add architectural overhaul to improve stability, portability, and user experience

ai_backend.py

@@ -12,7 +12,7 @@
 SCRIPT_DIR = Path(__file__).parent
 sys.path.insert(0, str(SCRIPT_DIR))
 
-from config import OLLAMA_MODEL, OLLAMA_API_URL, TEMPERATURE, TIMEOUT, KEEP_ALIVE, NUM_CTX, VAULT_PATH # noqa: E402
+from config import OLLAMA_MODEL, OLLAMA_API_URL, TEMPERATURES, TIMEOUT, KEEP_ALIVE, NUM_CTX, VAULT_PATH # noqa: E402
 
 # terminal colors
 R    = "\033[0m"
@@ -50,7 +50,7 @@ def check_ollama() -> bool:
 def check_vault() -> bool:
     """
     Check if the configured vault path exists.
-    Prints a friendly error if not found.
+    Prints a error if not found.
 
     Returns:
         True if vault exists, False otherwise.
@@ -83,32 +83,36 @@ def get_backend() -> str:
     return "ollama"
 
 
-def call_ai(prompt: str, backend: str = "ollama", timeout: int = None) -> str:
+def call_ai(prompt: str, backend: str = "ollama", timeout: int = None, temperature: float = None) -> str:
     """
     Send a prompt to the configured AI backend and return the response text.
 
     Args:
         prompt:  The full prompt string to send to the model.
         backend: Which backend to use. Currently only 'ollama' is supported.
         timeout: Override the default timeout from config.py (in seconds).
+        temperature: Override the global temperature for this specific call.
 
     Returns:
         The model's response as a stripped string.
     """
-    return _call_ollama(prompt, timeout or TIMEOUT)
+    return _call_ollama(prompt, timeout or TIMEOUT, temperature)
 
 
-def _call_ollama(prompt: str, timeout: int) -> str:
+def _call_ollama(prompt: str, timeout: int, temperature: float = None) -> str:
     """
     Internal function that makes the actual HTTP request to the Ollama API.
+    """
 
-    Args:
-        prompt:  The prompt to send.
-        timeout: Max seconds to wait for a response.
+    global_temp = TEMPERATURES.get("default", 0.2)
+    raw_temp = temperature if temperature is not None else global_temp
+
+    try:
+        final_temp = max(0.0, min(1.0, float(raw_temp)))
+    except (ValueError, TypeError):
+        print(f"\n[WARNING] Invalid temperature '{raw_temp}' in config. Defaulting to 0.2")
+        final_temp = 0.2
 
-    Returns:
-        The model's response text, stripped of leading/trailing whitespace.
-    """
     response = requests.post(
         OLLAMA_API_URL,
         json={
@@ -117,7 +121,7 @@ def _call_ollama(prompt: str, timeout: int) -> str:
             "stream":     False,
             "keep_alive": KEEP_ALIVE,
             "options": {
-                "temperature":    TEMPERATURE,
+                "temperature":    final_temp,
                 "top_p":          0.9,
                 "repeat_penalty": 1.1,
                 "num_ctx":        NUM_CTX,

config.py

@@ -3,8 +3,21 @@
 # -------------------------------------------------------------
 OLLAMA_MODEL = "llama3.1:8b"    # Ollama model to use
 
-TEMPERATURE  = 0.2              # Controls randomness. Lower = more factual, less hallucination.
-                                # Range: 0.0 (deterministic) to 1.0 (creative). Default: 0.2
+# Temperature controls the randomness and creativity of the AI.
+#   - 0.0 to 0.3: Rigid, factual, analytical (Good for summaries/coding)
+#   - 0.4 to 0.7: Balanced, conversational (Good for general writing)
+#   - 0.8 to 1.0: Highly creative, unpredictable (Good for brainstorming)
+TEMPERATURES = {
+    # The global fallback used if a script doesn't have a specific setting.
+    "default":  0.2,  
+
+    # Specific script overrides. 
+    # Replace 'None' with a float to override the default.
+    "insights": None, # generate_insights.py
+    "briefing": None, # morning_briefing.py
+    "recap":    None, # study_recap.py
+    "txt":      None, # txt_to_notes.py
+}
 
 # -------------------------------------------------------------
 # API Configuration
@@ -36,7 +49,7 @@
 
 MAX_NOTE_CHARS = 2000           # Max characters read per note. Higher = more detail but slower.
 
