.Net Gyaan, The blog on latest technologies

  I built advanced LLM (Large Language Model) Agents from the ground up using Python and the Google Generative AI package. I won't just show you the code; I'll explain exactly how agents work under the hood, from reasoning and planning to tool execution.     This tutorial is perfect for a developer, data scientist, or AI enthusiast ready to move from simple prompt engineering to building truly Agentic AI applications.       PREREQUISITES: Basic knowledge of Python.  A Google AI Studio API Key (it's free!). pip install google-generativeai python-dotenv  Create new file named "shopping_agent.py" and add below code inside the file.  def confirm_shopping_order(product, quantity,address) -> str: """ Function-callable API for Confirm a shopping order. params: dict with keys 'product', 'quantity', 'address', etc. """ #API call to search product availability can be added he...

 In previous article I have explained how to install Deepseek R1 model locally and create the RAG application using the locally downloaded models step by step. In this article I will explain Step-by-step to use the Chroma DB vector database in RAG application using llama index & Deepseek R1 local model with Ollama. Chroma DB is open source vector db which can be used to store the vector embeddings of documents and query the embeddings based on user query. I have used the same example used in previous article and added only Chroma DB integration code.   Prerequisites for this example is as follows: Visual studio code Python Ollama  Open visual studio code and create the file with name "sample.py". Now in visual studio code and go to terminal menu and click on New terminal link it will open new terminal. In terminal enter below command to install the LlamaIndex library, Chroma DB library, LlamaIndex Ollama and  LlamaIndex embedding Ollama library in your mac...

In this article I will explain Step-by-step to locally download and use the DeepSeek R1 Model with Ollama for free! and also explain how to set up AI-powered Retrieval Augmented Generation (RAG) using the nomic-embed-text:latest  embedding model and run the DeepSeek R1 Model locally via Ollama .   Prerequisites for this example is as follows: Visual studio code Python Ollama  Open visual studio code and create the file with name "sample.py". Now in visual studio code and go to terminal menu and click on New terminal link it will open new terminal. In terminal enter below command to install the LlamaIndex library and LlamaIndex Ollama and  LlamaIndex embedding Ollama library in your machine. pip install llama-index llama-index-llms-ollama llama-index-embeddings-ollama Create the folder named "doc" in root directory of the application as shown in below image and store the documents you want to query.   To get Ollama models list you can visit ollama ...