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fix(vertex_ai): inline json_schema $defs in Gemini batch rows and refuse rows BigQuery cannot load
Vertex AI loads a batch input file into BigQuery, which rejects '$' in column names, so one row whose response_format json_schema carried $defs or $ref failed the whole batch job. Gemini batch rows now carry the schema with every local $ref inlined, and a row that still has a '$' key (a recursive schema, tool-call arguments such as {"$gt": 5}) answers 400 at upload naming its line.
This commit is contained in:
mateo-berri
parent
a7b66dd8b9
commit
38c6327a12
3 changed files with 428 additions and 3 deletions
138
litellm/llms/vertex_ai/files/bigquery_loadable.py
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138
litellm/llms/vertex_ai/files/bigquery_loadable.py
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@ -0,0 +1,138 @@
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"""
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Vertex AI loads a batch input file into BigQuery before it runs the rows, so every object key in a row becomes a
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column name, and BigQuery rejects `$` in column names. A Gemini batch row's response schema therefore has every local
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`$ref` inlined, and a Gemini row that still carries a key starting with `$` is refused before upload
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"""
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import json
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from collections.abc import Iterator, Mapping
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from functools import partial, reduce
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from itertools import chain
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from typing import Final, TypeAlias
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from urllib.parse import unquote
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from pydantic import JsonValue, TypeAdapter
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from litellm.litellm_core_utils.core_helpers import as_str_mapping, iter_items
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from litellm.litellm_core_utils.prompt_templates.common_utils import DEFS_MAX_INLINED_BYTES
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_Pointer: TypeAlias = tuple[str, ...]
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_RefChain: TypeAlias = tuple[_Pointer, ...]
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_JSON_SCHEMA: Final = TypeAdapter(dict[str, JsonValue])
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_DROPPED_KEYWORDS: Final = frozenset(
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{"$ref", "$defs", "definitions", "$schema", "$id", "$comment", "$anchor", "$dynamicAnchor", "$vocabulary"}
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)
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_NAMED_SCHEMA_MAPS: Final = frozenset(
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{"properties", "patternProperties", "dependentSchemas", "dependentRequired", "dependencies"}
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)
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_DATA_KEYWORDS: Final = frozenset({"enum", "const", "default", "examples"})
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def _pointer(ref: JsonValue) -> _Pointer:
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fragment: Final = unquote(ref[1:]) if isinstance(ref, str) and ref.startswith("#") else None
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if fragment is None or (fragment and not fragment.startswith("/")):
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raise ValueError(
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f"response_format json_schema has $ref {ref!r}, which is not a JSON pointer into the schema itself, "
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"and a Vertex AI batch row can only carry a $ref it can inline"
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)
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return tuple(segment.replace("~1", "/").replace("~0", "~") for segment in fragment.split("/")[1:])
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def _child(ref: JsonValue, node: JsonValue, key: str) -> JsonValue:
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if isinstance(node, dict) and key in node:
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return node[key]
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if isinstance(node, list) and key.isdecimal() and int(key) < len(node):
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return node[int(key)]
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raise ValueError(f"response_format json_schema has $ref {ref!r}, which points at nothing in the schema")
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class _SchemaInliner:
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__slots__ = ("_budget_bytes", "_root")
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def __init__(self, root: JsonValue) -> None:
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self._root: Final = root
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self._budget_bytes: int = DEFS_MAX_INLINED_BYTES
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def inlined(self, node: JsonValue, ref_chain: _RefChain) -> JsonValue:
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if isinstance(node, list):
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return [self.inlined(item, ref_chain) for item in node]
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if isinstance(node, dict):
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return dict(self._inlined_items(node, ref_chain))
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return node
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def _inlined_items(self, node: Mapping[str, JsonValue], ref_chain: _RefChain) -> Iterator[tuple[str, JsonValue]]:
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target_items: Final = self._ref_target_items(node["$ref"], ref_chain) if "$ref" in node else iter(())
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return chain(
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target_items,
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(
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(key, self._inlined_keyword(key, value, ref_chain))
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for key, value in node.items()
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if key not in _DROPPED_KEYWORDS
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),
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)
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def _ref_target_items(self, ref: JsonValue, ref_chain: _RefChain) -> Iterator[tuple[str, JsonValue]]:
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pointer: Final = _pointer(ref)
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if pointer in ref_chain:
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raise ValueError(
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f"response_format json_schema is recursive through $ref {ref!r}, and a Vertex AI batch row must "
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"inline every $ref, so send this row as a regular request instead"
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)
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target: Final = reduce(partial(_child, ref), pointer, self._root)
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if not isinstance(target, dict):
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raise TypeError(
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f"response_format json_schema has $ref {ref!r}, which points at something other than a schema"
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)
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self._spend(target)
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return self._inlined_items(target, (*ref_chain, pointer))
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def _spend(self, target: Mapping[str, JsonValue]) -> None:
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self._budget_bytes -= len(json.dumps(target, separators=(",", ":")))
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if self._budget_bytes < 0:
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raise ValueError(
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f"response_format json_schema grows past {DEFS_MAX_INLINED_BYTES:,} bytes once its $ref targets "
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"are inlined, which a Vertex AI batch row needs"
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)
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def _inlined_keyword(self, keyword: str, value: JsonValue, ref_chain: _RefChain) -> JsonValue:
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if keyword in _DATA_KEYWORDS:
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return value
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if keyword in _NAMED_SCHEMA_MAPS and isinstance(value, dict):
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return {name: self.inlined(schema, ref_chain) for name, schema in value.items()}
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return self.inlined(value, ref_chain)
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def with_batch_loadable_response_json_schema(optional_params: Mapping[str, object]) -> Mapping[str, object]:
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schema: Final = optional_params.get("response_json_schema")
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if not isinstance(schema, dict):
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return optional_params
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root: Final = _JSON_SCHEMA.validate_python(schema)
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return {**optional_params, "response_json_schema": _SchemaInliner(root).inlined(root, ())}
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def _dollar_key_paths(value: object, path: str) -> Iterator[str]:
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mapping: Final = as_str_mapping(value)
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if mapping is not None:
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return chain.from_iterable(
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_key_paths(f"{path}.{key}" if path else key, key, child) for key, child in mapping.items()
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)
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if type(value) in (list, tuple):
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return chain.from_iterable(
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_dollar_key_paths(item, f"{path}[{index}]") for index, item in enumerate(iter_items(value))
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)
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return iter(())
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def _key_paths(path: str, key: str, child: object) -> Iterator[str]:
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return iter((path,)) if key.startswith("$") else _dollar_key_paths(child, path)
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def bigquery_loadable_row(row: Mapping[str, object]) -> Mapping[str, object]:
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dollar_key_path: Final = next(_dollar_key_paths(row, ""), None)
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if dollar_key_path is not None:
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raise ValueError(
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f"the row has the key {dollar_key_path}, but Vertex AI batch input cannot carry an object key starting "
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"with '$', so send this row as a regular request instead"
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)
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return row
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@ -61,6 +61,10 @@ from litellm.llms.vertex_ai.common_utils import (
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convert_vertex_datetime_to_openai_datetime,
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get_vertex_ai_fine_tuned_endpoint_id,
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)
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from litellm.llms.vertex_ai.files.bigquery_loadable import (
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bigquery_loadable_row,
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with_batch_loadable_response_json_schema,
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)
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from litellm.llms.vertex_ai.gemini.transformation import transform_request_body
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from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
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VertexGeminiConfig,
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@ -877,7 +881,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows(
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vertex_request_body: Final = transform_request_body(
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messages=chat_messages,
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model=chat_request_body.get("model", ""),
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optional_params=map_openai_to_vertex_params(chat_request_body),
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optional_params=dict(with_batch_loadable_response_json_schema(map_openai_to_vertex_params(chat_request_body))),
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custom_llm_provider="vertex_ai",
