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I changed the name to Pensieve because Memos was already taken.

Pensieve (previously named Memos)

Pensieve is a privacy-focused passive recording project. It can automatically record screen content, build intelligent indices, and provide a convenient web interface to retrieve historical records.

This project draws heavily from two other projects: one called Rewind and another called Windows Recall. However, unlike both of them, Pensieve allows you to have complete control over your data, avoiding the transfer of data to untrusted data centers.

Features

• 🚀 Simple installation: just install dependencies via pip to get started
• 🔒 Complete data control: all data is stored locally, allowing for full local operation and self-managed data processing
• 🔍 Full-text and vector search support
• 🤖 Integrates with Ollama, using it as the machine learning engine for Pensieve
• 🌐 Compatible with any OpenAI API models (e.g., OpenAI, Azure OpenAI, vLLM, etc.)
• 💻 Supports Mac and Windows (Linux support is in development)
• 🔌 Extensible functionality through plugins

📰 Latest News

• Intelligent Idle Processing Strategy: Starting from version v0.26.0, Pensieve introduces an intelligent idle processing strategy that automatically processes pending files during system idle time. This feature maximizes screenshot processing while minimizing performance impact during active system use. For more details, please refer to the Idle Processing Strategy section.

• PostgreSQL Support: Starting from version v0.25.4, Pensieve now fully supports using PostgreSQL as the backend database. This enhancement allows for improved retrieval performance, especially with large data volumes. If you have extensive screenshot data or require high-speed retrieval, we strongly recommend using PostgreSQL.

  For more details on setting up PostgreSQL, please refer to the Using PostgreSQL Database section.

Quick Start

Important

It seems that not all versions of Python's sqlite3 library support enable_load_extension. However, I'm not sure which environments or Python versions might encounter this issue. I use conda to manage Python, and Python installed via conda works fine on macOS, Windows x86, and Ubuntu 22.04.

Please ensure the following command works in your Python environment:

import sqlite3

# Check sqlite version
print(f"SQLite version: {sqlite3.sqlite_version}")

# Test if enable_load_extension is supported
try:
    conn = sqlite3.connect(':memory:')
    conn.enable_load_extension(True)
    print("enable_load_extension is supported")
except AttributeError:
    print("enable_load_extension is not supported")
finally:
    conn.close()

If you find that this does not work properly, you can install miniconda to manage your Python environment. Alternatively, check the current issue list to see if others have encountered the same problem.

1. Install Pensieve

pip install memos

2. Initialize

Initialize the pensieve configuration file and sqlite database:

memos init

Data will be stored in the ~/.memos directory.

3. Start the Service

memos enable
memos start

This command will:

• Begin recording all screens
• Start the Web service
• Set the service to start on boot

4. Access the Web Interface

Open your browser and visit http://localhost:8839

Mac Permission Issues

On Mac, Pensieve needs screen recording permission. When the program starts, Mac will prompt for screen recording permission - please allow it to proceed.

🚀 Using PostgreSQL Database

To use PostgreSQL with Pensieve, you need to install the package with PostgreSQL support:

pip install memos[postgresql]

Starting from version v0.25.4, Pensieve fully supports using PostgreSQL as the backend database. Compared to SQLite, PostgreSQL can maintain excellent retrieval performance even with large data volumes.

If your screenshot data is large or you require high retrieval response speed, it is strongly recommended to use PostgreSQL as the backend database.

1. Start PostgreSQL with Docker

Since Pensieve uses vector search functionality, it requires PostgreSQL with the pgvector extension. We recommend using the official pgvector image:

On Linux/macOS:

docker run -d \
    --name pensieve-pgvector \
    --restart always \
    -p 5432:5432 \
    -e POSTGRES_PASSWORD=mysecretpassword \
    -v pensieve-pgdata:/var/lib/postgresql/data \
    pgvector/pgvector:pg17

On Windows PowerShell:

docker run -d `
    --name pensieve-pgvector `
    --restart always `
    -p 5432:5432 `
    -e POSTGRES_PASSWORD=mysecretpassword `
    -v pensieve-pgdata:/var/lib/postgresql/data `
    pgvector/pgvector:pg17

On Windows Command Prompt:

docker run -d ^
    --name pensieve-pgvector ^
    --restart always ^
    -p 5432:5432 ^
    -e POSTGRES_PASSWORD=mysecretpassword ^
    -v pensieve-pgdata:/var/lib/postgresql/data ^
    pgvector/pgvector:pg17

This command will:

• Create a container named pensieve-pgvector
• Set the PostgreSQL password to mysecretpassword
• Map the container's port 5432 to the host's port 5432
• Use PostgreSQL version 17 with vector search support
• Create a data volume named pensieve-pgdata for persistent data storage
• Set the container to start automatically after Docker restarts

Note: If you are using Windows, make sure Docker Desktop is installed and running. You can download and install Docker Desktop from the Docker website.

2. Configure Pensieve to Use PostgreSQL

Modify the database configuration in the ~/.memos/config.yaml file:

# Change the original SQLite configuration:
database_path: database.db

# To PostgreSQL configuration:
database_path: postgresql://postgres:mysecretpassword@localhost:5432/postgres

Configuration explanation:

• postgres:mysecretpassword: Database username and password
• localhost:5432: PostgreSQL server address and port
• postgres: Database name

3. Migrate from SQLite to PostgreSQL

If you previously used SQLite and want to migrate to PostgreSQL, Pensieve provides a dedicated migration command:

# Stop the Pensieve service
memos stop

# Execute the migration
memos migrate \
  --sqlite-url "sqlite:///absolute/path/to/your/database.db" \
  --pg-url "postgresql://postgres:mysecretpassword@localhost:5432/postgres"

# Modify the configuration file to point to PostgreSQL
# Edit ~/.memos/config.yaml to update database_path

# Restart the service
memos start

Notes:

1. Ensure the PostgreSQL service is running before migration
2. The migration process will completely clear the target PostgreSQL database, ensure there is no important data
3. The migration will not affect the original SQLite database
4. The migration process may take some time depending on the data size
5. After migration, you can choose to backup and delete the original SQLite database file

Below are the migration commands for Mac and Windows:

# Mac
memos migrate \
  --sqlite-url "sqlite:///~/memos/database.db" \
  --pg-url "postgresql://postgres:mysecretpassword@localhost:5432/postgres"

# Windows PowerShell
memos migrate `
  --sqlite-url "sqlite:///$env:USERPROFILE/.memos/database.db" `
  --pg-url "postgresql://postgres:mysecretpassword@localhost:5432/postgres"

# Windows Command Line
memos migrate ^
  --sqlite-url "sqlite:///%USERPROFILE%/.memos/database.db" ^
  --pg-url "postgresql://postgres:mysecretpassword@localhost:5432/postgres"

User Guide

Using the Appropriate Embedding Model

1. Model Selection

Pensieve uses embedding models to extract semantic information and build vector indices. Therefore, choosing an appropriate embedding model is crucial. Depending on the user's primary language, different embedding models should be selected.

• For Chinese scenarios, you can use the jinaai/jina-embeddings-v2-base-zh model.
• For English scenarios, you can use the jinaai/jina-embeddings-v2-base-en model.

2. Adjust Memos Configuration

Open the ~/.memos/config.yaml file with your preferred text editor and modify the embedding configuration:

embedding:
  use_local: true
  model: jinaai/jina-embeddings-v2-base-en   # Model name used
  num_dim: 768                               # Model dimensions
  use_modelscope: false                      # Whether to use ModelScope's model

3. Restart Memos Service

memos stop
memos start

The first time you use the embedding model, Pensieve will automatically download and load the model.

4. Rebuild Index

If you switch the embedding model during use, meaning you have already indexed screenshots before, you need to rebuild the index:

memos reindex --force

The --force parameter indicates rebuilding the index table and deleting previously indexed screenshot data.

Using Ollama for Visual Search

By default, Pensieve only enables the OCR plugin to extract text from screenshots and build indices. However, this method significantly limits search effectiveness for images without text.

To achieve more comprehensive visual search capabilities, we need a multimodal image understanding service compatible with the OpenAI API. Ollama perfectly fits this role.

Important Notes Before Use

Before deciding to enable the VLM feature, please note the following:

1. Hardware Requirements

   • Recommended configuration: NVIDIA graphics card with at least 8GB VRAM or Mac with M series chip
   • The minicpm-v model will occupy about 5.5GB of storage space
   • CPU mode is not recommended as it will cause severe system lag

2. Performance and Power Consumption Impact

   • Enabling VLM will significantly increase system power consumption
   • Consider using other devices to provide OpenAI API compatible model services

1. Install Ollama

Visit the Ollama official documentation for detailed installation and configuration instructions.

2. Prepare the Multimodal Model

Download and run the multimodal model minicpm-v using the following command:

ollama run minicpm-v "Describe what this service is"

This command will download and run the minicpm-v model. If the running speed is too slow, it is not recommended to use this feature.

3. Configure Pensieve to Use Ollama

Open the ~/.memos/config.yaml file with your preferred text editor and modify the vlm configuration:

vlm:
  endpoint: http://localhost:11434  # Ollama service address
  modelname: minicpm-v              # Model name to use
  force_jpeg: true                  # Convert images to JPEG format to ensure compatibility
  prompt: Please describe the content of this image, including the layout and visual elements  # Prompt sent to the model

Use the above configuration to overwrite the vlm configuration in the ~/.memos/config.yaml file.

Also, modify the default_plugins configuration in the ~/.memos/plugins/vlm/config.yaml file:

default_plugins:
- builtin_ocr
- builtin_vlm

This adds the builtin_vlm plugin to the default plugin list.

4. Restart Pensieve Service

memos stop
memos start

After restarting the Pensieve service, wait a moment to see the data extracted by VLM in the latest screenshots on the Pensieve web interface:

If you do not see the VLM results, you can:

• Use the command memos ps to check if the Pensieve process is running normally
• Check for error messages in ~/.memos/logs/memos.log
• Confirm whether the Ollama model is loaded correctly (ollama ps)

Full Indexing

Pensieve is a compute-intensive application. The indexing process requires the collaboration of OCR, VLM, and embedding models. To minimize the impact on the user's computer, Pensieve calculates the average processing time for each screenshot and adjusts the indexing frequency accordingly. Therefore, not all screenshots are indexed immediately by default.

If you want to index all screenshots, you can use the following command for full indexing:

memos scan

This command will scan and index all recorded screenshots. Note that depending on the number of screenshots and system configuration, this process may take some time and consume significant system resources. The index construction is idempotent, and running this command multiple times will not re-index already indexed data.

Sampling Strategy

Pensieve dynamically adjusts the image processing interval based on the speed of screenshot generation and the speed of processing individual images. In environments without NVIDIA GPUs, it may be challenging to ensure that image processing keeps up with the rate of screenshot generation. To address this, Pensieve processes images on a sampled basis.

To prevent excessive system load, Pensieve's default sampling strategy is intentionally conservative. However, this conservative approach might limit the performance of devices with higher computational capacity. To provide more flexibility, additional control options have been introduced in ~/.memos/config.yaml, allowing users to configure the system for either more conservative or more aggressive processing strategies.

watch:
  # number of recent events to consider when calculating processing rates
  rate_window_size: 10
  # sparsity factor for file processing
  # a higher value means less frequent processing
  # 1.0 means process every file, can not be less than 1.0
  sparsity_factor: 3.0
  # initial processing interval for file processing, means process one file 
  # with plugins for every N files
  # but will be adjusted automatically based on the processing rate
  # 12 means processing one file every 12 screenshots generated
  processing_interval: 12

If you want every screenshot file to be processed, you can configure the settings as follows:

# A watch config like this means process every file with plugins at the beginning
# but if the processing rate is slower than file generated, the processing interval 
# will be increased automatically
watch:
  rate_window_size: 10
  sparsity_factor: 1.0
  processing_interval: 1

Remember to do memos stop && memos start to make the new config work.

Idle Processing Strategy

Pensieve implements an intelligent idle processing strategy to handle skipped files during system idle time. This helps ensure all screenshots are eventually processed while minimizing impact on system performance during active use.

Idle Detection and Processing

• The system enters idle state after 5 minutes of no new screenshot activity
• During idle state, Pensieve will attempt to process previously skipped files if:
  • The system is not running on battery power
  • The current time falls within the configured processing window
  • There are skipped files pending processing

Configuration

The idle processing behavior can be customized in ~/.memos/config.yaml:

watch:
  # seconds before marking state as idle
  idle_timeout: 300
  # time interval for processing skipped files
  # format: ["HH:MM", "HH:MM"]
  idle_process_interval: ["00:00", "07:00"]

• idle_timeout: How long (in seconds) the system should wait without activity before entering idle state
• idle_process_interval: The time window during which skipped files can be processed
  • Format is ["HH:MM", "HH:MM"] in 24-hour time
  • The interval can cross midnight (e.g., ["23:00", "07:00"] is valid)
  • For intervals crossing midnight, start time must be after 12:00 to avoid ambiguity

This strategy ensures that:

1. System resources are primarily available for active use during working hours
2. Background processing occurs during off-hours
3. Battery life is preserved by avoiding processing while on battery power

Remember to do memos stop && memos start to make any configuration changes take effect.

Privacy and Security

During the development of Pensieve, I closely followed the progress of similar products, especially Rewind and Windows Recall. I greatly appreciate their product philosophy, but they do not do enough in terms of privacy protection, which is a concern for many users (or potential users). Recording the screen of a personal computer may expose extremely sensitive private data, such as bank accounts, passwords, chat records, etc. Therefore, ensuring that data storage and processing are completely controlled by the user to prevent data leakage is particularly important.

The advantages of Pensieve are:

1. The code is completely open-source and easy-to-understand Python code, allowing anyone to review the code to ensure there are no backdoors.
2. Data is completely localized, all data is stored locally, and data processing is entirely controlled by the user. Data will be stored in the user's ~/.memos directory.
3. Easy to uninstall. If you no longer use Pensieve, you can close the program with memos stop && memos disable, then uninstall it with pip uninstall memos, and finally delete the ~/.memos directory to clean up all databases and screenshot data.
4. Data processing is entirely controlled by the user. Pensieve is an independent project, and the machine learning models used (including VLM and embedding models) are chosen by the user. Due to Pensieve' operating mode, using smaller models can also achieve good results.

Of course, there is still room for improvement in terms of privacy, and contributions are welcome to make Pensieve better.

Other Noteworthy Content

About Storage Space

Pensieve records the screen every 5 seconds and saves the original screenshots in the ~/.memos/screenshots directory. Storage space usage mainly depends on the following factors:

1. Screenshot Data:

   • Single screenshot size: about 40-400KB (depending on screen resolution and display complexity)
   • Daily data volume: about 400MB (based on 10 hours of usage, single screen 2560x1440 resolution)
   • Multi-screen usage: data volume increases with the number of screens
   • Monthly estimate: about 8GB based on 20 working days

   Screenshots are deduplicated. If the content of consecutive screenshots does not change much, only one screenshot will be retained. The deduplication mechanism can significantly reduce storage usage in scenarios where content does not change frequently (such as reading, document editing, etc.).

2. Database Space:

   • SQLite database size depends on the number of indexed screenshots
   • Reference value: about 2.2GB of storage space after indexing 100,000 screenshots

About Power Consumption

Pensieve requires two compute-intensive tasks by default:

• One is the OCR task, used to extract text from screenshots
• The other is the embedding task, used to extract semantic information and build vector indices

Resource Usage

• OCR Task: Executed using the CPU, and optimized to select the OCR engine based on different operating systems to minimize CPU usage

• Embedding Task: Intelligently selects the computing device

  • NVIDIA GPU devices prioritize using the GPU
  • Mac devices prioritize using Metal GPU
  • Other devices use the CPU

Performance Optimization Strategy

To avoid affecting users' daily use, Pensieve has adopted the following optimization measures:

• Dynamically adjust the indexing frequency, adapting to system processing speed
• Automatically reduce processing frequency when on battery power to save power

Development Guide

Peeling the First Layer of the Onion

In fact, after Pensieve starts, it runs three programs:

1. memos serve starts the web service
2. memos record starts the screenshot recording program
3. memos watch listens to the image events generated by memos record and dynamically submits indexing requests to the server based on actual processing speed

Therefore, if you are a developer or want to see the logs of the entire project running more clearly, you can use these three commands to run each part in the foreground instead of the memos enable && memos start command.