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| title: MLOps RAG Agent | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 6.18.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| python_version: "3.10" | |
| short_description: Level 3 Agentic RAG for MLOps/DevOps Q&A | |
| # 🤖 MLOps / DevOps Agentic RAG Agent | |
| A **Level 3 Agentic RAG** pipeline for MLOps and DevOps Q&A, featuring query rewriting, iterative retrieval, self-reflection, source citation, and fallback handling. | |
| ## Architecture | |
| ``` | |
| User Query | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────────────────────┐ | |
| │ Level 3 Agentic RAG Pipeline │ | |
| │ │ | |
| │ 1. Query Rewriting (Flan-T5-base) │ | |
| │ └─ Reformulates query for better retrieval │ | |
| │ │ | |
| │ 2. Semantic Retrieval │ | |
| │ ├─ Embeddings: all-MiniLM-L6-v2 (384-dim) │ | |
| │ ├─ Vector Store: ChromaDB (cosine similarity) │ | |
| │ └─ Indexing: LlamaIndex (SentenceSplitter) │ | |
| │ │ | |
| │ 3. Relevance Filtering (Self-Reflection) │ | |
| │ └─ Flan-T5 checks if each chunk is relevant │ | |
| │ │ | |
| │ 4. Answer Generation (Flan-T5-base) │ | |
| │ └─ Context-grounded answer synthesis │ | |
| │ │ | |
| │ 5. Answer Self-Reflection (Flan-T5-base) │ | |
| │ └─ Judges completeness → triggers re-retrieval │ | |
| │ │ | |
| │ 6. Iterative Retrieval (up to 2 iterations) │ | |
| │ └─ Follow-up queries if answer is inadequate │ | |
| │ │ | |
| │ 7. Fallback Handling │ | |
| │ └─ General answer when no relevant docs found │ | |
| │ │ | |
| │ 8. Source Citation │ | |
| │ └─ Source doc + cosine similarity per chunk │ | |
| └─────────────────────────────────────────────────────────┘ | |
| │ | |
| ▼ | |
| Gradio UI (Answer + Query Info + Reflection + Citations) | |
| ``` | |
| ## Tech Stack | |
| | Component | Technology | | |
| |-----------|------------| | |
| | Orchestration | LlamaIndex Core | | |
| | Vector Store | ChromaDB (persistent) | | |
| | Embeddings | sentence-transformers/all-MiniLM-L6-v2 | | |
| | Generator | google/flan-t5-base | | |
| | UI | Gradio | | |
| ## Knowledge Base | |
| The curated knowledge base covers five MLOps/DevOps domains: | |
| - **AWS SageMaker** — Training jobs, HPO, endpoints, Pipelines, Feature Store, Model Monitor, Clarify | |
| - **Kubernetes** — Pods, Deployments, HPA/KEDA, GPU management, Kubeflow, KServe, Helm | |
| - **CI/CD** — GitHub Actions, Jenkins, ArgoCD/GitOps, DVC, MLflow model registry, testing strategies | |
| - **Terraform** — HCL syntax, remote state, modules, EKS/SageMaker provisioning, Terragrunt | |
| - **Model Monitoring** — Data drift, concept drift, Evidently AI, Alibi Detect, Prometheus/Grafana, Great Expectations | |
| ## Example Questions | |
| - How do I configure auto-scaling for a SageMaker inference endpoint? | |
| - What is the difference between data drift and concept drift? | |
| - How do I use Terraform to provision an EKS cluster with GPU nodes? | |
| - Explain how to set up a CI/CD pipeline for model retraining with GitHub Actions. | |
| - How do I deploy a model with canary rollout on Kubernetes using KServe? | |
| - What metrics should I monitor for ML models in production? | |
| ## Running Locally | |
| ```bash | |
| git clone https://huggingface.co/spaces/atulkrs/mlops-rag-agent | |
| cd mlops-rag-agent | |
| pip install -r requirements.txt | |
| python app.py | |
| ``` | |