Add linting/formatting

2026-02-23 17:23:22 +00:00
parent fd07f4a3e3
commit 099f6a50d1
7 changed files with 109 additions and 35 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -216,3 +216,4 @@ __marimo__/
 .streamlit/secrets.toml

 ref/
+tmp/
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,3 +24,9 @@ va-evaluator = "va_evaluator.cli:app"
 [build-system]
 requires = ["uv_build>=0.8.3,<0.9.0"]
 build-backend = "uv_build"
+
+[dependency-groups]
+dev = [
+    "ruff>=0.15.2",
+    "ty>=0.0.18",
+]
--- a/src/va_evaluator/config.py
+++ b/src/va_evaluator/config.py
@@ -22,8 +22,8 @@ class IndexSettings(BaseModel):

 class AgentSettings(BaseModel):
    name: str = "default"
-    language_model: str = "gemini-2.0-flash"
-    embedding_model: str = "text-embedding-004"
+    language_model: str = "gemini-2.5-flash"
+    embedding_model: str = "gemini-embedding-001"


 class BigQuerySettings(BaseModel):
@@ -41,7 +41,9 @@ class BigQuerySettings(BaseModel):


 class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_nested_delimiter="__")
+    model_config = SettingsConfigDict(
+        env_nested_delimiter="__", env_file=".env", extra="ignore"
+    )

    project_id: str
    location: str = "us-central1"
--- a/src/va_evaluator/keypoint_metrics_evaluator.py
+++ b/src/va_evaluator/keypoint_metrics_evaluator.py
@@ -186,9 +186,7 @@ class KeypointRAGEvaluator:
        generated_answer: str,
        keypoints: list[str],
    ) -> tuple[dict[str, float], list[dict]]:
-        keypoints_list = "\n".join(
-            [f"{i + 1}. {kp}" for i, kp in enumerate(keypoints)]
-        )
+        keypoints_list = "\n".join([f"{i + 1}. {kp}" for i, kp in enumerate(keypoints)])
        prompt = EVALUATE_KEYPOINTS_PROMPT.format(
            generated_answer=generated_answer,
            keypoints_list=keypoints_list,
@@ -343,14 +341,13 @@ def load_data_from_bigquery(
    df.dropna(subset=["input", "expected_output", "response"], inplace=True)
    console.print(f"Loaded {len(df)} rows for evaluation.")
    if df.empty:
-        console.print(
-            "[bold yellow]Warning: No data found in BigQuery.[/bold yellow]"
-        )
+        console.print("[bold yellow]Warning: No data found in BigQuery.[/bold yellow]")
    return df


 # --- Core Logic ---

+
 def evaluate(
    input_file: str | None = None,
    output_file: str | None = None,
@@ -429,9 +426,7 @@ def evaluate(
                        )
                    )
                else:
-                    console.print(
-                        f"[bold red]Error evaluating row: {e}[/bold red]"
-                    )
+                    console.print(f"[bold red]Error evaluating row: {e}[/bold red]")
                    all_results.append(
                        {
                            "query": row["input"],
@@ -555,4 +550,3 @@ def evaluate(
                f"[bold red]An error occurred while saving to BigQuery: {e}[/bold red]"
            )
            raise typer.Exit(code=1)
-
--- a/src/va_evaluator/search_metrics_evaluator.py
+++ b/src/va_evaluator/search_metrics_evaluator.py
@@ -253,4 +253,3 @@ def evaluate(
                f"[bold red]An error occurred while saving to BigQuery: {e}[/bold red]"
            )
            raise typer.Exit(code=1)
-
--- a/src/va_evaluator/synthetic_question_generator.py
+++ b/src/va_evaluator/synthetic_question_generator.py
@@ -4,12 +4,12 @@ import random

 import pandas as pd
 import typer
-import vertexai
+from google import genai
 from google.cloud import storage
+from google.genai import types
 from pydantic import BaseModel
 from rich.console import Console
 from rich.progress import track
-from vertexai.generative_models import GenerationConfig, GenerativeModel

 from va_evaluator.config import Settings

@@ -89,20 +89,25 @@ class MultiStepResponseSchema(BaseModel):


 def generate_structured(
-    model: GenerativeModel,
+    client: genai.Client,
+    model: str,
    prompt: str,
    response_model: type[BaseModel],
 ) -> BaseModel:
-    generation_config = GenerationConfig(
-        response_mime_type="application/json",
-        response_schema=response_model,
+    response = client.models.generate_content(
+        model=model,
+        contents=prompt,
+        config=types.GenerateContentConfig(
+            response_mime_type="application/json",
+            response_schema=response_model,
+        ),
    )
-    response = model.generate_content(prompt, generation_config=generation_config)
    return response_model.model_validate_json(response.text)


