LangChain 01 - Getting Started: Quick Hello World Guide
This article introduces how to use the LangChain library with OpenAI API and GPT-3.5-turbo model to create a template for generating jokes about specific topics (like cat...
LangChain Journey
Mastering LangChain from basics to advanced, building production-ready LLM applications.
26 articles
Article 1 Article 2 Article 3 Article 4 Article 5 Article 6 Article 7 Article 8 Article 9 Article 10 Article 11 Article 12 Article 13 Article 14 Article 15 Article 16 Article 17 Article 18 Article 19 Article 20 Article 21 Article 22 Article 23 Article 24 Article 25 Article 26
LangChain 02 - JsonOutputParser and Streaming JSON Data Processing Guide
This article explains how to install and use LangChain and OpenAI API in Python, retrieve specified country and its population data through async functions.
LangChain 03 - astream_events Streaming Output with FAISS Practice
This article introduces how to use DocArrayInMemorySearch to vectorize text data, combined with OpenAIEmbeddings and GPT-3.5 model, to implement relevant information retr...
LangChain 04 - RAG Retrieval-Augmented Generation
This article explains in detail how to use RAG technology in LangChain, combined with OpenAI's GPT-3.5 model, to improve text generation quality through retrieval and gen...
LangChain 05 - RAG Enhanced Conversational Retrieval
Conversational Search is an intelligent search technology that combines natural language processing and context understanding capabilities.
LangChain 06 - RAG with Source Document
Retrieval-Augmented Generation (RAG) with Source Document is an AI technology framework that combines retrieval with large language model generation.
LangChain 07 - Multiple Chains
How to use Runnable and Prompts in LangChain to create chainable conversation flows for multi-stage question answering, with practical examples of sequential and parallel...
LangChain 08 - Query SQL DB with GPT
This article introduces how to use LangChain framework to import Chinook SQLite database through Python script and use GPT model to execute SQL queries, such as calculati...
LangChain 09 - Query SQL DB with RUN GPT
RUN GPT provides a powerful database query function, allowing users to input natural language to query database content.
LangChain 10 - Agents Langchainhub Guide
This article introduces how to use LangChainHub's Hub mechanism through Python code to easily access and share Prompts.
LangChain-11 Code Writing FunctionCalling: Autoregressive Language Modeling
This article introduces how GPT models work based on autoregressive language modeling, which generates coherent text by predicting the probability of the next token.
LangChain-12 Routing By Semantic Similarity
This article introduces a method using large models (like OpenAI) and Prompt templates to handle unexpected inputs in program design by calculating the similarity between...
LangChain-13 Memory ConversationBufferMemory: Conversation Context Management
This article introduces how to use tools in the LangChain library to manage conversation context of large models in Python.
LangChain-14 OpenAI Content Moderation (Moderation) Explained
Moderation refers to the process of reviewing and managing user-generated content (UGC) through manual or automated means.
LangChain-15 Intelligent Knowledge Retrieval: AgentExecutor Practice
Build an intelligent knowledge retrieval system using Wikipedia search plugin, AgentExecutor, and LangChain tools. Covers agent initialization, tool binding...
LangChain-16 Using Tools: Mastering LLM Tool Calling
LangChain is a powerful open-source framework designed to help developers more efficiently build and deploy applications based on Large Language Models (LLMs).
LangChain-17 Function Calling AI Function Calling Explained
Function Calling is a core technology for Large Language Models (like GPT-4, Claude, Gemini) to interact with external systems.
LangChain Cache Mechanism: InMemoryCache and SQLiteCache Explained
LangChain provides a comprehensive caching mechanism to significantly reduce LLM call latency and costs. Its core includes InMemoryCache (in-memory cache) and SQLiteCache...
LangChain-19 TokenUsage Callback Function Explained
Explains how to integrate OpenAI GPT-3 model in Python through LangChain library, demonstrating how to use the getopenaicallback function to obtain callbacks and execute...
LangChain-20 Document Loaders: TextLoader, CSVLoader, PyPDFLoader and More
This article introduces various document loaders provided by the LangChain library, such as TextLoader, CSVLoader, DirectoryLoader, etc., demonstrating how to load and pr...
LangChain Text Splitter: Character, Word, HTML and Code-based Splitting
This article introduces various TextSplitters in the LangChain library, including character-based, word-based, HTML tag-based, and programming language-based splitters...
LangChain-22 Text Embedding and FAISS Practical Explanation
Text Embedding involves the process of mapping high-dimensional data (such as text, images, etc.) to lower-dimensional spaces.
LangChain-23 Vector AI Semantic Search System: Vector Databases and Retrieval
Vector Storage, also known as Vector Database, is a database system specifically optimized for storing and retrieving high-dimensional vector data.
LangChain-24 AgentExecutor Comprehensive Guide
This article introduces how to use the Langchain library in Python for document retrieval, load web content, configure OpenAIEmbeddings, and integrate GPT-3.
LangChain-25 ReAct Framework Detailed Explanation and Integration Practice
This article introduces ReAct, a framework that uses logical reasoning and action sequences to achieve goal-oriented tasks through LLM decision-making and operations.
LangChain-26 Custom Agent Complete Tutorial: Building a Custom Agent
A Custom Agent refers to an intelligent agent program customized by users based on specific requirements, which can execute specific tasks or workflows.