Evaluation to Use Config Management

backend/app/alembic/versions/041_add_config_in_evals_run_table.py

@@ -0,0 +1,60 @@
+"""add config in evals run table
+
+Revision ID: 041
+Revises: 040
+Create Date: 2025-12-15 14:03:22.082746
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel.sql.sqltypes
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "041"
+down_revision = "040"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "evaluation_run",
+        sa.Column(
+            "config_id",
+            sa.Uuid(),
+            nullable=True,
+            comment="Reference to the stored config used",
+        ),
+    )
+    op.add_column(
+        "evaluation_run",
+        sa.Column(
+            "config_version",
+            sa.Integer(),
+            nullable=True,
+            comment="Version of the config used",
+        ),
+    )
+    op.create_foreign_key(None, "evaluation_run", "config", ["config_id"], ["id"])
+    op.drop_column("evaluation_run", "config")
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "evaluation_run",
+        sa.Column(
+            "config",
+            postgresql.JSONB(astext_type=sa.Text()),
+            autoincrement=False,
+            nullable=False,
+            comment="Evaluation configuration (model, instructions, etc.)",
+        ),
+    )
+    op.drop_constraint(None, "evaluation_run", type_="foreignkey")
+    op.drop_column("evaluation_run", "config_version")
+    op.drop_column("evaluation_run", "config_id")
+    # ### end Alembic commands ###

backend/app/api/routes/evaluation.py

@@ -3,12 +3,21 @@
 import logging
 import re
 from pathlib import Path
-
-from fastapi import APIRouter, Body, File, Form, HTTPException, Query, UploadFile
+from uuid import UUID
+
+from fastapi import (
+    APIRouter,
+    Body,
+    File,
+    Form,
+    HTTPException,
+    Query,
+    UploadFile,
+)
 
 from app.api.deps import AuthContextDep, SessionDep
 from app.core.cloud import get_cloud_storage
-from app.crud.assistants import get_assistant_by_id
+from app.crud.config.version import ConfigVersionCrud
 from app.crud.evaluations import (
     create_evaluation_dataset,
     create_evaluation_run,
@@ -27,13 +36,17 @@
     DatasetUploadResponse,
     EvaluationRunPublic,
 )
+from app.models.llm.request import LLMCallConfig
+from app.services.llm.jobs import resolve_config_blob
+from app.services.llm.providers import LLMProvider
 from app.utils import (
     APIResponse,
     get_langfuse_client,
     get_openai_client,
     load_description,
 )
 
+
 logger = logging.getLogger(__name__)
 
 # File upload security constants
@@ -430,20 +443,9 @@ def evaluate(
     experiment_name: str = Body(
         ..., description="Name for this evaluation experiment/run"
     ),
-    config: dict = Body(default_factory=dict, description="Evaluation configuration"),
-    assistant_id: str
-    | None = Body(
-        None, description="Optional assistant ID to fetch configuration from"
-    ),
+    config_id: UUID = Body(..., description="Stored config ID"),
+    config_version: int = Body(..., ge=1, description="Stored config version"),
 ) -> APIResponse[EvaluationRunPublic]:
-    logger.info(
-        f"[evaluate] Starting evaluation | experiment_name={experiment_name} | "
-        f"dataset_id={dataset_id} | "
-        f"org_id={auth_context.organization.id} | "
-        f"assistant_id={assistant_id} | "
-        f"config_keys={list(config.keys())}"
-    )
-
     # Step 1: Fetch dataset from database
     dataset = get_dataset_by_id(
         session=_session,
@@ -459,12 +461,6 @@ def evaluate(
             f"organization/project",
         )
 
-    logger.info(
-        f"[evaluate] Found dataset | id={dataset.id} | name={dataset.name} | "
-        f"object_store_url={'present' if dataset.object_store_url else 'None'} | "
-        f"langfuse_id={dataset.langfuse_dataset_id}"
-    )
-
     dataset_name = dataset.name
 