 def generate_synthetic_question(
-    model: GenerativeModel,
+    client: genai.Client,
+    model: str,
    file_content: str,
    file_path: str,
    q_type: str,
@@ -111,11 +116,12 @@ def generate_synthetic_question(
    prompt = PROMPT_TEMPLATE.format(
        context=file_content, id=file_path, qtype=q_type, qtype_def=q_def
    )
-    return generate_structured(model, prompt, ResponseSchema)
+    return generate_structured(client, model, prompt, ResponseSchema)


 def generate_synthetic_conversation(
-    model: GenerativeModel,
+    client: genai.Client,
+    model: str,
    file_content: str,
    file_path: str,
    num_turns: int,
@@ -123,7 +129,7 @@ def generate_synthetic_conversation(
    prompt = MULTI_STEP_PROMPT_TEMPLATE.format(
        context=file_content, num_turns=num_turns
    )
-    return generate_structured(model, prompt, MultiStepResponseSchema)
+    return generate_structured(client, model, prompt, MultiStepResponseSchema)


 def generate(
@@ -134,8 +140,12 @@ def generate(
    console = Console()
    settings = Settings()

-    vertexai.init(project=settings.project_id, location=settings.location)
-    model = GenerativeModel(settings.agent.language_model)
+    client = genai.Client(
+        vertexai=True,
+        project=settings.project_id,
+        location=settings.location,
+    )
+    model_name = settings.agent.language_model
    gcs_client = storage.Client(project=settings.project_id)
    bucket = gcs_client.bucket(settings.require_bucket)

@@ -165,9 +175,7 @@ def generate(
        console.print("[yellow]No files found. Skipping.[/yellow]")
        return ""

-    files_to_process = random.sample(
-        all_files, k=min(num_questions, len(all_files))
-    )
+    files_to_process = random.sample(all_files, k=min(num_questions, len(all_files)))
    console.print(
        f"Randomly selected {len(files_to_process)} files to generate questions from."
    )
@@ -182,7 +190,7 @@ def generate(
                conversation_data = None
                for attempt in range(3):
                    conversation_data = generate_synthetic_conversation(
-                        model, file_content, file_path, num_turns
+                        client, model_name, file_content, file_path, num_turns
                    )
                    if (
                        conversation_data
@@ -219,7 +227,7 @@ def generate(
                generated_data = None
                for attempt in range(3):
                    generated_data = generate_synthetic_question(
-                        model, file_content, file_path, q_type, q_def
+                        client, model_name, file_content, file_path, q_type, q_def
                    )
                    if (
                        generated_data
@@ -249,7 +257,9 @@ def generate(
                all_rows.append(row)

        except Exception as e:
-            console.print(f"[bold red]Error processing file {file_path}: {e}[/bold red]")
+            console.print(
+                f"[bold red]Error processing file {file_path}: {e}[/bold red]"
+            )

    if not all_rows:
        console.print("[bold yellow]No questions were generated.[/bold yellow]")
@@ -262,7 +272,9 @@ def generate(
            f"\n[bold green]Saving {len(df)} generated questions to {output_csv}...[/bold green]"
        )
        df.to_csv(output_csv, index=False, encoding="utf-8-sig")
-        console.print("[bold green]Synthetic question generation complete.[/bold green]")
+        console.print(
+            "[bold green]Synthetic question generation complete.[/bold green]"
+        )
    else:
        console.print(
            f"\n[bold green]Saving {len(df)} generated questions to BigQuery...[/bold green]"
@@ -295,4 +307,3 @@ def generate(

    console.print(f"[bold yellow]Finished run with ID: {run_id}[/bold yellow]")
    return run_id
-
--- a/uv.lock
+++ b/uv.lock
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 ]

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+]
+
 [[package]]
 name = "typer"
 version = "0.24.1"
@@ -2581,6 +2630,12 @@ dependencies = [
    { name = "typer" },
 ]

+[package.dev-dependencies]
+dev = [
+    { name = "ruff" },
+    { name = "ty" },
+]
+
 [package.metadata]
 requires-dist = [
    { name = "google-cloud-aiplatform" },
@@ -2596,6 +2651,12 @@ requires-dist = [
    { name = "typer" },
 ]

+[package.metadata.requires-dev]
+dev = [
+    { name = "ruff", specifier = ">=0.15.2" },
+    { name = "ty", specifier = ">=0.0.18" },
+]
+
 [[package]]
 name = "warc3-wet"
 version = "0.2.5"