LangChain 入门到进阶，构建生产级 LLM 应用。
26 articles
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 cats). The author demonstrates...
This article explains how to install and use LangChain and OpenAI API in Python, retrieve specified country and its population data through async functions, and demonstrates the process of progress...
This article introduces how to use DocArrayInMemorySearch to vectorize text data, combined with OpenAIEmbeddings and GPT-3.5 model, to implement relevant information retrieval and answer 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 generation. Provides installat...
This article introduces how to use tools in LangChain library, such as OpenAIEmbeddings and ChatModels, combined with document retrieval technology, to create a program that generates answers based...
Retrieval-Augmented Generation (RAG) with Source Document is an AI technology framework that combines retrieval with large language model generation. Its core...
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 chain composition.
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 calculating employee count.
This article introduces how to use Python libraries like langchain and ChatOpenAI (GPT-3.5-turbo) combined with SQLite database to create a program to execute SQL queries and return results in natu...
This article introduces how to use LangChainHub's Hub mechanism through Python code to easily access and share Prompts. Although the project hasn't been...
This article introduces how to use the GPT-3.5-Turbo model to write Python code to solve users' abstract calculation problems, such as 2+2 and complex mathematical expressions, demonstrating the mo...
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 queries and preset templates.
This article introduces how to use tools in the LangChain library to manage conversation context of large models in Python. Through components like...
Content moderation is a core component of modern internet platform safety and compliance, used to identify, filter, and manage user-generated content (UGC) to prevent the spread of illegal, low-qua...
Build an intelligent knowledge retrieval system using Wikipedia search plugin, AgentExecutor, and LangChain tools. Covers agent initialization, tool binding, and multi-step reasoning workflows.
LangChain is currently one of the most popular LLM application development frameworks, specifically designed for building intelligent assistants, automation...
Function Calling is a core technology for Large Language Models (like GPT-4, Claude, Gemini) to interact with external systems. It enables AI to not only understand language but also execute tasks,...
LangChain provides a comprehensive caching mechanism to significantly reduce LLM call latency and costs. Its core includes InMemoryCache (in-memory cache) and SQLiteCache (persistent cache).
Explains how to integrate OpenAI GPT-3 model in Python through LangChain library, demonstrating how to use the `get_openai_callback` function to obtain callbacks and execute requests.
This article introduces various document loaders provided by the LangChain library, such as TextLoader, CSVLoader, DirectoryLoader, etc., demonstrating how to load and process data in various formats.
This article introduces various TextSplitters in the LangChain library, including character-based, word-based, HTML tag-based, and programming language-based splitters, as well as their application...
This article introduces the key role of TextEmbedding in NLP, how to convert text into real number vectors to represent semantic relationships, and how to combine OpenAIEmbeddings and FAISS for eff...
This article introduces how to use Chroma vector database to process and retrieve high-dimensional vector embeddings from documents, vectorize them using...
This article introduces how to use the Langchain library in Python for document retrieval, load web content, configure OpenAIEmbeddings, and integrate GPT-3.5-turbo model for Q&A. It demonstrates h...
This article introduces ReAct, a framework that uses logical reasoning and action sequences to achieve goal-oriented tasks through LLM decision-making and operations. The core components include Th...
This article demonstrates how to create a chat agent using the Langchain library and GPT-4 model in Python by defining tool functions and integrating them with LLM to achieve queries for informatio...