Migrate keypoint eval to live queires

.gitignore

@@ -216,3 +216,4 @@ __marimo__/
 .streamlit/secrets.toml
 
 ref/
+tmp/

pyproject.toml

@@ -9,6 +9,7 @@ dependencies = [
     "google-cloud-bigquery>=3.40.1",
     "google-cloud-storage",
     "google-genai>=1.64.0",
+    "httpx",
     "pandas",
     "pandas-gbq",
     "pydantic",
@@ -24,3 +25,9 @@ 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",
+]

src/va_evaluator/cli.py

@@ -89,8 +89,20 @@ def eval_keypoint(
             help="Optional: The specific run_id to filter the evaluation data by."
         ),
     ] = None,
+    agent_url: Annotated[
+        str,
+        typer.Option(
+            "--agent-url",
+            help="Base URL of the agent server to query for responses.",
+        ),
+    ] = "http://localhost:8000",
 ):
     """Evaluates RAG responses using the keypoint methodology."""
     from va_evaluator.keypoint_metrics_evaluator import evaluate
 
-    evaluate(input_file=input_file, output_file=output_file, run_id=run_id)
+    evaluate(
+        input_file=input_file,
+        output_file=output_file,
+        run_id=run_id,
+        agent_url=agent_url,
+    )

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"

src/va_evaluator/keypoint_metrics_evaluator.py

@@ -4,6 +4,7 @@ import sqlite3
 from datetime import datetime
 from typing import Literal
 
+import httpx
 import pandas as pd
 import typer
 from google import genai
@@ -186,9 +187,7 @@ 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,
@@ -291,7 +290,7 @@ def load_data_from_local_file(
         )
         raise ValueError(f"Unsupported file type: {path.suffix}")
 
-    required_cols = {"input", "expected_output", "response"}
+    required_cols = {"input", "expected_output"}
     if not required_cols.issubset(df.columns):
         missing = required_cols - set(df.columns)
         console.print(
@@ -315,7 +314,7 @@ def load_data_from_local_file(
     if "type" in df.columns:
         df = df[df["type"] != "Unanswerable"].copy()
 
-    df.dropna(subset=["input", "expected_output", "response"], inplace=True)
+    df.dropna(subset=["input", "expected_output"], inplace=True)
     console.print(f"Loaded {len(df)} rows for evaluation.")
     return df
 
@@ -340,21 +339,38 @@ def load_data_from_bigquery(
         query += f" AND run_id = '{run_id}'"
 
     df = client.query(query).to_dataframe()
-    df.dropna(subset=["input", "expected_output", "response"], inplace=True)
+    df.dropna(subset=["input", "expected_output"], 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 fetch_agent_response(
+    http_client: httpx.Client, agent_url: str, query: str
+) -> str:
+    """Sends a query to the agent server and returns the response text."""
+    response = http_client.post(
+        f"{agent_url}/api/v1/query",
+        json={
+            "phone_number": "eval-user",
+            "text": query,
+            "type": "conversation",
+            "language_code": "es",
+        },
+    )
+    response.raise_for_status()
+    return response.json()["response_text"]
+
+
 def evaluate(
     input_file: str | None = None,
     output_file: str | None = None,
     run_id: str | None = None,
+    agent_url: str = "http://localhost:8000",
 ):
     """Core logic for running keypoint-based evaluation."""
     console = Console()
@@ -391,7 +407,11 @@ def evaluate(
     all_results = []
     total_skipped = 0
 
-    with Progress(
+    console.print(
+        f"[bold blue]Fetching responses from agent at: {agent_url}[/bold blue]"
+    )
+
+    with httpx.Client(timeout=120.0) as http_client, Progress(
         SpinnerColumn(),
         TextColumn("[progress.description]{task.description}"),
         BarColumn(),
@@ -405,9 +425,12 @@ def evaluate(
 
         for _, row in df.iterrows():
             try:
+                agent_response = fetch_agent_response(
+                    http_client, agent_url, row["input"]
+                )
                 result = evaluator.evaluate_response(
                     query=row["input"],
-                    response=row["response"],
+                    response=agent_response,
                     ground_truth=row["expected_output"],
                 )
                 result["agent"] = settings.agent.name
@@ -429,13 +452,11 @@ 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"],
-                            "response": row["response"],
+                            "response": "",
                             "ground_truth": row["expected_output"],
                             "completeness": 0.0,
                             "hallucination": 0.0,
@@ -555,4 +576,3 @@ def evaluate(
                 f"[bold red]An error occurred while saving to BigQuery: {e}[/bold red]"
             )
             raise typer.Exit(code=1)
-

src/va_evaluator/search_metrics_evaluator.py

@@ -3,7 +3,7 @@ import sqlite3
 
 import pandas as pd
 import typer
-import vertexai
+from google import genai
 from google.cloud import bigquery
 from google.cloud.aiplatform.matching_engine import MatchingEngineIndexEndpoint
 from ranx import Qrels, Run
@@ -11,7 +11,6 @@ from ranx import evaluate as ranx_evaluate
 from rich.console import Console
 from rich.progress import track
 from rich.table import Table
-from vertexai.language_models import TextEmbeddingModel
 
 from va_evaluator.config import Settings
 
@@ -111,8 +110,11 @@ def run_evaluation(
     )
     console.print(f"Index Name: [bold cyan]{settings.index.name}[/bold cyan]")
 
-    vertexai.init(project=settings.project_id, location=settings.location)
-    embedding_model = TextEmbeddingModel.from_pretrained(settings.agent.embedding_model)
+    client = genai.Client(
+        vertexai=True,
+        project=settings.project_id,
+        location=settings.location,
+    )
     index_endpoint = MatchingEngineIndexEndpoint(settings.index.require_endpoint)
 
     # Prepare qrels
@@ -126,8 +128,11 @@ def run_evaluation(
     run_data = {}
     detailed_results_list = []
     for _, row in track(df.iterrows(), total=len(df), description="Preparing run..."):
-        embeddings = embedding_model.get_embeddings([row["question"]])
-        question_embedding = embeddings[0].values
+        result = client.models.embed_content(
+            model=settings.agent.embedding_model,
+            contents=row["question"],
+        )
+        question_embedding = result.embeddings[0].values
 
         results = index_endpoint.find_neighbors(
             deployed_index_id=settings.index.require_deployment,
@@ -253,4 +258,3 @@ def evaluate(
                 f"[bold red]An error occurred while saving to BigQuery: {e}[/bold red]"
             )
             raise typer.Exit(code=1)
-

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 = 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
-

uv.lock

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+
 [[package]]
 name = "typer"
 version = "0.24.1"
@@ -2572,6 +2621,7 @@ dependencies = [
     { name = "google-cloud-bigquery" },
     { name = "google-cloud-storage" },
     { name = "google-genai" },
+    { name = "httpx" },
     { name = "pandas" },
     { name = "pandas-gbq" },
     { name = "pydantic" },
@@ -2581,12 +2631,19 @@ dependencies = [
     { name = "typer" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "ruff" },
+    { name = "ty" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "google-cloud-aiplatform" },
     { name = "google-cloud-bigquery", specifier = ">=3.40.1" },
     { name = "google-cloud-storage" },
     { name = "google-genai", specifier = ">=1.64.0" },
+    { name = "httpx" },
     { name = "pandas" },
     { name = "pandas-gbq" },
     { name = "pydantic" },
@@ -2596,6 +2653,12 @@ 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"