mirror of
https://github.com/aljazceru/gpt-engineer.git
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19a4c10b6e
* Added LangChain integration * Fixed issue created by git checkin process * Added ':' to characters to remove from end of file path * Tested initial migration to LangChain, removed comments and logging used for debugging * Tested initial migration to LangChain, removed comments and logging used for debugging * Converted camelCase to snake_case * Turns out we need the exception handling * Testing Hugging Face Integrations via LangChain * Added LangChain loadable models * Renames "qa" prompt to "clarify", since it's used in the "clarify" step, asking for clarification * Fixed loading model yaml files * Fixed streaming * Added modeldir cli option * Fixed typing * Fixed interaction with token logging * Fix spelling + dependency issues + typing * Fix spelling + tests * Removed unneeded logging which caused test to fail * Cleaned up code * Incorporated feedback - deleted unnecessary functions & logger.info - used LangChain ChatLLM instead of LLM to naturally communicate with gpt-4 - deleted loading model from yaml file, as LC doesn't offer this for ChatModels * Update gpt_engineer/steps.py Co-authored-by: Anton Osika <anton.osika@gmail.com> * Incorporated feedback - Fixed failing test - Removed parsing complexity by using # type: ignore - Replace every ocurence of ai.last_message_content with its content * Fixed test * Update gpt_engineer/steps.py --------- Co-authored-by: H <holden.robbins@gmail.com> Co-authored-by: Anton Osika <anton.osika@gmail.com>
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
import json
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import os
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from unittest.mock import MagicMock
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import pytest
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import rudderstack.analytics as rudder_analytics
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from gpt_engineer.collect import collect_learnings, steps_file_hash
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from gpt_engineer.db import DB, DBs
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from gpt_engineer.learning import extract_learning
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from gpt_engineer.steps import gen_code
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def test_collect_learnings(monkeypatch):
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monkeypatch.setattr(os, "environ", {"COLLECT_LEARNINGS_OPT_IN": "true"})
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monkeypatch.setattr(rudder_analytics, "track", MagicMock())
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model = "test_model"
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temperature = 0.5
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steps = [gen_code]
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dbs = DBs(DB("/tmp"), DB("/tmp"), DB("/tmp"), DB("/tmp"), DB("/tmp"), DB("/tmp"))
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dbs.input = {
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"prompt": "test prompt\n with newlines",
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"feedback": "test feedback",
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}
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code = "this is output\n\nit contains code"
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dbs.logs = {gen_code.__name__: json.dumps([{"role": "system", "content": code}])}
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dbs.workspace = {"all_output.txt": "test workspace\n" + code}
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collect_learnings(model, temperature, steps, dbs)
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learnings = extract_learning(
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model, temperature, steps, dbs, steps_file_hash=steps_file_hash()
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)
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assert rudder_analytics.track.call_count == 1
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assert rudder_analytics.track.call_args[1]["event"] == "learning"
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a = {
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k: v
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for k, v in rudder_analytics.track.call_args[1]["properties"].items()
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if k != "timestamp"
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}
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b = {k: v for k, v in learnings.to_dict().items() if k != "timestamp"}
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assert a == b
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assert json.dumps(code) in learnings.logs
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assert code in learnings.workspace
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if __name__ == "__main__":
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pytest.main(["-v"])
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