dspy-ai
There are 2 repositories under dspy-ai topic.
unoplat / unoplat-code-confluence
Maintain a live, pluggable context layer per repo that renders and updates Agents.md
Language:Python 71mbakgun / dspy-examples
This codebase demonstrates various DSPy functionalities through practical examples.
Language:Python 52sachink1729 / DSPy-Chain-of-Thought-RAG
Building a Chain of Thought RAG Model with DSPy, Qdrant and Ollama
Language:Jupyter Notebook 33Building Private Healthcare AI Assistant for Clinics Using Qdrant Hybrid Cloud, DSPy and Groq - Llama3
Language:Jupyter Notebook 22rachittshah / DSpy-KGs
LLM-driven automated knowledge graph construction from text using DSPy and Neo4j
Language:Python 17Discover 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.
Language:Jupyter Notebook 16Exploring advanced prompting tools to query SQL database with multiple tables in natural language using LLMs
Language:Jupyter Notebook 13Technoculture / dspy-agents
An AI agents framework addressing the two core challenges with real world agents - Optimisation and Deployement
kallemickelborg / agentic-ai
Stateful AI Agent for Knowledge Extraction
Language:Python 10- Language:Jupyter Notebook 10
Ashad001 / RoomAligner
A focus on aligning room elements for better flow and space utilization.
Language:Python 7- Language:Python 4
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- Language:Python 1
- DSpy_Ollama_Custom_ChromaDB_Examples
hr1juldey / DSpy_Ollama_Custom_ChromaDB_Examples
Examples created by the members of AI&U for DSPy
Language:Jupyter Notebook 1 jaseci-labs / mtllm-evaluation
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.
Language:Jupyter Notebook 1Sagor0078 / building-RAG-using-DSPy-and-Gemini-API
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.
Language:Python 1TorchonRAG / TorchON
TorchON : Optimized information retrieval application creation and deployment - easily make an good knowledge retrieval app, then share it securely with your colleagues
Language:Python 1A Multistep Question Answering Graphrag system with LLM routing to optimize answer quality
Language:Python 0robjsliwa / adventures_in_dspy
Learn DSPy framework by coding text adventure game
Language:Python 0shayantist / LLM-FactChecker
End-to-end pipeline for LLMs to fact-check complex statements, with evaluations and ablation studies across GPT-4, Calude Sonnet 3.5, LLaMa 3, etc.
Language:Jupyter Notebook 0ten24bytes / dspy-demo
A comprehensive demonstration project for DSPy, showcasing various advanced features and patterns for programming foundation models.
Language:Jupyter Notebook 0devadigapratham / DSPy
Experimental DSPy project exploring AI-powered movie analysis and resume evaluation using local language models, demonstrating LM pipeline techniques and prompt optimization.
Language:PythonKatashynskyi / multiclass_NER_LLM_UKR
It takes long texts in UKR and extract 13 classes of entities. Based on Gemma 2 9b. F1 = 0.35 for f1 metric on kaggle competition. DSPY powered
Language:Jupyter Notebook
