https://github.com/xerox563/agents-with-python
This repository contains a complete, structured learning path designed to take me build solid Python skills for becoming a Backend + AI Agent Engineer.
https://github.com/xerox563/agents-with-python
fastapi langchain langgraph postgresql python
Last synced: 4 months ago
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This repository contains a complete, structured learning path designed to take me build solid Python skills for becoming a Backend + AI Agent Engineer.
- Host: GitHub
- URL: https://github.com/xerox563/agents-with-python
- Owner: Xerox563
- Created: 2026-02-08T15:49:31.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2026-02-22T11:25:40.000Z (4 months ago)
- Last Synced: 2026-02-22T15:36:45.220Z (4 months ago)
- Topics: fastapi, langchain, langgraph, postgresql, python
- Language: Python
- Homepage:
- Size: 8.81 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Backend + AI Agent Engineering Roadmap
### _FastAPI • PostgreSQL • Auth • Deployment • LLMs • LangChain • AI Agents_
This repository contains a complete, structured learning path designed to take me build solid Python skills for becoming a **Backend + AI Agent Engineer**.
---
## 📌 What This Repository Covers
### 🔷 **1. FastAPI Fundamentals**
- Setting up FastAPI and project structure
- GET/POST endpoints
- Path & query parameters
- Request/response models
- Pydantic deep dive
- Error handling
- Dependency Injection
- Middleware & Background tasks
- CRUD Notes API project
---
### 🔷 **2. PostgreSQL + SQLAlchemy**
- SQL fundamentals
- Connecting FastAPI with PostgreSQL
- SQLAlchemy ORM models
- CRUD operations
- Relationships (1-many, many-many)
- Alembic migrations
- User + Tasks database project
---
### 🔷 **3. Authentication, Authorization & Performance**
- Password hashing (bcrypt)
- JWT authentication (access & refresh tokens)
- OAuth2PasswordBearer
- Role-based access
- Redis caching
- Rate limiting
- Background jobs (Celery / RQ)
- Full authentication system project
---
### 🔷 **4. Deployment & Best Practices**
- Docker fundamentals
- Containerizing FastAPI + PostgreSQL
- Docker Compose setup
- Deploying to Railway / Render
- Environment variables & secrets
- Logging & monitoring
- Production-ready backend API
---
### 🔷 **5. LLM Foundations**
- What LLMs can and cannot do
- Prompt engineering
- Embeddings & vector similarity
- Vector databases (FAISS, Chroma)
- RAG (Retrieval Augmented Generation)
- Mini Q&A RAG system project
---
### 🔷 **6. LangChain Core Concepts**
- Document loaders
- Text splitters
- Embeddings + VectorStore
- LLMChain & SequentialChain
- Conversational memory
- Tools & Agents
- AI chatbot with memory
---
### 🔷 **7. AI Agent Systems**
- Tool-enabled agents
- Creating custom Python tools
- Connecting FastAPI endpoints as tools
- Multi-agent orchestration
- RAG-enhanced agents
- Web-based agents
- Tool-using automation agent project
---
## 🧩 **Final Goal**
By the end of this repo, I will have:
✔ A fully deployed backend (FastAPI + PostgreSQL + Auth)
✔ A RAG-powered AI system
✔ LangChain-based agents using custom tools
✔ A multi-agent automation system
✔ A strong portfolio demonstrating backend + AI integration
---