[Bug]: CI flake - v1/entrypoints/llm/test_struct_output_generate.py::test_structured_output - JSONDecodeError: Expecting value: line 1 column 1 (char 0) #16053
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main commit 51d7c6a
Seen in #15894
FAILED v1/entrypoints/llm/test_struct_output_generate.py::test_structured_output[Qwen/Qwen2.5-1.5B-Instruct-guidance:disable-any-whitespace-auto] - json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
[2025-04-03T16:08:35Z] _ test_structured_output[Qwen/Qwen2.5-1.5B-Instruct-guidance:disable-any-whitespace-auto] _
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f318d89eb40>
[2025-04-03T16:08:35Z] sample_json_schema = {'properties': {'age': {'type': 'integer'}, 'name': {'type': 'string'}, 'skills': {'items': {'type': 'string'}, 'type'...ition'], 'type': 'object'}, 'type': 'array'}}, 'required': ['name', 'age', 'skills', 'work_history'], 'type': 'object'}
[2025-04-03T16:08:35Z] unsupported_json_schema = {'properties': {'email': {'pattern': '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$', 'type': 'string'}, 'grade': ...[a-z]{1,10}$', 'type': 'string'}, 'type': 'array'}}, 'required': ['score', 'grade', 'email', 'tags'], 'type': 'object'}
[2025-04-03T16:08:35Z] sample_sql_ebnf = '\nroot ::= select_statement\nselect_statement ::= "SELECT" column "from" table "where" condition\ncolumn ::= "col_1" | "col_2"\ntable ::= "table_1" | "table_2"\ncondition ::= column "=" number\nnumber ::= "1" | "2"\n'
[2025-04-03T16:08:35Z] sample_sql_lark = '\nstart: select_statement\nselect_statement: "SELECT" column "from" table "where" condition\ncolumn: "col_1" | "col_2"\ntable: "table_1" | "table_2"\ncondition: column "=" number\nnumber: "1" | "2"\n'
[2025-04-03T16:08:35Z] sample_regex = '((25[0-5]|(2[0-4]|1\\d|[1-9]|)\\d)\\.){3}(25[0-5]|(2[0-4]|1\\d|[1-9]|)\\d)'
[2025-04-03T16:08:35Z] sample_guided_choice = ['Python', 'Java', 'JavaScript', 'C++', 'C#', 'PHP', ...]
[2025-04-03T16:08:35Z] guided_decoding_backend = 'guidance:disable-any-whitespace'
[2025-04-03T16:08:35Z] tokenizer_mode = 'auto', model_name = 'Qwen/Qwen2.5-1.5B-Instruct'
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] @pytest.mark.skip_global_cleanup
[2025-04-03T16:08:35Z] @pytest.mark.parametrize("model_name, guided_decoding_backend, tokenizer_mode",
[2025-04-03T16:08:35Z] PARAMS_MODELS_BACKENDS_TOKENIZER_MODE)
[2025-04-03T16:08:35Z] def test_structured_output(
[2025-04-03T16:08:35Z] monkeypatch: pytest.MonkeyPatch,
[2025-04-03T16:08:35Z] sample_json_schema: dict[str, Any],
[2025-04-03T16:08:35Z] unsupported_json_schema: dict[str, Any],
[2025-04-03T16:08:35Z] sample_sql_ebnf: str,
[2025-04-03T16:08:35Z] sample_sql_lark: str,
[2025-04-03T16:08:35Z] sample_regex: str,
[2025-04-03T16:08:35Z] sample_guided_choice: str,
[2025-04-03T16:08:35Z] guided_decoding_backend: str,
[2025-04-03T16:08:35Z] tokenizer_mode: str,
[2025-04-03T16:08:35Z] model_name: str,
[2025-04-03T16:08:35Z] ):
[2025-04-03T16:08:35Z] monkeypatch.setenv("VLLM_USE_V1", "1")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # Use a single LLM instance for several scenarios to
[2025-04-03T16:08:35Z] # speed up the test suite.
