igpt-email-ask（igpt 电子邮件询问）

iGPT Email Ask

Ask questions about a user's email and get reasoned, structured answers. Powered by iGPT's Context Engine, which reconstructs conversations, decisions, ownership, and intent across time.

What This Skill Does

This skill queries iGPT's recall/ask endpoint to generate answers grounded in a user's connected email data. Unlike basic retrieval, the Context Engine:

• Reconstructs full conversation threads across replies, forwards, and CCs
• Identifies who decided what, who owns what, and what's still open
• Extracts structured data (tasks, deadlines, contacts, risks) from unstructured email
• Supports multiple quality tiers for different complexity levels
• Returns text, JSON, or schema-validated structured output
• Supports streaming (SSE) for real-time responses

When to Use This Skill

• Summarize what happened in a thread or across threads
• Extract action items, decisions, or open questions
• Analyze sentiment or risk in deal/customer threads
• Answer questions that require understanding context across multiple emails
• Generate structured data from email content (JSON, schema-validated)
• Prepare briefings before meetings based on recent correspondence

Prerequisites

1. An iGPT API key (get one at https://igpt.ai/hub/apikeys/)
2. A connected email datasource -- the user must have completed OAuth authorization via connectors/authorize before ask will return results. You can check connection status with datasources.list().
3. Python >= 3.8 with the igptai package installed

Setup

pip install igptai

Set your API key as an environment variable:

export IGPT_API_KEY="your-api-key-here"

Usage

Basic: Ask a question

from igptai import IGPT
import os

igpt = IGPT(api_key=os.environ["IGPT_API_KEY"], user="user_123")

res = igpt.recall.ask(input="Summarize key risks, decisions, and next steps from this week's meetings.")
if res is not None and res.get("error"):
    print("iGPT error:", res)
else:
    print(res)

Get JSON output

Pass output_format="json" for unstructured JSON, or provide a schema for validated structured output:

# Simple JSON output
res = igpt.recall.ask(
    input="What are the open action items from this week?",
    output_format="json"
)

# Schema-validated structured output
res = igpt.recall.ask(
    input="Open action items from this week",
    quality="cef-1-normal",
    output_format={
        "strict": True,
        "schema": {
            "type": "object",
            "required": ["action_items"],
            "additionalProperties": False,
            "properties": {
                "action_items": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "required": ["title", "owner", "due_date"],
                        "properties": {
                            "title": {"type": "string"},
                            "owner": {"type": "string"},
                            "due_date": {"type": "string"}
                        }
                    }
                }
            }
        }
    }
)
print(res)

Example response:

{
    "action_items": [
        {
            "title": "Approve revised Q1 budget allocation",
            "owner": "Dvir Ben-Aroya",
            "due_date": "2026-01-15"
        },
        {
            "title": "Approve final FY2026 strategic priorities",
            "owner": "Board of Directors",
            "due_date": "2026-01-31"
        }
    ]
}

Use quality tiers

iGPT's Context Engine has three quality tiers:

# Normal: fast, good for straightforward questions
res = igpt.recall.ask(
    input="When is my next meeting with Acme Corp?",
    quality="cef-1-normal"
)

# High: deeper reasoning, better for complex multi-thread analysis
res = igpt.recall.ask(
    input="What is the current negotiation status with Acme Corp and what leverage do we have?",
    quality="cef-1-high"
)

# Reasoning: maximum depth, for complex cross-thread synthesis
res = igpt.recall.ask(
    input="Across all communication with Acme over the past quarter, what patterns suggest risk and what should we do about it?",
    quality="cef-1-reasoning"
)

Stream responses

Streaming returns parsed JSON chunks (dicts), not raw text. Extract content from each chunk:

stream = igpt.recall.ask(
    input="Walk me through the timeline of the Acme deal from first contact to now.",
    stream=True
)

for chunk in stream:
    if isinstance(chunk, dict) and chunk.get("error"):
        print("Stream error:", chunk)
        break
    # Each chunk is a parsed JSON dict
    print(chunk)

Streaming is resilient: if the connection breaks, the iterator yields an error chunk and finishes rather than throwing.

Check datasource connection before asking

# Verify user has a connected datasource
status = igpt.datasources.list()
if status is not None and not status.get("error"):
    print("Connected datasources:", status)
else:
    # Connect a datasource first
    auth = igpt.connectors.authorize(service="spike", scope="messages")
    print("Open this URL to authorize:", auth.get("url"))

Parameters

Error Handling

The SDK does not throw exceptions. It returns normalized error objects:

res = igpt.recall.ask(input="What happened in yesterday's board meeting?")

if res is not None and res.get("error"):
    error = res["error"]
    if error == "auth":
        print("Check your API key")
    elif error == "params":
        print("Check your request parameters")
    elif error == "network_error":
        print("Network issue -- the SDK retries with exponential backoff (3 attempts by default) before returning this")
else:
    print(res)

External Endpoints

This skill communicates exclusively with:

• https://api.igpt.ai/v1/recall/ask/ -- the reasoning endpoint
• https://api.igpt.ai/v1/connectors/authorize/ -- only during initial datasource connection setup
• https://api.igpt.ai/v1/datasources/list/ -- to check connection status

No other external endpoints are contacted. No data is sent to any third-party service. The igptai PyPI package source is available at https://github.com/igptai/igpt-python.

Security & Privacy

• API-key scoped: All requests authenticate via IGPT_API_KEY sent as a Bearer token over HTTPS. No shell access, no filesystem access, no system commands.
• Per-user isolation: Every query is scoped to a specific user identifier. User A cannot access User B's email data. Isolation is enforced at the index and execution level, not as a filter layer.
• OAuth read-only: The email datasource connection uses OAuth with read-only scopes. The skill does not send, modify, or delete emails.
• No data retention: Prompts are discarded after execution. Memory is reconstructed on-demand, not stored.
• Transport encryption: All communication occurs over HTTPS. No plaintext endpoints.
• No local persistence: This skill does not write to disk, modify environment files, or create persistent configuration outside of the standard IGPT_API_KEY environment variable.
• Built-in retries: The SDK retries failed requests with exponential backoff (default: 3 attempts, 100ms base, 2x factor) before returning a network_error.

For the full security model, see https://docs.igpt.ai/docs/security/model.

What This Skill Does NOT Do

• Does not send, modify, forward, or delete emails
• Does not access the filesystem or execute shell commands
• Does not install persistent services or scheduled tasks
• Does not contact endpoints other than api.igpt.ai
• Does not store API keys or OAuth tokens outside the environment variable

Example Questions

These all work as natural language prompts:

• "Summarize key risks from this week's email threads" -- cross-thread analysis
• "What are the open action items from yesterday's board meeting?" -- task extraction
• "What's the current status of the Acme deal?" -- deal intelligence
• "Who owns the budget approval and when is it due?" -- ownership and deadline extraction
• "Are there any threads where tone has shifted negatively in the last 7 days?" -- sentiment analysis
• "Generate a briefing for my meeting with Sarah tomorrow" -- meeting prep

Resources

• Get API Key: https://igpt.ai/hub/apikeys/
• Documentation: https://docs.igpt.ai
• API Reference: https://docs.igpt.ai/docs/api-reference/ask
• Playground: https://igpt.ai/hub/playground/
• Python SDK: https://pypi.org/project/igptai/
• Node.js SDK: https://www.npmjs.com/package/igptai
• GitHub: https://github.com/igptai/igpt-python