Ok, solved my own issue in a work around way; still would like timestamps, but I can see this working generally since I realized that when I’m working, I naturally tell the AI that I’m taking a break, and when I take a break, it usually means that its been a certain amount of time (a couple hours) or that I’ve gotten to a stopping point with working with features – so taking that into consideration, and gonig back and asking previous Gemini conversations about my key phrases; I got soem exact phrases and close phrases and then used that to create a script to help me break up my larger script; and now that I know that is a part of the process, I’ll know to be sure to type out when I’m taking a break so that the script will catch it for the daily backup process.
sharing in case anyone else would find it helpful. (sorry if it is too wordy; still trying to get the tuning right and havent taken time to get things condensed down just right yet)
ReadMe
# AI Context Management and Daily Workflow
## Purpose
This directory and the associated scripts and procedures are used to manage the AI Prototyper's context in Firebase Studio. The goals are:
1. To prevent the AI from cutting off responses due to an overly large context window by enabling daily context clearing.
2. To maintain a daily backup of the AI's full interaction context for history, reference, and **long-term analysis**.
3. To establish a workflow for **segmenting daily interactions into meaningful "work sessions"** (currently based on user-indicated break phrases due to the lack of per-message timestamps in logs).
4. To create **concise, AI-assisted summaries from these sessions**, which can then be used to **re-contextualize the AI for subsequent sessions** (daily, weekly, monthly, or for specific projects/tasks), thereby overcoming its natural memory limitations and enhancing collaborative efficiency.
5. Ultimately, to **improve the human-AI partnership** by analyzing interaction patterns and building a more strategic approach to context management, as outlined in [community discussions](https://community.firebasestudio.dev/t/saving-context-window-history-with-timestamps/14309) and personal research on enhancing AI-assisted workflows.
## Directory Structure
- `/home/user/studio/context_backup/` (This directory)
- `README.md` (This file)
- `data/` (Contains all daily backup data)
- `YYYY-MM-DD/` (Subfolders created daily by `daily_backup_context.sh`, e.g., `2025-06-01/`)
- `prototyper-context-state-YYYYMMDDHHMMSS.json` (Full backup of context state)
- `prototyper-thread-YYYYMMDDHHMMSS.json` (Full backup of thread state)
- `sessions/` (Subfolder created by `split_sessions.py` if run)
- `thread_segment_1_X.json` (Segment 1 of X total segments)
- `thread_segment_2_X.json`
- `...`
- `daily_summaries/` (Subfolder for storing summaries)
- `session_1_summary_AI.txt` (AI-generated summary for segment 1)
- `daily_overview_summary_AI.txt` (AI-generated summary for the day, if applicable)
- `weekly_rollup_summary.txt` (Created periodically, potentially AI-assisted)
- `scripts/` (Contains utility scripts)
- `daily_backup_context.sh` (Main daily backup script)
- `split_sessions.py` (Script to segment thread logs based on break phrases)
## Daily Workflow (To Be Performed After Each AI Session)
Here are the recommended steps to follow daily:
### 1.a. Back Up Current AI Context (Full Backup)
Before clearing the live context, ensure you save its current state.
- **Action:** Open the Firebase Studio terminal. Navigate to the `scripts` directory and run the daily backup script:
```bash
cd /home/user/studio/context_backup/scripts/
./daily_backup_context.sh
```
- **Result & Verification:** This will create a new dated subfolder inside `/home/user/studio/context_backup/data/` (e.g., `data/2025-06-01/`). Check that this folder contains the two timestamped `.json` backup files. Note the exact filename of the `prototyper-thread-YYYYMMDDxxxxxx.json` file.
### 1.b. (Recommended) Segment Thread Log into Sessions
To make reviewing and summarizing easier (especially for the AI summarizer), split the full thread backup into smaller segments.
- **Action:** While still in the `scripts/` directory, run the Python splitting script.
```bash
# Example: Replace YYYY-MM-DD and the timestamped filename
python3 ./split_sessions.py \
--input_file /home/user/studio/context_backup/data/YYYY-MM-DD/prototyper-thread-YYYYMMDDxxxxxx.json \
--output_parent_dir /home/user/studio/context_backup/data/YYYY-MM-DD/
```
- **Result & Verification:** This creates a `sessions/` subfolder inside your dated backup directory (e.g., `data/YYYY-MM-DD/sessions/`) containing files like `thread_segment_1_X.json`.
### 2. Clear the AI Context Window
(Content of this section remains the same: Method A `/clear`, Method B manual edit)
* **Method A: Using the `/clear` command (Try this first)** ...
* **Method B: Manually Resetting the Context State File (If `/clear` is not effective or available)** ...
### 3. Generate AI-Assisted Summaries from Session Segments
Leverage another AI instance (e.g., a separate Gemini window) to create concise summaries from your segmented session logs. These summaries serve as "artifacts" of your work and help re-contextualize your primary AI Prototyper in future sessions.
