52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
"""
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Emma Adapter to provide a canonical, consistent interface to the Emma agent core.
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"""
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# This will be copied to /app/emma_identity.py by the Dockerfile
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from emma_identity import get_runtime_system_prompt
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class CanonicalEmma:
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"""
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A facade for the Emma agent that enforces a canonical identity and contract,
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while allowing the underlying chat function to be injected as a dependency.
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"""
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def __init__(self, chat_function, model, system_prompt=None):
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"""
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Initializes the CanonicalEmma adapter.
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Args:
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chat_function: The async function to call for the LLM interaction.
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model: The name of the model to use.
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system_prompt: An optional system prompt to override the default.
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"""
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self._chat_function = chat_function
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self._model = model
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self._system_prompt = system_prompt or get_runtime_system_prompt()
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async def run(self, prompt, history=None, system_context: str | None = None):
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"""
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Runs the Emma agent with the given prompt.
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In Phase 1, this is a simple pass-through to the injected chat_function,
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ensuring the canonical system prompt is used. History is ignored for now.
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Args:
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prompt: The user\'s prompt.
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history: The conversation history (currently ignored).
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system_context: Optional context to append to the system prompt.
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Returns:
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The raw dictionary response from the chat_function.
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"""
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# Phase 1 does not use history or memory.
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composed_system_prompt = self._system_prompt
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if system_context:
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composed_system_prompt = f"{self._system_prompt}\n\n{system_context.strip()}"
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return await self._chat_function(
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self._model,
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prompt,
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composed_system_prompt,
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history=history or [],
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)
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