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

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

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

Different approaches to RAG

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

Learn DSPy framework by coding text adventure game

STEP Web agent for WebArena using dspy.

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

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

Examples created by the members of AI&U for 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.

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

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

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.

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

The goal is to parse,process and ingest codebase/s to form a pluggable knowledge Graph and become the unified code context provider