Open-source AI-powered data science platform.

This codebase demonstrates various DSPy functionalities through practical examples.

Building a Chain of Thought RAG Model with DSPy, Qdrant and Ollama

Building Private Healthcare AI Assistant for Clinics Using Qdrant Hybrid Cloud, DSPy and Groq - Llama3

Discover advanced AI techniques in my repository combining Multi-Hop Chain of Thought (CoT) and Retrieval-Augmented Generation (RAG) using DSPy and Indexify. Enhance complex problem-solving with multi-step reasoning and external knowledge integration. Perfect for AI enthusiasts and researchers.

LLM-driven automated knowledge graph construction from text using DSPy and Neo4j

Stateful AI Agent for Knowledge Extraction

Exploring advanced prompting tools to query SQL database with multiple tables in natural language using LLMs

An AI agents framework addressing the two core challenges with real world agents - Optimisation and Deployement

A focus on aligning room elements for better flow and space utilization.

AI agents implementation to demostrate agentic programming frameworks and concepts like DSPy, MCP, etc.

🤖 Autonomous agentic system for visual analytics in the GenAI era

A multi-agent system built with DSPy that coordinates research and writing agents to produce comprehensive articles on any topic.

STEP Web agent for WebArena using dspy.

Useful modules that can be smoothly plugged into your DSPy projects.

A comprehensive demonstration project for DSPy, showcasing various advanced features and patterns for programming foundation models.

This codebase implements a Retrieval-Augmented Generation (RAG) chatbot using the Gemini API and DSPy framework, designed to answer questions based on the HotPotQA dataset. It includes components for loading data, generating responses, and evaluating model performance through various QA strategies, including basic QA and multi-hop retrieval.

Evaluation of DSpy, LMQL and Jaclang's MTLLM Feature on problem set of different difficulty levels based on the technique used and difficulty of the problem itself.

Different approaches to RAG