-MAX_FILE_SIZE = 1_000_000  # 1MB max per file, skip larger ones
+MAX_FILE_SIZE = 1_000_000       # 1MB max per file, skip larger ones
 
 #--------------------------------------------------------------
 # Ignore Folders
@@ -50,3 +63,34 @@
     # "Templates",
     # "Archive",
 ]
+
+#--------------------------------------------------------------
+# Tagging System (generate_insights.py)
+#--------------------------------------------------------------
+CANDIDATES = {
+        "productivity":  ["productiv", "task", "goal", "work", "focus"],
+        "mood":          ["mood", "emotion", "feel", "stress", "anxiet", "happy"],
+        "philosophy":    ["meaning", "values", "purpose", "reflect", "life"],
+        "habits":        ["habit", "routine", "pattern", "repeat", "daily"],
+        "health":        ["health", "sleep", "exercise", "energy", "body"],
+        "projects":      ["project", "build", "code", "ship", "launch", "develop"],
+        "relationships": ["friend", "family", "partner", "social", "connect"],
+        # "finances":    ["money", "budget", "spend", "invest", "finance"],
+        # Words that trigger specific tags
+    }
+
+# -------------------------------------------------------------
+# FILENAME FORMATS
+# -------------------------------------------------------------
+# Customize how your generated notes are named.
+# Placeholders: {date}, {period}
+INSIGHT_TITLE_FORMAT = "{date} {period} Insight.md"
+
+# Placeholders: {date}
+BRIEFING_TITLE_FORMAT = "{date} Morning Briefing.md"
+
+# Placeholders: {date}, {time}, {subject}
+RECAP_TITLE_FORMAT = "{date} ({time}) Study Recap — {subject}.md"
+
+# Placeholders: {title}
+TXT_TITLE_FORMAT = "{title}.md"

generate_insights.py

@@ -21,7 +21,7 @@
 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
+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, backend_label, run_startup_checks
 
 SCRIPT_DIR = Path(__file__).parent
@@ -39,6 +39,8 @@
 GROUNDING = PROMPTS["grounding"]
 LENSES    = PROMPTS["insights"]["lenses"]
 
+TEMP_INSIGHTS = TEMPERATURES.get("insights")
+
 def fill_prompt(template: str, **kwargs) -> str:
     """
     Safe placeholder replacement that won't crash on literal { } in the template.
@@ -188,7 +190,7 @@ def run_lens(lens: dict, notes_block: str, period: str, backend: str) -> dict:
         notes_block=notes_block,
     )
     print(f"   Running lens: {lens['name']}...")
-    return {"name": lens["name"], "result": call_ai(prompt, backend)}
+    return {"name": lens["name"], "result": call_ai(prompt, backend, temperature=TEMP_INSIGHTS)}
 
 
 def run_synthesis(lens_results: list[dict], period: str, state: str, backend: str) -> tuple:
@@ -224,7 +226,7 @@ def run_synthesis(lens_results: list[dict], period: str, state: str, backend: st
     )
 
     print("   Running final synthesis...")
-    raw = call_ai(prompt, backend)
+    raw = call_ai(prompt, backend, temperature=TEMP_INSIGHTS)
     return extract_state_from_synthesis(raw)
 
 
@@ -238,17 +240,8 @@ def extract_tags(lens_results: list[dict], synthesis: str) -> list[str]:
     base_tags = ["insights"]
     text = synthesis.lower() + " ".join(r["result"].lower() for r in lens_results)
 
-    candidates = {
-        "productivity":  ["productiv", "task", "goal", "work", "focus"],
-        "mood":          ["mood", "emotion", "feel", "stress", "anxiet", "happy"],
-        "philosophy":    ["meaning", "values", "purpose", "reflect", "life"],
-        "habits":        ["habit", "routine", "pattern", "repeat", "daily"],
-        "health":        ["health", "sleep", "exercise", "energy", "body"],
-        "projects":      ["project", "build", "code", "ship", "launch", "develop"],
-        "relationships": ["friend", "family", "partner", "social", "connect"],
-    }
-
-    for tag, keywords in candidates.items():
+
+    for tag, keywords in CANDIDATES.items():
         if any(kw in text for kw in keywords):
             base_tags.append(tag)
 
