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8 changes: 4 additions & 4 deletions apps/local-bot/README.md
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# 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:
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* *[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`

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### 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
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## 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:

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