Package: agentai
AgentAI: OpenAI Functions + Python FunctionsIt is designed to make it easy to use OpenAI models e.g. GPT3.5-Turbo and GPT4 with existing Python functions by adding a simple decorator.AgentAI is a simple Python library with these ethos:Let developers write code!Do not invent a new syntax!Make it easy to integrate with existing projects!Make it easy to extend!Have fun and use exclamations!Unlike some libraries, AgentAI does NOT require you to learn a new syntax. No chains!Instead, it empowers you to add OpenAI functions using Python decorators and then call them directly from your code.
This makes it easy to integrate AgentAI with your existing projects.Colab NotebooksExtract detailed entities using Pydantic:FeaturesAPI Calls: Use AgentAI to decorate your Python functions and make them magical!Nested Pydantic Objects for Extraction: Use nested Pydantic objects to extract information from the user.SQL Database Interaction: Seamlessly extract and utilize data from SQL databases.Function Execution: Generate and execute function calls based on conversation context.Conversation Management: Effectively manage and track the state of conversations. Easily define your own functions which can use messages, functions, and conversation history.Next WeekMultiple Functions: Call multiple functions in a single conversation with a DSL/DAG.Retrieval: Use AgentAI to retrieve information from a vector Database -- but only when needed!Rich Media: Support for rich media types e.g. images, audioFunction Generation: Generate Python functions based on conversation contextInstallationInstall AgentAI using pip:pipinstallagentaiGetting Started: Asking User for Missing Inputs till all inputs are availableImport required classes and functionsfromagentai.apiimportchat_complete,chat_complete_execute_fnfromagentai.openai_functionimporttool,ToolRegistryfromagentai.conversationimportConversationfromenumimportEnumweather_registry=ToolRegistry()Define a function with@tooldecoratorclassTemperatureUnit(Enum):celsius="celsius"fahrenheit="fahrenheit"@tool(regsitry=weather_registry)defget_current_weather(location:str,format:TemperatureUnit)->str:"""Get the current weatherArgs:location (str): The city and state, e.g. San Francisco, CAformat (str): The temperature unit to use. Infer this from the users location.Returns:str: The current weather"""# Your function implementation goes here.return""Note thatagentaiautomatically parses the Python Enum type (TemperatureUnit) and passes it to the model as a JSONSchema Enum. This saves you time in writing boilerplate JSONSchema which is required by OpenAI API.Create a Conversation object and add messagesconversation=Conversation()conversation.add_message("user","what is the weather like today?")Use thechat_completefunction to get a response from the modelchat_response=chat_complete(conversation.conversation_history,function_registry=weather_registry,model=GPT_MODEL)Output:{'role':'assistant','content':'In which city would you like to know the current weather?'}Add user response to conversation and callchat_completeagainOnce the user provides the required information, the model can generate the function arguments:conversation.add_message("user","I'm in Bengaluru, India")chat_response=chat_complete(conversation.conversation_history,function_registry=weather_registry,model=GPT_MODEL)eval(chat_response.json()["choices"][0]["message"]["function_call"]["arguments"])Output:{'location':'Bengaluru, India','format':'celsius'}Example: Doing a Database Query with Generated SQLDefine a function with@tooldecoratordb_registry=ToolRegistry()@tool(registry=db_registry)defask_database(query:str)->List[Tuple[str,str]]:"""Use this function to answer user questions about music. Input should be a fully formed SQL query.Args:query (str): SQL query extracting info to answer the user's question.SQL should be written using this database schema: <database_schema_string>IMPORTANT: Please return a fixed SQL in PLAIN TEXT.Your response should consist of ONLY the SQL query."""try:results=conn.execute(query).fetchall()returnresultsexceptExceptionase:raiseException(f"SQL error:{e}")Registering the function and using itagentai_functions=[json.loads(func.json_info)forfuncin[ask_database]]fromagentai.apiimportchat_complete_execute_fnagent_system_message="""You are ChinookGPT, a helpful assistant who gets answers to user questions from the Chinook Music Database.Provide as many details as possible to your usersBegin!"""sql_conversation=Conversation()sql_conversation.add_message(role="system",content=agent_system_message)sql_conversation.add_message("user","Hi, who are the top 5 artists by number of tracks")assistant_message=chat_complete_execute_fn(conversation=sql_conversation,functions=agentai_functions,model=GPT_MODEL,callable_function=ask_database)sql_conversation.display_conversation(detailed=True)Output:system: You are ChinookGPT, a helpful assistant who gets answers to user questions from the Chinook Music Database.
Provide as many details as possible to your users
Begin!


user: Hi, who are the top 5 artists by number of tracks


function: [('Iron Maiden', 213), ('U2', 135), ('Led Zeppelin', 114), ('Metallica', 112), ('Lost', 92)]


assistant: The top 5 artists by number of tracks are:

1. Iron Maiden - 213 tracks
2. U2 - 135 tracks
3. Led Zeppelin - 114 tracks
4. Metallica - 112 tracks
5. Lost - 92 tracksDetailed ExamplesCheck out our detailednotebooks with exampleswhere we demonstrate how to integrate AgentAI with a chatbot to create a powerful conversational assistant that can answer questions using a SQLite database.ContributingWe welcome contributions! Please see ourcontributing guidelinesfor more details.SupportIf you encounter any issues or require further assistance, please raise an issue on ourGitHub repository.We hope you enjoy using AgentAI and find it helpful in powering up your AI models. Happy coding!