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Tag: python

  • Building a Weather Chatbot with Langchain

    Building a Weather Chatbot with Langchain This article demonstrates how to create a simple chatbot using Langchain that can fetch and provide current weather information based on city names or zip codes. We’ll utilize the power of Large Language Models (LLMs) and a simple custom tool to achieve this. Prerequisites Python 3.6+ Langchain Library: Install… Read more

  • Building a Stock Price Chatbot with Langchain

    Building a Stock Price Chatbot with Langchain This article demonstrates how to create a simple chatbot using Langchain that can fetch and provide current and historical stock prices. We’ll leverage the power of Large Language Models (LLMs) and the yfinance library to build this interactive tool. Prerequisites Python 3.6+ Langchain Library: Install using pip install… Read more

  • Exploring the World of Graph Databases: A Detailed Comparison

    Exploring the World of Graph Databases: A Detailed Comparison for Novices (More Details & Links) Imagine data not just as tables with rows and columns, but as a rich tapestry of interconnected entities. This is the core idea behind graph databases. Unlike traditional relational databases optimized for structured data, graph databases are purpose-built to efficiently… Read more

  • Vector DB Weaviate Advanced Internal Concepts and Code Snippets

    Weaviate Internal Concepts and Code Snippets This document explores the core internal concepts of Weaviate, an open-source vector database, and provides illustrative code snippets using the Python client library to demonstrate its usage. Internal Concepts of Weaviate Schema and Collections Schema: Defines the structure of your data, including classes (now called Collections in newer versions),… Read more

  • Vector DB Pinecone Internal Concepts and Code Snippets

    Pinecone Internal Concepts and Code Snippets This document explores the inferred internal concepts of Pinecone, a vector database, and provides illustrative code snippets using the Python client library to demonstrate its usage. Internal Concepts of Pinecone (Inferred) Index Structure Sharding: Data is likely distributed across multiple servers for scalability. Replication: Redundancy is probably implemented for… Read more

  • Python Libraries Used in Robotics

    Python Libraries Used in Robotics Python has become a popular language in robotics due to its ease of use and extensive libraries. Here are some commonly used Python libraries: Robot Operating System (ROS) While a framework, ROS has extensive Python libraries (rospy) for robotics development. ROS GitHub rospy Documentation PyRobot A library from Facebook AI… Read more

  • Python Libraries for Video Motion Detection – Real-Life Use Cases

    Python Libraries for Video Motion Detection – Real-Life Use Cases Python libraries for video motion detection are employed in a wide array of real-world applications, leveraging their capabilities for various purposes. Here are some prominent examples, categorized by the libraries often used: OpenCV (cv2) – Use Cases OpenCV’s efficiency and versatility make it suitable for… Read more

  • Python Libraries for Video Motion Detection

    Python Libraries for Video Motion Detection Several Python libraries can be used for video motion detection, ranging from fundamental image processing techniques to sophisticated deep learning approaches. The choice often depends on the complexity of the scene, the type of motion you want to detect, and performance requirements. Here’s a more detailed look at some… Read more

  • Python Libraries for Image Object Identification

    Python Libraries for Image Object Identification Here’s a breakdown of popular Python libraries used for analyzing image object identification: High-Level Libraries (Easy to Use, Often with Pre-trained Models): TensorFlow Object Detection API (with Keras) A robust framework built on TensorFlow for constructing, training, and deploying object detection models. Keras simplifies building neural networks and offers… Read more

  • Data Structure of Trained ML Models

    Data Structure of Trained ML Models Once a machine learning model is trained, its “knowledge” is stored in a specific data structure that allows it to make predictions on new, unseen data. The exact structure varies depending on the type of model and the library used for training. However, the core idea is to save… Read more

  • Detailed Exploration of LangChain Chains and Use Cases

    Detailed Exploration of LangChain Chains and Use Cases LangChain’s “Chains” are composable sequences of components, allowing you to build sophisticated applications by linking together Language Models (LLMs), prompts, utilities, and other chains. Let’s explore each of the core chain types with more detail and practical use cases. 1. LLMChain: Structuring Language Model Interactions Detail: The… Read more

  • Exploring LangChain MCP Features with Sample Code

    Exploring LangChain MCP Features with Sample Code LangChain provides integration with the Model Context Protocol (MCP), allowing LLM agents to interact with external tools and data sources managed by an MCP server. This enables powerful capabilities like real-time information retrieval and action execution. Here’s an exploration of key LangChain MCP features with illustrative Python code… Read more

  • Exploring LangChain, LangGraph, and LangSmith

    Exploring LangChain, LangGraph, and LangSmith The LangChain ecosystem provides a comprehensive suite of tools for building, deploying, and managing applications powered by Large Language Models (LLMs). It consists of three key components: LangChain, LangGraph, and LangSmith. LangChain: The Building Blocks LangChain is an open-source framework designed to simplify the development of LLM-powered applications. It provides… Read more

  • Top 5 SCA Tools Comparison & Other Options

    Top 5 SCA Tools Comparison &amp Other Options 1. Snyk Open Source Snyk Open Source is a developer-first SCA tool that focuses on identifying and helping developers fix vulnerabilities in open-source dependencies. Key Features: Developer-friendly interface and integration with IDEs. Comprehensive vulnerability database (Snyk Intel). Automatic fix suggestions and remediation advice. License compliance management. Integration… Read more

