gpt-4 / networks /message_parser.py
Hansimov's picture
:zap: [Enhance] Prettify format of searching queries and results
0bade77
raw
history blame
4.67 kB
import json
from utils.logger import logger
from networks import OpenaiStreamOutputer
class MessageParser:
def __init__(self, outputer=OpenaiStreamOutputer()):
self.delta_content_pointer = 0
self.outputer = outputer
def parse(self, data, return_output=False):
arguments = data["arguments"][0]
if arguments.get("throttling"):
throttling = arguments.get("throttling")
# pprint.pprint(throttling)
if arguments.get("messages"):
for message in arguments.get("messages"):
message_type = message.get("messageType")
# Message: Displayed answer
if message_type is None:
# content = message["adaptiveCards"][0]["body"][0]["text"]
content = message["text"]
delta_content = content[self.delta_content_pointer :]
logger.line(delta_content, end="")
self.delta_content_pointer = len(content)
# Message: Suggested Questions
if message.get("suggestedResponses"):
logger.note("\nSuggested Questions: ")
suggestion_texts = [
suggestion.get("text")
for suggestion in message.get("suggestedResponses")
]
for suggestion_text in suggestion_texts:
logger.file(f"- {suggestion_text}")
if return_output:
completions_output = self.outputer.output(
delta_content, content_type="Completions"
)
if message.get("suggestedResponses"):
suggestions_output = self.outputer.output(
suggestion_texts,
content_type="SuggestedResponses",
)
return [completions_output, suggestions_output]
else:
return completions_output
# Message: Search Query
elif message_type in ["InternalSearchQuery"]:
search_query_str = message.get("hiddenText")
if return_output:
# output_str = self.outputer.output(
# search_query_str, content_type="InternalSearchQuery"
# )
# logger.note(output_str)
# return output_str
return None
# Message: Internal Search Results
elif message_type in ["InternalSearchResult"]:
if message.get("groundingInfo"):
web_search_results = message.get("groundingInfo").get(
"web_search_results"
)
invocation = message.get("invocation")
if return_output:
search_results_str = self.outputer.output(
{
"invocation": invocation,
"web_search_results": web_search_results,
},
content_type="InternalSearchResult",
)
data = json.loads(search_results_str)
logger.note(data["choices"][0]["delta"]["content"])
return search_results_str
# Message: Loader status, such as "Generating Answers"
elif message_type in ["InternalLoaderMessage"]:
# logger.note("[Generating answers ...]\n")
pass
# Message: Internal thoughts, such as "I will generate my response to the user message"
elif message_type in ["Internal"]:
pass
# Message: Internal Action Marker, no value
elif message_type in ["InternalActionMarker"]:
continue
# Message: Render Cards for Webpages
elif message_type in ["RenderCardRequest"]:
continue
elif message_type in ["ChatName"]:
continue
# Message: Not Implemented
else:
raise NotImplementedError(
f"Not Supported Message Type: {message_type}"
)
return None