[2025-04-03T16:08:35Z] llm = LLM(model=model_name,
[2025-04-03T16:08:35Z] enforce_eager=True,
[2025-04-03T16:08:35Z] max_model_len=1024,
[2025-04-03T16:08:35Z] guided_decoding_backend=guided_decoding_backend,
[2025-04-03T16:08:35Z] tokenizer_mode=tokenizer_mode)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 1: Generate JSON output based on a provided schema
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=1.0,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(json=sample_json_schema))
[2025-04-03T16:08:35Z] outputs = llm.generate(prompts=[
[2025-04-03T16:08:35Z] f"Give an example JSON for an employee profile "
[2025-04-03T16:08:35Z] f"that fits this schema: {sample_json_schema}"
[2025-04-03T16:08:35Z] ] * 2,
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z] if 'disable-any-whitespace' in guided_decoding_backend:
[2025-04-03T16:08:35Z] assert "\n" not in generated_text
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z] output_json = json.loads(generated_text)
[2025-04-03T16:08:35Z] jsonschema.validate(instance=output_json, schema=sample_json_schema)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 2: Generate JSON object without a schema
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=1.0,
[2025-04-03T16:08:35Z] max_tokens=100,
[2025-04-03T16:08:35Z] n=2,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(json_object=True))
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts=("Generate a JSON object with curly braces for a person with "
[2025-04-03T16:08:35Z] "name and age fields for John Smith who is 31 years old."),
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] for i in range(2):
[2025-04-03T16:08:35Z] generated_text = output.outputs[i].text
[2025-04-03T16:08:35Z] print(generated_text)
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # Parse to verify it is valid JSON
[2025-04-03T16:08:35Z] parsed_json = json.loads(generated_text)
[2025-04-03T16:08:35Z] allowed_types: tuple[type, ...] = (dict, )
[2025-04-03T16:08:35Z] if guided_decoding_backend.startswith("xgrammar"):
[2025-04-03T16:08:35Z] # TODO - we are currently too permissive with xgrammar and
[2025-04-03T16:08:35Z] # allow # any valid json (typically comes back as a list or
[2025-04-03T16:08:35Z] # object). We can fix this by specifying a jsonschema of
[2025-04-03T16:08:35Z] # {"type": "object"}, # but we need this fix in a release
[2025-04-03T16:08:35Z] # first: https://github.com/mlc-ai/xgrammar/pull/264
[2025-04-03T16:08:35Z] allowed_types = (dict, list)
[2025-04-03T16:08:35Z] assert isinstance(parsed_json, allowed_types)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 3: test a jsonschema incompatible with xgrammar
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=1.0,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(json=unsupported_json_schema))
[2025-04-03T16:08:35Z] if guided_decoding_backend.startswith("xgrammar"):
[2025-04-03T16:08:35Z] with pytest.raises(ValueError,
[2025-04-03T16:08:35Z] match="The provided JSON schema contains features "
[2025-04-03T16:08:35Z] "not supported by xgrammar."):
[2025-04-03T16:08:35Z] llm.generate(prompts=[
[2025-04-03T16:08:35Z] f"Give an example JSON for an employee profile "
[2025-04-03T16:08:35Z] f"that fits this schema: {unsupported_json_schema}"
[2025-04-03T16:08:35Z] ] * 2,
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z] else:
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts=("Give an example JSON object for a grade "
[2025-04-03T16:08:35Z] "that fits this schema: "
[2025-04-03T16:08:35Z] f"{unsupported_json_schema}"),
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z] print(generated_text)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # Parse to verify it is valid JSON
[2025-04-03T16:08:35Z] parsed_json = json.loads(generated_text)
[2025-04-03T16:08:35Z] assert isinstance(parsed_json, dict)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 4: Generate SQL statement using EBNF grammar
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=0.8,
[2025-04-03T16:08:35Z] top_p=0.95,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(grammar=sample_sql_ebnf))
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts=("Generate a sql statement that selects col_1 from "
[2025-04-03T16:08:35Z] "table_1 where it is equal to 1"),
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True,
[2025-04-03T16:08:35Z] )
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # remove spaces for comparison b/c we removed them in the grammar
[2025-04-03T16:08:35Z] ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
[2025-04-03T16:08:35Z] " ", "")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert generated_text.strip() == ground_truth
