ragas

There are 0 repository under ragas topic.

Hungreeee / Resume-Screening-RAG-Pipeline
An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.
llm rag rag-fusion gpt-3 gpt-4 langchain openai ragas resume-screening adaptive
Language:Jupyter Notebook 61
benitomartin / rag-langchain-ragas
RAG project for QA retrieval using LanChain and Ragas
faiss jupyter-notebook langchain llm python rag ragas
Language:Jupyter Notebook 6
Edouard-Legoupil / rag_extraction
A tutorial on how to build Summary Brief from Evaluation Report - Offline+Open Source
langchain llm mixtral-8x7b-instruct rag ragas ollama
Language:HTML 4
rag-optimization-cnn-dailymail
hillaryke / rag-optimization-cnn-dailymail
Optimizing a Retrieval-Augmented Generation (RAG) system on the CNN/Daily Mail dataset using LangChain, with performance benchmarking and analysis via RAGAS.
chromadb langchain rag rag-implementation ragas
Language:Jupyter Notebook 3
mistir-nigusse / LLM_Prompt_Engine
AI-driven prompt generation and evaluation system, designed to optimize the use of Language Models (LLMs) in various industries. The project consists of both frontend and backend components, facilitating prompt generation, automatic evaluation data generation, and prompt testing.
gpt-3 langchain prompt-engineering rag-pipeline ragas
Language:Python 2
temesgen5335 / Legal_Expert_Contract_Advisor_RAG
This project focuses on developing a Retrieval-Augmented Generation (RAG) system tailored for Contract Q&A.
genai-chatbot generative-ai langchain openai-api rag ragas vector-database weaviate
Language:Jupyter Notebook 2
devgargd7 / CorrectiveRAG
Multi-step Agentic Self-Corrective RAG with websearch
docker google-search-api langchain ollama openai-api ragas streamlit
Language:Python 1
temesgen5335 / Precision--RAG
This project focuses on Automatic Prompt Engineering (APE) for Retrieval-Augmented Generation (RAG) systems.
openai-api pinecone rag ragas vector-database
Language:Python 1
Yohanes213 / Contract-Advisor-RAG
The objective is to build, evaluate, and improve a Retrieval-Augmented Generation (RAG) system for Contract Q&A, simulating interaction with a contract by asking questions and getting precise answers.
llm rag ragas streamlit
Language:HTML 1
asvch / ask_docs
LLM AI chatbot using Advanced Retrieval Augmented Generation (RAG), Langchain, and Streamlit to answer questions about information contained in numerous files.
chromadb langchain rag ragas streamlit
Language:Python 0
hillaryke / contract-qa-high-precision-rag
A RAG system for Contract Q&A that enables chatting with a contract and asking questions about the contract. It has an interface build with React and FastAPI in backend integrating rag-pipeline with Autogen agents and websockets for communication. Evaluation of the RAG is done using RAGAS.
embedding-models fastapi llms rag ragas reactjs vector-database websockets
Language:Jupyter Notebook 0
paulomuraroferreira / langflow_ragas
This repository implements the evaluation metrics from RAGAS to a RAG pipeline on langflow.
genai langflow rag ragas llm
0
RhiteshKS / QA-chatbot-using-RAG-SMAI
SMAI Project. Made an abstractive qa RAG chatbot using Langchain and experimented with variety of vector stores and retrievers and evaluated them using Ragas
chromadb cohere faiss-vector-database langchain-python matryoshka nlp openai-api ragas
Language:Jupyter Notebook 0
amruta33 / Rag_evaluation
Rag Evaluation using ragas
embedded evaluation genai langchain llm openai python rag ragas
Language:Jupyter Notebook
cheronodaisy / contract-advisor-rag
A Contract Q&A Retrieval-Augmented Generation (RAG) system with LangChain's advanced retrieval methods. Evaluation is done with RAGAs metrics.
gpt-4o langchain prompt-engineering rag ragas
Language:Jupyter Notebook
GetachewAgegnehu / PrecisionRAG
This project aims to develop an enterprise-grade Retrieval-Augmented Generation (RAG) system by automating the prompt engineering process. The goal is to create a comprehensive solution that simplifies the task of crafting effective prompts for Language Models (LLMs), enabling businesses to leverage advanced AI capabilities more efficiently.
elo prompt-engineering rag ragas
Language:Jupyter Notebook
shaopyshaozi / Rag_Eval_Pipeline
检索生成系统评价模型 - 检索内容排序+生成标准答案+RAGAs评分
rag ragas sort
Language:Python

ragas

Hungreeee / Resume-Screening-RAG-Pipeline

benitomartin / rag-langchain-ragas

Edouard-Legoupil / rag_extraction

hillaryke / rag-optimization-cnn-dailymail

mistir-nigusse / LLM_Prompt_Engine

temesgen5335 / Legal_Expert_Contract_Advisor_RAG

devgargd7 / CorrectiveRAG

temesgen5335 / Precision--RAG

Yohanes213 / Contract-Advisor-RAG

asvch / ask_docs

hillaryke / contract-qa-high-precision-rag

paulomuraroferreira / langflow_ragas

RhiteshKS / QA-chatbot-using-RAG-SMAI

amruta33 / Rag_evaluation

cheronodaisy / contract-advisor-rag

GetachewAgegnehu / PrecisionRAG

shaopyshaozi / Rag_Eval_Pipeline