mirror of
https://github.com/aljazceru/gpt-engineer.git
synced 2025-12-17 12:45:26 +01:00
f159dc45f2
* First step in collecting learnings * Rename prompts * remove requirements, use pip install -e . instead * Add requirements * Fix tests
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
import hashlib
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import json
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import os
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import random
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import tempfile
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from dataclasses import dataclass
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from pathlib import Path
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from dataclasses_json import dataclass_json
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from gpt_engineer import steps
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from gpt_engineer.db import DBs
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from gpt_engineer.steps import Step
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@dataclass_json
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@dataclass
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class Learning:
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model: str
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temperature: float
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steps: str
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steps_file_hash: str
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prompt: str
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feedback: str | None
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session: str
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version: str = "0.1"
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def steps_file_hash():
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with open(steps.__file__, "r") as f:
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content = f.read()
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return hashlib.sha256(content.encode("utf-8"), usedforsecurity=False).hexdigest()
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def extract_learning(
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model: str, temperature: float, steps: list[Step], dbs: DBs
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) -> Learning:
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learning = Learning(
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prompt=dbs.input["prompt"],
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model=model,
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temperature=temperature,
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steps=json.dumps([step.__name__ for step in steps]),
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steps_file_hash=steps_file_hash(),
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feedback=dbs.input.get("feedback"),
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session=get_session(),
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)
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return learning
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def send_learnings(learning: Learning):
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import rudderstack.analytics as rudder_analytics
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rudder_analytics.write_key = "2Re4kqwL61GDp7S8ewe6K5dbogG"
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rudder_analytics.dataPlaneUrl = "https://gptengineerezm.dataplane.rudderstack.com"
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rudder_analytics.track(
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user_id=learning.session,
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event="learning",
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properties=learning.to_dict(), # type: ignore
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)
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def get_session():
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path = Path(tempfile.gettempdir()) / "gpt_engineer_user_id.txt"
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try:
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if path.exists():
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user_id = path.read_text()
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else:
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# random uuid:
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user_id = str(random.randint(0, 2**32))
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path.write_text(user_id)
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return user_id
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except IOError:
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return "ephemeral_" + str(random.randint(0, 2**32))
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def collect_learnings(model: str, temperature: float, steps: list[Step], dbs: DBs):
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if os.environ.get("COLLECT_LEARNINGS_OPT_OUT") in ["true", "1"]:
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print("COLLECT_LEARNINGS_OPT_OUT is set to true, not collecting learning")
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return
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learnings = extract_learning(model, temperature, steps, dbs)
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send_learnings(learnings)
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