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yzlin 2024-02-02 19:58:56 +08:00
parent 3894334b52
commit 4938896dd8
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1
.gitignore vendored
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@ -178,3 +178,4 @@ cov.xml
*.faiss
*-structure.csv
*-structure.json
metagpt/tools/schemas

55
metagpt/tools/libs/gpt_v_generator.py
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@ -30,9 +30,18 @@ As the design pays tribute to large companies, sometimes it is normal for some c
Now, please generate the corresponding webpage code including HTML, CSS and JavaScript:"""
@register_tool(tool_type=ToolTypes.IMAGE2WEBPAGE.type_name)
@register_tool(
tool_type=ToolTypes.IMAGE2WEBPAGE.type_name, include_functions=["__init__", "generate_webpages", "save_webpages"]
)
class GPTvGenerator:
"""Class for generating webpages at once.
This class provides methods to generate webpages including all code (HTML, CSS, and JavaScript) based on an image.
It utilizes a vision model to analyze the layout from an image and generate webpage codes accordingly.
"""
def __init__(self):
"""Initialize GPTvGenerator class with default values from the configuration."""
from metagpt.config2 import config
self.api_key = config.llm.api_key
@ -41,15 +50,42 @@ class GPTvGenerator:
self.max_tokens = config.vision_max_tokens
def analyze_layout(self, image_path):
"""Analyze the layout of the given image and return the result.
This is a helper method to generate a layout description based on the image.
Args:
image_path (str): Path of the image to analyze.
Returns:
str: The layout analysis result.
"""
return self.get_result(image_path, ANALYZE_LAYOUT_PROMPT)
def generate_webpages(self, image_path):
"""Generate webpages including all code (HTML, CSS, and JavaScript) in one go based on the image.
Args:
image_path (str): The path of the image file.
Returns:
str: Generated webpages content.
"""
layout = self.analyze_layout(image_path)
prompt = GENERATE_PROMPT + "\n\n # Context\n The layout information of the sketch image is: \n" + layout
result = self.get_result(image_path, prompt)
return result
def get_result(self, image_path, prompt):
"""Get the result from the vision model based on the given image path and prompt.
Args:
image_path (str): Path of the image to analyze.
prompt (str): Prompt to use for the analysis.
Returns:
str: The model's response as a string.
"""
base64_image = self.encode_image(image_path)
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
payload = {
@ -74,11 +110,28 @@ class GPTvGenerator:
@staticmethod
def encode_image(image_path):
"""Encode the image at the given path to a base64 string.
Args:
image_path (str): Path of the image to encode.
Returns:
str: The base64 encoded string of the image.
"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
@staticmethod
def save_webpages(image_path, webpages) -> Path:
"""Save webpages including all code (HTML, CSS, and JavaScript) at once.
Args:
image_path (str): The path of the image file.
webpages (str): The generated webpages content.
Returns:
Path: The path of the saved webpages.
"""
# 在workspace目录下，创建一个名为下webpages的文件夹，用于存储html、css和js文件
webpages_path = DEFAULT_WORKSPACE_ROOT / "webpages" / Path(image_path).stem
os.makedirs(webpages_path, exist_ok=True)

61
metagpt/tools/libs/sd_engine.py
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@ -53,10 +53,22 @@ payload = {
default_negative_prompt = "(easynegative:0.8),black, dark,Low resolution"
@register_tool(tool_type=ToolTypes.STABLE_DIFFUSION.type_name)
@register_tool(
tool_type=ToolTypes.STABLE_DIFFUSION.type_name,
include_functions=["__init__", "simple_run_t2i", "run_t2i", "construct_payload", "save"],
)
class SDEngine:
"""Generate image using stable diffusion model.
This class provides methods to interact with a stable diffusion service to generate images based on text inputs.
"""
def __init__(self, sd_url=""):
# Initialize the SDEngine with configuration
"""Initialize the SDEngine instance with configuration.
Args:
sd_url (str, optional): URL of the stable diffusion service. Defaults to "".
"""
self.sd_url = sd_url
self.sd_t2i_url = f"{self.sd_url}/sdapi/v1/txt2img"
# Define default payload settings for SD API
@ -71,7 +83,18 @@ class SDEngine:
height=512,
sd_model="galaxytimemachinesGTM_photoV20",
):
# Configure the payload with provided inputs
"""Modify and set the API parameters for image generation.
Args:
prompt (str): Text input for image generation.
negtive_prompt (str, optional): Text input for negative prompts. Defaults to None.
width (int, optional): Width of the generated image in pixels. Defaults to 512.
height (int, optional): Height of the generated image in pixels. Defaults to 512.
sd_model (str, optional): The model to use for image generation. Defaults to "galaxytimemachinesGTM_photoV20".
Returns:
dict: Updated parameters for the stable diffusion API.
"""
self.payload["prompt"] = prompt
self.payload["negative_prompt"] = negtive_prompt
self.payload["width"] = width
@ -81,12 +104,27 @@ class SDEngine:
return self.payload
def save(self, imgs, save_name=""):
"""Save generated images to the output directory.
Args:
imgs (str): Generated images.
save_name (str, optional): Output image name. Default is empty.
"""
save_dir = SOURCE_ROOT / SD_OUTPUT_FILE_REPO
if not save_dir.exists():
save_dir.mkdir(parents=True, exist_ok=True)
batch_decode_base64_to_image(imgs, str(save_dir), save_name=save_name)
def simple_run_t2i(self, payload: dict, auto_save: bool = True):
"""Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images.
Args:
payload (dict): Dictionary of input parameters for the stable diffusion API.
auto_save (bool, optional): Save generated images automatically. Defaults to True.
Returns:
list: The generated images as a result of the API call.
"""
with requests.Session() as session:
logger.debug(self.sd_t2i_url)
rsp = session.post(self.sd_t2i_url, json=payload, timeout=600)
@ -98,7 +136,11 @@ class SDEngine:
return results
async def run_t2i(self, payloads: List):
# Asynchronously run the SD API for multiple prompts
"""Run the stable diffusion API for multiple prompts asynchronously.
Args:
payloads (list): List of payload, each payload is a dictionary of input parameters for the stable diffusion API.
"""
session = ClientSession()
for payload_idx, payload in enumerate(payloads):
results = await self.run(url=self.sd_t2i_url, payload=payload, session=session)
@ -106,7 +148,16 @@ class SDEngine:
await session.close()
async def run(self, url, payload, session):
# Perform the HTTP POST request to the SD API
"""Perform the HTTP POST request to the SD API.
Args:
url (str): The API URL.
payload (dict): The payload for the request.
session (ClientSession): The session for making HTTP requests.
Returns:
list: Images generated by the stable diffusion API.
"""
async with session.post(url, json=payload, timeout=600) as rsp:
data = await rsp.read()

6
metagpt/tools/schemas/__init__.py
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@ -1,6 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/11/16 16:33
# @Author : lidanyang
# @File : __init__.py
# @Desc :

61
metagpt/tools/schemas/data_preprocess/FillMissingValue.yml
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@ -1,61 +0,0 @@
FillMissingValue:
type: class
description: "Completing missing values with simple strategies"
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "columns to be processed"
strategy:
type: str
description: "the imputation strategy, notice mean/median can only be used for numeric features"
default: mean
enum:
- mean
- median
- most_frequent
- constant
fill_value:
type: int
description: "fill_value is used to replace all occurrences of missing_values"
default: null
required:
- features
fit:
description: "Fit the FillMissingValue model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/data_preprocess/LabelEncode.yml
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@ -1,48 +0,0 @@
LabelEncode:
type: class
description: "Apply label encoding to specified categorical columns in-place."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "Categorical columns to be label encoded"
required:
- features
fit:
description: "Fit the LabelEncode model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/data_preprocess/MaxAbsScale.yml
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@ -1,48 +0,0 @@
MaxAbsScale:
type: class
description: "cale each feature by its maximum absolute value"
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "columns to be processed"
required:
- features
fit:
description: "Fit the MaxAbsScale model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/data_preprocess/MinMaxScale.yml
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@ -1,48 +0,0 @@
MinMaxScale:
type: class
description: "Transform features by scaling each feature to a range, witch is (0, 1)"
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "columns to be processed"
required:
- features
fit:
description: "Fit the MinMaxScale model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/data_preprocess/OneHotEncode.yml
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@ -1,48 +0,0 @@
OneHotEncode:
type: class
description: "Apply one-hot encoding to specified categorical columns, the original columns will be dropped."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "Categorical columns to be one-hot encoded and dropped"
required:
- features
fit:
description: "Fit the OneHotEncoding model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

46
metagpt/tools/schemas/data_preprocess/OrdinalEncode.yml
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@ -1,46 +0,0 @@
OrdinalEncode:
type: class
description: Encode categorical features as ordinal integers.
methods:
__init__:
description: 'Initialize the OrdinalEncode instance with feature names. '
parameters:
properties:
features:
type: list
description: List of categorical feature names to be encoded.
required:
- features
fit:
description: 'Learn the ordinal encodings for the features. '
parameters:
properties:
df:
type: pd.DataFrame
description: Dataframe containing the categorical features.
required:
- df
fit_transform:
description: 'Fit and transform the input DataFrame. '
parameters:
properties:
df:
type: pd.DataFrame
description: The input DataFrame.
required:
- df
returns:
- type: pd.DataFrame
description: The transformed DataFrame.
transform:
description: 'Convert the categorical features to ordinal integers. '
parameters:
properties:
df:
type: pd.DataFrame
description: Dataframe containing the categorical features to be encoded.
required:
- df
returns:
- type: pd.DataFrame
description: A new dataframe with the encoded features.

47
metagpt/tools/schemas/data_preprocess/RobustScale.yml
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@ -1,47 +0,0 @@
RobustScale:
type: class
description: Apply the RobustScaler to scale features using statistics that are
robust to outliers.
methods:
__init__:
description: 'Initialize the RobustScale instance with feature names. '
parameters:
properties:
features:
type: list
description: List of feature names to be scaled.
required:
- features
fit:
description: 'Compute the median and IQR for scaling. '
parameters:
properties:
df:
type: pd.DataFrame
description: Dataframe containing the features.
required:
- df
fit_transform:
description: 'Fit and transform the input DataFrame. '
parameters:
properties:
df:
type: pd.DataFrame
description: The input DataFrame.
required:
- df
returns:
- type: pd.DataFrame
description: The transformed DataFrame.
transform:
description: 'Scale features using the previously computed median and IQR. '
parameters:
properties:
df:
type: pd.DataFrame
description: Dataframe containing the features to be scaled.
required:
- df
returns:
- type: pd.DataFrame
description: A new dataframe with scaled features.

48
metagpt/tools/schemas/data_preprocess/StandardScale.yml
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@ -1,48 +0,0 @@
StandardScale:
type: class
description: "Standardize features by removing the mean and scaling to unit variance"
methods:
__init__:
description: "Initialize self."
parameters:
properties:
features:
type: list
description: "columns to be processed"
required:
- features
fit:
description: "Fit the StandardScale model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/feature_engineering/CatCount.yml
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@ -1,48 +0,0 @@
CatCount:
type: class
description: "Add value counts of a categorical column as new feature."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column for value counts."
required:
- col
fit:
description: "Fit the CatCount model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

52
metagpt/tools/schemas/feature_engineering/CatCross.yml
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@ -1,52 +0,0 @@
CatCross:
type: class
description: "Add pairwise crossed features and convert them to numerical features."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
cols:
type: list
description: "Columns to be pairwise crossed, at least 2 columns."
max_cat_num:
type: int
description: "Maximum unique categories per crossed feature."
default: 100
required:
- cols
fit:
description: "Fit the CatCross model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

48
metagpt/tools/schemas/feature_engineering/GeneralSelection.yml
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@ -1,48 +0,0 @@
GeneralSelection:
type: class
description: "Drop all nan feats and feats with only one unique value."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
required:
- label_col
fit:
description: "Fit the GeneralSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

58
metagpt/tools/schemas/feature_engineering/GroupStat.yml
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@ -1,58 +0,0 @@
GroupStat:
type: class
description: "Aggregate specified column in a DataFrame grouped by another column, adding new features named '<agg_col>_<agg_func>_by_<group_col>'."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
group_col:
type: str
description: "Column used for grouping."
agg_col:
type: str
description: "Column on which aggregation is performed."
agg_funcs:
type: list
description: >-
List of aggregation functions to apply, such as ['mean', 'std'].
Each function must be supported by pandas.
required:
- group_col
- agg_col
- agg_funcs
fit:
description: "Fit the GroupStat model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

60
metagpt/tools/schemas/feature_engineering/KFoldTargetMeanEncoder.yml
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@ -1,60 +0,0 @@
KFoldTargetMeanEncoder:
type: class
description: "Adds a new feature to the DataFrame by k-fold mean encoding of a categorical column using the label column."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column to be k-fold mean encoded."
label:
type: str
description: "Predicted label column."
n_splits:
type: int
description: "Number of splits for K-fold."
default: 5
random_state:
type: int
description: "Random seed."
default: 2021
required:
- col
- label
fit:
description: "Fit the KFoldTargetMeanEncoder model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

548
metagpt/tools/schemas/feature_engineering/PolynomialExpansion.yml
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@ -1,548 +0,0 @@
PolynomialExpansion:
type: class
description: "Add polynomial and interaction features from selected numeric columns to input DataFrame."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
cols:
type: list
description: "Columns for polynomial expansion."
label_col:
type: str
description: "Label column name."
degree:
type: int
description: "The degree of the polynomial features."
default: 2
required:
- cols
- label_col
fit:
description: "Fit the PolynomialExpansion model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame without duplicated columns."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame without duplicated columns."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
CatCount:
type: class
description: "Add value counts of a categorical column as new feature."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column for value counts."
required:
- col
fit:
description: "Fit the CatCount model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
TargetMeanEncoder:
type: class
description: "Encodes a categorical column by the mean of the label column, and adds the result as a new feature."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column to be mean encoded."
label:
type: str
description: "Predicted label column."
required:
- col
- label
fit:
description: "Fit the TargetMeanEncoder model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
KFoldTargetMeanEncoder:
type: class
description: "Adds a new feature to the DataFrame by k-fold mean encoding of a categorical column using the label column."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column to be k-fold mean encoded."
label:
type: str
description: "Predicted label column."
n_splits:
type: int
description: "Number of splits for K-fold."
default: 5
random_state:
type: int
description: "Random seed."
default: 2021
required:
- col
- label
fit:
description: "Fit the KFoldTargetMeanEncoder model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
CatCross:
type: class
description: "Add pairwise crossed features and convert them to numerical features."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
cols:
type: list
description: "Columns to be pairwise crossed, at least 2 columns."
max_cat_num:
type: int
description: "Maximum unique categories per crossed feature."
default: 100
required:
- cols
fit:
description: "Fit the CatCross model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
GroupStat:
type: class
description: "Aggregate specified column in a DataFrame grouped by another column, adding new features named '<agg_col>_<agg_func>_by_<group_col>'."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
group_col:
type: str
description: "Column used for grouping."
agg_col:
type: str
description: "Column on which aggregation is performed."
agg_funcs:
type: list
description: >-
List of aggregation functions to apply, such as ['mean', 'std'].
Each function must be supported by pandas.
required:
- group_col
- agg_col
- agg_funcs
fit:
description: "Fit the GroupStat model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
SplitBins:
type: class
description: "Inplace binning of continuous data into intervals, returning integer-encoded bin identifiers directly."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
cols:
type: list
description: "Columns to be binned inplace."
strategy:
type: str
description: "Strategy used to define the widths of the bins."
default: quantile
enum:
- quantile
- uniform
- kmeans
required:
- cols
fit:
description: "Fit the SplitBins model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
GeneralSelection:
type: class
description: "Drop all nan feats and feats with only one unique value."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
required:
- label_col
fit:
description: "Fit the GeneralSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
TreeBasedSelection:
type: class
description: "Select features based on tree-based model and remove features with low importance."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
task_type:
type: str
description: "Task type, 'cls' for classification, 'mcls' for multi-class classification, 'reg' for regression."
enum:
- cls
- mcls
- reg
required:
- label_col
- task_type
fit:
description: "Fit the TreeBasedSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."
VarianceBasedSelection:
type: class
description: "Select features based on variance and remove features with low variance."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
threshold:
type: float
description: "Threshold for variance."
default: 0.0
required:
- label_col
fit:
description: "Fit the VarianceBasedSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."

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metagpt/tools/schemas/feature_engineering/SplitBins.yml
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SplitBins:
type: class
description: "Inplace binning of continuous data into intervals, returning integer-encoded bin identifiers directly."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
cols:
type: list
description: "Columns to be binned inplace."
strategy:
type: str
description: "Strategy used to define the widths of the bins."
default: quantile
enum:
- quantile
- uniform
- kmeans
required:
- cols
fit:
description: "Fit the SplitBins model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

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metagpt/tools/schemas/feature_engineering/TargetMeanEncoder.yml
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TargetMeanEncoder:
type: class
description: "Encodes a categorical column by the mean of the label column, and adds the result as a new feature."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
col:
type: str
description: "Column to be mean encoded."
label:
type: str
description: "Predicted label column."
required:
- col
- label
fit:
description: "Fit the TargetMeanEncoder model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame."

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metagpt/tools/schemas/feature_engineering/TreeBasedSelection.yml
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TreeBasedSelection:
type: class
description: "Select features based on tree-based model and remove features with low importance."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
task_type:
type: str
description: "Task type, 'cls' for classification, 'mcls' for multi-class classification, 'reg' for regression."
enum:
- cls
- mcls
- reg
required:
- label_col
- task_type
fit:
description: "Fit the TreeBasedSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."

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metagpt/tools/schemas/feature_engineering/VarianceBasedSelection.yml
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VarianceBasedSelection:
type: class
description: "Select features based on variance and remove features with low variance."
methods:
__init__:
description: "Initialize self."
parameters:
properties:
label_col:
type: str
description: "Label column name."
threshold:
type: float
description: "Threshold for variance."
default: 0.0
required:
- label_col
fit:
description: "Fit the VarianceBasedSelection model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
transform:
description: "Transform the input DataFrame with the fitted model."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."
fit_transform:
description: "Fit and transform the input DataFrame."
parameters:
properties:
df:
type: DataFrame
description: "The input DataFrame."
required:
- df
returns:
df:
type: DataFrame
description: "The transformed DataFrame contain label_col."

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metagpt/tools/schemas/image2webpage/GPTvGenerator.yml
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GPTvGenerator:
type: class
description: "Class for generating webpages at once."
methods:
__init__:
description: "Initialize Vision class with default values."
generate_webpages:
description: "Generate webpages including all code(HTML, CSS and JavaScript) in one go based on the image."
parameters:
properties:
image_path:
type: str
description: "The path of the image file"
required:
- image_path
returns:
type: str
description: "Generated webpages content."
save_webpages:
description: "Save webpages including all code(HTML, CSS and JavaScript) at once"
parameters:
properties:
image_path:
type: str
description: "The path of the image file"
webpages:
type: str
description: "The generated webpages content"
required:
- image_path
- webpages
returns:
type: Path
description: "The path of the saved webpages"

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metagpt/tools/schemas/stable_diffusion/SDEngine.yml
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SDEngine:
type: class
description: "Generate image using stable diffusion model"
methods:
__init__:
description: "Initialize the SDEngine instance."
parameters:
properties:
sd_url:
type: str
description: "URL of the stable diffusion service."
simple_run_t2i:
description: "Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images."
parameters:
properties:
payload:
type: dict
description: "Dictionary of input parameters for the stable diffusion API."
auto_save:
type: bool
description: "Save generated images automatically."
required:
- prompts
run_t2i:
type: async function
description: "Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images."
parameters:
properties:
payloads:
type: list
description: "List of payload, each payload is a dictionary of input parameters for the stable diffusion API."
required:
- payloads
construct_payload:
description: "Modify and set the API parameters for image generation."
parameters:
properties:
prompt:
type: str
description: "Text input for image generation."
required:
- prompt
returns:
payload:
type: dict
description: "Updated parameters for the stable diffusion API."
save:
description: "Save generated images to the output directory."
parameters:
properties:
imgs:
type: str
description: "Generated images."
save_name:
type: str
description: "Output image name. Default is empty."
required:
- imgs

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metagpt/tools/schemas/web_scraping/scrape_web_playwright.yml
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scrape_web_playwright:
type: async funciton
description: "Scrape and save the HTML structure and inner text content of a web page using Playwright."
parameters:
properties:
url:
type: str
description: "web url"
\*url:
type: Non-Keyword Arguments
description: "other web urls, you can assagin sub url link to it."
required:
- url
returns:
inner_text:
type: str
description: The inner text content of the web page.
html:
type: str
description: The html structure of the web page.