The goal is to understand the codebase/s to form a pluggable knowledge Graph and become the unified code context provider

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

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

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.

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

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

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

STEP Web agent for WebArena using dspy.

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

TorchON : Optimized information retrieval application creation and deployment - easily make an good knowledge retrieval app, then share it securely with your colleagues

A Multistep Question Answering Graphrag system with LLM routing to optimize answer quality

Learn DSPy framework by coding text adventure game

Examples created by the members of AI&U for DSPy

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.