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Fully functional AI agent RAG app with step-by-step instructions (100% opensource). This article provides a detailed guide on building a Local AI RAG (Retrieval-Augmented Generation) Agent using DeepSeek R1, which runs entirely on your local machine. The agent processes information like a human, connecting different pieces of information, drawing logical conclusions, and showing its thinking process. The best part? No cloud is needed, ensuring complete privacy and full control over your data.
Tech Stack:
- DeepSeek (via Ollama)
- Snowflake for embeddings (via Ollama)
- Qdrant for vector storage
- Agno for agentic RAG
What It Can Process:
- PDFs
- Web content
- Previous conversations
- Combine them all to give reasoned answers
Steps to Get Started:
- Subscribe to Unwind AI (for free): theunwindai.com
- Star the repo: https://lnkd.in/dW6b_dEn
Practice Verified Codes and Commands:
1. Install Ollama:
curl -fsSL https://ollama.ai/install.sh | sh
2. Pull DeepSeek R1 Model:
ollama pull deepseek-r1
3. Run DeepSeek R1 Locally:
ollama run deepseek-r1
4. Set Up Qdrant for Vector Storage:
docker run -p 6333:6333 qdrant/qdrant
5. Install Agno for Agentic RAG:
pip install agno
6. Run the Streamlit Dashboard:
streamlit run app.py
What Undercode Say:
Building a Local AI RAG Agent with DeepSeek R1 is a groundbreaking approach to handling data locally, ensuring privacy, and reducing costs. This setup is particularly beneficial for developers who need to process sensitive information without relying on cloud services. The integration of DeepSeek, Snowflake, Qdrant, and Agno provides a robust framework for creating intelligent agents that can reason and process information effectively.
For those interested in further exploring AI and RAG, consider diving into more advanced topics like fine-tuning models, optimizing vector storage, and enhancing agent reasoning capabilities. Here are some additional resources and commands to get you started:
- Fine-Tuning Models:
ollama fine-tune deepseek-r1 --data=your_dataset.json
-
Optimizing Qdrant:
docker exec -it qdrant_container qdrant optimize
-
Enhancing Agent Reasoning:
agno enhance --model=deepseek-r1 --data=your_data_folder
For more detailed tutorials and updates, visit theunwindai.com and follow Shubham Saboo for daily tips and tutorials on LLMs, RAG, and AI Agents.
By leveraging these tools and commands, you can build a powerful local AI agent that processes information efficiently and securely. This approach not only enhances your data privacy but also gives you full control over your AI models, making it a valuable addition to any developer’s toolkit.
References:
Hackers Feeds, Undercode AI
