BigQuery Adapter (#86)

* BigQuery

* removing unneeded

Author: xzrderek

eval_protocol/adapters/__init__.py

@@ -6,42 +6,64 @@
 Available adapters:
 - LangfuseAdapter: Pull data from Langfuse deployments
 - HuggingFaceAdapter: Load datasets from HuggingFace Hub
+- BigQueryAdapter: Query data from Google BigQuery
 - Braintrust integration (legacy)
 - TRL integration (legacy)
 """
 
 # Conditional imports based on available dependencies
 try:
     from .langfuse import LangfuseAdapter, create_langfuse_adapter
+
     __all__ = ["LangfuseAdapter", "create_langfuse_adapter"]
 except ImportError:
     __all__ = []
 
 try:
     from .huggingface import (
-        HuggingFaceAdapter, 
-        create_huggingface_adapter,
+        HuggingFaceAdapter,
         create_gsm8k_adapter,
+        create_huggingface_adapter,
         create_math_adapter,
     )
-    __all__.extend([
-        "HuggingFaceAdapter", 
-        "create_huggingface_adapter",
-        "create_gsm8k_adapter",
-        "create_math_adapter",
-    ])
+
+    __all__.extend(
+        [
+            "HuggingFaceAdapter",
+            "create_huggingface_adapter",
+            "create_gsm8k_adapter",
+            "create_math_adapter",
+        ]
+    )
+except ImportError:
+    pass
+
+try:
+    from .bigquery import (
+        BigQueryAdapter,
+        create_bigquery_adapter,
+    )
+
+    __all__.extend(
+        [
+            "BigQueryAdapter",
+            "create_bigquery_adapter",
+        ]
+    )
 except ImportError:
     pass
 
 # Legacy adapters (always available)
 try:
     from .braintrust import reward_fn_to_scorer, scorer_to_reward_fn
+
     __all__.extend(["scorer_to_reward_fn", "reward_fn_to_scorer"])
 except ImportError:
     pass
 
 try:
     from .trl import create_trl_adapter
+
     __all__.extend(["create_trl_adapter"])
 except ImportError:
     pass

eval_protocol/adapters/bigquery.py

@@ -0,0 +1,285 @@
+"""Google BigQuery adapter for Eval Protocol.
+
+This adapter allows querying data from Google BigQuery tables and converting it
+to EvaluationRow format for use in evaluation pipelines.
+"""
+
+import logging
+from typing import Any, Callable, Dict, Iterator, List, Optional, Union
+
+from eval_protocol.models import CompletionParams, EvaluationRow, InputMetadata, Message
+
+logger = logging.getLogger(__name__)
+
+try:
+    from google.auth.exceptions import DefaultCredentialsError
+    from google.cloud import bigquery
+    from google.cloud.exceptions import Forbidden, NotFound
+    from google.oauth2 import service_account
+
+    BIGQUERY_AVAILABLE = True
+except ImportError:
+    BIGQUERY_AVAILABLE = False
+    logger.warning("Google Cloud BigQuery not installed. Install with: pip install 'eval-protocol[bigquery]'")
+
+# Type alias for transformation function
+TransformFunction = Callable[[Dict[str, Any]], Dict[str, Any]]
+
+
+class BigQueryAdapter:
+    """Adapter to query data from Google BigQuery and convert to EvaluationRow format.
+
+    This adapter connects to Google BigQuery, executes SQL queries, and applies
+    a user-provided transformation function to convert each row to the format
+    expected by EvaluationRow.
+
+    The transformation function should take a BigQuery row dictionary and return:
+    {
+        'messages': List[Dict] - list of message dictionaries with 'role' and 'content'
+        'ground_truth': Optional[str] - expected answer/output
+        'metadata': Optional[Dict] - any additional metadata to preserve
+        'tools': Optional[List[Dict]] - tool definitions for tool calling scenarios
+    }
+    """
+
+    def __init__(
+        self,
+        transform_fn: TransformFunction,
+        dataset_id: Optional[str] = None,
+        credentials_path: Optional[str] = None,
+        location: Optional[str] = None,
+        **client_kwargs,
+    ):
+        """Initialize the BigQuery adapter.
+
+        Args:
+            transform_fn: Function to transform BigQuery rows to evaluation format
+            dataset_id: Google Cloud project ID (if None, uses default from environment)
+            credentials_path: Path to service account JSON file (if None, uses default auth)
+            location: Default location for BigQuery jobs
+            **client_kwargs: Additional arguments to pass to BigQuery client
+
+        Raises:
+            ImportError: If google-cloud-bigquery is not installed
+            DefaultCredentialsError: If authentication fails
+        """
+        if not BIGQUERY_AVAILABLE:
+            raise ImportError(
+                "Google Cloud BigQuery not installed. Install with: pip install 'eval-protocol[bigquery]'"
+            )
+
+        self.transform_fn = transform_fn
+        self.dataset_id = dataset_id
+        self.location = location
+
+        # Initialize BigQuery client
+        try:
+            client_args = {}
+            if dataset_id:
+                client_args["project"] = dataset_id
+            if credentials_path:
+                credentials = service_account.Credentials.from_service_account_file(credentials_path)
+                client_args["credentials"] = credentials
+            if location:
+                client_args["location"] = location
+
+            client_args.update(client_kwargs)
+            self.client = bigquery.Client(**client_args)
+
+        except DefaultCredentialsError as e:
+            logger.error("Failed to authenticate with BigQuery: %s", e)
+            raise
+        except Exception as e:
+            logger.error("Failed to initialize BigQuery client: %s", e)
+            raise
+
+    def get_evaluation_rows(
+        self,
+        query: str,
+        query_params: Optional[List[Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]]] = None,
+        limit: Optional[int] = None,
+        offset: int = 0,
+        model_name: str = "gpt-3.5-turbo",
+        temperature: float = 0.0,
+        max_tokens: Optional[int] = None,
+        **completion_params_kwargs,
+    ) -> Iterator[EvaluationRow]:
+        """Execute BigQuery query and convert results to EvaluationRow format.
+
+        Args:
+            query: SQL query to execute
+            query_params: Optional list of query parameters for parameterized queries
+            limit: Maximum number of rows to return (applied after BigQuery query)
+            offset: Number of rows to skip (applied after BigQuery query)
+            model_name: Model name for completion parameters
+            temperature: Temperature for completion parameters
+            max_tokens: Max tokens for completion parameters
+            **completion_params_kwargs: Additional completion parameters
+
+        Yields:
+            EvaluationRow: Converted evaluation rows
+
+        Raises:
+            NotFound: If the query references non-existent tables/datasets
+            Forbidden: If insufficient permissions
+        """
+        try:
+            # Configure query job
+            job_config = bigquery.QueryJobConfig()
+            if query_params:
+                job_config.query_parameters = query_params
+            if self.location:
+                job_config.location = self.location
+
+            query_job = self.client.query(query, job_config=job_config)
+
+            results = query_job.result()
+
+            completion_params: CompletionParams = {
+                "model": model_name,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                **completion_params_kwargs,
+            }
+
+            # Convert rows with offset/limit
+            row_count = 0
+            processed_count = 0
+
+            for raw_row in results:
+                # Apply offset
+                if row_count < offset:
+                    row_count += 1
+                    continue
+
+                # Apply limit
+                if limit is not None and processed_count >= limit:
+                    break
+
+                try:
+                    eval_row = self._convert_row_to_evaluation_row(raw_row, processed_count, completion_params)
+                    if eval_row:
+                        yield eval_row
+                        processed_count += 1
+
+                except (AttributeError, ValueError, KeyError) as e:
+                    logger.warning("Failed to convert row %d: %s", row_count, e)
+
+                row_count += 1
+
+        except (NotFound, Forbidden) as e:
+            logger.error("BigQuery access error: %s", e)
+            raise
+        except Exception as e:
+            logger.error("Error executing BigQuery query: %s", e)
+            raise
+
+    def _convert_row_to_evaluation_row(
+        self,
+        raw_row: Dict[str, Any],
+        row_index: int,
+        completion_params: CompletionParams,
+    ) -> EvaluationRow:
+        """Convert a single BigQuery row to EvaluationRow format.
+
+        Args:
+            raw_row: BigQuery row dictionary
+            row_index: Index of the row in the result set
+            completion_params: Completion parameters to use
+
+        Returns:
+            EvaluationRow object or None if conversion fails
+        """
+        # Apply user transformation
+        transformed = self.transform_fn(raw_row)
+
+        # Validate required fields
+        if "messages" not in transformed:
+            raise ValueError("Transform function must return 'messages' field")
+
+        # Convert message dictionaries to Message objects
+        messages = []
+        for msg_dict in transformed["messages"]:
+            if not isinstance(msg_dict, dict):
+                raise ValueError("Each message must be a dictionary")
+            if "role" not in msg_dict:
+                raise ValueError("Each message must have a 'role' field")
+
+            messages.append(
+                Message(
+                    role=msg_dict["role"],
+                    content=msg_dict.get("content"),
+                    name=msg_dict.get("name"),
+                    tool_call_id=msg_dict.get("tool_call_id"),
+                    tool_calls=msg_dict.get("tool_calls"),
+                    function_call=msg_dict.get("function_call"),
+                )
+            )
+
+        # Extract other fields
+        ground_truth = transformed.get("ground_truth")
+        tools = transformed.get("tools")
+        user_metadata = transformed.get("metadata", {})
+
+        # Create dataset info
+        dataset_info = {
+            "source": "bigquery",
+            "dataset_id": self.dataset_id or self.client.project,
+            "row_index": row_index,
+            "transform_function": (
+                self.transform_fn.__name__ if hasattr(self.transform_fn, "__name__") else "anonymous"
+            ),
+        }
+
+        # Add user metadata
+        dataset_info.update(user_metadata)
+
+        # Add original row data (with prefix to avoid conflicts)
+        for key, value in raw_row.items():
+            # Convert BigQuery types to JSON-serializable types
+            dataset_info[f"original_{key}"] = value
+
+        # Create input metadata (following HuggingFace pattern)
+        input_metadata = InputMetadata(
+            row_id=f"{self.dataset_id}_{row_index}",
+            completion_params=completion_params,
+            dataset_info=dataset_info,
+            session_data={
+                "dataset_source": "bigquery",
+            },
+        )
+
+        return EvaluationRow(
+            messages=messages,
+            tools=tools,
+            input_metadata=input_metadata,
+            ground_truth=str(ground_truth) if ground_truth is not None else None,
+        )
+
+
+def create_bigquery_adapter(
+    transform_fn: TransformFunction,
+    dataset_id: Optional[str] = None,
+    credentials_path: Optional[str] = None,
+    location: Optional[str] = None,
+    **client_kwargs,
+) -> BigQueryAdapter:
+    """Factory function to create a BigQuery adapter.
+
+    Args:
+        transform_fn: Function to transform BigQuery rows to evaluation format
+        dataset_id: Google Cloud project ID
+        credentials_path: Path to service account JSON file
+        location: Default location for BigQuery jobs
+        **client_kwargs: Additional arguments for BigQuery client
+
+    Returns:
+        BigQueryAdapter instance
+    """
+    return BigQueryAdapter(
+        transform_fn=transform_fn,
+        dataset_id=dataset_id,
+        credentials_path=credentials_path,
+        location=location,
+        **client_kwargs,
+    )