Overview
This workflow empowers users to chat with their email history directly through Telegram, leveraging advanced AI and vector database technologies for efficient retrieval and response generation.
Key Features
- Telegram Integration: Initiates conversations via Telegram, allowing users to send queries as chat messages.
- AI-Powered Retrieval: Utilizes Mistral and Ollama embeddings to process and understand user queries.
- Vector Database Search: Employs PGVector with PostgreSQL to store and retrieve relevant email content using semantic search (RAG).
- Automated Processing: Splits and processes large email datasets in batches for scalable performance.
- Dynamic Responses: Beautifies and formats AI-generated responses before sending them back to users on Telegram.
Benefits
- Seamless Communication: Users can access and search their email history without leaving Telegram.
- Time Savings: Automates complex search and summarization tasks, reducing manual effort.
- Enhanced Productivity: Quick, AI-driven insights from email data support faster decision-making.
Use Cases
- Personal Knowledge Management: Retrieve important information from past emails instantly.
- Customer Support: Agents can quickly reference historical email threads during live chats.
- Sales & CRM: Sales teams can access client communication history on the go.
Integrations & Automation
Key integrations include Telegram, PostgreSQL with PGVector, Mistral, and Ollama for embeddings, orchestrated via n8n's automation platform.
