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233 | class ResponseService(Service):
"""
ResponseService orchestrates response generation using retrieved engrams,
conversation history, and plugin-based LLM processing. It integrates with
plugins, websockets, and metrics to generate contextual AI responses.
Attributes:
plugin_manager (PluginManager): Manages access to LLM and DB plugins.
web_socket_manager (WebsocketManager): Handles streaming responses.
db_document_plugin (dict): Document DB plugin instance.
engram_repository (EngramRepository): Repository to load Engram data.
llm_main (dict): LLM plugin for main response generation.
instructions (Prompt): Placeholder prompt for prompt engineering.
metrics_tracker (MetricsTracker): Tracks internal performance metrics.
Methods:
start(): Subscribe to service topics and initialize websocket manager.
stop(): Shutdown websocket manager and stop service.
init_async(): Establish DB connection via plugin.
on_retrieve_complete(retrieve_result_in): Handle retrieval completion,
fetch engrams and history, and trigger main prompt.
on_fetch_data_complete(fut): Callback after data is fetched.
on_main_prompt_complete(fut): Callback after main prompt completes.
on_acknowledge(message_in): Reset and send metrics status packet.
_fetch_history(): Fetch history from the database.
_fetch_retrieval(prompt_str, analysis, retrieve_result): Fetch engrams
using the retrieval result.
main_prompt(prompt_str, analysis, engram_array, retrieve_result, history_array):
Run LLM with contextual prompt data and return Response.
"""
def __init__(self, host: Host) -> None:
super().__init__(host)
self.plugin_manager: PluginManager = host.plugin_manager
self.web_socket_manager: WebsocketManager = WebsocketManager(host)
self.db_document_plugin = self.plugin_manager.get_plugin('db', 'document')
self.engram_repository: EngramRepository = EngramRepository(self.db_document_plugin)
self.llm_main = self.plugin_manager.get_plugin('llm', 'response_main')
self.instructions: Prompt = Prompt('Placeholder for prompt engineering for main prompt.')
self.metrics_tracker: MetricsTracker[ResponseMetric] = MetricsTracker[ResponseMetric]()
##
# Many methods are not ready to be until their async component is running.
# Do not call async context methods in the constructor.
def start(self) -> None:
self.subscribe(Service.Topic.ACKNOWLEDGE, self.on_acknowledge)
self.subscribe(Service.Topic.RETRIEVE_COMPLETE, self.on_retrieve_complete)
self.web_socket_manager.init_async()
def stop(self) -> None:
self.run_task(self.web_socket_manager.shutdown())
super().stop()
def init_async(self) -> None:
self.db_document_plugin['func'].connect(args=None)
return super().init_async()
def on_retrieve_complete(self, retrieve_result_in: dict[str, Any]) -> None:
if __debug__:
self.host.update_mock_data_input(self, retrieve_result_in)
prompt_str = retrieve_result_in['prompt_str']
prompt_analysis = PromptAnalysis(**retrieve_result_in['analysis'])
retrieve_result = RetrieveResult(**retrieve_result_in['retrieve_response'])
self.metrics_tracker.increment(ResponseMetric.RETRIEVES_RECIEVED)
fetch_engrams_task = self.run_tasks([
self._fetch_retrieval(prompt_str=prompt_str, analysis=prompt_analysis, retrieve_result=retrieve_result),
self._fetch_history(),
])
fetch_engrams_task.add_done_callback(self.on_fetch_data_complete)
"""
### Fetch History & Engram
Fetch engrams based on the IDs provided by the retrieve service.
"""
async def _fetch_history(self) -> dict[str, Any]:
plugin = self.db_document_plugin
args = plugin['args']
args['history'] = 1
ret_val = await asyncio.to_thread(plugin['func'].fetch, table=DB.DBTables.HISTORY, ids=[], args=args)
history: dict[str, Any] = ret_val[0]
return history
async def _fetch_retrieval(
self, prompt_str: str, analysis: PromptAnalysis, retrieve_result: RetrieveResult
) -> dict[str, Any]:
engram_array: list[Engram] = await asyncio.to_thread(
self.engram_repository.load_batch_retrieve_result, retrieve_result
)
# assembled main_prompt, render engrams.
return {
'prompt_str': prompt_str,
'analysis': analysis,
'retrieve_result': retrieve_result,
'engram_array': engram_array,
}
def on_fetch_data_complete(self, fut: Future[Any]) -> None:
exc = fut.exception()
if exc is not None:
raise exc
result = fut.result()
retrieval = result['_fetch_retrieval'][0]
history = result['_fetch_history'][0]
main_prompt_task = self.run_task(
self.main_prompt(
retrieval['prompt_str'],
retrieval['analysis'],
retrieval['engram_array'],
retrieval['retrieve_result'],
history,
)
)
main_prompt_task.add_done_callback(self.on_main_prompt_complete)
"""
### Main Prompt
Combine the previous stages to generate the response.
"""
async def main_prompt(
self,
prompt_str: str,
analysis: PromptAnalysis,
engram_array: list[Engram],
retrieve_result: RetrieveResult,
history_array: dict[str, Any],
) -> Response:
self.metrics_tracker.increment(ResponseMetric.ENGRAMS_FETCHED, len(engram_array))
engram_dict_list = [asdict(engram) for engram in engram_array]
# build main prompt here
prompt = PromptMainPrompt(
prompt_str=prompt_str,
input_data={
'engram_list': engram_dict_list,
'history': history_array,
'working_memory': retrieve_result.conversation_direction,
},
)
plugin = self.llm_main
response = await asyncio.to_thread(
plugin['func'].submit_streaming,
prompt=prompt,
websocket_manager=self.web_socket_manager,
args=self.host.mock_update_args(plugin),
)
if __debug__:
main_prompt = prompt.render_prompt()
self.send_message_async(
Service.Topic.DEBUG_MAIN_PROMPT_INPUT, {'main_prompt': main_prompt, 'ask_id': retrieve_result.ask_id}
)
self.host.update_mock_data(self.llm_main, response)
model = ''
if plugin['args'].get('model'):
model = plugin['args']['model']
response_inst = Response(
str(uuid.uuid4()), response[0]['llm_response'], retrieve_result, prompt.prompt_str, analysis, model
)
return response_inst
def on_main_prompt_complete(self, fut: Future[Any]) -> None:
result = fut.result()
self.metrics_tracker.increment(ResponseMetric.MAIN_PROMPTS_RUN)
self.send_message_async(Service.Topic.MAIN_PROMPT_COMPLETE, asdict(result))
if __debug__:
self.host.update_mock_data_output(self, asdict(result))
"""
### Ack
Acknowledge and return metrics
"""
def on_acknowledge(self, message_in: str) -> None:
del message_in
metrics_packet: MetricPacket = self.metrics_tracker.get_and_reset_packet()
self.send_message_async(
Service.Topic.STATUS,
{'id': self.id, 'name': self.__class__.__name__, 'timestamp': time.time(), 'metrics': metrics_packet},
)
|