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                                                  actual screenshot taken of the knowledge bot Introducing the Local Copilot Chatbot Application: Your Ultimate Document-Based Query Assistant In today's fast-paced world, finding precise information quickly can make a significant difference. Our Local Copilot Chatbot Application offers a cutting-edge solution for accessing and querying document-based knowledge with remarkable efficiency. This Flask-based application utilizes the powerful Ollama and Phi3 models to deliver an interactive, intuitive chatbot experience. Here's a deep dive into what our application offers and how it leverages modern technologies to enhance your productivity. What is the Local Copilot Chatbot Application? The Local Copilot Chatbot Application is designed to serve as your personal assistant for document-based queri...

Streamlit apps rely on WebSockets, which can create challenges when embedding them directly in an iframe, especially in some browsers due to security restrictions. Instead, consider an alternative approach such as creating a simple JavaScript-based frontend that can interact with your Streamlit backend via an API, ensuring easy integration into client websites. Here is the demo Chat Bot application approaches: Backend Development 1. Model Setup:    - Use Ollama and Llama3 for natural language understanding and generation.    - Train your models with data specific to each business for better performance. 2. API Development:    - Create an API using a framework like FastAPI or Flask to handle requests and responses between the frontend and the backend models.    - Ensure the API supports multitenancy by handling different businesses' data separately. 3. Vector Store with FAISS:    - Use FAISS to create a vector store database for each busi...

  Chatbot Application with Local LLM, RAG, LangChain, and Guardrail I've developed a chatbot application designed for informative and engaging conversationAs you already aware that Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds, RAG can significantly improve the value of generative AI systems. Developers must consider a variety of factors when building a RAG pipeline: from LLM response benchmarking to selecting the right chunk size. In tapplication demopost, I demonstrate how to build a RAG pipeline uslocal LLM which can be converted to ing NVIDIA AI Endpoints for LangChain. FirI have you crdeate a vector storeconnecting with one of the ...

  pexel To develop a Telegram bot for monitoring, summarizing, and sending periodic overviews of channel posts, follow these steps: Step 1: Set Up Your Environment 1. Install Python : Ensure you have Python installed on your system. 2. Install Required Libraries :     ```python     pip install python-telegram-bot requests beautifulsoup4     ``` Step 2: Create the Telegram Bot 1. Create a Bot on Telegram : Talk to [@BotFather](https://telegram.me/BotFather) to create a new bot. Note the API token provided. Step 3: Develop the Bot 1. Monitor Telegram Channels :     ```python     from telegram import Bot, Update     from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext     import requests     from bs4 import BeautifulSoup     TOKEN = 'YOUR_TELEGRAM_BOT_TOKEN'     CHANNELS = ['@example_channel_1', '@example_channel_2']     SUMMARY_PERIOD = 6...

  Photo by Kindel Media at pexel If you want to develop a ChatBot with Azure and OpenAi in a few simple steps. You can follow the steps below. 1. Design and Requirements Gathering:    - Define the purpose and functionalities of the chatbot.    - Gather requirements for integration with Azure, OpenAI, Langchain, Promo Engineering, Document Intelligence System, KNN-based question similarities with Redis, vector database, and Langchain memory. 2. Azure Setup :    - Create an Azure account if you don't have one.    - Set up Azure Functions for serverless architecture.    - Request access to Azure OpenAI Service. 3. OpenAI Integration :    - Obtain API access to OpenAI.    - Integrate OpenAI's GPT models for natural language understanding and generation into your chatbot. 4. Langchain Integration :    - Explore Langchain's capabilities for language processing and understanding.    - Integrate Langc...

pexel: Shantanu Kumar Improving the performance of your chatbot involves several steps. Let’s address this issue: Latency Diagnosis : Begin by diagnosing the causes of latency in your chatbot application. Use tools like LangSmith to analyze and understand where delays occur. Identify Bottlenecks : Check if any specific components are causing delays: Language Models (LLMs) : Are they taking too long to respond? Retrievers : Are they retrieving historical messages efficiently? Memory Stores : Is memory retrieval slowing down the process? Streamline Prompt Engineering : Optimize your prompts: Contextual Information : Include only relevant context in prompts. Prompt Length : Avoid overly long prompts that increase LLM response time. Retriever Queries : Optimize queries to vector databases. Memory Store Optimization : If you’re using a memory store (e.g., Zep), consider: Caching : Cache frequently accessed data. Indexing : Optimize data retrieval using efficient indexing. Memory Size : Ens...