Best of Langchain — August 2024

1. 1
   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.

   39
2. 2
   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.

   21
3. 3
   Article

   Towards Data Science·2y

   How to Implement a GenAI Agent using Autogen or LangGraph

   GenAI agents can automate parts of business processes that involve tasks like text summarization, question answering, and code generation. This post demonstrates implementing a GenAI agent using two frameworks: Autogen, which treats workflows as conversations between agents, and LangGraph, which represents workflows as graphs. Step-by-step guides include setting up an agent framework to query weather information using APIs, handling location extraction, geocoding, and obtaining the final answer from the NWS API. Both frameworks are showcased with configurations for various AI models.

   18