- **Process:**
1. In your separate AI (Gemini) window used for summarization, provide it with your research document ("[Saving Context Windows on the Daily...](https://github.com/its-a-lisa/personal-handbook/blob/main/Saving%20Context%20Windows%20on%20the%20Daily%3A%20Enhancing%20AI-Assisted%20Workflows%3A%20A%20Strategic%20Framework%20for%20Log%20Synthesis%2C%20Trend%20Analysis%2C%20and%20Goal%20Achievement.md)") as a guiding prompt or foundational context for how summaries should be structured or what they should achieve.
2. Then, provide the content of one of your segmented `thread_segment_X_Y.json` files (from, e.g., `data/YYYY-MM-DD/sessions/`) to this AI.
3. Request the AI to generate a summary of that session segment based on your framework and the provided log segment.
- **Storage:** Save the AI-generated summary as a text file (e.g., `session_1_summary_AI.txt`) inside a `daily_summaries/` subfolder within the corresponding dated backup folder (e.g., `/data/YYYY-MM-DD/daily_summaries/session_1_summary_AI.txt`).
- **Long-Term Strategy:** These AI-generated session summaries can be periodically reviewed, refined, and potentially fed back to an AI (perhaps the same one used for summarization, or even the Prototyper itself) to create rolled-up weekly or monthly summaries, or combined for specific projects, to provide broader and more targeted context when needed.
## Start of Next Session
1. **Select Relevant Summary:** Choose the appropriate summary to provide context. This might be a summary from the last session, a daily overview, or a compiled weekly/monthly/project-specific summary if you've started creating those.
2. **Provide Summary to AI Prototyper:** Copy and paste the chosen summary into the AI Prototyper as one of your first prompts. For example:
```
Hello AI. To bring you up to speed from our previous work (ref: YYYY-MM-DD, Session Z / Project Topic):
[Paste your relevant AI-generated summary here]
Our goal for this session is [your starting point for today].
```
## Important Files & Scripts
- **Main Daily Backup Script:** `/home/user/studio/context_backup/scripts/daily_backup_context.sh`
- *Purpose:* Automates the backup of the full original AI context files into date-stamped folders within `data/`.
- **Session Splitting Script:** `/home/user/studio/context_backup/scripts/split_sessions.py`
- *Purpose:* Splits the daily `prototyper-thread.json` backup into smaller session segments based on detected user break phrases, saving them into a `sessions/` subfolder.
- **Source AI Context Files (Backed up by `daily_backup_context.sh`):**
- `/home/user/.idx/ai/prototyper-context-state.json`
- `/home/user/.idx/ai/prototyper-thread.json`
## Future Enhancements (Ideas)
This workflow can be expanded over time. Some ideas include:
- Further automating the summarization pipeline, potentially chaining the output of `split_sessions.py` to an AI summarization script.
- **Developing systematic methods for creating, storing, and retrieving various levels of summaries (session, daily, weekly, monthly, project-specific) to provide highly relevant and efficient context to the AI.**
- Using AI to assist in identifying key decisions, code snippets, and action items from session logs or summaries for more structured knowledge management.
- Implementing ways to search or pinpoint specific information from past backed-up contexts or the structured summaries.
- Exploring community or official solutions for per-message timestamps to enable more precise, automated session splitting.
---
Remember to adapt file paths or commands if your setup changes.
Daily Backup Context
`#!/bin/bash
Get current date in YYYY-MM-DD format
current_date=$(date +%Y-%m-%d)
Define the parent backup directory
parent_backup_dir=“/home/user/studio/context_backup/data”
Define the specific directory for today’s backup
This creates a path like /home/user/studio/context_backup/2025-06-01
todays_backup_dir=“${parent_backup_dir}/${current_date}”
Create today’s backup directory (and parent if it doesn’t exist)
echo “Ensuring backup directory for today exists: ${todays_backup_dir}”
mkdir -p “${todays_backup_dir}”
Define a timestamp for unique backup filenames within the day
timestamp=$(date +%Y%m%d%H%M%S) # e.g., 20250601103055
Source files - double check these paths are correct for your environment
source_context_file=“/home/user/.idx/ai/prototyper-context-state.json”
source_thread_file=“/home/user/.idx/ai/prototyper-thread.json”
Destination filenames including the path to today’s dated folder
dest_context_file=“${todays_backup_dir}/prototyper-context-state-${timestamp}.json”
dest_thread_file=“${todays_backup_dir}/prototyper-thread-${timestamp}.json”
Check if source files exist before attempting to copy
if [ -f “${source_context_file}” ] && [ -f “${source_thread_file}” ]; then
echo “Source files found. Proceeding with backup…”
# Copy the files
cp “${source_context_file}” “${dest_context_file}”
cp “${source_thread_file}” “${dest_thread_file}”
echo "Backup successfully created in ${todays_backup_dir}/"
ls -l "${todays_backup_dir}/"
else
echo “Error: Source context file or thread file not found.”
echo “Checked for: ${source_context_file}”
echo “And for: ${source_thread_file}”
echo “Backup aborted.”
