add chat feature

.gitignore

@@ -3,4 +3,5 @@ __pycache__/
 venv/
 yo.txt
 .ruff_cache
-.venv/
+.venv/
+chat_history.db

chat.py

@@ -0,0 +1,318 @@
+# =============================================================
+# vaultmind — chat.py
+# =============================================================
+# Interactive AI chat interface for your Obsidian Vault.
+# Finds relevant notes based on your question and answers
+# using your vault as context.
+#
+# Memory modes (configured in config.py):
+#   SAVE_CHAT_HISTORY = True  → persistent memory via SQLite
+#   SAVE_CHAT_HISTORY = False → amnesiac mode (no history saved)
+#
+# Usage:
+#   python chat.py
+# =============================================================
+
+import sys
+import re
+import sqlite3
+import datetime
+from pathlib import Path
+from config import (
+    VAULT_PATH,
+    EXCLUDED_FOLDERS,
+    TEMPERATURES,
+    MAX_CONTENT_NOTES,
+    MAX_NOTE_CHARS,
+    SAVE_CHAT_HISTORY,
+    HISTORY_LIMIT,
+)
+from ai_backend import call_ai, get_backend, run_startup_checks
+
+SCRIPT_DIR = Path(__file__).parent
+sys.path.insert(0, str(SCRIPT_DIR))
+
+
+# --- COLORS ---
+CYAN   = "\033[96m"
+GREEN  = "\033[92m"
+YELLOW = "\033[93m"
+RED    = "\033[91m"
+DIM    = "\033[2m"
+BOLD   = "\033[1m"
+RESET  = "\033[0m"
+
+DB_FILE = SCRIPT_DIR / "chat_history.db"
+
+TEMP_CHAT = TEMPERATURES.get("chat") or TEMPERATURES.get("default") or 0.2
+
+# =============================================================
+# DATABASE
+# =============================================================
+
+def init_db():
+    """Create the exchanges table if it doesn't exist."""
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute("""
+            CREATE TABLE IF NOT EXISTS exchanges (
+                id        INTEGER PRIMARY KEY AUTOINCREMENT,
+                user_msg  TEXT,
+                ai_msg    TEXT,
+                timestamp DATETIME
+            )
+        """)
+
+
+def add_exchange(user_msg: str, ai_msg: str):
+    """Save a user/AI exchange to the database."""
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute(
+            "INSERT INTO exchanges (user_msg, ai_msg, timestamp) VALUES (?, ?, ?)",
+            (user_msg, ai_msg, datetime.datetime.now().isoformat()),
+        )
+
+
+def get_recent_history(limit: int) -> list[dict]:
+    """Return the last `limit` exchanges as a message list."""
+    with sqlite3.connect(DB_FILE) as conn:
+        rows = conn.execute(
+            "SELECT user_msg, ai_msg FROM exchanges ORDER BY id DESC LIMIT ?", (limit,)
+        ).fetchall()
+
+    history = []
+    for user_msg, ai_msg in reversed(rows):
+        history.append({"role": "user",      "content": user_msg})
+        history.append({"role": "assistant", "content": ai_msg})
+    return history
+
+
+def forget_exchange(item_id: int) -> bool:
+    """Delete a single exchange by ID. Returns True if deleted."""
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute("DELETE FROM exchanges WHERE id = ?", (item_id,))
+        return conn.total_changes > 0
+
+
+def clear_all_history():
+    """Delete all saved exchanges."""
+    with sqlite3.connect(DB_FILE) as conn:
+        conn.execute("DELETE FROM exchanges")
+
+
+def print_history():
+    """Print all saved exchanges in a readable format."""
+    with sqlite3.connect(DB_FILE) as conn:
+        rows = conn.execute(
+            "SELECT id, user_msg, ai_msg FROM exchanges ORDER BY id ASC"
+        ).fetchall()
+
+    if not rows:
+        print(f"\n{DIM}Memory bank is empty.{RESET}")
+        return
+
+    print(f"\n{CYAN}{BOLD}SAVED EXCHANGES{RESET}")
+    print(f"{DIM}{'─' * 50}{RESET}")
+    for row_id, user_msg, ai_msg in rows:
+        u = (user_msg.replace("\n", " ")[:35] + "...") if len(user_msg) > 35 else user_msg
+        a = (ai_msg.replace("\n",  " ")[:35] + "...") if len(ai_msg)  > 35 else ai_msg
+        print(f"{CYAN}│{RESET} {DIM}[{row_id}]{RESET} {YELLOW}You:{RESET} {u} {DIM}→{RESET} {GREEN}AI:{RESET} {a}")
+    print(f"{DIM}{'─' * 50}{RESET}\n")
+
+
+# =============================================================
+# VAULT
+# =============================================================
+
+def index_vault() -> list[Path]:
+    """Return all .md paths in the vault, excluding configured folders."""
+    vault = Path(VAULT_PATH).expanduser().resolve()
+    return [
+        p for p in vault.rglob("*.md")
+        if not any(skip in str(p) for skip in EXCLUDED_FOLDERS)
+    ]
+
+
+def clean_filename(text: str) -> str:
+    """Strip leading numbers, bullets, and whitespace from a filename."""
+    return re.sub(r'^[\d\s.\-*]+', '', text).strip()
+
+
+# =============================================================
+# RETRIEVAL
+# =============================================================
+
+def retrieve_context(query: str, all_paths: list[Path], backend: str) -> tuple:
+    """
+    Two-step retrieval:
+      1. Keyword force  — exact filename matches for query words
+      2. Librarian      — AI ranks the full file list and picks top 10
+    Then reads the content of matched files.
+
+    Returns:
+        Tuple of (context_blocks, matched_filenames)
+    """
+    # step 1: keyword force — catch exact filename matches
+    query_words = [w for w in re.findall(r'\w+', query.lower()) if len(w) > 3]