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litellm_params={},
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cached_content=None,
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@ -889,7 +893,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows(
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vertex_request_body["labels"] = {}
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_set_litellm_batch_custom_id_labels(vertex_request_body["labels"], custom_id)
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return ({"request": vertex_request_body},)
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return (bigquery_loadable_row({"request": vertex_request_body}),)
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def _decode_batch_input_line(lineno: int, raw: str | bytes | bytearray) -> str:
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@ -12,10 +12,11 @@ from urllib.parse import parse_qs, urlparse
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import httpx
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import pytest
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from pydantic import TypeAdapter
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from pydantic import JsonValue, TypeAdapter
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from unittest.mock import MagicMock
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import litellm
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from litellm.litellm_core_utils.prompt_templates.common_utils import DEFS_MAX_INLINED_BYTES
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from litellm.llms.base_llm.files.transformation import BaseFileUploadStream
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from litellm.llms.vertex_ai.files.transformation import (
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VertexAIFilesConfig,
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@ -23,6 +24,7 @@ from litellm.llms.vertex_ai.files.transformation import (
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_openai_batch_jsonl_entry_to_vertex_rows,
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_sanitize_gcp_label_value,
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)
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from litellm.llms.vertex_ai.common_utils import VertexAIError
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from litellm.types.llms.openai import OpenAIFileObject, HttpxBinaryResponseContent
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from openai.types.file_deleted import FileDeleted
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@ -2474,3 +2476,284 @@ def test_batch_row_for_an_unflagged_gemini_model_keeps_the_knobs(monkeypatch: py
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monkeypatch.setattr(litellm, "drop_params", False)
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(row,) = _wrap_entries([_chat_entry("gemini-2.5-flash", _SAMPLING_KNOBS)])
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assert _generation_config(row) == {"max_output_tokens": 5, **_SAMPLING_KNOBS}
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_GEMINI_DEPLOYMENT: Final = MappingProxyType({"gcs_bucket_name": "my-bucket", "model": "vertex_ai/gemini-3.8-flash"})
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_ADDRESS: Final = MappingProxyType(
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{
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"properties": {"city": {"title": "City", "type": "string"}, "country": {"title": "Country", "type": "string"}},
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"required": ["city", "country"],
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"title": "Address",
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"type": "object",
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}
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)
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_FLAT_SCHEMA: Final = MappingProxyType(
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{
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"type": "object",
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"properties": {
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"answer": {"type": "string", "enum": ["yes", "no"], "examples": [{"definitions": "kept as data"}]},
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"definitions": {"type": "string"},
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},
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"required": ["answer", "definitions"],
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"additionalProperties": False,
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}
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)
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def _json_schema_entry(schema: Mapping[str, object]) -> Mapping[str, object]:
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return dict(
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_chat_entry(
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"gemini-3.8-flash",
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{"response_format": {"type": "json_schema", "json_schema": {"name": "answer", "schema": dict(schema)}}},
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)
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)
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def _uploaded_response_schema(config: VertexAIFilesConfig, schema: Mapping[str, object]) -> JsonValue:
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(row,) = _uploaded_rows(config, _GEMINI_DEPLOYMENT, [_json_schema_entry(schema)])
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return TypeAdapter(dict[str, JsonValue]).validate_python(_generation_config(row))["response_json_schema"]
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def test_batch_row_inlines_a_nested_pydantic_schema(config):
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person: Final = {
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"$defs": {"Address": dict(_ADDRESS)},
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"properties": {
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"name": {"title": "Name", "type": "string"},
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"home": {"$ref": "#/$defs/Address"},
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"work": {"$ref": "#/$defs/Address"},
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},
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"required": ["name", "home", "work"],
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"title": "Person",
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"type": "object",
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}
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assert _uploaded_response_schema(config, person) == {
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"properties": {"name": {"title": "Name", "type": "string"}, "home": dict(_ADDRESS), "work": dict(_ADDRESS)},
