POC Python Realtime API o1 assistant

This is a proof of concept for using the OpenAI's Realtime API to chain tools, call o1-preview & o1-mini, structure output responses, and glimpse into the future of AI assistant powered engineering.

See video where we use SQL methods and discuss AI engineering in 2025

See video where we use and discuss this POC

See video where we add memory and tools to the assistant

This codebase is a v0.3, poc. It's buggy, but contains the core ideas for realtime personal ai assistants & AI Agents.

Setup

Install uv, the hyper modern Python package manager.
Setup environment cp .env.sample .env add your OPENAI_API_KEY and a FIRECRAWL_API_KEY for scraping.
Update personalization.json to fit your setup
Install dependencies uv sync
Run the realtime assistant uv run main or uv run main --prompts "Hello, how are you?|What time is it?|Open Hacker News"

Assistant Tools

See TOOLS.md for a detailed list of available tools and their descriptions.

Personalization

You can customize the behavior of the assistant by modifying the personalization.json file. Here are the available options:

browser_urls: A list of URLs that the assistant can open in the browser.
browser_command: The command used to open the browser.
ai_assistant_name: The name of the AI assistant.
human_name: The name of the human user.
sql_dialect: The SQL dialect to use for database operations. Supported options are:
- sqlite: For SQLite databases
- postgres: For PostgreSQL databases (untested)
- duckdb: For DuckDB databases
system_message_suffix: A string that will be appended to the end of the system instructions for the AI assistant.

Example personalization.json:

{
  "browser_urls": [
    "https://chat.openai.com",
    "https://github.com",
    "https://stackoverflow.com"
  ],
  "browser_command": "open -a 'Google Chrome'",
  "ai_assistant_name": "Ada",
  "human_name": "User",
  "sql_dialect": "sqlite",
  "system_message_suffix": "Always be helpful and concise in your responses."
}

The system_message_suffix allows you to add custom instructions or personality traits to your AI assistant. This suffix will be appended to the end of the default system instructions, giving you more control over how the assistant behaves and responds.

Try This

Voice Commands

Here are some voice commands you can try with the assistant:

"Hey Ada, how are you?"
"What's the current time?"
"Generate a random number."
"Open ChatGPT, Claude, and Hacker News."
"Create a new CSV file called user analytics with 10 mock rows."
"Update the user analytics file, add 20 additional mock rows, use a reasoning model."
"Get the current time, then add the current time to memory with the key 'current_time'."
"Hey Ada, generate a diagram outlining the architecture of a minimal tiktok clone."
"Hey Ada, check if example.py is runnable."
"Hey Ada, run example.py."
"Hey Ada, load the tables into memory.|Ada ingest active memory.|Ada select all Users"

CLI Text Prompts

You can also pass text prompts to the assistant via the CLI. Use '|' to separate prompts to chain commands.

uv run main --prompts "Hello, how are you?"
uv run main --prompts "Open Hacker News"
uv run main --prompts "Hey Ada!|What time is it?|Open Hacker News|Open Simon Willison blog|Open Aider"
uv run main --prompts "copy my current clipboard to memory"
uv run main --prompts "copy my current clipboard to memory with the key 'url'"
uv run main --prompts "call remove_variable_from_memory to delete the clipboard_content variable from active memory"
uv run main --prompts "Create a new CSV file called user analytics with 10 mock rows."
uv run main --prompts "read file user analytics into memory"
uv run main --prompts "reset active memory"
uv run main --prompts "add to memory the key 'project_status' with the value 'in progress'"
uv run main --prompts "read all files in the scratch pad directory into memory"
uv run main --prompts "scrape the URL from my clipboard and save it to a file"
uv run main --prompts "ada update the openai_structured_outputs file. clean it up and focus on the coding examples and the key usecases of structured outputs. use a fast model"
uv run main --prompts "Generate a diagram outlining the architecture of a minimal tiktok clone"
uv run main --prompts "Check if example.py is runnable code"
uv run main --prompts "Run example.py"
uv run main --prompts "Hey Ada, load the tables into memory|Ada ingest active memory|Ada execute sql select all Users and save to csv file"

Code Breakdown

Code Organization

The codebase is organized within the src/realtime_api_async_python directory. The application is modularized, with core functionality divided into separate Python modules located in the modules/ directory. Tests are located in the tests/ directory, providing a starting point for testing the application's components.

Important Files and Directories

main.py: This is the entry point of the application. It sets up the WebSocket connection, handles audio input/output, and manages the interaction between the user and the AI assistant.
modules/ Directory: Contains various modules handling different functionalities of the assistant:
- audio.py: Handles audio playback, including adding silence padding to prevent audio clipping.
- async_microphone.py: Manages asynchronous audio input from the microphone.
- database.py: Provides database interfaces for different SQL dialects (e.g., SQLite, DuckDB, PostgreSQL) and executes SQL queries.
- llm.py: Interfaces with language models, including functions for structured output parsing and chat prompts.
- logging.py: Configures logging for the application using Rich for formatted and colorful logs.
- memory_management.py: Manages the assistant's memory with operations to create, read, update, and delete memory entries.
- mermaid.py: Generates Mermaid diagrams based on prompts and renders them as images.
- tools.py: Contains definitions of tools and functions that the assistant can use to perform various actions.
- utils.py: Provides utility functions used across the application, such as timing decorators, model enumerations, audio configurations, and helper methods.
tests/ Directory: Contains tests for the application's modules, providing a starting point for testing the application's components.
active_memory.json: Stores the assistant's active memory state, allowing it to persist information between interactions.
personalization.json: Contains configuration settings used to personalize the assistant's behavior.
db/ Directory: Holds mock database files and SQL scripts for testing database functionalities.
scratchpad/ Directory: Used for temporary file storage and manipulation by the assistant.

Memory Management

The assistant uses the MemoryManager class in memory_management.py to handle memory operations. This class provides methods to create, read, update, delete, and list memory entries. Memory is stored persistently in active_memory.json, enabling the assistant to access and manipulate memory across sessions.

Tools Framework

Tools are functions defined in modules/tools.py that extend the assistant's capabilities. These tools are mapped in function_map and are available for the assistant to perform actions based on user requests. The assistant uses these tools to execute specific tasks, enhancing its functionality and allowing for dynamic interactions.

Improvements

Up for a challenge? Here are some ideas on how to improve the experience:

Add interruption handling. Current version prevents it for simplicity.
Add transcript logging.
Make personalization.json a pydantic type.
Let tools run in parallel.
Fix audio randomly cutting out near the end.

Mock Database (sqlite and duckdb)

Reset duckdb rm db/mock_duck.duckdb && duckdb db/mock_duck.duckdb < db/mock_data_for_duckdb.sql
Reset sqlite rm db/mock_sqlite.db && sqlite3 db/mock_sqlite.db < db/mock_data_for_sqlite.sql

disler/poc-realtime-ai-assistant