Add Projects, Milestones, RAG auto-inject, push notifications, PWA, tag normalisation

## Projects & Milestones (Phases A + G)
- New models: Project, Milestone (Project → Milestone → Task hierarchy)
- notes table: project_id + milestone_id FKs; parent_id FK constraint activated
- Migrations: 0017 (projects), 0018 (push_subscriptions), 0019 (events), 0020 (milestones)
- Services: projects.py, milestones.py (CRUD + progress tracking)
- Routes: /api/projects + /api/projects/<id>/milestones
- LLM tools: create/list/get/update project; create/list milestone; project + milestone + parent_task params on note/task tools
- Frontend: ProjectListView (stacked milestone bars), ProjectView (milestone-grouped kanban), ProjectSelector, MilestoneSelector, NoteEditorView + TaskEditorView updated

## RAG Auto-injection (Phase B)
- Notes ≥0.60 cosine similarity auto-injected into system prompt (max 3, 800 chars each)
- excluded_note_ids param; ChatView "Auto-included" sidebar section

## Summarisation improvements (Phase C)
- Threshold 20→30, keep-recent 6→8, max_tokens 200→400
- Two-pass summarisation for histories >50 messages

## Browser push notifications (Phase E)
- PushSubscription model + migration; pywebpush dependency
- /api/push routes; VAPID config; fire-and-forget on generation complete
- Frontend: sw.js, push store, Settings toggle

## PWA manifest (Phase F)
- manifest.json, Apple meta tags, service worker registration in main.ts

## Tag normalisation
- All tags lowercased + deduplicated at backend (create_note/update_note) and frontend (TagInput sanitize)
- Note/Task types gain project_id + milestone_id fields; store signatures updated

