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Mlflow With Rag model by AnasAber
0
🧠
Model

Mlflow With Rag

by AnasAber gh-model--anasaber--mlflow_with_rag
Nexus Index
0.0 Top 18%
P: Popularity 0
F: Freshness 0
C: Completeness 0
U: Utility 0
Tech Context
Vital Performance
0 DL / 30D
0.0%

This project is for people that want to deploy a RAG pipeline using MLflow. The project uses: - and as orchestrators - and - as an MLOps framework for deploying and tracking !Project Overview Diagram 1. Clone the repository 2. Install the dependencies Make sure to put your api_keys into the , and rename it to 3. Notebook Prep: - Put your own data files in the data/ folder - Go to the notebook, and replace "api_key_here" with your huggingface_api_key - If you have GPU, you're fine, if not, run...

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Model Information Summary
Entity Passport
Registry ID gh-model--anasaber--mlflow_with_rag
Provider github
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{gh_model__anasaber__mlflow_with_rag,
  author = {AnasAber},
  title = {Mlflow With Rag Model},
  year = {2026},
  howpublished = {\url{https://github.com/AnasAber/MLflow_with_RAG}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
AnasAber. (2026). Mlflow With Rag [Model]. Free2AITools. https://github.com/AnasAber/MLflow_with_RAG

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone
git clone https://github.com/AnasAber/MLflow_with_RAG

⚖ī¸ Nexus Index V16.5

Methodology Index Protocol
0.0
TOP 18% SYSTEM IMPACT
Popularity (P) 0
Freshness (F) 0
Completeness (C) 0
Utility (U) 0

đŸ’Ŧ Index Insight

The Free2AITools Nexus Index for Mlflow With Rag aggregates Popularity (P:0), Freshness (F:0), and Completeness (C:0). The Utility score (U:0) represents deployment readiness and ecosystem adoption.

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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Deployment Guide

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Technical Deep Dive

MLflow Deployement of a RAG pipeline đŸĨ€

This project is for people that want to deploy a RAG pipeline using MLflow.

The project uses:

  • LlamaIndex and langchain as orchestrators
  • Ollama and HuggingfaceLLMs
  • MLflow as an MLOps framework for deploying and tracking

Project Overview Diagram

How to start

  1. Clone the repository
bash 
git clone https://github.com/AnasAber/RAG_in_CPU.git
  1. Install the dependencies
bash 
pip install -r requirements.txt

Make sure to put your api_keys into the example.env, and rename it to .env

  1. Notebook Prep:
  • Put your own data files in the data/ folder
  • Go to the notebook, and replace "api_key_here" with your huggingface_api_key
  • If you have GPU, you're fine, if not, run it on google colab, and make sure to download the json file output at the end of the run.
  1. Go to deployement folder, and open two terminals:
bash 
python workflow.py

And after the run, go to your mlflow run, and pick the run ID: Run ID Place it into this command:

bash 
mlflow models serve -m runs://rag_deployement -p 5001

In the other terminal, make sure to run

bash 
app.py
  1. Open another terminal, and move to the frontend folder, and run:
bash 
npm start

Now, you should be seeing a web interface, and the two terminals are running. Interface

If you got errors, try to see what's missing in the requirements.txt.

Enjoy!

🚀 Quick Start

bash 
git clone https://github.com/AnasAber/RAG_in_CPU.git

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
  • â€ĸ Source: Unknown
🐙 Data Source: GitHub ↗
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AI Summary: Based on GitHub metadata. Not a recommendation.

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🆔 Identity & Source

id
gh-model--anasaber--mlflow_with_rag
source
github
author
AnasAber
tags
evaluation-metricsmlflowmlflow-projectsmlflow-trackingmlflow-tracking-servermlflow-uimlopsmlops-templateragrag-evaluationrag-pipelinellamaindexllamaindex-ragcicddeploymentmlops-projectmlflow-deployementpython

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
feature-extraction

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