fi`
Split Sessions
#!/usr/bin/env python3
import json
import os
import argparse
import re
# Define break phrases using regular expressions (case-insensitive by default in search function)
# List combines specific phrases and more generalized patterns
BREAK_PHRASES_PATTERNS = [
r"going to stop now", # Still useful if explicitly said
r"going to stop (?:for|until) (?:tonight|the night|the day|later|a bit)",
r"going to stop here",
r"stop here for tonight",
r"i will take a break",
# Matches "taking a break", "taking a small break", "taking a short break now", "taking a bit of a break later", etc.
r"taking (?:a|some|my)?\s*(?:small|short|long|quick|little|bit of a|good|nice|)\s*break(?: now| later| soon| in a bit| for a while| for \d+ minutes| for an hour)?",
r"i'll be back in a while",
r"i'll be back later",
r"be right back", # handles "brb" if user types it out, or just "be right back"
r"brb",
r"take about \S+ break", # e.g., "take about an hour break", "take about 20 minutes break"
r"(?:and )?then (?:we'll|i'll|i will) take a break",
r"going to go to bed",
r"my stopping point is (?:around|somewhere around|at) \S+",
r"weeknights, my stopping point is (?:around|at) \S+",
r"now i'm stopping at \S+", # e.g., "now i'm stopping at 12:00 instead"
r"not going to do that.*i'm going to stop here", # Compound phrase indicating stop
# Patterns for time-bound session endings or explicit stops
r"(?:lets|let's|let us) work until \S+(?: today| tonight)?",
r"(?:lets|let's|let us) go until \S+(?: today| tonight)?",
r"(?:lets|let's|let us) expect to go until \S+",
r"we'll go another \d+ minutes",
r"i say somewhere between \S+ for this session",
r"i plan on working until \S+(?: today| tonight)?"
]
def find_break_in_message_regex(message_text, patterns):
"""Checks if any regex pattern matches the message text (case-insensitive)."""
# text_lower = message_text.lower() # Handled by re.IGNORECASE now
for pattern in patterns:
if re.search(pattern, message_text, re.IGNORECASE): # Added re.IGNORECASE flag
return True
return False
def split_thread_into_sessions(input_file_path, output_parent_dir):
"""
Splits a prototyper-thread.json file's history into session segments
based on user messages indicating a break, using regex for phrase matching.
"""
try:
with open(input_file_path, 'r', encoding='utf-8') as f:
thread_data = json.load(f)
except FileNotFoundError:
print(f"Error: Input file not found at {input_file_path}")
return
except json.JSONDecodeError:
print(f"Error: Could not decode JSON from {input_file_path}")
return
history = thread_data.get("history", [])
if not history:
print("No history found in the input file.")
return
all_segments_messages = []
current_segment_messages = []
for message in history:
current_segment_messages.append(message)
speaker_info = message.get("speaker", {})
if speaker_info.get("type") == "user":
body_array = message.get("body", [])
if body_array and isinstance(body_array, list) and len(body_array) > 0:
# Ensure markdown_content is a string
markdown_item = body_array[0]
if isinstance(markdown_item, dict) and "markdown" in markdown_item:
markdown_content = markdown_item.get("markdown", "")
if not isinstance(markdown_content, str): # Safety check
markdown_content = ""
elif isinstance(markdown_item, str): # If body[0] is directly a string (unlikely for this JSON)
markdown_content = markdown_item
else: # If body[0] is not a dict with 'markdown' or a string
markdown_content = ""
if markdown_content and find_break_in_message_regex(markdown_content, BREAK_PHRASES_PATTERNS):
all_segments_messages.append(list(current_segment_messages))
current_segment_messages = []
if current_segment_messages:
all_segments_messages.append(list(current_segment_messages))
if not all_segments_messages and history:
print("No break phrases found. Treating the entire log as a single segment.")
all_segments_messages.append(list(history))
total_segments = len(all_segments_messages)
if total_segments == 0:
print("No messages found or no segments created.")
return
sessions_output_dir = os.path.join(output_parent_dir, "sessions")
os.makedirs(sessions_output_dir, exist_ok=True)
print(f"Found {total_segments} segment(s) based on break phrases. Saving to: {sessions_output_dir}")
for i, segment_messages in enumerate(all_segments_messages):
segment_num = i + 1
filename = f"thread_segment_{segment_num}_{total_segments}.json"
output_path = os.path.join(sessions_output_dir, filename)
try:
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(segment_messages, f, indent=2)
print(f" Saved: {filename}")
except IOError as e:
print(f" Error saving {filename}: {e}")
except TypeError as e:
print(f" Error serializing segment {filename} to JSON: {e}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Split a Prototyper thread log into session files based on user break phrases using regex.")
parser.add_argument("--input_file", required=True, help="Path to the input prototyper-thread-YYYYMMDDHHMMSS.json file.")
parser.add_argument("--output_parent_dir", required=True, help="Path to the dated parent output directory (e.g., /path/to/context_backup/YYYY-MM-DD). The 'sessions' subfolder will be created here.")
args = parser.parse_args()
split_thread_into_sessions(args.input_file, args.output_parent_dir)