# jamwithai/production-agentic-rag-course

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6,972 stars · 1,564 forks · Python · MIT

## Links

- GitHub: https://github.com/jamwithai/production-agentic-rag-course
- awesome-repositories: https://awesome-repositories.com/repository/jamwithai-production-agentic-rag-course.md

## Description

This project is an educational course and technical blueprint for building production-ready retrieval-augmented generation systems. It provides a curriculum and implementation strategies for designing agentic workflows, containerized AI infrastructure, and retrieval pipelines using large language models.

The materials focus on agentic design patterns, utilizing state-based decision nodes to rewrite queries and grade retrieved documents. It differentiates its approach by providing a deployment framework for managing databases, search engines, and API services through container orchestration.

The project covers a broad range of architectural capabilities, including hybrid search with reciprocal rank fusion, OCR-based document parsing for PDF ingestion, and input-validation guardrails to prevent hallucinations. It also addresses operational requirements such as distributed request tracing, automatic query caching, and server-sent event streaming for real-time responses.

## Tags

### Artificial Intelligence & ML

- [Agentic RAG Development](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-rag-development.md) — Provides a blueprint for building intelligent, self-correcting retrieval systems using agentic orchestration. ([source](https://github.com/jamwithai/production-agentic-rag-course/blob/main/notebooks/week7/week7_agentic_rag.ipynb))
- [Hybrid Search Retrievers](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-rag-development/knowledge-base-retrieval/hybrid-search-retrievers.md) — Combines semantic vector embeddings with keyword search using reciprocal rank fusion for improved retrieval. ([source](https://github.com/jamwithai/production-agentic-rag-course#readme))
- [Agentic Workflow Automation](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-workflow-automation.md) — Uses intelligent agents to coordinate tasks and adapt retrieval based on result quality. ([source](https://github.com/jamwithai/production-agentic-rag-course#readme))
- [Data Ingestion Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/data-ingestion-pipelines.md) — Automatically processes, chunks, and vectorizes raw data from APIs and PDFs for use in LLMs. ([source](https://github.com/jamwithai/production-agentic-rag-course#readme))
- [Agentic Retrieval Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/agentic-retrieval-workflows.md) — Implements state-based agentic workflows for intelligent query rewriting and retrieved document grading.
- [Ingestion Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/document-collections/ingestion-pipelines.md) — Provides automated pipelines for extracting text from PDFs and external APIs to populate vector stores.
- [RAG Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/rag-pipelines.md) — Implements workflows that augment model outputs by retrieving and integrating relevant external data from document sources. ([source](https://github.com/jamwithai/production-agentic-rag-course#readme))
- [RAG Data Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/rag-data-pipelines.md) — Provides a practical blueprint for constructing data pipelines that process PDFs and store embeddings for semantic search.
- [State-Based Workflow Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/state-based-workflow-engines.md) — Manages complex agent execution flows using state graphs to coordinate query rewriting and document grading.
- [LLM Response Streaming](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-response-streaming.md) — Configures incremental delivery of language model outputs to reduce perceived latency for users. ([source](https://github.com/jamwithai/production-agentic-rag-course/blob/main/README.md))

### Data & Databases

- [Automated Document Ingestion](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/data-extraction-ingestion/document-processing-tools/automated-document-ingestion.md) — Automates the retrieval of external papers and transformation of PDFs into structured text for processing. ([source](https://github.com/jamwithai/production-agentic-rag-course/tree/main/airflow))
- [Query Result Caching](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/caching-performance/caching-strategies/query-result-caching.md) — Implements temporary storage of language model request results to reduce repeated computation and latency.
- [Search Result Fusion Algorithms](https://awesome-repositories.com/f/data-databases/search-result-fusion-algorithms.md) — Combines semantic vector embeddings with keyword search results using rank fusion to improve retrieval precision.

