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intact can be removed
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ultmaster committed Jan 24, 2024
1 parent b78766b commit 5b6bf4d
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Showing 2 changed files with 17 additions and 3 deletions.
16 changes: 14 additions & 2 deletions coml/core.py
Original file line number Diff line number Diff line change
Expand Up @@ -118,6 +118,8 @@ class CoMLAgent:
ensemble_shuffle: Shuffle the examples in the prompt before ensemble.
example_ranking: A model that ranks the examples. If provided, the examples
will be ranked by the model before selecting the examples.
intact_instruction: Whether to instruct LLM to keep the variables unmodified.
For experimenting purposes only.
"""

def __init__(
Expand All @@ -134,6 +136,7 @@ def __init__(
ensemble: int | None = None,
ensemble_shuffle: bool = True,
example_ranking: Embeddings | None = None,
intact_instruction: bool = True,
):
self.llm = llm
self.prompt_version = prompt_version
Expand All @@ -145,6 +148,7 @@ def __init__(
self.ensemble = ensemble
self.ensemble_shuffle = ensemble_shuffle
self.example_ranking = example_ranking
self.intact_instruction = intact_instruction

def _fix_context_from_any_context(
self, context: GenerateContext | FixContext, **kwargs: Any
Expand Down Expand Up @@ -282,9 +286,17 @@ def generate_code(
messages: list[BaseMessage] = []

if self.chain_of_thought:
messages.append(SystemMessage(content=GENERATE_INSTRUCTION_COT))
generate_instruction = GENERATE_INSTRUCTION_COT
else:
messages.append(SystemMessage(content=GENERATE_INSTRUCTION))
generate_instruction = GENERATE_INSTRUCTION
if not self.intact_instruction:
generate_instruction = re.sub(r"- Do not overwrite or modify.*\n", "", generate_instruction)
for shot in fewshots:
if "answer_wo_intact" in shot:
shot["answer"] = shot.pop("answer_wo_intact")
if "rationale_wo_intact" in shot:
shot["rationale"] = shot.pop("rationale_wo_intact")
messages.append(SystemMessage(content=generate_instruction))

for shot in self._select_examples(request, fewshots):
question, answer = render_generate_context(
Expand Down
4 changes: 3 additions & 1 deletion coml/prompts/generate_fewshots_v2.json
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,9 @@
],
"request": "Compare the average weight of surface products and macbook products?",
"answer": "\n# Import pandas as it seems unimported.\nimport pandas as pd\n\n# Create a new variable to avoid unintentional modifications to raw DataFrame.\ndf_product_weight = pd.DataFrame({\n \"product\": df[\"product\"].apply(lambda x: \"Macbook\" if \"Macbook\" in x else \"Surface\"),\n \"weight\": df[\"weight\"].apply(lambda x: float(x.replace(\" lbs\", \"\"))),\n})\ndf_product_weight.groupby(\"product\").mean()",
"rationale": "To compare the average weight of Surface products and MacBook products, we'll need to:\n\n1. Extract the weight as a numerical value from the \"weight\" column.\n2. Create a new column to classify each product as either 'Surface' or 'Macbook'.\n3. Use the `groupby` function to calculate the average weight for each product group.\n\nWe need to avoid unintentional modifications to the raw DataFrame, as the request didn't ask for in-place changes to the data."
"rationale": "To compare the average weight of Surface products and MacBook products, we'll need to:\n\n1. Extract the weight as a numerical value from the \"weight\" column.\n2. Create a new column to classify each product as either 'Surface' or 'Macbook'.\n3. Use the `groupby` function to calculate the average weight for each product group.\n\nWe need to avoid unintentional modifications to the raw DataFrame, as the request didn't ask for in-place changes to the data.",
"answer_wo_intact": "# Import pandas as it seems unimported.\nimport pandas as pd\n\ndf[\"product\"] = df[\"product\"].apply(lambda x: \"Macbook\" if \"Macbook\" in x else \"Surface\")\ndf[\"weight\"] = df[\"weight\"].apply(lambda x: float(x.replace(\" lbs\", \"\")))\ndf.groupby(\"product\").mean()",
"rationale_wo_intact": "To compare the average weight of Surface products and MacBook products, we'll need to:\n\n1. Extract the weight as a numerical value from the \"weight\" column.\n2. Classify each product as either 'Surface' or 'Macbook'.\n3. Use the `groupby` function to calculate the average weight for each product group."
},
{
"variables": {
Expand Down

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