chnages

github_readme.md

@@ -1,43 +1,59 @@
-📧 AI-Driven Email Agent 🧠
+# 📧 AI-Driven Email Agent 🧠
 
-A production-grade, multi-agent system built with LangGraph and FastAPI that automates email triage, context retrieval, and drafting. This project demonstrates advanced implementation of Long-term Memory, State Persistence, and Human-in-the-Loop Interrupts using LangGraph's Functional API Command pattern.
+A production-grade, multi-agent system built with LangGraph and FastAPI that automates email triage, context retrieval, and drafting. This project demonstrates advanced implementation of **Long-term Memory**, **State Persistence**, and **Human-in-the-Loop Interrupts** using LangGraph's Functional API Command pattern.
 
-🚀 Key Features
+---
 
-Advanced Learning Implementation: Throughout this project, I successfully implemented Semantic Memory, Checkpointer Persistence, and Functional Interrupts, allowing the agent to maintain state and handle user feedback reliably.
+## 🚀 Key Features
 
-Multi-Agent Workflow: Specialized agents for Triage, Context Synthesis, and Email Drafting.
+- **Advanced Learning Implementation**  
+  Implemented **Semantic Memory**, **Checkpointer Persistence**, and **Functional Interrupts**, enabling the agent to maintain state and handle user feedback reliably.
 
-Intelligent Triage: Automatically classifies emails, assigns priority, and determines if a reply is required.
+- **Multi-Agent Workflow**  
+  Specialized agents for:
+  - Triage
+  - Context Synthesis
+  - Email Drafting
 
-Semantic Memory: Uses langmem and PostgresStore to retrieve past interactions, ensuring the agent "remembers" previous project details.
+- **Intelligent Triage**  
+  Automatically classifies emails, assigns priority, and determines if a reply is required.
 
-Resource Management: A dedicated Token Count Node ensures large emails (like deployment logs) are summarized before processing to optimize costs.
+- **Semantic Memory**  
+  Uses `langmem` and `PostgresStore` to retrieve past interactions, allowing the agent to remember previous project details.
 
-Human-in-the-Loop: The graph pauses using interrupt() to allow users to review, approve, or provide feedback on drafts via Command(resume=...).
+- **Resource Management**  
+  A dedicated **Token Count Node** ensures large emails (e.g., deployment logs) are summarized before processing to optimize costs.
 
-Scalable Architecture: Built with FastAPI, Docker, and a modular folder structure for enterprise-level deployment.
+- **Human-in-the-Loop**  
+  The graph pauses using `interrupt()` to allow users to:
+  - Review drafts  
+  - Approve responses  
+  - Provide feedback via `Command(resume=...)`
 
-🛠️ Tech Stack
+- **Scalable Architecture**  
+  Built with **FastAPI**, **Docker**, and a modular structure for enterprise-grade deployment.
 
-Orchestration: langgraph (Functional API), langchain
+---
 
-LLM Interface: langchain-groq 
+## 🛠️ Tech Stack
 
-Memory & Persistence: langmem, PostgresCheckpoint, PostgresStore (via Neon/PostgreSQL)
+- **Orchestration:** `langgraph` (Functional API), `langchain`  
+- **LLM Interface:** `langchain-groq`  
+- **Memory & Persistence:**  
+  - `langmem`  
+  - `PostgresCheckpoint`  
+  - `PostgresStore` (Neon/PostgreSQL)  
+- **Database ORM:** SQLAlchemy 2.0  
+- **Embeddings:** `langchain_huggingface` (DistilBERT)  
+- **Backend:** FastAPI + Uvicorn  
+- **Configuration:** `pydantic-settings` (.env management)  
+- **Authentication:** `google-auth` (Gmail API Integration)  
 
-Database ORM: SQLAlchemy 2.0
+---
 
-Embeddings: langchain_huggingface (DistilBERT)
-
-Backend: FastAPI + Uvicorn
-
-Configuration: pydantic-settings (Type-safe .env management)
-
-Authentication: google-auth (Gmail API Integration)
-
-📂 Project Structure
+## 📂 Project Structure
 
+```bash
 app/
 ├── agents/             # Brains: Specialized LLM logic (Triage, Writer, Context)
 ├── database/           # Data: SQLAlchemy models and Connection Pooling
@@ -46,32 +62,4 @@ app/
 ├── state/              # Schema: Pydantic & TypedDict state definitions
 ├── utils/              # Toolbox: Token counters, Embeddings, and Auth helpers
 ├── graph.py            # Logic: StateGraph construction and compilation
-└── main.py             # Entry: FastAPI app and Controller logic
-
-
-🔄 Graph Architecture
-
-The system follows a pre-processing pipeline before reaching the agentic loop:
-
-Safety Check: Filters out malicious content.
-
-Token Check: Routes large bodies to a Summarization Node.
-
-Triage: Analyzes intent and priority.
-
-Context Retrieval: Queries PostgresStore for relevant past facts.
-
-Drafting Agent: Creates a reply based on the full context.
-
-Interrupt: Pauses for User Review and feedback.
-
-🛡️ Integrated Security
-
-The safety classification in this agent is informed by my specialized project on threat detection:
-
-Hybrid Phishing Detection Model: GitHub Repository
-
-👨‍💻 Author
-
-Atharva Gaykar AI Engineer | IIIT Nagpur
-
+└── main.py             # Entry: FastAPI app and Controller logic