Gen AI eval: MULTI_TURN_GENERAL_QUALITY fails with "conversation_history is required but not provided" even when present

### Summary

`Gen AI eval: MULTI_TURN_GENERAL_QUALITY fails with "conversation_history is required but not provided" even when column is present in dataset`

---

#### Environment details

- **OS type and version**: Colab / Colab Enterprise (Linux, managed runtime)
- **Python version**: `Python 3.10` (from Colab)
- **pip version**: `pip 24.x` (from Colab)
- **`google-cloud-aiplatform` version**: `1.134.0`
(also reproducible on `1.135.0`)
- **`google-genai` version**: `1.61.0`
- **API usage**: `vertexai.Client(..., http_options=genai_types.HttpOptions(api_version="v1beta1"))`

---

#### Steps to reproduce

1. **Install the SDK in a fresh Colab / Colab Enterprise runtime**

```python
%pip install -U -q "google-cloud-aiplatform[evaluation]==1.134.0"

import google.cloud.aiplatform as aiplatform
import google.genai as genai
print("aiplatform:", aiplatform.__version__) # 1.134.0
print("genai:", genai.__version__) # 1.61.0
```

2. **Initialize Vertex and define `agent_info`**

```python
import os
import vertexai
from google.cloud import storage
from google.genai import types as genai_types
from vertexai import Client
from vertexai import types
import pandas as pd

PROJECT_ID = os.getenv("PROJECT_ID", "")
LOCATION = os.getenv("LOCATION", "us-central1")
AGENT = os.getenv("AGENT", "")
GCS_DEST = os.getenv("GCS_DEST", "")
AUTOMATED_RUN = os.getenv("AUTOMATED_RUN", "false")
AGENT_DISPLAY_NAME = os.getenv("AGENT_DISPLAY_NAME", AGENT.split("/")[-1])

vertexai.init(project=PROJECT_ID, location=LOCATION)

client = Client(
project=PROJECT_ID,
location=LOCATION,
http_options=genai_types.HttpOptions(api_version="v1beta1"),
)

# Define agent_info (simplified)
agent_info = types.evals.AgentInfo(
agent_resource_name=AGENT,
name="orchestrator_agent",
# instruction + tools omitted for brevity
)
```

3. **Build a multi‑turn dataset with `history` and run inference**

```python
from vertexai import generative_models as genai_models

multi_turn_conversations = [
{
"history": [
genai_models.Content(
role="user",
parts=[genai_models.Part.from_text("First question")]
),
genai_models.Content(
role="model",
parts=[genai_models.Part.from_text("First response")]
),
],
"prompt": "Second question",
"session_inputs": types.evals.SessionInput(
user_id="user_1",
state={"agent_type": "engineering", "conversation_id": "1"},
),
},
# ... a few more rows ...
]

prompts = [conv["prompt"] for conv in multi_turn_conversations]
histories = [conv["history"] for conv in multi_turn_conversations]
session_inputs_list = [conv["session_inputs"] for conv in multi_turn_conversations]

def content_to_dict(content):
parts_list = []
for part in content.parts:
if hasattr(part, "text"):
parts_list.append({"text": part.text})
return {
"role": content.role,
"parts": parts_list,
}

histories_as_dicts = [
[content_to_dict(content) for content in history]
for history in histories
]

# Create DataFrame with required columns
df = pd.DataFrame({
"prompt": prompts,
"history": histories_as_dicts, # list[dict] with role + parts[text]
"session_inputs": session_inputs_list,
})

multi_turn_dataset = types.EvaluationDataset(eval_dataset_df=df)

print(multi_turn_dataset.eval_dataset_df.columns)
# Index(['prompt', 'history', 'session_inputs'], dtype='object')

# Run inference
eval_dataset = client.evals.run_inference(
src=multi_turn_dataset,
agent=AGENT,
)

# Workaround attempt: explicitly add conversation_history
df2 = eval_dataset.eval_dataset_df.copy()
df2["conversation_history"] = df2["history"]
eval_dataset = types.EvaluationDataset(eval_dataset_df=df2)

print(eval_dataset.eval_dataset_df.columns)
# Index([... 'history', 'conversation_history', ...], dtype='object')
```

4. **Create the multi‑turn evaluation run with `MULTI_TURN_GENERAL_QUALITY`**

```python
import datetime
import time

print("📊 Running multi-turn evaluation...")
run_type = "Auto" if AUTOMATED_RUN.lower() == "true" else "Manual"

evaluation_run = client.evals.create_evaluation_run(
display_name=f"{run_type}-MultiTurn-Eval-{AGENT_DISPLAY_NAME}-{datetime.datetime.now().strftime('%Y%m%d-%H%M%S')}",
dataset=eval_dataset,
agent_info=agent_info,
metrics=[
types.RubricMetric.MULTI_TURN_GENERAL_QUALITY,
],
dest=GCS_DEST,
)

evaluation_run.show()

# Poll to completion
while evaluation_run.state not in {"SUCCEEDED", "FAILED", "CANCELLED"}:
evaluation_run = client.evals.get_evaluation_run(name=evaluation_run.name)
time.sleep(10)

eval_result = client.evals.get_evaluation_run(
name=evaluation_run.name,
include_evaluation_items=True,
)

eval_result.show()
```

---

#### Stack trace / error

The evaluation run consistently fails with `FAILED_PRECONDITION`:

```text
📈 Multi-Turn Evaluation Results:

Status: FAILED

Error:
code=9 details=None message='code=FAILED_PRECONDITION, message=Evaluation items failed with errors:
Item ...: INVALID_ARGUMENT: code=INVALID_ARGUMENT, message=Error rendering metric prompt template: Variable conversation_history is required but not provided.., cause=null,
Item ...: INVALID_ARGUMENT: code=INVALID_ARGUMENT, message=Error rendering metric prompt template: Variable conversation_history is required but not provided.., cause=null,
...
cause=null'
```

This happens even when `eval_dataset.eval_dataset_df` clearly contains a `conversation_history` column which is a copy of `history` (each value is a list of `{"role": ..., "parts": [{"text": ...}]}` dicts).

Single‑turn evaluations in the same environment work as expected.

---

#### Expected behavior

- Either `MULTI_TURN_GENERAL_QUALITY` should accept the documented `history` field for multi‑turn datasets, **or**
- If `conversation_history` is now required by the metric prompt template, then providing `conversation_history = history` in the dataset should allow the metric to run successfully instead of returning:

> `Error rendering metric prompt template: Variable conversation_history is required but not provided.`

If a different schema is now required for multi‑turn agent evaluation (e.g. a `request` object wrapper or a different field name/structure), updated documentation or a validation error before running the metric would be very helpful.

Thanks!