@@ -267,14 +260,13 @@ def write_insight_note(lens_results: list[dict], synthesis: str, note_count: int
 
     is_month     = DAYS_BACK > 7
     period_label = "Week" if DAYS_BACK <= 7 else "Monthly"
-    filename     = f"{date_str} {period_label} Insight.md"
+    filename     = INSIGHT_TITLE_FORMAT.format(date=date_str, period=period_label)
 
     if is_month:
         time_property = "month: " + now.strftime("%B %Y")
     else:
         time_property = "week: " + get_week_label()
 
-
     INSIGHT_FOLDER.mkdir(parents=True, exist_ok=True)
     filepath = INSIGHT_FOLDER / filename
     tags = extract_tags(lens_results, synthesis)

morning_briefing.py

@@ -16,7 +16,7 @@
 import json
 import datetime
 
-from config import EXCLUDED_FOLDERS, MAX_FILE_SIZE, VAULT_PATH, MAX_NOTE_CHARS
+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, backend_label, run_startup_checks
 
 SCRIPT_DIR = Path(__file__).parent
@@ -33,6 +33,8 @@
 
 GROUNDING = PROMPTS["grounding"]
 
+TEMP_BRIEFING = TEMPERATURES.get("briefing")
+
 def fill_prompt(template: str, **kwargs) -> str:
     """
     Safe placeholder replacement that won't crash on literal { } in the template.
@@ -46,8 +48,6 @@ def fill_prompt(template: str, **kwargs) -> str:
 def collect_notes(days_back: float) -> list[dict]:
     """
     Collect notes modified within the last `days_back` days.
-    Skips Briefings and Insights folders to avoid feeding
-    generated content back into the briefing.
 
     Args:
         days_back: How many days back to look. Use 0 for today only,
@@ -129,13 +129,12 @@ def generate_briefing(yesterday_notes: list[dict], today_notes: list[dict], back
         today_block=today_block,
     )
 
-    return call_ai(prompt, backend)
+    return call_ai(prompt, backend, temperature=TEMP_BRIEFING)
 
 def write_briefing(content: str) -> str:
     """
     Write the briefing to the Briefings folder in the vault.
     Creates the folder if it doesn't exist.
-    Frontmatter is built line by line to avoid f-string issues.
 
     Args:
         content: The briefing Markdown content from generate_briefing().
@@ -146,7 +145,7 @@ def write_briefing(content: str) -> str:
     BRIEFING_FOLDER.mkdir(parents=True, exist_ok=True)
 
     date_str = datetime.datetime.now().strftime("%Y-%m-%d")
-    filename = f"{date_str} Morning Briefing.md"
+    filename = BRIEFING_TITLE_FORMAT.format(date=date_str)
     filepath = BRIEFING_FOLDER / filename
 
     fm_lines = [
@@ -186,7 +185,7 @@ def main():
     print(f"   found {total} note(s) modified in the last 24h\n")
 
     if not yesterday_notes and not today_notes:
-        print("   no recent notes found — briefing will be minimal\n")
+        print("   no recent notes found\n")
 
     print("   generating briefing...")
     content  = generate_briefing(yesterday_notes, today_notes, backend)
@@ -196,4 +195,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

study_recap.py

@@ -15,7 +15,7 @@
 import datetime
 import re
 
-from config import EXCLUDED_FOLDERS, MAX_FILE_SIZE, VAULT_PATH, HOURS_BACK, MAX_NOTE_CHARS
+from config import EXCLUDED_FOLDERS, MAX_FILE_SIZE, VAULT_PATH, HOURS_BACK, MAX_NOTE_CHARS, TEMPERATURES, RECAP_TITLE_FORMAT
 from ai_backend import get_backend, call_ai, backend_label, run_startup_checks
 
 SCRIPT_DIR = Path(__file__).parent
@@ -30,6 +30,8 @@
 
 GROUNDING = PROMPTS["grounding"]
 
