Backend Deep Dive

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Understanding the Omi Ecosystem 🗺️

• Processing and analyzing data: Converting audio to text, extracting meaning, and creating structured information from user interactions.
• Storing and managing memories: Building a rich knowledge base of user experiences that Omi can draw upon to provide context and insights.
• Facilitating intelligent conversations: Understanding user requests, retrieving relevant information, and generating personalized responses.
• Integrating with external services: Extending Omi’s capabilities and connecting it to other tools and platforms.

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System Architecture

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The Flow of Information: From User Interaction to Memory 🌊

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A. User Initiates a Recording 🎤

1. Recording Audio: The user starts a recording session using the Omi app, capturing a conversation or their thoughts.

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B. Real-Time Transcription with Deepgram 🎧

2. WebSocket Connection: The Omi app establishes a real-time connection with the backend using WebSockets (at the /listen endpoint in routers/transcribe.py).
3. Streaming Audio: The app streams audio data continuously through the WebSocket to the backend.
4. Deepgram Processing: The backend forwards the audio data to the Deepgram API for real-time speech-to-text conversion.
5. Transcription Results: As Deepgram transcribes the audio, it sends results back to the backend.
6. Live Feedback: The backend relays these transcription results back to the Omi app, allowing for live transcription display as the user is speaking.

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C. Creating a Lasting Memory 💾

7. API Request to /v1/memories: When the conversation session ends, the Omi app sends a POST request to the /v1/memories endpoint in routers/memories.py.
8. Data Formatting: The request includes information about the start and end time of the recording, the language, optional geolocation data, and the transcribed text segments from Deepgram.
9. Memory Creation (routers/memories.py): The create_memory function in this file receives the request and performs basic validation on the data.
10. Processing the Memory (utils/memories/process_memory.py):
    • The create_memory function delegates the core memory processing logic to the process_memory function. This function is where the real magic happens!
    • Structure Extraction: OpenAI’s powerful large language model (LLM) is used to analyze the transcript and extract key information, creating a structured representation of the memory. This includes:
      • title: A short, descriptive title.
      • overview: A concise summary of the main points.
      • category: A relevant category to organize memories (work, personal, etc.).
      • action_items: Any tasks or to-dos mentioned.
      • events: Events that might need to be added to a calendar.
    • Embedding Generation: The LLM is also used to create a vector embedding of the memory, capturing its semantic meaning for later retrieval.
    • App Execution: If the user has enabled any apps, relevant apps are run to enrich the memory with additional insights, external actions, or other context-specific information.
    • Storage in Firestore: The fully processed memory, including the transcript, structured data, app results, and other metadata, is stored in Firebase Firestore (a NoSQL database) for persistence.
    • Embedding Storage in Pinecone: The memory embedding is sent to Pinecone, a vector database, to enable fast and efficient similarity searches later.

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D. Enhancing the Memory (Optional)

11. Post-Processing: The user can optionally trigger post-processing of the memory to improve the quality of the transcript. This involves:
    • Sending the audio to a more accurate transcription service (like WhisperX through a FAL.ai function).
    • Updating the memory in Firestore with the new transcript.
    • Re-generating the embedding to reflect the updated content.

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The Core Components: A Closer Look 🔎

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1. database/memories.py: The Memory Guardian 🛡️

• upsert_memory: Creates or updates a memory document in Firestore, ensuring efficient storage and handling of updates.
• get_memory: Retrieves a specific memory by its ID.
• get_memories: Fetches a list of memories for a user, allowing for filtering, pagination, and optional inclusion of discarded memories.
• OpenGlass Functions: Handles the storage and retrieval of photos associated with memories created through OpenGlass.
• Post-Processing Functions: Manages the storage of data related to transcript post-processing (status, model used, alternative transcription segments).

class Memory(BaseModel):
    id: str  # Unique ID
    created_at: datetime  # Creation timestamp
    started_at: Optional[datetime]
    finished_at: Optional[datetime]

    source: Optional[MemorySource]
    language: Optional[str]

    structured: Structured  # Contains extracted title, overview, action items, etc. 
    transcript_segments: List[TranscriptSegment]
    geolocation: Optional[Geolocation]
    photos: List[MemoryPhoto]

    apps_results: List[AppResult]
    external_data: Optional[Dict]
    postprocessing: Optional[MemoryPostProcessing]

    discarded: bool
    deleted: bool 

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2. database/vector_db.py: The Embedding Expert 🌲

• upsert_vector: Adds or updates a memory embedding in Pinecone.
• upsert_vectors: Efficiently adds or updates multiple embeddings.
• query_vectors: Performs similarity search to find memories relevant to a user query.
• delete_vector: Removes a memory embedding.

• Contextual Retrieval: Finding memories that are semantically related to a user’s request, even if they don’t share exact keywords.
• Efficient Search: Quickly retrieving relevant memories from a large collection.
• Scalability: Handling the growing number of memory embeddings as the user creates more memories.

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3. utils/llm.py: The AI Maestro 🧠

• Memory Processing:
  • Determines if a conversation should be discarded.
  • Extracts structured information from transcripts (title, overview, categories, etc.).
  • Runs apps on memory data.
  • Handles post-processing of transcripts to improve accuracy.
• OpenGlass and External Integration Processing:
  • Creates structured summaries from photos and descriptions (OpenGlass).
  • Processes data from external sources (like ScreenPipe) to generate memories.
• Chat and Retrieval:
  • Generates initial chat messages.
  • Analyzes chat conversations to determine if context is needed.
  • Extracts relevant topics and dates from chat history.
  • Retrieves and summarizes relevant memory content for chat responses.
• Emotional Processing:
  • Analyzes conversation transcripts for user emotions.
  • Generates emotionally aware responses based on context and user facts.
• Fact Extraction: Identifies and extracts new facts about the user from conversation transcripts.

• llm.py leverages OpenAI’s ChatOpenAI model (specifically gpt-4o in the code, but you can use other models) for language understanding, generation, and reasoning.
• It uses OpenAI’s OpenAIEmbeddings model to generate vector embeddings for memories and user queries.

• The Brain of Omi: This module enables Omi’s core AI capabilities, including natural language understanding, content generation, and context-aware interactions.
• Memory Enhancement: It enriches raw data by extracting meaning and creating structured information.
• Personalized Responses: It helps Omi provide responses that are tailored to individual users, incorporating their unique facts, memories, and even emotional states.
• Extensibility: The app system and integration with external services make Omi highly versatile.

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4. utils/other/storage.py: The Cloud Storage Manager ☁️

• upload_profile_audio(file_path: str, uid: str):
  • Uploads a user’s speech profile audio recording to the GCS bucket specified by the BUCKET_SPEECH_PROFILES environment variable.
  • Organizes audio files within the bucket using the user’s ID (uid).
  • Returns the public URL of the uploaded file.
• get_profile_audio_if_exists(uid: str) -> str:
  • Checks if a speech profile already exists for a given user ID in the GCS bucket.
  • Downloads the speech profile audio to a local temporary file if it exists and returns the file path.
  • Returns None if the profile does not exist.

• The upload_profile_audio function is called when a user uploads a new speech profile recording through the /v3/upload-audio endpoint (defined in routers/speech_profile.py).
• The get_profile_audio_if_exists function is used to retrieve a user’s speech profile when needed, for example, during speaker identification in real-time transcription or post-processing.

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5. database/redis_db.py: The Data Speedster 🚀

• User Speech Profiles:
  • Storage: When a user uploads a speech profile, the raw audio data, along with its duration, is stored in Redis.
  • Retrieval: During real-time transcription or post-processing, the user’s speech profile is retrieved from Redis to aid in speaker identification.
• Enabled Apps:
  • Storage: A set of app IDs is stored for each user, representing the apps they have enabled.
  • Retrieval: When processing a memory or handling a chat request, the backend checks Redis to see which apps are enabled for the user.
• App Reviews:
  • Storage: Reviews for each app (score, review text, date) are stored in Redis, organized by app ID and user ID.
  • Retrieval: When displaying app information, the backend retrieves reviews from Redis.
• Cached User Names:
  • Storage: User names are cached in Redis to avoid repeated lookups from Firebase.
  • Retrieval: The backend first checks Redis for a user’s name before querying Firestore, improving performance.

• store_user_speech_profile, get_user_speech_profile: For storing and retrieving speech profiles.
• store_user_speech_profile_duration, get_user_speech_profile_duration: For managing speech profile durations.
• enable_app, disable_app, get_enabled_apps: For handling app enable/disable states.
• get_app_reviews: Retrieves reviews for a app.
• cache_user_name, get_cached_user_name: For caching user names. Why Redis is Important:
• Performance: Caching data in Redis significantly improves the backend’s speed, as frequently accessed data can be retrieved from memory very quickly.
• User Data Management: Redis provides a flexible and efficient way to manage user-specific data, such as app preferences and speech profiles.
• Real-time Features: The low-latency nature of Redis makes it ideal for supporting real-time features like live transcription and instant app interactions.
• Scalability: As the number of users grows, Redis helps maintain performance by reducing the load on primary databases.

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6. routers/transcribe.py: The Real-Time Transcription Engine 🎙️

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1. WebSocket Communication: The Lifeline of Real-Time Interactions 🔌

• /listen Endpoint: The Omi app initiates a WebSocket connection with the backend at the /listen endpoint, which is defined in the websocket_endpoint function of routers/transcribe.py.
• Bidirectional Communication: WebSockets enable a two-way communication channel, allowing:
  • The Omi app to stream audio data to the backend continuously.
  • The backend to send back transcribed text segments as they become available from Deepgram.
• Real-Time Feedback: This constant back-and-forth ensures that users see their words being transcribed in real-time, creating a more interactive and engaging experience.

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2. Deepgram Integration: Converting Speech to Text with Precision 🎧➡️📝

• process_audio_dg Function: The process_audio_dg function (found in utils/stt/streaming.py) manages the interaction with Deepgram.
• Deepgram API: The audio chunks streamed from the Omi app are sent to the Deepgram API for transcription. Deepgram’s sophisticated speech recognition models process the audio and return text results.
• Options Configuration: The process_audio_dg function configures various Deepgram options, including:
  • punctuate: Automatically adds punctuation to the transcribed text.
  • no_delay: Minimizes latency for real-time feedback.
  • language: Sets the language for transcription.
  • interim_results: (Set to False in the code) Controls whether to send interim (partial) transcription results or only final results.
  • diarize: Enables speaker diarization (identifying different speakers in the audio).
  • encoding, sample_rate: Sets audio encoding and sample rate for compatibility with Deepgram.

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3. Transcription Flow: A Step-by-Step Breakdown 🌊

1. App Streams Audio: The Omi app captures audio from the user’s device and continuously sends chunks of audio data through the WebSocket to the backend’s /listen endpoint.
2. Backend Receives and Forwards: The backend’s websocket_endpoint function receives the audio chunks and immediately forwards them to Deepgram using the process_audio_dg function.
3. Deepgram Processes: Deepgram’s speech recognition models transcribe the audio data in real-time.
4. Results Sent Back: Deepgram sends the transcribed text segments back to the backend as they become available.
5. Backend Relays to App: The backend immediately sends these transcription results back to the Omi app over the WebSocket connection.
6. App Displays Transcript: The Omi app updates the user interface with the newly transcribed text, providing instant feedback.

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4. Key Considerations

• Speaker Identification: The code uses Deepgram’s speaker diarization feature to identify different speakers in the audio. This information is included in the transcription results, allowing the app to display who said what.
• User Speech Profile Integration: If a user has uploaded a speech profile, the backend can use this information (retrieved from Redis or Google Cloud Storage) to improve the accuracy of speaker identification.
• Latency Management: Real-time transcription requires careful attention to latency to ensure a seamless user experience. The no_delay option in Deepgram and the efficient handling of data in the backend are essential for minimizing delays.
• Error Handling: The code includes error handling mechanisms to gracefully handle any issues that may occur during the WebSocket connection or Deepgram transcription process.

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5. Example Code Snippet (Simplified):

from fastapi import APIRouter, WebSocket

# ... other imports ...

router = APIRouter()


@router.websocket("/listen")
async def websocket_endpoint(websocket: WebSocket, uid: str, language: str = 'en', ...):
    await websocket.accept()  # Accept the WebSocket connection

    # Start Deepgram transcription
    transcript_socket = await process_audio_dg(uid, websocket, language, ...)

    # Receive and process audio chunks from the app
    async for data in websocket.iter_bytes():
        transcript_socket.send(data)

        # ... other logic for speaker identification, error handling, etc. 

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Other Important Components 🧩

• routers/transcribe.py: Manages real-time audio transcription using Deepgram, sending the transcribed text back to the Omi app for display.
• routers/workflow.py, routers/screenpipe.py: Define API endpoints for external integrations to trigger memory creation.

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Contributing 🤝

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Support 🆘

• GitHub Issues: 🐛 For bug reports and feature requests
• Community Forum: 💬 Join our community forum for discussions and questions
• Documentation: 📚 Check out our full documentation for in-depth guides