     # Get API clients
@@ -487,63 +483,35 @@ def evaluate(
             "Please ensure Langfuse credentials were configured when the dataset was created.",
         )
 
-    # Handle assistant_id if provided
-    if assistant_id:
-        # Fetch assistant details from database
-        assistant = get_assistant_by_id(
-            session=_session,
-            assistant_id=assistant_id,
-            project_id=auth_context.project.id,
-        )
-
-        if not assistant:
-            raise HTTPException(
-                status_code=404, detail=f"Assistant {assistant_id} not found"
-            )
+    config_version_crud = ConfigVersionCrud(
+        session=_session, config_id=config_id, project_id=auth_context.project.id
+    )
 
-        logger.info(
-            f"[evaluate] Found assistant in DB | id={assistant.id} | "
-            f"model={assistant.model} | instructions="
-            f"{assistant.instructions[:50] if assistant.instructions else 'None'}..."
+    config, error = resolve_config_blob(
+        config_crud=config_version_crud,
+        config=LLMCallConfig(id=config_id, version=config_version),
+    )
+    if error:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Failed to resolve config from stored config: {error}",
         )
-
-        # Build config from assistant (use provided config values to override
-        # if present)
-        config = {
-            "model": config.get("model", assistant.model),
-            "instructions": config.get("instructions", assistant.instructions),
-            "temperature": config.get("temperature", assistant.temperature),
-        }
-
-        # Add tools if vector stores are available
-        vector_store_ids = config.get(
-            "vector_store_ids", assistant.vector_store_ids or []
+    elif config.completion.provider != LLMProvider.OPENAI:
+        raise HTTPException(
+            status_code=422,
+            detail="Only 'openai' provider is supported for evaluation configs",
         )
-        if vector_store_ids and len(vector_store_ids) > 0:
-            config["tools"] = [
-                {
-                    "type": "file_search",
-                    "vector_store_ids": vector_store_ids,
-                }
-            ]
-
-        logger.info("[evaluate] Using config from assistant")
-    else:
-        logger.info("[evaluate] Using provided config directly")
-        # Validate that config has minimum required fields
-        if not config.get("model"):
-            raise HTTPException(
-                status_code=400,
-                detail="Config must include 'model' when assistant_id is not provided",
-            )
 
-    # Create EvaluationRun record
+    logger.info("[evaluate] Successfully resolved config from config management")
+
+    # Create EvaluationRun record with config references
     eval_run = create_evaluation_run(
         session=_session,
         run_name=experiment_name,
         dataset_name=dataset_name,
         dataset_id=dataset_id,
-        config=config,
+        config_id=config_id,
+        config_version=config_version,
         organization_id=auth_context.organization.id,
         project_id=auth_context.project.id,
     )
@@ -555,7 +523,7 @@ def evaluate(
             openai_client=openai_client,
             session=_session,
             eval_run=eval_run,
-            config=config,
+            config=config.completion.params,
         )
 
         logger.info(

backend/app/crud/evaluations/__init__.py

@@ -5,6 +5,7 @@
     create_evaluation_run,
     get_evaluation_run_by_id,
     list_evaluation_runs,
+    resolve_model_from_config,
 )
 from app.crud.evaluations.cron import (
     process_all_pending_evaluations,
@@ -39,6 +40,7 @@
     "create_evaluation_run",
     "get_evaluation_run_by_id",
     "list_evaluation_runs",
+    "resolve_model_from_config",
     # Cron
     "process_all_pending_evaluations",
     "process_all_pending_evaluations_sync",

backend/app/crud/evaluations/core.py

@@ -1,12 +1,16 @@
 import logging
 from typing import Any
+from uuid import UUID
 
 from langfuse import Langfuse
 from sqlmodel import Session, select
 
 from app.core.util import now
+from app.crud.config.version import ConfigVersionCrud
 from app.crud.evaluations.langfuse import fetch_trace_scores_from_langfuse
 from app.models import EvaluationRun
+from app.models.llm.request import LLMCallConfig
+from app.services.llm.jobs import resolve_config_blob
 
 logger = logging.getLogger(__name__)
 