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"required": ["name", "home", "work"],
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"title": "Person",
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"type": "object",
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}
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@pytest.mark.parametrize(
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("defs", "ref"),
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[
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({"$defs": {"Address": dict(_ADDRESS)}}, "#/%24defs/Address"),
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({"definitions": {"Address": dict(_ADDRESS)}}, "#/definitions/Address"),
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({"$defs": {"a/b~c": dict(_ADDRESS)}}, "#/$defs/a~1b~0c"),
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({"$defs": {"Either": {"anyOf": [{"type": "string"}, dict(_ADDRESS)]}}}, "#/$defs/Either/anyOf/1"),
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({"$defs": {"Home": {"$ref": "#/$defs/Address"}, "Address": dict(_ADDRESS)}}, "#/$defs/Home"),
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],
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)
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def test_batch_row_resolves_every_spelling_of_a_local_ref(config, defs, ref):
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schema: Final = {**defs, "type": "object", "properties": {"home": {"$ref": ref}}}
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assert _uploaded_response_schema(config, schema) == {"type": "object", "properties": {"home": dict(_ADDRESS)}}
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def test_batch_row_keeps_keys_beside_a_ref_over_the_targets_own(config):
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schema: Final = {
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"$defs": {"Address": {**_ADDRESS, "description": "Any postal address"}},
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"type": "object",
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"properties": {"home": {"$ref": "#/$defs/Address", "description": "Where the person lives"}},
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}
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assert _uploaded_response_schema(config, schema) == {
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"type": "object",
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"properties": {"home": {**_ADDRESS, "description": "Where the person lives"}},
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}
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def test_batch_row_passes_a_schema_without_dollar_keys_through_unchanged(config):
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assert _uploaded_response_schema(config, _FLAT_SCHEMA) == dict(_FLAT_SCHEMA)
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def _object_with(home: Mapping[str, object], **keywords: object) -> Mapping[str, object]:
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return {"type": "object", "properties": {"home": dict(home)}, "required": ["home"], **keywords}
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@pytest.mark.parametrize(
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("schema", "reason"),
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[
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pytest.param(
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{
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"$defs": {
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"TreeNode": {
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"type": "object",
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"properties": {"children": {"type": "array", "items": {"$ref": "#/$defs/TreeNode"}}},
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}
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},
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"$ref": "#/$defs/TreeNode",
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},
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"is recursive through $ref '#/$defs/TreeNode', and a Vertex AI batch row must inline every $ref, "
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"so send this row as a regular request instead",
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id="recursive",
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),
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pytest.param(
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{
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"$defs": {"TreeNode": {"type": "object", "properties": {"children": {"items": {"$ref": "#"}}}}},
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"$ref": "#/$defs/TreeNode",
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},
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"is recursive through $ref '#/$defs/TreeNode'",
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id="recursive-through-root",
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),
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pytest.param(
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_object_with({"$ref": "https://example.com/address.json"}),
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"$ref 'https://example.com/address.json', which is not a JSON pointer into the schema itself",
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id="remote",
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),
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pytest.param(
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_object_with({"$ref": "address.json#/Address"}),
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"$ref 'address.json#/Address', which is not a JSON pointer into the schema itself",
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id="other-file",
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),
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pytest.param(
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_object_with({"$ref": "#Address"}, **{"$defs": {"Address": {"$anchor": "Address"}}}),
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"$ref '#Address', which is not a JSON pointer into the schema itself",
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id="anchor",
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),
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pytest.param(
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_object_with({"$ref": "#/$defs/Missing"}, **{"$defs": {"Address": dict(_ADDRESS)}}),
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"$ref '#/$defs/Missing', which points at nothing in the schema",
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id="missing-def",
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),
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pytest.param(
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_object_with({"$ref": "#/$defs/Either/anyOf/2"}, **{"$defs": {"Either": {"anyOf": [{}, {}]}}}),
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"$ref '#/$defs/Either/anyOf/2', which points at nothing in the schema",
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id="index-past-end",
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),
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pytest.param(
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_object_with({"$ref": "#/$defs/Either/anyOf/first"}, **{"$defs": {"Either": {"anyOf": [{}, {}]}}}),
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"$ref '#/$defs/Either/anyOf/first', which points at nothing in the schema",
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id="index-not-a-number",
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),
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pytest.param(
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_object_with({"$ref": "#/required"}),
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"$ref '#/required', which points at something other than a schema",
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id="not-a-schema",
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),
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],
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)
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def test_batch_upload_refuses_a_schema_vertex_cannot_load_naming_its_line(config, schema, reason):
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with pytest.raises(VertexAIError) as exc_info:
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_uploaded_rows(config, _GEMINI_DEPLOYMENT, [_json_schema_entry(_FLAT_SCHEMA), _json_schema_entry(schema)])
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assert exc_info.value.status_code == 400
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assert exc_info.value.message.startswith("Batch input file line 2 could not be converted to a Vertex AI request: ")
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assert reason in exc_info.value.message
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def test_batch_upload_refuses_a_schema_that_inlines_past_the_byte_budget(config):
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doublings: Final = 6
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leaf: Final = {"type": "string", "description": "x" * (DEFS_MAX_INLINED_BYTES * 2 >> doublings)}
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levels: Final = {
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f"L{level}": {
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"type": "object",
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"properties": {"a": {"$ref": f"#/$defs/L{level - 1}"}, "b": {"$ref": f"#/$defs/L{level - 1}"}},
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}
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for level in range(1, doublings + 1)
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}
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bomb: Final = {"$defs": {"L0": leaf, **levels}, "$ref": f"#/$defs/L{doublings}"}
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with pytest.raises(VertexAIError, match=f"grows past {DEFS_MAX_INLINED_BYTES:,} bytes") as exc_info:
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_uploaded_rows(config, _GEMINI_DEPLOYMENT, [_json_schema_entry(bomb)])
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assert exc_info.value.status_code == 400
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|
||||
def _chat_entry_with(model: str, body: Mapping[str, object]) -> Mapping[str, object]:
|
||||
return {**_chat_entry(model, {}), "body": {"model": model, "max_tokens": 5, **body}}
|
||||
|
||||
|
||||
_FIND_TOOL: Final = MappingProxyType(
|
||||
{"type": "function", "function": {"name": "find", "parameters": {"type": "object", "properties": {}}}}
|
||||
)
|
||||
_GREATER_THAN_FIVE_CALL: Final = MappingProxyType(
|
||||
{
|
||||
"tools": [dict(_FIND_TOOL)],
|
||||
"messages": [
|
||||
{"role": "user", "content": "find the cheap ones"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{"id": "call-1", "type": "function", "function": {"name": "find", "arguments": '{"$gt": 5}'}}
|
||||
],
|
||||
},
|
||||
{"role": "tool", "tool_call_id": "call-1", "content": "[]"},
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("deployment", "body", "path"),
|
||||
[
|
||||
pytest.param(
|
||||
_GEMINI_DEPLOYMENT,
|
||||
{
|
||||
"messages": [{"role": "user", "content": "quote it"}],
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "quote",
|
||||
"parameters": {"type": "object", "properties": {"$price": {"type": "number"}}},
|
||||
},
|
||||
}
|
||||
],
|
||||
},
|
||||
"request.tools[0].function_declarations[0].parameters.properties.$price",
|
||||
id="tool-parameter-name",
|
||||
),
|
||||
pytest.param(
|
||||
_GEMINI_DEPLOYMENT,
|
||||
_GREATER_THAN_FIVE_CALL,
|
||||
"request.contents[1].parts[0].function_call.args.$gt",
|
||||
id="tool-call-arguments",
|
||||
),
|
||||
pytest.param(
|
||||
_GEMINI_DEPLOYMENT,
|
||||
{
|
||||
"messages": [{"role": "user", "content": "hi"}],
|
||||
"response_format": {
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": "answer",
|
||||
"schema": _object_with({"type": "object", "enum": [{"kind": {"$tag": "home"}}]}),
|
||||
},
|
||||
},
|
||||
},
|
||||
"request.generationConfig.response_json_schema.properties.home.enum[0].kind.$tag",
|
||||
id="schema-enum-value",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_batch_upload_refuses_a_row_with_a_dollar_key_naming_its_line_and_path(config, deployment, body, path):
|
||||
model: Final = str(deployment["model"]).removeprefix("vertex_ai/")
|
||||
with pytest.raises(VertexAIError) as exc_info:
|
||||
_uploaded_rows(config, deployment, [dict(_chat_entry(model, {})), _chat_entry_with(model, body)])
|
||||
|
||||
assert exc_info.value.status_code == 400
|
||||
assert exc_info.value.message == (
|
||||
"Batch input file line 2 could not be converted to a Vertex AI request: "
|
||||
f"the row has the key {path}, but Vertex AI batch input cannot carry an object key starting with '$', "
|
||||
"so send this row as a regular request instead"
|
||||
)
|
||||
|
||||
|
||||
def test_batch_upload_keeps_a_claude_tool_schema_with_defs_unchanged(config):
|
||||
parameters: Final = {
|
||||
"$defs": {"Address": dict(_ADDRESS)},
|
||||
"type": "object",
|
||||
"properties": {"home": {"$ref": "#/$defs/Address"}},
|
||||
}
|
||||
body: Final = {
|
||||
"messages": [{"role": "user", "content": "where do they live"}],
|
||||
"tools": [{"type": "function", "function": {"name": "locate", "parameters": parameters}}],
|
||||
}
|
||||
|
||||
(row,) = _uploaded_rows(config, CLAUDE_DEPLOYMENT, [_chat_entry_with("claude-sonnet-4-6", body)])
|
||||
|
||||
assert row["request"]["tools"][0]["input_schema"] == parameters
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue