Storing Memories

🔄 Overview of the Process

1. Memory object is processed

2. Object is converted to a dictionary

3. Data is organized into specific fields

4. Memory is saved to Firestore

   

🧠 Detailed Steps

1. 📥 Memory Object Received

• The process_memory function in utils/memories/process_memory.py processes a new or updated memory
• The complete Memory object is then sent to the upsert_memory function in database/memories.py

2. 🔄 Convert to Dictionary

• The upsert_memory function converts the Memory object into a Python dictionary
• This conversion is necessary because Firestore stores data in a JSON-like format

3. 📊 Data Fields

The dictionary contains the following key fields:

Field	Description
id	Unique ID of the memory
created_at	Timestamp of memory creation
started_at	Timestamp when the associated event started
finished_at	Timestamp when the associated event ended
source	Source of the memory (e.g., "friend", "openglass", "workflow")
language	Language code of the conversation
structured	Dictionary of structured information (see below)
transcript_segments	List of transcript segments (see below)
geolocation	Location data (if available)
plugins_results	Results from any plugins run on the memory
external_data	Additional data from external integrations
postprocessing	Information about post-processing status
discarded	Boolean indicating if the memory is low-quality
deleted	Boolean indicating if the memory has been deleted
visibility	Visibility setting of the memory

📋 Structured Information

The structured field contains:

• title: Topic of the memory
• overview: Summary of the memory
• emoji: Representing emoji
• category: Category (e.g., "personal", "business")
• action_items: List of derived action items
• events: List of extracted calendar events

🗣️ Transcript Segments

Each segment in transcript_segments includes:

• speaker: Speaker label (e.g., "SPEAKER_00")
• start: Start time in seconds
• end: End time in seconds
• text: Transcribed text
• is_user: Boolean indicating if spoken by the user
• person_id: ID of a person from user's profiles (if applicable)

🔄 Postprocessing Information

The postprocessing field contains:

• status: Current status (e.g., "not_started", "in_progress")
• model: Post-processing model used (e.g., "fal_whisperx")
• fail_reason: (Optional) Reason for failure

4. 💾 Save to Firestore

• database/memories.py uses the Firebase Firestore API to store the memory data dictionary
• Data is saved in the memories collection within the user's document

📁 Firestore Structure

Users Collection

└── memories Collection
├── Memory Document 1
├── Memory Document 2
└── ...

This structure allows for efficient querying and management of user-specific memory data.