https://github.com/spidy20/deepseek-rag-app
This POC demonstrates how to deploy the DeepSeek model on AWS EC2 and build a RAG (Retrieval-Augmented Generation) application using LangChain & ChromaDB. You'll learn to set up an EC2 instance, configure dependencies, run the DeepSeek Ollama API, and integrate it with a Streamlit-based chat app to process and analyze PDF documents with AI-powered
https://github.com/spidy20/deepseek-rag-app
deepseek deepseek-chat deepseek-r1 ollama
Last synced: 6 months ago
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This POC demonstrates how to deploy the DeepSeek model on AWS EC2 and build a RAG (Retrieval-Augmented Generation) application using LangChain & ChromaDB. You'll learn to set up an EC2 instance, configure dependencies, run the DeepSeek Ollama API, and integrate it with a Streamlit-based chat app to process and analyze PDF documents with AI-powered
- Host: GitHub
- URL: https://github.com/spidy20/deepseek-rag-app
- Owner: Spidy20
- Created: 2025-02-08T13:03:52.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2025-02-08T13:17:55.000Z (8 months ago)
- Last Synced: 2025-02-08T14:23:37.772Z (8 months ago)
- Topics: deepseek, deepseek-chat, deepseek-r1, ollama
- Language: Python
- Homepage:
- Size: 516 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deploy DeepSeek Model on AWS EC2 🚀 | Build a RAG App 🤖 with LangChain & ChromaDB! 🔥
### [Watch this tutorial►](https://youtu.be/YWmnD_QcZQU)
- This video demonstrates how to deploy the DeepSeek model on AWS EC2 and build a RAG (Retrieval-Augmented Generation) application using LangChain & ChromaDB. You'll learn to set up an EC2 instance, configure dependencies, run the DeepSeek Ollama API, and integrate it with a Streamlit-based chat app to process and analyze PDF documents with AI-powered responses. 🚀
### Implementation Architecture
### Used Services
- **AWS EC2**: Responsible for managing the backend of the Document Extractor using the Boto3 SDK.
- **AWS EC2**: Deploy and run the DeepSeek model efficiently on a scalable cloud instance.
- **Streamlit**: Build an interactive chat interface to test DeepSeek’s AI responses.
- **ChromaDB**: Store and retrieve vector embeddings for RAG-based document processing.
- **Ollama**: Serve and run the DeepSeek model locally on EC2 with optimized inference.
### Implementation Setup
1. **Set Up EC2 Instance** – Launch an AWS EC2 instance and configure security settings.
2. **Connect to EC2** – Access the instance via SSH and install necessary dependencies.
3. **Install Ollama** – Set up Ollama to run and manage the DeepSeek model.
4. **Test DeepSeek Model** – Run a quick shell test to verify model functionality.
5. **Install App Dependencies** – Install Streamlit, ChromaDB, and LangChain for the RAG app.
6. **Develop the Chat App** – Integrate DeepSeek with Streamlit for real-time chat interaction.
7. **Configure Security & API Access** – Set up EC2 security groups and expose the API.
8. **Test & Deploy** – Upload a PDF, query the model, analyze responses, and finalize deployment.
# Commands
# Ubuntu Commands
```sh
# Update System Packages
apt update
# To download Ollama
curl -fsSL https://ollama.com/install.sh | sh
# To download Model visit
# https://ollama.com/library/deepseek-r1:7b
# Download model
ollama run deepseek-r1:7b
# Check API Serving
ollama serve
# Model API
curl http://localhost:11434/api/chat -d '{
"model": "deepseek-r1:8b",
"messages": [{ "role": "user", "content": "Write python script for hello world" }],
"stream": false
}'
# To install requirements
python3 -m pip install -r requirements.txt
# To run App
python3 -m streamlit run app.py
```
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