Agentic RAG

agentic_rag is a starter repository for building an agentic retrieval-augmented generation (RAG) system.

The goal of this project is to combine:

• retrieval over external knowledge sources
• an LLM-powered reasoning layer
• agentic workflows for tool use, planning, and answer generation

Status

This repository is currently in an early scaffold state. At the moment, it does not yet contain application source code, runtime scripts, or configuration beyond Git metadata and ignore rules.

This README is intended to give the project a clear direction and provide a place to document the implementation as it grows.

Planned Goals

• ingest documents from one or more data sources
• index content for semantic retrieval
• retrieve relevant context for user queries
• let an agent decide when to search, reason, or use tools
• generate grounded answers with cited context
• provide an interface for local development and experimentation

Proposed Architecture

As the project is implemented, it will likely include components such as:

• ingestion/ for loading and preprocessing source documents
• indexing/ for embeddings and vector storage
• retrieval/ for search and reranking logic
• agent/ for orchestration and tool-calling workflows
• app/ or api/ for a user-facing interface
• tests/ for automated coverage

Setup

Setup instructions will depend on the stack chosen for the implementation. A typical workflow may look like this:

git clone git@github.com:Mohammad-Aljafary/agentic_rag.git
cd agentic_rag

Once the codebase is added, this section should be expanded with:

• language/runtime requirements
• dependency installation steps
• environment variable configuration
• local development commands

Running the Project

There is no runnable application in the repository yet.

When implementation is added, document commands here for tasks such as:

• starting the app
• running ingestion jobs
• building or refreshing the vector index
• executing tests

Roadmap

Suggested next steps for the project:

1. choose the core stack and framework
2. define the data ingestion pipeline
3. implement retrieval and indexing
4. add the agent orchestration layer
5. expose a CLI, API, or UI
6. add tests and deployment documentation

License

Add a license file and update this section with the chosen license.