Best of Langchain — 2024

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Article
freeCodeCamp·2y
Learn Generative AI for Developers
Generative AI is transforming AI by enabling machines to produce text, images, and audio. A new 21-hour course on the freeCodeCamp.org YouTube channel offers a comprehensive guide for developers, covering foundational concepts, advanced methods, hands-on projects, and deployment. Key tools include Hugging Face, OpenAI, LangChain, and vector databases, with practical applications like chatbots and text summarizers. The course also delves into Retrieval-Augmented Generation (RAG) and deploying AI apps on Google Cloud and AWS.
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2
Article
Faun·2y
Building AI Agents and Workflow
With advancements in AI, creating AI Agents and AI-driven Workflows has become a growing interest. An AI Agent uses a Language Model (LLM) for reasoning and utilizes tools to perform tasks autonomously, while an Agentic Workflow combines these agents to execute a series of tasks. The post provides a step-by-step guide to build an AI Agent using langchain and OpenAI, including examples of sending emails and generating responses based on queries. The process involves defining tools, creating agents, setting up workflows, and executing tasks, with a focus on flexibility and extensibility.
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Article
Towards Data Science·2y
Using LLMs to Learn From YouTube
Learn how to build a chatbot using LangChain, Pinecone, Flask, and React that allows users to ask questions about YouTube videos. The chatbot uses the RAG framework to generate answers that take the conversation history into account.
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4
Article
GoPenAI·2y
Building a Custom Chatbot with Next.js, Langchain, OpenAI, and Supabase.
Build a custom chatbot with Next.js for the frontend, MaterialUI for UI components, Langchain and OpenAI for language model interactions, and Supabase to store data and embeddings. The chatbot can be trained with custom data from PDF files and answer questions based on the content.
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5
Article
DEV·2y
GPT-4o: Learn how to Implement a RAG on the new model, step-by-step!
Learn how to implement a RAG on the new GPT-4o model step-by-step, including setting up your OpenAI Account and API Key, using Langchain's approach on talkdai/dialog, and using GPT-4o in your content.
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6
Article
freeCodeCamp·2y
Learn LangChain and Gen AI by Building 6 Projects
Learn how to build six end-to-end projects using LangChain and various LLMs in this course on the freeCodeCamp.org YouTube channel. The course covers the integration of LangChain with GPT-4, Google Gemini Pro, and Llama 2, enabling the creation of practical, real-world applications.
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Article
Getting started with AI·2y
LangGraph Tutorial with Practical Example
This tutorial guides you through building a LangGraph application designed to enhance resumes for job seekers. LangGraph, a low-level framework from LangChain, enables developers to create graph-based AI applications. It involves using graph structures to manage state and node interactions, where each node performs specific tasks such as calling an API or processing data. The tutorial includes an example of integrating OpenAI's GPT-4o to create a resume expert system that interacts with job and resume data.
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Article
Faun·2y
Building a End to End Multi-Modal RAG System Using AWS Bedrock And Langchain
Learn how to build an end-to-end Retrieval Augmented Generation (RAG) application using AWS Bedrock and Langchain. This project involves loading PDF documents, creating vector embeddings using the Titan model, storing them in a vector store, integrating Cloudy and Llama 2 language models, and building a user-friendly interface with Streamlit. The system efficiently retrieves and summarizes information from PDF files based on user queries.
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Article
LangChain·2y
Announcing LangChain v0.3
LangChain v0.3 has been released, introducing significant changes for both Python and JavaScript users. In Python, all packages are upgraded to Pydantic 2, with Pydantic 1 and Python 3.8 support ending in 2024. In the JavaScript ecosystem, LangChain packages now use '@langchain/core' as a peer dependency, and callbacks are non-blocking by default. Multiple deprecated entry points and objects have been removed. The release also includes new integrations, revamped documentation, additional utilities for chat models, and a simplified tool definition. Guides are available for migration, and LangGraph remains the recommended way to build agents.
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10
Video
Tech With Tim·2y
Python AI Agent Tutorial - Build a Coding Assistant w/ RAG & LangChain
Learn how to build a custom AI agent using Lang chain and retrieval augmented generation. Query information about GitHub repositories and create a coding or GitHub assistant. Utilize tools like GitHub Vector search database to store and retrieve issues. Build an AI agent that can summarize and respond to different issues.
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Article
freeCodeCamp·2y
How to Build Better AI Workflows with Langchain
Learn how to use Large Language Models like ChatGPT in your app with Langchain. Langchain is a powerful tool that simplifies building and deploying large language models, allowing you to integrate multiple services and enhance functionality. With Langchain, you can access and learn from newer data without limits.
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12
Article
Machine Learning Mastery·2y
5 Essential Free Tools for Getting Started with LLMs
This post introduces 5 essential free tools for getting started with LLMs: Transformers, LlamaIndex, Langchain, Ollama, and Llamafile. Each tool has its own unique set of tasks, advantages, and features to help beginners grasp the subtleties of LLM development and interact with it.
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Article
neo4j·2y
Turn Your CSVs Into Graphs Using LLMs
The post explores how large language models (LLMs) can assist in creating data models from CSV files for use in Neo4j, emphasizing iterative approaches to avoid data complexity distractions. It discusses using LangChain, prompt engineering for generating consistent outputs, and converting CSV data into Cypher statements for Neo4j. The post also highlights important considerations like adding unique identifiers and creating data import scripts, offering a step-by-step methodology to streamline the process.
