Hello, Guys, I am Spidy. I am back with another video.
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 responses. 🚀
Chapters:
00:00 - Introduction
00:43 - Implementation Flow Overview
01:32 - Architecture Explanation
02:34 - Creating an EC2 Instance
04:30 - Connecting to EC2
05:25 - Installing Dependencies
06:10 - Installing Ollama
07:45 - Testing DeepSeek Model in Shell
09:15 - Installing App Requirements (Streamlit, ChromaDB)
10:35 - Setting Up EC2 Security Groups
11:18 - Testing DeepSeek Ollama API
12:51 - App Code Walkthrough
15:16 - Running the App on EC2
15:43 - Uploading a PDF for Testing
15:57 - Chat Testing
17:34 - Analyzing the Generated Answer
20:00 - POC Conclusion & Resource Cleanup
Used Services
-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.
Code & Commands ► https://github.com/Spidy20/Deepseek-RAG-App
AWS Tutorials ► https://youtube.com/playlist?list=PLsT53VV2LIIGnKRdYHMo-KO9uQcIhbFPs
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Note: If you want me to solve your errors and make the project run into the system, I will do it using a remote desktop, and it will be paid. You can reach me at [email protected] for your queries.
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