## CalDAV
- Radicale embedded server reverted; back to user-configured external CalDAV

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-03-02 20:52:21 -05:00
parent 3d7be5888e
commit 012eb1d46b
52 changed files with 4319 additions and 62 deletions
+110 -19
View File
@@ -27,6 +27,10 @@ STOP_WORDS = frozenset({
"but", "if", "so", "than", "too", "very", "just", "about", "up",
})
RAG_AUTO_THRESHOLD = 0.60
RAG_AUTO_LIMIT = 3
RAG_AUTO_SNIPPET = 800
async def get_installed_models() -> set[str]:
"""Return set of installed Ollama model names (with and without :latest)."""
@@ -302,8 +306,8 @@ def _find_urls(text: str) -> list[str]:
# History summarization thresholds
_HISTORY_SUMMARY_THRESHOLD = 20 # total messages before summarizing
_HISTORY_KEEP_RECENT = 6 # verbatim tail to preserve (3 exchanges)
_HISTORY_SUMMARY_THRESHOLD = 30 # total messages before summarizing
_HISTORY_KEEP_RECENT = 8 # verbatim tail to preserve (4 exchanges)
async def summarize_history_for_context(
@@ -324,13 +328,55 @@ async def summarize_history_for_context(
to_summarize = history[:-_HISTORY_KEEP_RECENT]
recent = history[-_HISTORY_KEEP_RECENT:]
lines: list[str] = []
for m in to_summarize:
role = m.get("role", "")
content = (m.get("content") or "").strip()
if role in ("user", "assistant") and content:
label = "User" if role == "user" else "Assistant"
lines.append(f"{label}: {content[:400]}")
# Two-pass for very long histories: summarize first half, combine with second half
if len(to_summarize) > 50:
mid = len(to_summarize) // 2
first_half = to_summarize[:mid]
second_half = to_summarize[mid:]
# Summarize first half
first_lines = []
for m in first_half:
role = m.get("role", "")
content = (m.get("content") or "").strip()
if role in ("user", "assistant") and content:
label = "User" if role == "user" else "Assistant"
first_lines.append(f"{label}: {content[:400]}")
if first_lines:
try:
first_summary_messages = [
{"role": "system", "content": "Summarize this conversation in 3-4 sentences covering topics, notes/tasks created, and key decisions."},
{"role": "user", "content": "\n".join(first_lines)},
]
summary_a = await generate_completion(first_summary_messages, model, max_tokens=300)
summary_a = summary_a.strip()
except Exception:
summary_a = ""
else:
summary_a = ""
# Build lines for final pass from second half
second_lines = []
for m in second_half:
role = m.get("role", "")
content = (m.get("content") or "").strip()
if role in ("user", "assistant") and content:
label = "User" if role == "user" else "Assistant"
second_lines.append(f"{label}: {content[:400]}")
if summary_a:
lines = [f"[Earlier summary: {summary_a}]"] + second_lines
else:
lines = second_lines
else:
lines: list[str] = []
for m in to_summarize:
role = m.get("role", "")
content = (m.get("content") or "").strip()
if role in ("user", "assistant") and content:
label = "User" if role == "user" else "Assistant"
lines.append(f"{label}: {content[:400]}")
if not lines:
return history, None
@@ -339,17 +385,19 @@ async def summarize_history_for_context(
{
"role": "system",
"content": (
"Summarize this conversation history in 3-5 concise sentences. "
"Capture: topics discussed, any notes/tasks/events created or modified, "
"decisions made, and context needed to continue the conversation naturally. "
"Be specific and factual. Output only the summary, nothing else."
"Summarize this conversation history. Capture: "
"(1) All notes, tasks, and projects created or modified — include their exact names. "
"(2) Key decisions made and conclusions reached. "
"(3) Open questions and next steps mentioned. "
"(4) The overall topic arc so the conversation can continue naturally. "
"Be specific and factual. Output 4-8 concise sentences. Nothing else."
),
},
{"role": "user", "content": "\n".join(lines)},
]
try:
summary = await generate_completion(prompt_messages, model, max_tokens=200)
summary = await generate_completion(prompt_messages, model, max_tokens=400)
summary = summary.strip()
if summary:
logger.info(
@@ -371,6 +419,7 @@ async def build_context(
exclude_note_ids: list[int] | None = None,
history_summary: str | None = None,
include_note_ids: list[int] | None = None,
excluded_note_ids: list[int] | None = None,
) -> tuple[list[dict], dict]:
"""Build messages array for Ollama with system prompt and context.
@@ -426,6 +475,7 @@ async def build_context(
"context_note_id": None,
"context_note_title": None,
"auto_notes": [],
"auto_injected_notes": [],
}
# Include current note context if provided — full body, no truncation
@@ -441,10 +491,9 @@ async def build_context(
f"--- End Note ---"
)
# Search for related notes to populate sidebar candidates.
# Results are NOT injected into the system prompt automatically — this keeps
# the system prompt prefix stable so Ollama's KV cache can reuse prefill state.
# Users can explicitly include notes via the sidebar (include_note_ids).
# Search for related notes. High-confidence results (>=0.60) are auto-injected
# into the system prompt; lower-confidence results populate the sidebar only.
# Users can also explicitly include notes via the sidebar (include_note_ids).
search_exclude = set(exclude_set)
if current_note_id:
search_exclude.add(current_note_id)
@@ -473,12 +522,54 @@ async def build_context(
except Exception:
logger.warning("Failed to search notes for context", exc_info=True)
# Populate sidebar candidates (never auto-injected into system prompt).
# Separate high-confidence results for auto-injection vs sidebar display
excluded_inject_set = set(excluded_note_ids or [])
auto_inject: list[tuple[float, object]] = []
sidebar_only: list[tuple[float | None, object]] = []
for score, n in found_scored:
if (
score is not None
and score >= RAG_AUTO_THRESHOLD
and len(auto_inject) < RAG_AUTO_LIMIT
and n.id not in excluded_inject_set
):
auto_inject.append((score, n))
else:
sidebar_only.append((score, n))
# Inject high-scoring notes into system prompt
if auto_inject:
snippets = []
for score, n in auto_inject:
body_snippet = (n.body or "")[:RAG_AUTO_SNIPPET]
snippets.append(f"**{n.title}** (relevance: {round(score * 100)}%)\n{body_snippet}")
context_meta["auto_injected_notes"].append({
"id": n.id,
"title": n.title,
"score": round(score, 2),
})
system_parts.append(
"\n\n--- Relevant Notes ---\n"
+ "\n\n".join(snippets)
+ "\n--- End Relevant Notes ---"
)
# Populate sidebar candidates (auto-injected notes also appear here for reference,
# but sidebar_only are the ones not yet in the prompt)
for score, n in auto_inject:
context_meta["auto_notes"].append({
"id": n.id,
"title": n.title,
"score": round(score, 2) if score is not None else None,
"auto_injected": True,
})
for score, n in sidebar_only:
context_meta["auto_notes"].append({
"id": n.id,
"title": n.title,
"score": round(score, 2) if score is not None else None,
"auto_injected": False,
})
context_meta["auto_note_ids"] = [n.id for _, n in found_scored]