mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-14 23:21:35 +00:00
Fix lint
This commit is contained in:
Sameer Kankute
parent
cc1100b4a9
commit
493405c129
2 changed files with 60 additions and 59 deletions
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@ -199,7 +199,7 @@ class VertexAIFilesHandler(GCSBucketBase):
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# Apply transformation to convert Vertex AI batch outputs to OpenAI format
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config = VertexAIFilesConfig()
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# Create a logging object for transformation
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logging_obj = Logging(
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model="",
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@ -210,11 +210,9 @@ class VertexAIFilesHandler(GCSBucketBase):
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litellm_call_id="",
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function_id="",
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)
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return config.transform_file_content_response(
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raw_response=mock_response,
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logging_obj=logging_obj,
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litellm_params={}
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raw_response=mock_response, logging_obj=logging_obj, litellm_params={}
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)
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def file_content(
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@ -46,15 +46,15 @@ _GCP_LABEL_VALUE_MAX_LEN = 63
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def _sanitize_gcp_label_value(value: str) -> str:
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"""
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Sanitize a string to meet GCP label value constraints.
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GCP label values must:
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- Be lowercase
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- Contain only letters, numbers, underscores, and hyphens
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- Be max 63 characters
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Args:
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value: The string to sanitize
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Returns:
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A sanitized string that meets GCP label constraints
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"""
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@ -293,14 +293,16 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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litellm_params={},
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cached_content=None,
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)
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# Add custom_id as a label for correlation in batch outputs
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custom_id = _openai_jsonl_content.get("custom_id")
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if custom_id:
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if "labels" not in vertex_request_body:
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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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_set_litellm_batch_custom_id_labels(
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vertex_request_body["labels"], custom_id
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)
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vertex_jsonl_content.append({"request": vertex_request_body})
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return vertex_jsonl_content
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@ -509,10 +511,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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) -> HttpxBinaryResponseContent:
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"""
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Transform file content response, converting Vertex AI batch output to OpenAI format if applicable.
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This method automatically detects and transforms Vertex AI batch prediction outputs
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(predictions.jsonl files) into OpenAI-compatible batch response format.
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If the file is not a batch output or transformation fails, the original content
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is returned as-is to maintain backward compatibility.
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"""
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@ -526,11 +528,11 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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if transformed_content != content:
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# Create a new response with transformed content and updated Content-Length
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import httpx
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# Update headers with correct Content-Length
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new_headers = dict(raw_response.headers)
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new_headers["content-length"] = str(len(transformed_content))
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mock_response = httpx.Response(
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status_code=raw_response.status_code,
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content=transformed_content,
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@ -541,16 +543,14 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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except Exception:
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# If transformation fails, return as-is
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pass
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return HttpxBinaryResponseContent(response=raw_response)
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def _try_transform_vertex_batch_output_to_openai(
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self, content: bytes
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) -> bytes:
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def _try_transform_vertex_batch_output_to_openai(self, content: bytes) -> bytes:
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"""
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Try to transform Vertex AI batch output to OpenAI format.
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If conversion fails at any point, return the original content as-is.
