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

This codebase demonstrates various DSPy functionalities through practical examples.

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

Stateful AI Agent for Knowledge Extraction

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

STEP Web agent for WebArena using dspy.

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

Different approaches to RAG

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

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

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.

Learn DSPy framework by coding text adventure game

Experimental DSPy project exploring AI-powered movie analysis and resume evaluation using local language models, demonstrating LM pipeline techniques and prompt optimization.