self - prompt tuning

[Eran-BA]

how is it possible to refine the self-prompt of an agent?
Can the agent re-create it self prompt for the next iteration?
is it possible to create even 2 agents, agent-x observes the second agent-y and tunes it while not tackling stop condition/s.

[dosubot[bot]]

🤖

Yes, it is possible for an agent to refine its self-prompt for the next iteration. This is achieved by generating self-criticism and a new instruction using the meta-prompt at the end of each episode. The agent doesn't have memory between episodes except for the instruction it modifies for itself each time. This allows the agent to learn over time and self-improve by incorporating useful details into its instructions.

As for one agent observing and tuning another agent, the LangChain implementation of Meta-Prompt does allow for this. In the provided code, two chains are defined: one serves as the Assistant, and the other is a "meta-chain" that critiques the Assistant's performance and modifies the instructions to the Assistant.

In the context shared, the AgentExecutor class in LangChain has a max_iterations attribute that sets the maximum number of steps to take before ending the execution loop. If the agent never returns AgentFinish, the early_stopping_method attribute determines the method to use for early stopping. If it's set to 'force', it returns a string saying that it stopped because it met a time or iteration limit. If it's set to 'generate', it does one final forward pass and tries to extract a final answer. If it can extract, but the tool is not the final tool, it just returns the full output. This shows that LangChain does have a mechanism to tackle stop conditions for the agents. However, this mechanism is based on the number of iterations or the presence of specific phrases (success_phrase or failed_phrase) in the user's input, and not on the agents' performance or behavior.

The LangChain framework implements the self-criticism and instruction modification process for an agent to refine its self-prompt for the next iteration through the use of prompt templates and conditional prompt selectors. The DEFAULT_REFINE_PROMPT_TMPL is a template that instructs the agent to refine the existing answer with more context if needed. If the context isn't useful, the agent is instructed to return the original answer. This template is then converted into a PromptTemplate object and stored in DEFAULT_REFINE_PROMPT.

The CHAT_REFINE_PROMPT is a ChatPromptTemplate that is created from a list of messages. It is used when the model is a chat model.

The REFINE_PROMPT_SELECTOR is a ConditionalPromptSelector that selects the appropriate prompt based on the conditionals provided. If the model is a chat model, it selects the CHAT_REFINE_PROMPT, otherwise, it selects the DEFAULT_REFINE_PROMPT.

The DEFAULT_TEXT_QA_PROMPT_TMPL and CHAT_QUESTION_PROMPT are similar to the refine prompts but are used for question answering instead of refining.

The QUESTION_PROMPT_SELECTOR is another ConditionalPromptSelector that selects the appropriate question answering prompt based on the conditionals provided.

You can find more details in the following files:

• docs/extras/use_cases/more/agents/autonomous_agents/meta_prompt.ipynb
• libs/langchain/langchain/agents/agent.py
• libs/langchain/langchain/chains/question_answering/refine_prompts.py

I hope this answers your question. If you need further clarification, feel free to ask.

Sources

• docs/extras/use_cases/more/agents/autonomous_agents/meta_prompt.ipynb
• libs/langchain/langchain/agents/agent.py
• libs/langchain/langchain/chains/question_answering/refine_prompts.py

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