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Vertex AI batch output format (predictions.jsonl):
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{
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"request": {"contents": [...], "labels": {"litellm_custom_id": "request-1", "litellm_custom_id_raw": "..."}},
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@ -558,7 +558,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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"response": {"candidates": [...], "modelVersion": "gemini-2.5-flash", ...},
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"processed_time": "2026-04-13T10:18:18.102004+00:00"
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}
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OpenAI batch output format:
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{
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"id": "batch_req_...",
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@ -574,15 +574,15 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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try:
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# Decode content
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content_str = content.decode("utf-8")
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# Check if it's JSONL (multiple lines)
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lines = content_str.strip().split("\n")
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if not lines:
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return content
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# Try to parse the first line to see if it's Vertex AI batch output
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first_line = json.loads(lines[0])
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# Check if it has Vertex AI batch output structure with discriminating fields
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# Must have request, response, and processed_time
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# Plus either candidates (success) or status (error)
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@ -592,37 +592,38 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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and "processed_time" in first_line
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)
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has_success_or_error = (
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"candidates" in first_line.get("response", {})
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or "status" in first_line
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"candidates" in first_line.get("response", {}) or "status" in first_line
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)
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if not (has_base_structure and has_success_or_error):
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# Not a Vertex AI batch output, return as-is
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return content
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# Transform all lines
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transformed_lines = []
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for line in lines:
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if not line.strip():
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continue
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try:
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vertex_output = json.loads(line)
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openai_output = self._transform_single_vertex_batch_output_to_openai(
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vertex_output
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openai_output = (
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self._transform_single_vertex_batch_output_to_openai(
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vertex_output
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)
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)
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transformed_lines.append(json.dumps(openai_output))
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except Exception:
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# If any line fails, return original content
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return content
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# Return transformed content
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return "\n".join(transformed_lines).encode("utf-8")
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except Exception:
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# If anything fails, return original content
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return content
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def _transform_single_vertex_batch_output_to_openai(
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self, vertex_output: Dict[str, Any]
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) -> Dict[str, Any]:
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@ -634,11 +635,11 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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request_data = vertex_output.get("request", {})
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labels = request_data.get("labels", {}) or {}
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custom_id = _get_litellm_batch_custom_id_from_labels(labels)
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# Check if there's an error
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status = vertex_output.get("status", "")
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has_error = bool(status)
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if has_error:
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# Return error response in OpenAI format
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return {
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@ -651,25 +652,25 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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"error": {
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"message": status,
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"type": "vertex_ai_error",
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"code": "vertex_ai_error"
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"code": "vertex_ai_error",
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}
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}
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},
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},
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"error": {
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"message": status,
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"type": "vertex_ai_error",
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"code": "vertex_ai_error"
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}
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"code": "vertex_ai_error",
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},
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}
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# Transform successful response using existing transformation
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vertex_response = vertex_output.get("response", {})
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# Extract model from response
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model = vertex_response.get("modelVersion", "gemini-1.5-flash-001")
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if "@" in model:
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model = model.split("@")[0]
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# Create logging object for transformation
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logging_obj = Logging(
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model=model,
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@ -681,7 +682,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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function_id="",
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)
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logging_obj.optional_params = {}
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# Create mock httpx response for transformation
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mock_httpx_response = httpx.Response(
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status_code=200,
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@ -689,12 +690,12 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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headers={"content-type": "application/json"},
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request=httpx.Request(method="POST", url="https://example.com"),
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)
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try:
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# Use existing VertexGeminiConfig transformation
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vertex_gemini_config = VertexGeminiConfig()
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model_response = ModelResponse()
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transformed_response = vertex_gemini_config._transform_google_generate_content_to_openai_model_response(
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completion_response=vertex_response,
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model_response=model_response,
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@ -702,10 +703,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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logging_obj=logging_obj,
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raw_response=mock_httpx_response,
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)
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# Convert ModelResponse to dict
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response_dict = transformed_response.model_dump()
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# Return in OpenAI batch format
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return {
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"id": f"batch_req_{uuid.uuid4()}",
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@ -713,11 +714,11 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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"response": {
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"status_code": 200,
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"request_id": response_dict.get("id", ""),
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"body": response_dict
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"body": response_dict,
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},
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"error": None
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"error": None,
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}
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except Exception as e:
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# If transformation fails, return error
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return {
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@ -730,15 +731,15 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
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"error": {
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"message": f"Failed to transform response: {str(e)}",
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"type": "transformation_error",
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"code": "transformation_error"
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"code": "transformation_error",
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}
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}
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},
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},
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"error": {
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"message": f"Failed to transform response: {str(e)}",
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"type": "transformation_error",
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"code": "transformation_error"
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}
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"code": "transformation_error",
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},
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}
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@ -799,14 +800,16 @@ class VertexAIJsonlFilesTransformation(VertexGeminiConfig):
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litellm_params={},
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cached_content=None,
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)
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# Add custom_id as a label for correlation in batch outputs
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custom_id = _openai_jsonl_content.get("custom_id")
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if custom_id:
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if "labels" not in vertex_request_body:
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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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_set_litellm_batch_custom_id_labels(
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vertex_request_body["labels"], custom_id
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)
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vertex_jsonl_content.append({"request": vertex_request_body})
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return vertex_jsonl_content
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