### DevOps & Infrastructure

- [Container Orchestration & Deployment](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration-deployment.md) — Provides configurations for managing application deployment within isolated containerized environments for databases and API services.
- [Application Containerization](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration/container-runtimes/runtime-configuration-interfaces/docker-socket-orchestrators/docker-target-configurators/docker-container-deployments/application-containerizers/application-containerization.md) — Packages software and dependencies into portable container images to ensure consistent execution across environments. ([source](https://github.com/jamwithai/production-agentic-rag-course/blob/main/Dockerfile))
- [AI Service Orchestration](https://awesome-repositories.com/f/devops-infrastructure/containerized-deployment-orchestration/ai-service-orchestration.md) — Provides a framework for orchestrating containerized AI services, including databases, search engines, and LLM APIs.
- [Containerized RAG Services](https://awesome-repositories.com/f/devops-infrastructure/deployment-management-strategies/execution-platforms-and-targets/deployment-infrastructure/containerized-rag-services.md) — Employs deployment patterns for running RAG engines and their dependencies within containerized environments.
- [Production Deployment Guides](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/self-hosted-infrastructure-management/self-hosted-deployment-infrastructure/production-deployment-guides.md) — Provides technical guidance for configuring high-availability production environments using container orchestration for RAG services. ([source](https://github.com/jamwithai/production-agentic-rag-course/blob/main/README.md))

### Education & Learning Resources

- [RAG Development Courses](https://awesome-repositories.com/f/education-learning-resources/educational-resources/courses-training-certifications/courses-structured-learning/courses/generative-ai-courses/autonomous-agent-development-courses/rag-development-courses.md) — Offers a structured learning curriculum for building production-ready retrieval-augmented generation systems.
- [LLM Architecture Guides](https://awesome-repositories.com/f/education-learning-resources/llm-architecture-guides.md) — Provides a technical reference for implementing hybrid search, guardrails, and request tracing in LLM workflows.

### Security & Cryptography

- [LLM Input Guardrails](https://awesome-repositories.com/f/security-cryptography/llm-input-guardrails.md) — Implements security layers that filter prompt injections and validate model inputs to prevent hallucinations.
- [AI Guardrails](https://awesome-repositories.com/f/security-cryptography/safety-and-validation-layers/ai-guardrails.md) — Implements validation layers that inspect inputs and detect domain boundaries to prevent hallucinations.

### Part of an Awesome List

- [OCR Document Parsers](https://awesome-repositories.com/f/awesome-lists/data/document-parsing-and-extraction/ocr-document-parsers.md) — Uses optical character recognition to extract structured text and metadata from PDF files for vector indexing.
- [PDF Text Extractors](https://awesome-repositories.com/f/awesome-lists/data/document-parsing-and-extraction/pdf-text-extractors.md) — Parses PDF files to extract structured text and metadata for use in RAG pipelines. ([source](https://github.com/jamwithai/production-agentic-rag-course/tree/main/airflow))

### Networking & Communication

- [Server-Sent Events](https://awesome-repositories.com/f/networking-communication/server-sent-events.md) — Pushes real-time model outputs from the server to the client using standard event streams.

### Software Engineering & Architecture

- [Performance and Reliability](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability.md) — Implements retry logic, rate limiting, and local caching to ensure stable data ingestion pipelines. ([source](https://github.com/jamwithai/production-agentic-rag-course/tree/main/airflow))
- [RAG Pipeline Optimizers](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/data-handling-throughput/rag-pipeline-optimizers.md) — Implements request tracing and caching strategies to optimize retrieval latency and reduce operational costs. ([source](https://github.com/jamwithai/production-agentic-rag-course#readme))

### System Administration & Monitoring

- [Request Tracing](https://awesome-repositories.com/f/system-administration-monitoring/request-tracing.md) — Integrates observability tools to monitor end-to-end request paths and analyze system latency. ([source](https://github.com/jamwithai/production-agentic-rag-course/blob/main/README.md))

### Testing & Quality Assurance

- [Distributed Request Tracing](https://awesome-repositories.com/f/testing-quality-assurance/api-call-loggers/distributed-request-tracing.md) — Captures timing and processing data across backend operations to analyze latency and operational costs.