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from python.helpers import persist_chat, tokens
from python.helpers.extension import Extension
from agent import LoopData
import asyncio
from python.helpers.log import LogItem
from python.helpers import log
import math
from python.extensions.before_main_llm_call._10_log_for_stream import build_heading, build_default_heading
class LogFromStream(Extension):
async def execute(
self,
loop_data: LoopData = LoopData(),
text: str = "",
parsed: dict = {},
**kwargs,
):
heading = build_default_heading(self.agent)
if "headline" in parsed:
heading = build_heading(self.agent, parsed['headline'])
elif "tool_name" in parsed:
heading = build_heading(self.agent, f"Using tool {parsed['tool_name']}") # if the llm skipped headline
elif "thoughts" in parsed:
# thought length indicator
thoughts = "\n".join(parsed["thoughts"])
pipes = "|" * math.ceil(math.sqrt(len(thoughts)))
heading = build_heading(self.agent, f"Thinking... {pipes}")
# create log message and store it in loop data temporary params
if "log_item_generating" not in loop_data.params_temporary:
loop_data.params_temporary["log_item_generating"] = (
self.agent.context.log.log(
type="agent",
heading=heading,
)
)
# update log message
log_item = loop_data.params_temporary["log_item_generating"]
# keep reasoning from previous logs in kvps
kvps = {}
if log_item.kvps is not None and "reasoning" in log_item.kvps:
kvps["reasoning"] = log_item.kvps["reasoning"]
kvps.update(parsed)
# update the log item
log_item.update(heading=heading, content=text, kvps=kvps) |