SmartEMailAssistant

[Kiruthika369]

No description provided.

[Kiruthika369]

📌 Project Overview

The Voice-Based Smart Email Assistant using RAG and Endee Vector Database is an AI-powered application that enables users to interact with email data using natural language queries. Instead of relying on traditional keyword-based search, the system leverages vector embeddings and semantic search to understand the meaning behind user queries.

This project integrates Retrieval-Augmented Generation (RAG) to fetch relevant emails from a vector database and generate intelligent, context-aware responses. It provides a smarter way to search, summarize, and understand email communication.

✨ Features
🔍 Semantic Email Search
Find emails based on meaning, not just exact keywords
🤖 AI-Powered Q&A (RAG)
Ask questions like:
“What did my manager say about the project?”
📊 Context-Aware Responses
Retrieves relevant emails and generates accurate answers
⚡ Fast Vector Search with Endee
Efficient similarity search using embeddings
🖥️ Simple User Interface
Built with Streamlit for easy interaction
📁 Preloaded Email Dataset
Includes sample emails for testing
🎯 Real-World Use Case
Simulates intelligent email assistants like Gmail AI

🧠 How It Uses Vector Search Concepts

This project is built on vector search and embedding-based retrieval, powered by Endee.

🔹 Step 1: Text → Embeddings
Each email (subject + body) is converted into a vector representation (embedding) using a language model
These embeddings capture the semantic meaning of the text
🔹 Step 2: Store in Endee
All email embeddings are stored in the Endee vector database
This allows fast and efficient similarity search
🔹 Step 3: Query Embedding
When the user enters a query, it is also converted into an embedding
🔹 Step 4: Similarity Search
Endee compares the query vector with stored email vectors
Retrieves the most relevant emails based on meaning (not keywords)
🔹 Step 5: Retrieval-Augmented Generation (RAG)
Retrieved emails are passed to an AI model
The model generates a context-aware answer
🔁 Flow Summary

User Query → Embedding → Endee Search → Relevant Emails → AI Response

🚀 Result

This approach enables:

Smarter search
Better understanding of user intent
More accurate and human-like responses

[github-actions]

VectorDB Benchmark — — Failed

Triggered by @hemant-endee · Commit ``

Step	Status
Provision Servers	Up
Deploy Endee Server	Done
Run Benchmark	Failed
Results	See reason below
Teardown	Done ✓

[hemant-endee]

Thanks for your contribution! However, this type of project should not be included directly in this server repository.

Please create a separate repository for your project and just mention endee vectordatabase. You can refer to this example structure: https://github.com/iamdainwi/codemind

For this reason, we will be closing this PR.