Explore open-source implementations leveraging Qdrant for agent tool use, vector indexing, evaluation, and LLM orchestration.
A Streamlit application that simulates a full-service legal team using multiple AI agents to analyze legal documents and provide comprehensive legal insights. Each agent represents a different legal specialist role, from research and...
This Streamlit app implements an AI-powered customer support agent for synthetic data generated using GPT-4o. The agent uses OpenAI's GPT-4o model and maintains a memory of past interactions using the Mem0 library with Qdrant as the...
This Streamlit app implements a multi-agent career coaching assistant built with Google's Agent Development Kit (ADK). A career orchestrator agent delegates each question to the right specialist sub-agent (resume, interview practice,...
This Streamlit app implements an AI-powered research assistant that helps users search for academic papers on arXiv while maintaining a memory of user interests and past interactions. It utilizes OpenAI's GPT-4o-mini model for...
This Streamlit app implements an AI-powered travel assistant that remembers user preferences and past interactions. It utilizes OpenAI's GPT-4o for generating responses and Mem0 with Qdrant for maintaining conversation history.
This Streamlit app is an AI-powered chatbot that uses OpenAI's GPT-4o model with a persistent memory feature. It allows users to have conversations with the AI while maintaining context across multiple interactions.
This Streamlit application implements a fully local ChatGPT-like experience using Llama 3.1, featuring personalized memory storage for each user. All components, including the language model, embeddings, and vector store, run locally...
This Streamlit application demonstrates a multi-LLM system with a shared memory layer, allowing users to interact with different language models while maintaining conversation history and context across sessions.
A RAG Agentic system built with the new Gemini 2.0 Flash Thinking model and gemini-exp-1206, Qdrant for vector storage, and Agno (phidata prev) for agent orchestration. This application features intelligent query rewriting, document...
AI Blog Search is an Agentic RAG application designed to enhance information retrieval from AI-related blog posts. This system leverages LangChain, LangGraph, and Google's Gemini model to fetch, process, and analyze blog content,...
A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via Ollama), Snowflake for embeddings, Qdrant for vector storage, and Agno for agent orchestration, this application offers both...
An open-source agentic RAG system for mathematical problem solving and symbolic reasoning using LLMs and calculation tools.
This RAG Application demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using locally running Qwen 3 and Gemma 3 models via Ollama. It combines document processing, vector search, and web search...
A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get...
A Streamlit application that demonstrates an advanced implementation of RAG Agent with intelligent query routing. The system combines multiple specialized databases with smart fallback mechanisms to ensure reliable and accurate...
This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database. Built with Agno v2.0.
An OpenAI SDK powered customer support agent application that delivers voice-powered responses to questions about your knowledge base using OpenAI's GPT-4o and TTS capabilities. The system crawls through documentation websites with...
This script demonstrates how to build a voice-enabled Retrieval-Augmented Generation (RAG) system using OpenAI's SDK and Streamlit. The application allows users to upload PDF documents, ask questions, and receive both text and voice...