@@ -16,7 +20,8 @@ def create_evaluation_run(
     run_name: str,
     dataset_name: str,
     dataset_id: int,
-    config: dict,
+    config_id: UUID,
+    config_version: int,
     organization_id: int,
     project_id: int,
 ) -> EvaluationRun:
@@ -28,7 +33,8 @@ def create_evaluation_run(
         run_name: Name of the evaluation run/experiment
         dataset_name: Name of the dataset being used
         dataset_id: ID of the dataset
-        config: Configuration dict for the evaluation
+        config_id: UUID of the stored config
+        config_version: Version number of the config
         organization_id: Organization ID
         project_id: Project ID
 
@@ -39,7 +45,8 @@ def create_evaluation_run(
         run_name=run_name,
         dataset_name=dataset_name,
         dataset_id=dataset_id,
-        config=config,
+        config_id=config_id,
+        config_version=config_version,
         status="pending",
         organization_id=organization_id,
         project_id=project_id,
@@ -56,7 +63,10 @@ def create_evaluation_run(
         logger.error(f"Failed to create EvaluationRun: {e}", exc_info=True)
         raise
 
-    logger.info(f"Created EvaluationRun record: id={eval_run.id}, run_name={run_name}")
+    logger.info(
+        f"Created EvaluationRun record: id={eval_run.id}, run_name={run_name}, "
+        f"config_id={config_id}, config_version={config_version}"
+    )
 
     return eval_run
 
@@ -293,3 +303,47 @@ def save_score(
                 f"traces={len(score.get('traces', []))}"
             )
         return eval_run
+
+
+def resolve_model_from_config(
+    session: Session,
+    eval_run: EvaluationRun,
+) -> str:
+    """
+    Resolve the model name from the evaluation run's config.
+
+    Args:
+        session: Database session
+        eval_run: EvaluationRun instance
+
+    Returns:
+        Model name from config
+
+    Raises:
+        ValueError: If config is missing, invalid, or has no model
+    """
+    if not eval_run.config_id or not eval_run.config_version:
+        raise ValueError(
+            f"Evaluation run {eval_run.id} has no config reference "
+            f"(config_id={eval_run.config_id}, config_version={eval_run.config_version})"
+        )
+
+    config_version_crud = ConfigVersionCrud(
+        session=session,
+        config_id=eval_run.config_id,
+        project_id=eval_run.project_id,
+    )
+
+    config, error = resolve_config_blob(
+        config_crud=config_version_crud,
+        config=LLMCallConfig(id=eval_run.config_id, version=eval_run.config_version),
+    )
+
+    if error or config is None:
+        raise ValueError(
+            f"Config resolution failed for evaluation {eval_run.id} "
+            f"(config_id={eval_run.config_id}, version={eval_run.config_version}): {error}"
+        )
+
+    model = config.completion.params.get("model")
+    return model

backend/app/crud/evaluations/embeddings.py

@@ -364,19 +364,7 @@ def start_embedding_batch(
         logger.info(f"Starting embedding batch for evaluation run {eval_run.id}")
 
         # Get embedding model from config (default: text-embedding-3-large)
-        embedding_model = eval_run.config.get(
-            "embedding_model", "text-embedding-3-large"
-        )
-
-        # Validate and fallback to default if invalid
-        try:
-            validate_embedding_model(embedding_model)
-        except ValueError as e:
-            logger.warning(
-                f"Invalid embedding model '{embedding_model}' in config: {e}. "
-                f"Falling back to text-embedding-3-large"
-            )
-            embedding_model = "text-embedding-3-large"
+        embedding_model = "text-embedding-3-large"
 
         # Step 1: Build embedding JSONL with trace_ids
         jsonl_data = build_embedding_jsonl(