Best of Bots — July 2024
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Hacker News·2y
yunginnanet/HellPot: HellPot is a cross-platform portal to endless suffering meant to punish unruly HTTP bots.
HellPot is a cross-platform honeypot designed to trap and punish rogue HTTP bots by sending them endless streams of data. Implemented in Go, it uses a toml configuration file, JSON logging, and offers substantial performance gains. When misconfigured or when bots ignore the standard `robots.txt`, HellPot subjects them to an eternity of useless data generated by a Markov engine. It has easy setup steps and integrates well with web servers like nginx and Apache.
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Tech With Tim·2y
Building a $1500 AI Chat Bot in 20 Minutes - Full Tutorial
Learn how to build an AI chatbot valued at $1500 using the Coes platform in just 20 minutes. This comprehensive tutorial covers setting up the chatbot, using GPT-4, adding functionalities like weather updates and Google searches, creating a knowledge base, and integrating the bot into a website. The guide highlights the potential of utilizing AI to drive business engagements and streamline tasks such as client consultations.
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Towards AI·2y
Langchain + Graph RAG + GPT-4o Python Project: Easy AI/Chat for...
A tutorial on creating a fully local chatbot using Langchain, Graph RAG, and GPT-4o. The guide emphasizes the advantages of Graph RAGs in reducing hallucinations by offering more reliable and engaging data. It simplifies the process, making it accessible for both business and personal use.
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Community Picks·2y
How to build a Perplexity-like Chatbot in Slack?
Learn how to build a Slack chatbot that can search the internet and return information with citations using AI. The process involves configuring a SlackBot, setting up event listeners, and utilizing the Composio platform with tools like Exa and Tavily. Follow step-by-step instructions to install necessary libraries, configure integrations, and build the agent workflow. The guide also explains how to define and deploy AI agents capable of responding to Slack messages by retrieving information through internet searches.
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Improving RAG Answer Quality Through Complex Reasoning
Multi-hop retrieval enhances the capabilities of Retrieval-Augmented Generation (RAG) systems by enabling complex reasoning over multiple pieces of information. This method is especially powerful for advanced question-answering systems. The post demonstrates building a Q&A chatbot for the healthcare domain using Indexify, OpenAI, and DSPy, showcasing how multi-hop retrieval can significantly improve answer quality in complex queries.
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Towards Data Science·2y
Improving RAG Answer Quality Through Complex Reasoning
Explore how multi-hop retrieval can enhance the quality of answers in Retrieval-Augmented Generation (RAG) systems, particularly in complex reasoning tasks. Using DSPy and Indexify, the post demonstrates the construction of a question-answering chatbot for the healthcare domain. The setup includes the installation of necessary packages, data ingestion, and creating multi-hop retrieval logic for efficient question handling. The integration allows for dynamic context generation, deduplication, and chain-of-thought reasoning, showcasing significant improvements in handling complex queries.
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GoPenAI·2y
LocalRAG: Deploy a production-grade RAG chatbot on your Local Machine or Server
LocalRAG is a self-hosting application designed to deploy a production-level RAG chatbot on a local machine or server, ensuring data privacy and security. The application utilizes LangChain for backend processing, Streamlit for the frontend, Qdrant as the vector store, and Redis for storing chat messages. Prerequisites include setting up a Python virtual environment and Docker. Users also need to install Ollama for running large language models locally. The guide provides steps to clone the necessary repository and run the application using Streamlit.
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LangChain·2y
UX for Agents, Part 1: Chat
Agentic systems face unique challenges compared to traditional computer systems, such as latency, unreliability, and natural language interfaces. This has led to the development of new UX paradigms, with chat being the most dominant so far. Streaming chat allows users to see intermediate actions and guide the AI, while non-streaming chat provides responses in batches, requiring trust but potentially allowing for more complex tasks. Both methods have their pros and cons, and there are likely other UX paradigms worth exploring for AI interactions.