+    forced = [p for p in all_paths if any(w in p.name.lower() for w in query_words)]
+
+    # step 2: librarian — ask AI to rank all files
+    print(f"{DIM}  ranking files...{RESET}", end="\r")
+    file_list = "\n".join(p.name for p in all_paths)
+    librarian_prompt = (
+        f"QUESTION: {query}\n"
+        f"FILES:\n{file_list}\n"
+        f"Return ONLY the top 10 most relevant filenames, one per line."
+    )
+    raw = call_ai(librarian_prompt, backend, temperature=0.0)
+    suggested = [clean_filename(line) for line in raw.splitlines() if line.strip()]
+
+    # step 3: read content of matched files
+    context_blocks, matched_names = [], []
+    seen = set()
+
+    for name in [p.name for p in forced] + suggested:
+        if name in seen:
+            continue
+        match = next((p for p in all_paths if p.name.lower() == name.lower()), None)
+        if not match:
+            continue
+        try:
+            content = match.read_text(errors="ignore")[:MAX_NOTE_CHARS]
+            context_blocks.append(f"### {match.name}\n{content}")
+            matched_names.append(match.name)
+            seen.add(name)
+        except OSError:
+            continue
+        if len(matched_names) >= MAX_CONTENT_NOTES:
+            break
+
+    return context_blocks, matched_names
+
+
+# =============================================================
+# COMMANDS
+# =============================================================
+
+def handle_command(query: str) -> bool:
+    """
+    Handle slash commands. Returns True if a command was handled
+    (so the main loop skips AI generation).
+    """
+    cmd = query.lower()
+
+    if not SAVE_CHAT_HISTORY:
+        print(f"{DIM}  memory commands are disabled (SAVE_CHAT_HISTORY = False){RESET}")
+        return True
+
+    if cmd == "/clear":
+        clear_all_history()
+        print(f"{DIM}  history cleared{RESET}")
+
+    elif cmd in ("/memories", "/history"):
+        print_history()
+
+    elif cmd.startswith("/forget"):
+        parts = query.split()
+        if len(parts) == 2 and parts[1].isdigit():
+            if forget_exchange(int(parts[1])):
+                print(f"{DIM}  deleted{RESET}")
+            else:
+                print(f"{DIM}  id not found{RESET}")
+        else:
+            print(f"{DIM}  usage: /forget <id>{RESET}")
+
+    else:
+        print(f"{DIM}  unknown command{RESET}")
+
+    return True
+
+
+# =============================================================
+# OUTPUT
+# =============================================================
+
+def print_response(content: str, sources: list[str]):
+    """Print the AI response in a clean box with source files."""
+    source_str = ", ".join(sources[:4])
+    if len(sources) > 4:
+        source_str += f" +{len(sources) - 4} more"
+
+    print(f"\n{GREEN}{BOLD}AI{RESET} {DIM}· {source_str}{RESET}")
+    print(f"{GREEN}│{RESET}")
+    for line in content.split("\n"):
+        print(f"{GREEN}│{RESET} {line}")
+    print()
+
+
+# =============================================================
+# MAIN
+# =============================================================
+
+def main():
+    backend    = get_backend()
+    all_paths  = index_vault()
+
+    run_startup_checks()
+
+    if SAVE_CHAT_HISTORY:
+        init_db()
+        mem_label = f"{GREEN}on{RESET}"
+        cmd_hint  = "/memories  /forget <id>  /clear  /exit"
+    else:
+        mem_label = f"{YELLOW}off{RESET}"
+        cmd_hint  = "/exit"
+
+    print(f"\n{CYAN}{BOLD}  vault chat{RESET}")
+    print(f"{DIM}  {len(all_paths)} notes indexed  ·  memory: {mem_label}  ·  temp: {TEMP_CHAT}{RESET}")
+    print(f"{DIM}  {cmd_hint}{RESET}\n")
+
+    while True:
+        try:
+            query = input(f"{YELLOW}{BOLD}you > {RESET}").strip()
+        except (KeyboardInterrupt, EOFError):
+            print(f"\n{DIM}bye{RESET}\n")
+            break
+
+        if not query:
+            continue
+
+        if query.lower() in ("/exit", "/quit"):
+            print(f"\n{DIM}bye{RESET}\n")
+            break
+
+        if query.lower() in ("exit", "quit"):
+            print(f"{DIM}  use /exit to leave{RESET}")
+            continue
+
+        if query.startswith("/"):
+            handle_command(query)
+            continue
+
+        # retrieve relevant notes
+        context_blocks, matched = retrieve_context(query, all_paths, backend)
+
+        if not context_blocks:
+            print(f"{DIM}  no relevant notes found{RESET}\n")
+            continue
+
+        # build prompt
+        print(f"{DIM}  generating...{RESET}", end="\r")
+
+        if SAVE_CHAT_HISTORY:
+            history     = get_recent_history(HISTORY_LIMIT)
+            history_str = "\n".join(f"{m['role'].upper()}: {m['content']}" for m in history)
+            prompt      = f"HISTORY:\n{history_str}\n\nVAULT NOTES:\n{' '.join(context_blocks)}\n\nQUESTION: {query}"
+        else:
+            prompt = f"VAULT NOTES:\n{' '.join(context_blocks)}\n\nQUESTION: {query}"
+
+        answer = call_ai(prompt, backend, temperature=TEMP_CHAT)
+
+        if SAVE_CHAT_HISTORY:
+            add_exchange(query, answer)
+
+        print(" " * 40, end="\r")
+        print_response(answer, matched)
+
+
+if __name__ == "__main__":
+    main()

