multi1: create o1-like reasoning chains with multiple AI providers (and locally). Supports LiteLLM as backend too for 100+ providers at once.

This is a repository to develop a web interface to enble configuration of nftables via GUI.

Транслятор запросов к LLM Yandex Cloud из SDK OpenAI

[F]inding [T]hings [I]n [S]tuff

Repository for my Talk "Malicious Learning". Includes pigbrother tool.

My implementations for "AI for Developers" course assignments, in Python and JavaScript.

This is an End-to-End LangChain Study Ride to learn about all Framework tools. Motive to make to gain the more practical knowledge about all the algo and self study,

Chat with AI in the terminal

Python code for label clusters chains - multilabel classification

Probabilistic Robotics - Problem 2 of chapter 2, Simulator of Markov Chain

Prova de código de senioridade Pleno do Fiesc. Onde Obtive aprovação e uma boa colocação entre centenas de candidatos de todo o Brasil.

Ice Breaker is comprehensive fullstack app leveraging generative AI and LangChain to find LinkedIn profiles and generate engaging ice breakers. LangChain ReAct agents ensure accurate URL retrieval and JSON cleaning, identifying a summary, facts, topics, and ice breakers. The frontend is built with HTML/CSS, and Flask powers the backend development.

Open-source LangChain toolkit with custom Chains, ChatModels, Embeddings, and Output Parsers. Build powerful AI workflows effortlessly. Perfect for developers and businesses leveraging LLMs.

Learning and Implementing INGESTION, RETRIVAL-AUGMENTED-GENERATION. LLMS | PINECONE | LANGCHAIN | LANGSMITH |

This repository covers extensive tutorials on how to integrate LangSmith with LangChain with LangGraph to incorporate observability, monitoring, alerting, evaluation, etc. within complex LLM workflows and applications.

This repository demonstrates how to use Chains in LangChain to build modular AI pipelines.

Smart Quiz Generator is a Streamlit-based app that uses GPT-4 to create quizzes (MCQ, True/False, or Fill-in-the-Blank) from your own documents (PDF/TXT) or web pages. It processes content, stores it in a FAISS vector store for quick retrieval, and generates customized quizzes based on a chosen topic.

The chatbot converts user input into a SQL query using GPT-4, executes it against a SQL database, and returns results in natural language. This involves data processing, interaction with the OpenAI API, and integration within a Streamlit application.