From e0f817c11be46966b52f008473ce41ef34bfe297 Mon Sep 17 00:00:00 2001 From: murrayhu-mdb Date: Thu, 21 May 2026 12:09:30 +0100 Subject: [PATCH] Update README.md --- apps/local-bot/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/apps/local-bot/README.md b/apps/local-bot/README.md index c6ab48f9..ed0f3c1f 100644 --- a/apps/local-bot/README.md +++ b/apps/local-bot/README.md @@ -1,6 +1,6 @@ # Step-by-Step Guide: Building a Local Chatbot with Streamlit, LangChain, Ollama, and MongoDB Atlas -In this tutorial, we'll set up a local chatbot using **Streamlit**, **LangChain**, **Ollama**, and **MongoDB Atlas Search**. This bot will leverage MongoDB's powerful Atlas Search capabilities alongside local LLMs (Large Language Models) via Ollama, allowing you to enhance user queries with context from chat history. +In this tutorial, we'll set up a local chatbot using **Streamlit**, **LangChain**, **Ollama**, and **MongoDB Search**. This bot will leverage MongoDB's powerful MongoDB Search capabilities alongside local LLMs (Large Language Models) via Ollama, allowing you to enhance user queries with context from chat history. ## Prerequisites Before starting, make sure you have the following installed: @@ -40,7 +40,7 @@ Here’s a quick rundown of the tools we’re using in this project: * *[Streamlit](https://streamlit.io)*: A Python library for easily creating data-based web applications. We'll use it to create a local chatbot interface. * *[LangChain](https://langchain.com)*: A framework that simplifies working with LLMs and document processing. It will assist processing user queries and generate responses. * *[Ollama](https://ollama.com)*: A solution for deploying LLMs locally without external API dependency. It to host our models. -* *[MongoDB Atlas Search](https://www.mongodb.com/products/platform/atlas-search)*: Adds a powerful, flexible vector search functionality to our app. It will store user queries and responses in MongoDB. +* *[MongoDB Search](https://www.mongodb.com/products/platform/atlas-search)*: Adds a powerful, flexible vector search functionality to our app. It will store user queries and responses in MongoDB. ### Setting Up `requirements.txt` @@ -173,7 +173,7 @@ except Exception as e: ### Step 3.2: Loading Documents and Creating a Vector Search Index -Now, load documents, process them with LangChain, and store them as vector embeddings in MongoDB. This setup allows MongoDB Atlas to perform fast vector-based searches. +Now, load documents, process them with LangChain, and store them as vector embeddings in MongoDB. This setup allows MongoDB to perform fast vector-based searches. ```python from langchain_ollama import OllamaEmbeddings @@ -268,7 +268,7 @@ At this point, you can start prompting with inputs like “Who started AT&T?” ## Conclusion and Next Steps -In this tutorial, we built a local chatbot setup using MongoDB Atlas Search and local LLMs via Ollama, integrated through Streamlit. This project forms a robust foundation for further development and deployment. +In this tutorial, we built a local chatbot setup using MongoDB Search and local LLMs via Ollama, integrated through Streamlit. This project forms a robust foundation for further development and deployment. Possible Extensions: