Integrate MongoDB
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parent
750ba7b0d4
commit
cf0ac4b161
1 changed files with 1003 additions and 748 deletions
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@ -1,5 +1,5 @@
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from math import ceil
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from typing import Any, Dict, List, Sequence, Tuple
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
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import pandas as pd
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import plotly.express as px
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@ -8,12 +8,12 @@ from dash import Dash, dcc, html
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from dash.dependencies import Input, Output
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from flask import Flask
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from great_ai.utilities.unique import unique
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from great_ai.utilities import unique
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from ....constants import DASHBOARD_PATH, ONLINE_TAG_NAME
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from ....context import get_context
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from ....helper import snake_case_to_text, text_to_hex_color
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from ....views import SortBy
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from ....views import SortBy, Trace
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from .get_description import get_description
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from .get_filter_from_datatable import get_filter_from_datatable
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from .get_footer import get_footer
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@ -105,19 +105,32 @@ def create_dash_app(function_name: str, function_docs: str) -> Flask:
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@app.callback(
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Output(table, "data"),
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Output(table, "page_count"),
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Output(table, "columns"),
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Output(traces_table_container, "style"),
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Output(execution_time_histogram_container, "children"),
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Output(parallel_coordinates, "figure"),
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Output(parallel_coordinates, "style"),
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Input(table, "page_current"),
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Input(table, "page_size"),
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Input(table, "sort_by"),
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Input(table, "filter_query"),
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Input(interval, "n_intervals"),
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)
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def update_table(
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def update_page(
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page_current: int,
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page_size: int,
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sort_by: List[SortBy],
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sort_by: List[Dict[str, Union[str, int]]],
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filter_query: str,
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n_intervals: int,
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) -> Tuple[List[Dict[str, Any]], int]:
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) -> Tuple[
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List[Dict[str, Any]],
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int,
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List[Dict[str, Sequence[str]]],
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Dict[str, Any],
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Any,
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go.Figure,
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Dict[str, Any],
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]:
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conjunctive_filters = (
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[get_filter_from_datatable(f) for f in filter_query.split(" && ")]
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if filter_query
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@ -128,114 +141,98 @@ def create_dash_app(function_name: str, function_docs: str) -> Flask:
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elements, count = get_context().tracing_database.query(
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skip=page_current * page_size,
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take=page_size,
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conjunctive_tags=[ONLINE_TAG_NAME],
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conjunctive_filters=non_null_conjunctive_filters,
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sort_by=sort_by,
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conjunctive_tags=[ONLINE_TAG_NAME],
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sort_by=[SortBy.parse_obj(s) for s in sort_by],
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)
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columns, style = update_layout(elements[0] if elements else None)
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execution_time_histogram, parallel_coords_fig, style = update_charts(
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elements=elements, function_name=function_name, accent_color=accent_color
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)
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return (
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[e.to_flat_dict() for e in elements],
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[e.to_flat_dict(include_original=False) for e in elements],
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max(1, ceil(count / page_size)),
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)
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@app.callback(
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Output(table, "columns"),
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Output(traces_table_container, "style"),
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Input(interval, "n_intervals"),
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)
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def update_layout(
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n_intervals: int,
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) -> Tuple[List[Dict[str, Sequence[str]]], Dict[str, Any]]:
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elements, count = get_context().tracing_database.query(
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take=1, conjunctive_tags=[ONLINE_TAG_NAME]
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)
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if elements:
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keys = list(elements[0].to_flat_dict().keys())
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header_height = max(len(i.split(":")) for i in keys)
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columns = [
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{
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"name": [""] * (header_height - len(k.split(":")))
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+ k.replace("_flat", "").split(":"),
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"id": k,
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}
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for k in keys
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]
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else:
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columns = []
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return (
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columns,
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{"display": "block" if count > 0 else "none"},
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style,
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execution_time_histogram,
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parallel_coords_fig,
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style,
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)
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@app.callback(
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Output(execution_time_histogram_container, "children"),
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Output(parallel_coordinates, "figure"),
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Output(parallel_coordinates, "style"),
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Input(table, "filter_query"),
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Input(interval, "n_intervals"),
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)
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def update_charts(
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filter_query: str, n_intervals: int
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) -> Tuple[Any, go.Figure, Dict[str, Any]]:
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conjunctive_filters = (
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[get_filter_from_datatable(f) for f in filter_query.split(" && ")]
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if filter_query
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else []
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)
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non_null_conjunctive_filters = [f for f in conjunctive_filters if f is not None]
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elements, count = get_context().tracing_database.query(
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conjunctive_tags=[ONLINE_TAG_NAME],
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conjunctive_filters=non_null_conjunctive_filters,
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)
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if not elements:
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return (
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html.Span(
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f"No traces yet: call your function ({function_name}) to create one.",
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className="placeholder",
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),
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go.Figure(),
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{"display": "none"},
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)
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flat_elements = [e.to_flat_dict() for e in elements]
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execution_time_histogram = dcc.Graph(config={"displaylogo": False})
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df = pd.DataFrame(flat_elements)
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fig = px.histogram(
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df,
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x="original_execution_time_ms",
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labels={"original_execution_time_ms": "Execution time (ms)"},
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nbins=20,
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height=400,
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log_y=True,
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color_discrete_sequence=[accent_color],
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)
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fig.update_layout(
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margin=dict(l=0, r=0, b=0, t=0, pad=0),
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)
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execution_time_histogram.figure = fig
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parallel_coords_fig = go.Figure(
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go.Parcoords(
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dimensions=[
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get_dimension_descriptor(df, c)
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for c in df.columns
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if c
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not in {"trace_id", "created", "output", "exception", "feedback"}
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and "_flat" not in c
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],
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line_color=accent_color,
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)
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)
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return execution_time_histogram, parallel_coords_fig, {}
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return app.server
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def update_layout(
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first_element: Optional[Trace],
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) -> Tuple[List[Dict[str, Sequence[str]]], Dict[str, Any]]:
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if first_element:
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keys = list(first_element.to_flat_dict(include_original=False).keys())
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header_height = max(len(i.split(":")) for i in keys)
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columns = [
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{
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"name": [""] * (header_height - len(k.split(":")))
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+ k.replace("_flat", "").split(":"),
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"id": k,
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}
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for k in keys
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]
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else:
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columns = []
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return (
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columns,
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{"display": "none" if first_element is None else "block"},
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)
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def update_charts(
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elements: List[Trace], function_name: str, accent_color: str
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) -> Tuple[Any, go.Figure, Dict[str, Any]]:
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if not elements:
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return (
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html.Span(
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f"No traces yet: call your function ({function_name}) to create one.",
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className="placeholder",
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),
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go.Figure(),
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{"display": "none"},
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)
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flat_elements = [e.to_flat_dict(include_original=False) for e in elements]
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execution_time_histogram = dcc.Graph(config={"displaylogo": False})
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df = pd.DataFrame(flat_elements)
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fig = px.histogram(
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df,
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x="original_execution_time_ms",
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labels={"original_execution_time_ms": "Execution time (ms)"},
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nbins=20,
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height=400,
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log_y=True,
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color_discrete_sequence=[accent_color],
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)
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fig.update_layout(
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margin=dict(l=0, r=0, b=0, t=0, pad=0),
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)
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execution_time_histogram.figure = fig
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parallel_coords_fig = go.Figure(
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go.Parcoords(
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dimensions=[
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get_dimension_descriptor(df, c)
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for c in df.columns
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if c not in {"trace_id", "created", "output", "exception", "feedback"}
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and "_flat" not in c
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],
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line_color=accent_color,
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)
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)
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return execution_time_histogram, parallel_coords_fig, {}
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def get_dimension_descriptor(df: pd.DataFrame, column: str) -> Dict[str, Any]:
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dimension: Dict[str, Any] = {
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"label": snake_case_to_text(column),
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