generate_insights.py

@@ -12,7 +12,7 @@
 # Output: Vault/Insights/YYYY-MM-DD Week Insight.md
 #
 # Schedule with cron to run automatically:
-#   0 8 * * 0 /path/to/venv/bin/python /path/to/generate_insights.py
+# 0 8 * * 0 /path/to/venv/bin/python /path/to/generate_insights.py
 # =============================================================
 
 import sys
@@ -21,7 +21,6 @@
 import datetime
 from pathlib import Path
 from concurrent.futures import ThreadPoolExecutor, as_completed
-
 from config import MAX_FILE_SIZE, VAULT_PATH, DAYS_BACK, MAX_NOTE_CHARS, EXCLUDED_FOLDERS, CANDIDATES, TEMPERATURES, INSIGHT_TITLE_FORMAT
 from ai_backend import get_backend, call_ai, run_startup_checks
 
@@ -40,7 +39,7 @@
 with PROMPTS_PATH.open(encoding="utf-8") as f:
     PROMPTS = json.load(f)
 
-TEMP_INSIGHTS = TEMPERATURES.get("insights")
+TEMP_INSIGHTS = TEMPERATURES.get("insights") or TEMPERATURES.get("default") or 0.2
 
 GROUNDING = PROMPTS["grounding"]
 LENSES    = PROMPTS["insights"]["lenses"]

morning_briefing.py

@@ -8,14 +8,13 @@
 # Output: Vault/Briefings/YYYY-MM-DD Morning Briefing.md
 #
 # Schedule with cron to run automatically every morning:
-#   0 8 * * * /path/to/venv/bin/python /path/to/morning_briefing.py
+# 0 8 * * * /path/to/venv/bin/python /path/to/morning_briefing.py
 # =============================================================
 
 from pathlib import Path
 import sys
 import json
 import datetime
-
 from config import EXCLUDED_FOLDERS, MAX_FILE_SIZE, VAULT_PATH, MAX_NOTE_CHARS, TEMPERATURES, BRIEFING_TITLE_FORMAT
 from ai_backend import get_backend, call_ai, run_startup_checks
 
@@ -27,13 +26,12 @@
 VAULT_PATH  = Path(VAULT_PATH).expanduser().resolve()
 BRIEFING_FOLDER = VAULT_PATH / "Briefings"
 
-
 # load prompts from prompts.json
 PROMPTS_PATH = SCRIPT_DIR / "prompts.json"
 with PROMPTS_PATH.open(encoding="utf-8") as f:
     PROMPTS = json.load(f)
 
-TEMP_BRIEFING = TEMPERATURES.get("briefing")
+TEMP_BRIEFING = TEMPERATURES.get("briefing") or TEMPERATURES.get("default") or 0.2
 
 GROUNDING = PROMPTS["grounding"]
 

study_recap.py

@@ -33,7 +33,8 @@
 with PROMPTS_PATH.open(encoding="utf-8") as f:
     PROMPTS = json.load(f)
 
-TEMP_RECAP = TEMPERATURES.get("recap", 0.5)
+TEMP_RECAP = TEMPERATURES.get("recap") or TEMPERATURES.get("default") or 0.2
+
 GROUNDING  = PROMPTS["grounding"]
 
 def format_obsidian_tag(text: str) -> str: