salesgpt.agents module

class salesgpt.agents.SalesGPT(*, name: str | None = None, memory: BaseMemory | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, verbose: bool = None, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, callback_manager: BaseCallbackManager | None = None, conversation_history: List[str] = [], conversation_stage_id: str = '1', current_conversation_stage: str = 'Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are calling.', stage_analyzer_chain: StageAnalyzerChain = FieldInfo(annotation=NoneType, required=True), sales_agent_executor: CustomAgentExecutor | None = FieldInfo(annotation=NoneType, required=True), knowledge_base: RetrievalQA | None = FieldInfo(annotation=NoneType, required=True), sales_conversation_utterance_chain: SalesConversationChain = FieldInfo(annotation=NoneType, required=True), conversation_stage_dict: Dict = {'1': 'Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are calling.', '2': 'Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.', '3': 'Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.', '4': "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.", '5': "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.", '6': 'Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.', '7': 'Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.', '8': "End conversation: It's time to end the call as there is nothing else to be said."}, model_name: str = 'gpt-3.5-turbo-0613', use_tools: bool = False, salesperson_name: str = 'Ted Lasso', salesperson_role: str = 'Business Development Representative', company_name: str = 'Sleep Haven', company_business: str = 'Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.', company_values: str = "Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.", conversation_purpose: str = 'find out whether they are looking to achieve better sleep via buying a premier mattress.', conversation_type: str = 'call')

Bases: Chain

Controller model for the Sales Agent.

acall(inputs: Dict[str, Any]) → Dict[str, Any]

Executes one step of the sales agent.

This function overrides the input temporarily with the current state of the conversation, generates the agent’s utterance using either the sales agent executor or the sales conversation utterance chain, adds the agent’s response to the conversation history, and returns the AI message.

Parameters

inputsDict[str, Any]

The initial inputs for the sales agent.

Returns

Dict[str, Any]

The AI message generated by the sales agent.

async acompletion_with_retry(llm: Any, **kwargs: Any) → Any

Use tenacity to retry the async completion call.

This method uses the tenacity library to retry the asynchronous completion call in case of failure. It creates a retry decorator using the ‘_create_retry_decorator’ method and applies it to the ‘_completion_with_retry’ function which makes the actual asynchronous completion call.

Parameters

llmAny

The language model to be used for the completion.

**kwargsAny

Additional keyword arguments to be passed to the completion function.

Returns

Any

The result of the completion function call.

Raises

Exception

If the completion function call fails after the maximum number of retries.

adetermine_conversation_stage()

Determines the current conversation stage based on the conversation history.

This method uses the stage_analyzer_chain to analyze the conversation history and determine the current stage. The conversation history is joined into a single string, with each entry separated by a newline character. The current conversation stage ID is also passed to the stage_analyzer_chain.

The method then prints the determined conversation stage ID and retrieves the corresponding conversation stage from the conversation_stage_dict dictionary using the retrieve_conversation_stage method.

Finally, the method prints the determined conversation stage.

Returns:

None

astep(stream: bool = False)

Executes an asynchronous step in the conversation.

If the stream argument is set to False, it calls the _acall method with an empty dictionary as input. If the stream argument is set to True, it returns a streaming generator object for manipulating streaming chunks in downstream applications.

Args:

stream (bool, optional): A flag indicating whether to return a streaming generator object. Defaults to False.

Returns:

Generator: A streaming generator object if stream is set to True. Otherwise, it returns None.

company_business: str

company_name: str

company_values: str

conversation_history: List[str]

conversation_purpose: str

conversation_stage_dict: Dict

conversation_stage_id: str

conversation_type: str

current_conversation_stage: str

determine_conversation_stage()

Determines the current conversation stage based on the conversation history.

This method uses the stage_analyzer_chain to analyze the conversation history and determine the current stage. The conversation history is joined into a single string, with each entry separated by a newline character. The current conversation stage ID is also passed to the stage_analyzer_chain.

The method then prints the determined conversation stage ID and retrieves the corresponding conversation stage from the conversation_stage_dict dictionary using the retrieve_conversation_stage method.

Finally, the method prints the determined conversation stage.

Returns:

None

classmethod from_llm(llm: ChatLiteLLM, verbose: bool = False, **kwargs) → SalesGPT

Class method to initialize the SalesGPT Controller from a given ChatLiteLLM instance.

This method sets up the stage analyzer chain and sales conversation utterance chain. It also checks if custom prompts are to be used and if tools are to be set up for the agent. If tools are to be used, it sets up the knowledge base, gets the tools, sets up the prompt, and initializes the agent with the tools. If tools are not to be used, it sets the sales agent executor and knowledge base to None.

Parameters

llmChatLiteLLM

The ChatLiteLLM instance to initialize the SalesGPT Controller from.

verbosebool, optional

If True, verbose output is enabled. Default is False.

**kwargsdict

Additional keyword arguments.

Returns

SalesGPT

The initialized SalesGPT Controller.

human_step(human_input)

Processes the human input and appends it to the conversation history.

This method takes the human input as a string, formats it by adding “User: “ at the beginning and “ <END_OF_TURN>” at the end, and then appends this formatted string to the conversation history.

Args:

human_input (str): The input string from the human user.

Returns:

None

property input_keys: List[str]

Property that returns a list of input keys.

This property is currently set to return an empty list. It can be overridden in a subclass to return a list of keys that are used to extract input data from a dictionary.

Returns:

List[str]: An empty list.

knowledge_base: RetrievalQA | None

model_name: str

property output_keys: List[str]

Property that returns a list of output keys.

This property is currently set to return an empty list. It can be overridden in a subclass to return a list of keys that are used to extract output data from a dictionary.

Returns:

List[str]: An empty list.

retrieve_conversation_stage(key)

Retrieves the conversation stage based on the provided key.

This function uses the key to look up the corresponding conversation stage in the conversation_stage_dict dictionary. If the key is not found in the dictionary, it defaults to “1”.

Args:

key (str): The key to look up in the conversation_stage_dict dictionary.

Returns:

str: The conversation stage corresponding to the key, or “1” if the key is not found.

sales_agent_executor: CustomAgentExecutor | None

sales_conversation_utterance_chain: SalesConversationChain

salesperson_name: str

salesperson_role: str

seed_agent()

This method seeds the conversation by setting the initial conversation stage and clearing the conversation history.

The initial conversation stage is retrieved using the key “1”. The conversation history is reset to an empty list.

Returns:

None

stage_analyzer_chain: StageAnalyzerChain

step(stream: bool = False)

Executes a step in the conversation. If the stream argument is set to True, it returns a streaming generator object for manipulating streaming chunks in downstream applications. If the stream argument is set to False, it calls the _call method with an empty dictionary as input.

Args:

stream (bool, optional): A flag indicating whether to return a streaming generator object. Defaults to False.

Returns:

Generator: A streaming generator object if stream is set to True. Otherwise, it returns None.

use_tools: bool