rag-pipeline

There are 0 repository under rag-pipeline topic.

Julian-AT / synth-ui
Generate & Ship UI with minimal effort - Open Source Generative UI with natural language
ai artificial-intelligence open-source rag-pipeline ui user-interface v0
Language:TypeScript 11
Clearedge-AI / clearedge
Build a RAG preprocessing pipeline
document-parser haystack langchain llamaindex llm ocr pdf pdf-ocr-extraction pdf-to-json pdf-to-text rag-pipeline retrieval-augmented-generation table-detection table-recognition
Language:Jupyter Notebook 10
MaxMLang / RAG-nificent
RAG-nificent is a state-of-the-art framework leveraging Retrieval-Augmented Generation (RAG) to provide instant answers and references from a curated directory of PDFs containing information on any given topic. Supports Llama3.1 and OpenAI Models via the Groq API.
chainlit chatgpt gemma groq groqapi langchain llama3 llama3-rag mistral-7b mistralai pinecone rag rag-implementation rag-pipeline vector-database
Language:Python 9
AnasAber / RAG_in_CPU
This repo is for advanced RAG systems, each branch will represent a project based on RAG.
advanced-rag generative-ai llms rag rag-implementation rag-pipeline chromadb cohere cohere-api groq groq-api langchain chat-application chat-with-pdf faiss faiss-vector-database redis redis-cache
Language:Python 5
Dynatrace / obslab-llm-observability
Search for a holiday and get destination advice from an LLM. Observability by Dynatrace.
demo llm ollama pinecone ai-observability genai openai rag-pipeline application-observability dynatrace
Language:HTML 5
teddy-ambona / openai-rag-chatbot
Demo LLM (RAG pipeline) web app running locally using docker-compose. LLM and embedding models are consumed as services from OpenAI.
langchain llm mlops rag-pipeline vector-database
Language:Python 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
nelima22 / hybrid_search_rag_pipeline
Hybrid Search RAG Pipeline integrating BM25 and vector search techniques using LangChain
hybrid-search-technique langchain rag-chatbot rag-pipeline
Language:Jupyter Notebook 0
dynamicanupam / Fashion_Recommendation_System_using_RAG_pipeline
A GenAI based search system that scans numerous fashion product descriptions to recommend suitable options based on user queries.
cross-encoder genai llm openai rag-pipeline text-embedding vector-database
Language:Jupyter Notebook
olifarhaan / rag-console-chat
This project implements document ingestion, embedding generation, and retrieval-augmented generation (RAG). If you are looking for a small project to understand the implementation of basic RAG then this project is good to go.
chromadb console-application rag rag-pipeline retrieval-augmented-generation retrieval-chatbot vector-database
Language:Python
siddhantprateek / bitrac-rag
This is a production-ready applications using RAG-based Language Model.
llm mlops mlops-workflow rag rag-pipeline
simranjeet97 / Learn_RAG_from_Scratch_LLM
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
artificial-intelligence datascience-machinelearning genai-domain genai-usecase generative-ai llm-apps llm-evaluation llm-framework llm-training rag rag-application rag-chatbot rag-embeddings rag-evaluation rag-implementation rag-llm rag-model rag-pipeline retrieval-augmented-generation
Language:Jupyter Notebook

rag-pipeline

Julian-AT / synth-ui

Clearedge-AI / clearedge

MaxMLang / RAG-nificent

AnasAber / RAG_in_CPU

Dynatrace / obslab-llm-observability

teddy-ambona / openai-rag-chatbot

mistir-nigusse / LLM_Prompt_Engine

nelima22 / hybrid_search_rag_pipeline

dynamicanupam / Fashion_Recommendation_System_using_RAG_pipeline

olifarhaan / rag-console-chat

siddhantprateek / bitrac-rag

simranjeet97 / Learn_RAG_from_Scratch_LLM