Email & File Data Extraction to CSV

This workflow is designed to automate the extraction, structuring, and management of data from emails and associated documents, such as file attachments and URLs. It leverages advanced language models and prompt engineering to process unstructured information and output structured summaries, making it particularly useful for tasks like email triage, customer support, or large-scale data extraction from communication channels.

Overview

The flow connects several components that handle user input, file and URL content retrieval, prompt construction, large language model (LLM) processing, agent-based reasoning, and structured data output. Its key benefits are scalability, automation, and the ability to handle complex or high-volume data extraction tasks with minimal manual intervention.

Step-by-Step Process

1. User Input and Attachments

• Chat Input: The workflow starts by accepting user input (an email or message) and optional file attachments via a chat interface.
• File Retriever: Any attached files are processed to extract their textual content, using strategies like OCR (if needed) and token limits to ensure efficiency.

2. Enriching Context

• URL Retriever: The workflow can also retrieve content from specified URLs, parsing and chunking the information for downstream use. This is useful when emails reference external resources or knowledge bases.

• Chat History: The system maintains a memory of the last 5 chat messages (up to 800 tokens), providing context for better understanding and continuity.

3. Prompt Engineering

• Prompt Templates: The workflow uses templates to dynamically construct prompts for the LLM and agent, incorporating:

  • The email/message content
  • Extracted file content
  • Chat history for context
  • System-level instructions

  These prompts are designed to maximize the LLM’s ability to understand and structure the incoming information.

4. LLM and Agent Orchestration

• Google Gemini LLM: The workflow uses Gemini 2.5 Flash for high-quality language understanding and generation, with temperature set to 0 for deterministic outputs.

• Tool Calling Agent: An advanced agent receives the composed prompt, chat history, and tools (such as file/URL retrievers) to:

  • Review and organize email data
  • Extract and structure relevant information
  • Provide a comprehensive overview based on emails and attached files
  • Use external knowledge via tools if necessary

  The agent is guided by a system message to focus on efficiency and data structuring.

5. Structuring and Output

• Structured Output Generator: The agent’s response, along with additional context, is passed through another prompt and LLM (also Gemini) to produce a structured output. The required fields are:

  • User Name: Name of the user
  • Email: Patient’s email address
  • Message: The message mentioned in the email

• CSV Output: The structured data is then exported as a CSV file, making it easy to process, analyze, or import into other systems.

6. User Feedback

• Chat Output: The workflow also provides the agent’s overview and answers as a chat response, ensuring the user receives immediate feedback.

Component Summary Table

Use Cases and Benefits

• Scalability: Automates repetitive data extraction and structuring from emails and documents, reducing manual labor.
• Consistency: Uses LLM and prompt templates for uniform processing across large volumes of data.
• Extensibility: Easily adapts to new input types (files, URLs) and output formats (structured objects, CSV).
• Automation: Suitable for customer support, medical records processing, or any workflow requiring structured data from unstructured sources.

Why This Workflow is Useful

This workflow dramatically reduces the time and effort required to extract actionable, structured data from emails and their attachments. It is highly scalable—capable of handling multiple messages and file types in bulk—and automates a process that would otherwise require significant human effort. By integrating advanced LLMs, tool agents, and prompt engineering, it ensures both high precision and adaptability, making it a powerful asset for businesses and organizations aiming to streamline their information processing pipelines.