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https://github.com/Significant-Gravitas/AutoGPT.git
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1f1288d623
During Tally data extraction, the system now also generates personalized quick-action prompts as part of the existing LLM extraction call (configurable model, defaults to GPT-4o-mini, `temperature=0.0`). The prompt asks the LLM for 5 candidates, then the code validates (filters prompts >20 words) and keeps the top 3. These prompts are stored in the existing `CoPilotUnderstanding.data` JSON field (at the top level, not under `business`) and served to the frontend via a new API endpoint. The copilot chat page uses them instead of hardcoded defaults when available. ### Changes 🏗️ **Backend – Data models** (`understanding.py`): - Added `suggested_prompts` field to `BusinessUnderstandingInput` (optional) and `BusinessUnderstanding` (default empty list) - Updated `from_db()` to deserialize `suggested_prompts` from top-level of the data JSON - Updated `merge_business_understanding_data()` with overwrite strategy for prompts (full replace, not append) - `format_understanding_for_prompt()` intentionally does **not** include `suggested_prompts` — they are UI-only **Backend – Prompt generation** (`tally.py`): - Extended `_EXTRACTION_PROMPT` to request 5 suggested prompts alongside the existing business understanding fields — all extracted in a single LLM call (`temperature=0.0`) - Post-extraction validation filters out prompts exceeding 20 words and slices to the top 3 - Model is now configurable via `tally_extraction_llm_model` setting (defaults to `openai/gpt-4o-mini`) **Backend – API endpoint** (`routes.py`): - Added `GET /api/chat/suggested-prompts` (auth required) - Returns `{prompts: string[]}` from the user's cached business understanding (48h Redis TTL) - Returns empty array if no understanding or no prompts exist **Frontend** (`EmptySession/`): - `helpers.ts`: Extracted defaults to `DEFAULT_QUICK_ACTIONS`, `getQuickActions()` now accepts optional custom prompts and falls back to defaults - `EmptySession.tsx`: Calls `useGetV2GetSuggestedPrompts` hook (`staleTime: Infinity`) and passes results to `getQuickActions()` with hardcoded fallback - Fixed `useEffect` resize handler that previously used `window.innerWidth` as a dependency (re-ran every render); now uses a proper resize event listener - Added skeleton loading state while prompts are being fetched **Generated** (`__generated__/`): - Regenerated Orval API client with new endpoint types and hooks ### Checklist 📋 #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Backend format + lint + pyright pass - [x] Frontend format + lint pass - [x] All existing tally tests pass (28/28) - [x] All chat route tests pass (9/9) - [x] All invited_user tests pass (7/7) - [x] E2E: New user with tally data sees custom prompts on copilot page - [x] E2E: User without tally data sees hardcoded default prompts - [x] E2E: Clicking a custom prompt sends it as a chat message
103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
"""Tests for business understanding merge and format logic."""
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from datetime import datetime, timezone
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from typing import Any
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from backend.data.understanding import (
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BusinessUnderstanding,
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BusinessUnderstandingInput,
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format_understanding_for_prompt,
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merge_business_understanding_data,
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)
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def _make_input(**kwargs: Any) -> BusinessUnderstandingInput:
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"""Create a BusinessUnderstandingInput with only the specified fields."""
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return BusinessUnderstandingInput.model_validate(kwargs)
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# ─── merge_business_understanding_data: suggested_prompts ─────────────
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def test_merge_suggested_prompts_overwrites_existing():
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"""New suggested_prompts should fully replace existing ones (not append)."""
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existing = {
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"name": "Alice",
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"business": {"industry": "Tech", "version": 1},
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"suggested_prompts": ["Old prompt 1", "Old prompt 2"],
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}
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input_data = _make_input(
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suggested_prompts=["New prompt A", "New prompt B", "New prompt C"],
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)
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result = merge_business_understanding_data(existing, input_data)
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assert result["suggested_prompts"] == [
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"New prompt A",
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"New prompt B",
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"New prompt C",
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]
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def test_merge_suggested_prompts_none_preserves_existing():
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"""When input has suggested_prompts=None, existing prompts are preserved."""
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existing = {
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"name": "Alice",
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"business": {"industry": "Tech", "version": 1},
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"suggested_prompts": ["Keep me"],
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}
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input_data = _make_input(industry="Finance")
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result = merge_business_understanding_data(existing, input_data)
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assert result["suggested_prompts"] == ["Keep me"]
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assert result["business"]["industry"] == "Finance"
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def test_merge_suggested_prompts_added_to_empty_data():
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"""Suggested prompts are set at top level even when starting from empty data."""
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existing: dict[str, Any] = {}
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input_data = _make_input(suggested_prompts=["Prompt 1"])
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result = merge_business_understanding_data(existing, input_data)
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assert result["suggested_prompts"] == ["Prompt 1"]
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def test_merge_suggested_prompts_empty_list_overwrites():
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"""An explicit empty list should overwrite existing prompts."""
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existing: dict[str, Any] = {
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"suggested_prompts": ["Old prompt"],
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"business": {"version": 1},
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}
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input_data = _make_input(suggested_prompts=[])
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result = merge_business_understanding_data(existing, input_data)
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assert result["suggested_prompts"] == []
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# ─── format_understanding_for_prompt: excludes suggested_prompts ──────
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def test_format_understanding_excludes_suggested_prompts():
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"""suggested_prompts is UI-only and must NOT appear in the system prompt."""
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understanding = BusinessUnderstanding(
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id="test-id",
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user_id="user-1",
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created_at=datetime.now(tz=timezone.utc),
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updated_at=datetime.now(tz=timezone.utc),
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user_name="Alice",
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industry="Technology",
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suggested_prompts=["Automate reports", "Set up alerts", "Track KPIs"],
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)
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formatted = format_understanding_for_prompt(understanding)
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assert "Alice" in formatted
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assert "Technology" in formatted
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assert "suggested_prompts" not in formatted
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assert "Automate reports" not in formatted
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assert "Set up alerts" not in formatted
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assert "Track KPIs" not in formatted
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