building a Agent API with LangServe: Integrating Currency Exchange and Trip Planning
    • 08-SyntheticDataset
      • Synthetic Dataset Generation using RAG
    • 09-Monitoring
      • Langfuse Selfhosting
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On this page
  • 🛠️ Contribution Process
  • Steps (in developing)
  • 📚 References
  • Licence
  • 🧑‍💻 Core Contributors

🦜️🔗 The LangChain Open Tutorial for Everyone

Next01-Basic

Last updated 28 days ago

This tutorial delves into , starting from an overview then providing practical examples.

The LangChain community in Seoul is excited to announce the LangChain OpenTutorial, a brand-new resource designed for everyone. This tutorial builds upon the foundation of the existing tutorial available here: written in Korean.

Within this new repository, we offer the following enhancements to benefit users of all skill levels:

  • Addressing global use cases for international users,

  • Diving deep into cutting-edge features including the recent updates available at the latest version of LangChain and LangGraph release, and

  • Demonstrating additional goodies that showcase real-world uses and further applications.

At this base repository, it serves as a home for both beginners and seasoned LangChain users. The tutorial whould provide a roadmap for learning LangChain, while also offering a valuable refresher for those already familiar with its functionalities.

🛠️ Contribution Process

Steps (in developing)

  1. Open a Pull request (PR): Develop at least one existing or new content file (.ipynb). Optionally, add examples related to open LLMs. Then, Submit a PR with the developed content. - Note: Self-Check Before PR Submission (Recommended)

    • License Compliance & Copyright Issues: Verify that all dataset and content comply with licensing requirements. Confirm that there are no copyright infringements.

    • Template Compliance: Follow the provided templates in or in

    • Execution Platform: Individual files should be executable on Google Colab.

    • Specifications Submission (if Required): If using open models or additional packages, specify the required environment to Infra Team.

  2. Team Peer Reviews: Assign at least two team members as reviewers. Reviewers will evaluate the code and content quality and check tutorials are compatible with Mac, Windows, and Linux environments. Approve the Pull Request if there are no issues.

  3. Merge Pull Request: Once the Pull Request has been approved by more than two reviewers, the original author can merge the PR into the 'main' branch.

  4. Proofreading: During the following week, the proofreading team will review the submitted content for typos, template compliance, and proper citations. If the proofreading team requests any modifications, the original author must make the necessary corrections and resubmit the PR.

📚 References

Licence

🧑‍💻 Core Contributors

Name
Profile
Name
Profile
Name
Profile

Minji Kang

Dowoung Kong

Dooil Kwak

Namyoung Kim

Musang Kim

Sunworl Kim

Sungchul Kim

Youngin Kim

Yongdam Kim (codingiscoffee)

Jaeho Kim

Jongcheol Kim

JungWook Kim

Junseong Kim

JoonHo Kim

Taylor (Jihyun) Kim

Taehwan Kim

Harheem Kim

Heeah Kim

Kyusik Moon

Bokyung Moon

HyeonJong Moon

Heesun Moon

Sunyoung Park (architectyou)

Erika Park

Yejin Park

JeongGi Park

Gimin Bae

Injin Bae (Ivy)

Pyoungwon Seo

Joonho Song

HoJun Song

HeeWung Song (Dan)

Aera Shin

Jeongho Shin

Haseom Shin

kkamDragon

Yoonji Oh

Jooyeong Oh

Byunggil Yoon

Kane

Donghakl

Chester (Sangyoon Lee)

Suhyun Lee

Sunhyoung Lee

Wonyoung Lee

YuChul Lee

Jongho Lee

Juni Lee

Chang-Jun Lee

Sohyeon Yim

Mark Lim

Yookyung Jeon

Joonha Jeon

Changwon Jeon

Gwangwon Jung (Pupba)

Wooseok Jeong

Ilgyun Jeong

Youngjun Cho

Jinu Cho

Hwayoung Cha

Ash-hun

Yeonhee Han

Sooyoung Her

Seongmin Hong (Solon)

Jaemin Hong

Chaeyoon Kim

✨ Want to join? Add your info and submit a PR!

📖

Unless stated otherwise, the codebase is released under the . This covers both the codebase and any sample code in the documentation.

langchain-ai
LangGraph GitHub
LangChain Documentation
MIT Licence
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