AI Frameworks · microsoft

rag-time

RAG Time is a Microsoft-authored educational repository containing a 5-week video course series, sample code, and guides on Retrieval-Augmented Generation (RAG) fundamentals and techniques. It covers vector indexing, multimodal data retrieval, and real-world RAG use cases with hands-on Jupyter notebooks and expert interviews.

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Source: GitHub — github.com/microsoft/rag-time

893

GitHub stars

317

Forks

Jupyter Notebook

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	microsoft/rag-time
Owner	microsoft
Primary language	Jupyter Notebook
License	MIT — OSI-approved
Stars	893
Forks	317
Open issues	4
Latest release	Unknown
Last updated	2025-06-17
Source	https://github.com/microsoft/rag-time

What rag-time is

The repository provides practical implementations of RAG pipelines using Azure AI Search, vector indexing (HNSW), quantization methods (binary and scalar), hybrid search strategies, and agentic RAG patterns. Content spans knowledge retrieval architectures, vector compression for scale, and multimodal indexing approaches, with code samples targeting LLM integration workflows.

Quickstart

Get the rag-time source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/microsoft/rag-time.git2cd rag-time3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Team RAG Training & Upskilling

Use as structured self-paced or instructor-led learning material for engineering teams building LLM applications. Video content and working code samples accelerate onboarding to RAG concepts and Azure AI Search integration patterns.

Proof-of-Concept Development

Leverage reference implementations and sample notebooks to rapidly prototype RAG solutions—particularly for vector indexing optimization, multimodal retrieval, and agentic patterns without building from scratch.

Architecture Decision Support

Review Journey 2 and Journey 3 content to evaluate retrieval system design patterns, scaling strategies, and quantization trade-offs when deciding on vector DB, indexing, and retrieval infrastructure for production systems.

Implementation considerations

Clone and run Jupyter notebooks locally or in Azure notebooks; requires Python environment setup, Azure credentials, and optional Azure OpenAI/Search resources.
Code samples are illustrative prototypes; production deployments require error handling, logging, authentication, input validation, and scaling considerations not covered in samples.
Vector indexing examples (HNSW, quantization) are specific to Azure AI Search; porting to alternative vector DBs (Pinecone, Weaviate, Milvus) requires API translation.
Multimodal and agentic RAG journeys assume familiarity with LLM APIs and prompt engineering; teams new to LLMs should start with Journey 1 fundamentals.
No automated testing, CI/CD pipelines, or deployment manifests provided; integration into existing DevOps workflows is the responsibility of the adopting team.

When to avoid it — and what to weigh

Seeking Production-Ready Framework or Library — This is educational content, not a deployable framework. Code samples are illustrative; production use requires integration into your own systems, error handling, monitoring, and security hardening.
Requiring Non-Microsoft AI Services Integration — Content is heavily focused on Azure AI services (Azure AI Search, Azure OpenAI). If your tech stack mandates non-Azure LLMs or vector databases, adapting examples may require significant rework.
Need Immediate Support or SLA-Backed Maintenance — This is an educational open-source repository, not a supported product. No SLA, no dedicated support channel beyond community Discord. For critical production systems, engage Microsoft professional services separately.
Avoiding Time-Bound Content — Content references a scheduled live video series (March–April) and may contain time-sensitive examples. If evergreen, fully async learning is required, verify sample currency before heavy reliance.

License & commercial use

Licensed under MIT (Massachusetts Institute of Technology License). MIT is a permissive OSI-approved license that allows commercial use, modification, and redistribution with minimal restrictions, provided the license notice and copyright are retained.

MIT license permits commercial use of the code samples and materials. However, this is educational content, not a supported commercial product. Any commercial deployment of code derived from this repository is the responsibility of the adopting organization. Microsoft provides no warranty, support, or SLA. Verify that your use of Azure AI services (if applicable) complies with your Azure licensing agreement.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Code samples handle API credentials (Azure keys, OpenAI tokens) via environment variables, which is reasonable for development. Production deployments must implement secure secret management (Azure Key Vault, HashiCorp Vault) and rotate credentials regularly. No explicit security audit, SAST analysis, or vulnerability disclosure process mentioned. Input validation in samples is minimal; production code must sanitize LLM inputs and outputs. Vector data sensitivity (embeddings may leak training data patterns) is not discussed; assess data classification requirements independently.

Alternatives to consider

LangChain & LlamaIndex Documentation

Comparable open-source frameworks with production-grade libraries for RAG pipelines; broader integration support beyond Azure; more extensive security and testing guidance.

Pinecone or Weaviate Official Guides

Vector database-specific tutorials with native SDKs; if not Azure-locked, may reduce integration friction and offer more direct support.

Azure AI Foundry Samples Repository

Sibling Microsoft resource with broader AI development patterns, enterprise integration examples, and potentially tighter alignment with production Azure deployments.

Software development agency

Build on rag-time with DEV.co software developers

Fork the repository, follow the 5-week learning journeys, and build production-ready RAG pipelines. Watch the video series, run code samples, and join the Azure AI Community Discord for expert Q&A.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

RAG-Driven-Generative-AI RAG-Retrieval rag-cookbooks

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rag-time FAQ

Can I use RAG Time code in production?

Yes, under the MIT license. However, code samples are illustrative prototypes. Production use requires security hardening (secret management, input validation), error handling, logging, monitoring, and load testing. You are responsible for operationalization and support.

Do I need an Azure account to follow RAG Time?

The content is tool-agnostic in theory, but Journey 2 and beyond heavily use Azure AI Search and Azure OpenAI examples. You can adapt examples to non-Azure services, but provided samples will require Azure credentials to run as-is.

How long does it take to complete RAG Time?

The repo describes a 5-week schedule with live Wednesday sessions (March–April). Individual journey completion varies; 2–4 hours per journey is typical for video + code review. Self-paced learners can progress faster or slower.

Is there professional support available?

No. This is community-supported via the Azure AI Community Discord. For production support, engage Microsoft through separate commercial support channels or partner with a systems integrator.

Work with a software development agency

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If rag-time is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Master RAG?

Fork the repository, follow the 5-week learning journeys, and build production-ready RAG pipelines. Watch the video series, run code samples, and join the Azure AI Community Discord for expert Q&A.

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