  • Top 5 SAST Tools Comparison & Other Options

    Top 5 SAST Tools Comparison & Other Options Top 5 SAST Tools Comparison 1. Checkmarx SAST Checkmarx SAST examines application source code, bytecode, or binaries without execution, identifying security weaknesses early in the SDLC. Key Features: Supports a wide range of languages and frameworks (35 languages, 80+ frameworks). Incremental scanning for faster performance. Highly accurate… Read more

  • Top 5 Code Generation Models (May 5, 2025)

    Top 5 Code Generation LLMs (May 5, 2025) The landscape of Large Language Models for code generation is dynamic. This list highlights five prominent models based on their performance, features, and recognition as of today. 1. GPT-4o Provider: OpenAI Key Details: Often cited as a leader in overall LLM benchmarks, including code generation. Known for… Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers… Read more

  • Using local LLM for Document Extraction

    Non-Cloud LLM for Document Extraction This guide explains how to use a non-cloud version of a pretrained Large Language Model (LLM) for document extraction, focusing on open-source models and local execution. Phase 1: Setting Up Your Local Environment 1. Hardware Requirements Ensure your system meets the following recommendations: CPU/GPU: An NVIDIA GPU with sufficient VRAM… Read more

  • Pretrained Models for Document Extraction

    Pretrained Models for Document Extraction Cloud-Based Pretrained Models Google Cloud Document AI: Offers pretrained models for various document types (invoices, receipts, IDs, etc.) for key-value pair, table extraction, and classification. AWS Textract: Provides pretrained models for OCR, key-value pair extraction, and table extraction from documents and images. Azure Form Recognizer (now Document Intelligence): Offers pretrained… Read more

  • Automating PDF to JSON Extraction with AI/ML

    Automating PDF to JSON Extraction with AI/ML 1. Understanding the Problem and Defining Key Values for AI/ML When leveraging AI/ML for PDF to JSON extraction, the initial problem definition remains crucial, but with a focus on how AI/ML can address challenges posed by unstructured or highly variable documents. Identify the Key Values: As before, define… Read more

  • Reinforcement Learning Explained with Python Code (Simplified)

    Reinforcement Learning Explained with Python Code (Simplified) To illustrate the core concepts of Reinforcement Learning, we’ll use a very simplified example in Python. Imagine an agent trying to learn the best way to navigate a small grid world to reach a goal. 1. The Environment Our environment will be a 1D grid with a starting… Read more

  • Sample Agentic AI Orchestrating Complex Cybersecurity Workflow in GCP

    Agentic AI Orchestrating Complex Workflow in GCP (Sample) Agentic AI Orchestrating Complex Workflow in GCP (Sample) This sample outlines a conceptual implementation of an agentic AI system orchestrating a complex cybersecurity workflow in Google Cloud Platform (GCP), focusing on automatically investigating and responding to potential phishing incidents reported by employees. Conceptual Architecture +———————+ +———————+ +——————–+… Read more

  • Sample Autonomous Threat Identification and Mitigation in AWS (Sample)

    Autonomous Threat Identification and Mitigation in AWS (Sample) This sample outlines a conceptual architecture and key AWS services for building an Autonomous Threat Identification and Mitigation system, focusing on detecting and responding to suspicious network traffic. Conceptual Architecture +—————–+ +—————–+ +———————+ +———————+ +———————+ | Network Traffic | –> | VPC Flow Logs / | –>… Read more

  • Real-time Recommendation Engine AI Agent on AWS

    Real-time Recommendation Engine AI Agent on AWS Implementing a real-time recommendation engine AI agent on AWS requires a robust and scalable architecture. Here are implementation examples for key services in the tech stack: 1. Real-time Data Ingestion (Amazon Kinesis Data Streams): You would use the AWS SDK (Boto3 in Python) in your application backend to… Read more

  • AI Agent with Short-Term Memory on Google Cloud

    AI Agent with Short-Term Memory on Google Cloud Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Google Cloud Platform (GCP) offers a range of services that… Read more

  • AI Agent with Long-Term Memory on Google Cloud

    AI Agent with Long-Term Memory on Google Cloud Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Google Cloud Platform (GCP) offers… Read more

  • AI Agent with Long-Term Memory on Azure

    AI Agent with Long-Term Memory on Azure Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Microsoft Azure offers a comprehensive suite… Read more

  • AI Agent with Short-Term Memory on Azure

    AI Agent with Short-Term Memory on Azure Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Microsoft Azure offers a range of services that can be utilized… Read more

  • AI Agent with Long-Term Memory on AWS

    AI Agent with Long-Term Memory on AWS Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Amazon Web Services (AWS) offers a… Read more

  • Python Multithreading in API Backend

    Python Multithreading in API Backend Python Multithreading in API Backend Multithreading in Python can improve the performance of an API backend by allowing it to handle multiple requests concurrently. This is particularly useful for I/O-bound operations, such as fetching data from external APIs or databases. Understanding the GIL Before diving into the code, it’s crucial… Read more