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Article
Medium·2y
How to Build RAG Applications with Pinecone Serverless, OpenAI, Langchain and Python
Learn how to build RAG applications using Pinecone Serverless, OpenAI, Langchain, and Python. Discover why Pinecone is a preferred vector database, and follow a step-by-step guide on building RAG apps. Upsert data to the vector database and query it to retrieve relevant information.
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Article
freeCodeCamp·2y
How to Use LangChain and GPT to Analyze Multiple Documents
LangChain is a versatile project designed to provide easy integrations with various large language models (LLMs). This guide demonstrates how to use LangChain with GPT to programmatically access, summarize, and analyze online documents, with examples using financial reports from companies like Alphabet, Cisco, and IBM. The process involves setting up a Python environment, loading multiple PDF files, and utilizing tools such as PyPDFLoader, CharacterTextSplitter, and FAISS for text processing and embedding. The tutorial highlights the ability to automate AI tasks on real-world data efficiently.
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Article
Hacker News·2y
gregpr07/browser-use
Browser-Use allows Language Models (LLMs) to interact with websites naturally, offering features like universal LLM support, smart element detection, multi-tab management, and vision model support. Users can customize browser interactions and persist browser states across multiple agents. It supports all LangChain chat models and provides examples and quick start guides to help users get started.
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Article
freeCodeCamp·2y
How to Use LangChain to Build With LLMs – A Beginner's Guide
Learn how to use LangChain to build with LLMs, a beginner's guide to the popular framework for creating LLM-powered apps.
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Video
Sam Witteveen·2y
Gemma 2 - Local RAG with Ollama and LangChain
Gemma 2 has been released for multiple formats including Keras, PyTorch, and Hugging Face transformers. This post details the author's experience using the 9B and 27B models in Ollama, highlighting the better performance of the 9B model for real-time responses. A straightforward script is provided to create a fully local Retrieval-Augmented Generation (RAG) system using Gemma 2, Nomic embeddings, and ChromaDB, all executed within VSCode. The steps involve setting up an indexer, embedding transcripts from Alex Hormozi's YouTube channel, and handling text splitting methods. Debugging tips and additional add-ons for the RAG system are also discussed.
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Article
DevBlogs·2y
Build a chatbot on your own data in 1 hour with Azure SQL, Langchain and Chainlit
Creating a custom chatbot using your own data has become straightforward with the help of LangChain, Chainlit, and Azure SQL. By leveraging Azure SQL's new vector support, you can store and query data embeddings. LangChain assists in defining prompts for the chatbot and retrieving relevant session data from the database. Finally, the integration with Chainlit ensures seamless Conversational AI implementation. This process takes less than an hour and can be further expanded based on your requirements.
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Article
Towards Data Science·2y
Building Knowledge Graphs with LLM Graph Transformer
This post explores building knowledge graphs using the LLM Graph Transformer from LangChain. It covers techniques for extracting structured data from unstructured text to create knowledge graphs, highlighting the advantages and challenges of both tool-based and prompt-based modes. The guide includes steps for setting up a Neo4j environment, defining graph schemas, and ensuring consistency in extraction. Additionally, it addresses how to import graph documents into databases like Neo4j for further analysis and application.
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Article
Towards AI·2y
Retrieval-Augmented Generation (RAG) using LangChain, LlamaIndex, and OpenAI
Large Language Models (LLMs) can sometimes provide incorrect information due to outdated knowledge, a phenomenon known as 'hallucination.' Retrieval-Augmented Generation (RAG) addresses this by dynamically fetching relevant data from external databases, ensuring responses are accurate and up-to-date. This guide explains how RAG works, from cleaning and indexing data to retrieving and generating responses, and provides implementation steps using LangChain and LlamaIndex. Advanced techniques like Parent Document Retriever are also discussed for enhanced specificity and context.
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Article
SingleStore·2y
How to Create Open-Source AI Apps with LangChain
LangChain is an open-source AI framework that simplifies building custom AI applications using Large Language Models (LLMs). It provides various modules/components to enhance the capabilities of LLMs' problem-solving strategies. This post includes a tutorial on how to build AI applications using LangChain, complete with installing the framework, loading a PDF, splitting its content, storing it in a database, and retrieving accurate responses.
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Article
LangChain·2y
How Rexera’s AI agents drive quality control with LangGraph
Rexera is transforming the real estate transaction industry with advanced AI agents, leveraging LangChain and LangGraph to automate complex workflows and reduce errors. Initially using single-prompt LLMs, which had limitations in handling intricate scenarios, Rexera transitioned to a multi-agent approach with CrewAI and eventually to LangGraph. LangGraph's tree-like structure for Quality Control (QC) has significantly improved accuracy and efficiency, minimizing false positives and negatives, particularly for rush orders.
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Article
GoPenAI·2y
Lab #3: Implementing RAG to build a “Chat with Multiple PDFs” app
This post explains how to build a 'Chat with Multiple PDFs' app using Retrieval-Augmented Generation (RAG), and covers its benefits, such as reducing model hallucination and enhancing reliability. It details phases for pre-processing and inference, including loading, chunking, and embedding data into a vector database, and setting up a retrieval chain using Langchain and OpenAI integration.
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Article
Community Picks·2y
LLM app dev using AWS Bedrock and Langchain
The post explains how to develop applications using Large Language Models (LLMs) with Amazon Bedrock and Langchain to perform tasks like Question Answering over large document corpora. It introduces the concept of retrieval-augmented generation (RAG), which uses document processing and vector embedding to fetch relevant document chunks for question answering. The process includes setting up LLM and embedding models, loading and splitting documents into chunks, creating a vector database using SingleStoreDB, and performing similarity searches to generate context-aware responses.
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