+TEMP_RECAP = TEMPERATURES.get("recap")
+
 R      = "\033[0m"
 DIM    = "\033[2m"
 BOLD   = "\033[1m"
@@ -98,14 +100,13 @@ def find_recent_notes(hours: int) -> list[dict]:
 def index_all_notes() -> dict[str, str]:
     """
     Build an index of all notes in the vault for connection finding.
-    Only reads the first 500 chars of each note to keep memory usage low.
     """
     notes = {}
     for path_obj in VAULT_PATH.rglob("*.md"):
         try:
             if path_obj.stat().st_size > MAX_FILE_SIZE:
                 continue
-            content = path_obj.read_text(encoding="utf-8", errors="ignore")[:500]
+            content = path_obj.read_text(encoding="utf-8", errors="ignore")[:2000]
             notes[path_obj.stem] = content
         except Exception:
             continue
@@ -192,7 +193,7 @@ def generate_recap(notes_data: list[dict], all_notes: dict, backend: str) -> str
         notes_block=notes_block,
         all_titles=all_titles,
     )
-    return call_ai(prompt, backend, timeout=600)
+    return call_ai(prompt, backend, temperature=TEMP_RECAP)
 
 
 def write_recap(content: str, note_names: list[str]) -> str:
@@ -210,8 +211,8 @@ def write_recap(content: str, note_names: list[str]) -> str:
     else:
         subject = f"{stems[0]}, {stems[1]} (+{len(stems)-2})"
 
-    filename   = f"{date_str} {time_str} Recap - {subject}.md"
-    filename = re.sub(r'[\\/*?:"<>|]', "", filename)
+    safe_subject = re.sub(r'[\\/*?:"<>|]', "", subject)
+    filename   = RECAP_TITLE_FORMAT.format(date=date_str, time=time_str, subject=safe_subject)
     filepath   = RECAP_FOLDER / filename
 
     tags_lines = [

txt_to_notes.py

@@ -3,7 +3,7 @@
 # =============================================================
 # Converts any text file into one or more structured Obsidian
 # notes. Works with conversations, meeting notes, articles,
-# braindumps — anything you can paste into a .txt file.
+# anything you can paste into a .txt file.
 #
 # Two-pass process:
 #   Pass 1: AI reads the text and decides how many notes to create,
@@ -25,7 +25,7 @@
 import datetime
 from pathlib import Path
 
-from config import MAX_FILE_SIZE, VAULT_PATH
+from config import MAX_FILE_SIZE, VAULT_PATH, TEMPERATURES, TXT_TITLE_FORMAT
 from ai_backend import get_backend, call_ai, backend_label, run_startup_checks
 
 SCRIPT_DIR = Path(__file__).parent
@@ -48,6 +48,8 @@
 GREEN  = "\033[32m"
 RED    = "\033[31m"
 
+TEMP_TXT = TEMPERATURES.get("txt")
+
 def format_obsidian_tag(text: str) -> str:
     text = text.lower().replace(" ", "-")
     return re.sub(r'[^a-z0-9_-]', '', text)
@@ -147,7 +149,7 @@ def plan_notes(text, instructions, existing_tags, existing_titles, backend) -> l
         titles_hint=titles_hint,
     )
 
-    raw = call_ai(prompt, backend)
+    raw = call_ai(prompt, backend, temperature=TEMP_TXT)
     raw = re.sub(r"```(?:json)?", "", raw).strip()
 
     match = re.search(r"\[.*\]", raw, re.DOTALL)
@@ -182,15 +184,15 @@ def write_note_content(text, note_plan, all_titles_in_batch, instructions, exist
         related_links=related_links,
         tags_hint=tags_hint,
     )
-    return call_ai(prompt, backend, timeout=300)
+    return call_ai(prompt, backend, temperature=TEMP_TXT)
 
 
 def write_file(note_plan, body, folder_path, date_str) -> str:
     """Write a single note to disk with Obsidian frontmatter."""
     title      = note_plan.get("title", "Untitled")
     tags = [format_obsidian_tag(t) for t in note_plan.get("tags", [])]    
     safe_title = re.sub(r'[\\/*?:"<>|]', "", title)
-    filename   = f"{safe_title}.md"
+    filename   = TXT_TITLE_FORMAT.format(title=safe_title)
 
     filepath   = Path(folder_path) / filename
 
@@ -280,4 +282,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()