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 5: Generate SQL statement using Lark grammar
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=0.8,
[2025-04-03T16:08:35Z] top_p=0.95,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(grammar=sample_sql_lark))
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts=("Generate a sql statement that selects col_1 from "
[2025-04-03T16:08:35Z] "table_1 where it is equal to 1"),
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True,
[2025-04-03T16:08:35Z] )
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # use Lark to parse the output, and make sure it's a valid parse tree
[2025-04-03T16:08:35Z] from lark import Lark
[2025-04-03T16:08:35Z] parser = Lark(sample_sql_lark)
[2025-04-03T16:08:35Z] parser.parse(generated_text)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] # remove spaces for comparison b/c we removed them in the grammar
[2025-04-03T16:08:35Z] ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
[2025-04-03T16:08:35Z] " ", "")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert generated_text.strip() == ground_truth
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 6: Test invalid grammar input
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=0.8,
[2025-04-03T16:08:35Z] top_p=0.95,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
[2025-04-03T16:08:35Z] with pytest.raises(ValueError, match="Failed to convert the grammar "):
[2025-04-03T16:08:35Z] llm.generate(
[2025-04-03T16:08:35Z] prompts=("Generate a sql statement that selects col_1 from "
[2025-04-03T16:08:35Z] "table_1 where it is equal to 1"),
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True,
[2025-04-03T16:08:35Z] )
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 7: Generate text based on a regex pattern
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=0.8,
[2025-04-03T16:08:35Z] top_p=0.95,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(regex=sample_regex))
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts=[
[2025-04-03T16:08:35Z] f"Give an example IPv4 address with this regex: {sample_regex}"
[2025-04-03T16:08:35Z] ] * 2,
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True,
[2025-04-03T16:08:35Z] )
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] print(generated_text)
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z] assert re.fullmatch(sample_regex, generated_text) is not None
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 8: Generate text based on a choices
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=0.8,
[2025-04-03T16:08:35Z] top_p=0.95,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts="The best language for type-safe systems programming is ",
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] print(generated_text)
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z] assert generated_text in sample_guided_choice
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] # Test 9: Generate structured output using a Pydantic model with an enum
[2025-04-03T16:08:35Z] #
[2025-04-03T16:08:35Z] json_schema = CarDescription.model_json_schema()
[2025-04-03T16:08:35Z] sampling_params = SamplingParams(
[2025-04-03T16:08:35Z] temperature=1.0,
[2025-04-03T16:08:35Z] max_tokens=1000,
[2025-04-03T16:08:35Z] guided_decoding=GuidedDecodingParams(json=json_schema))
[2025-04-03T16:08:35Z] outputs = llm.generate(
[2025-04-03T16:08:35Z] prompts="Generate a JSON with the brand, model and car_type of"
[2025-04-03T16:08:35Z] "the most iconic car from the 90's",
[2025-04-03T16:08:35Z] sampling_params=sampling_params,
[2025-04-03T16:08:35Z] use_tqdm=True)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] assert outputs is not None
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] for output in outputs:
[2025-04-03T16:08:35Z] assert output is not None
[2025-04-03T16:08:35Z] assert isinstance(output, RequestOutput)
[2025-04-03T16:08:35Z] prompt = output.prompt
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] generated_text = output.outputs[0].text
[2025-04-03T16:08:35Z] assert generated_text is not None
[2025-04-03T16:08:35Z] print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
[2025-04-03T16:08:35Z] > output_json = json.loads(generated_text)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] v1/entrypoints/llm/test_struct_output_generate.py:332:
[2025-04-03T16:08:35Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2025-04-03T16:08:35Z] /usr/lib/python3.12/json/__init__.py:346: in loads
[2025-04-03T16:08:35Z] return _default_decoder.decode(s)
[2025-04-03T16:08:35Z] /usr/lib/python3.12/json/decoder.py:338: in decode
[2025-04-03T16:08:35Z] obj, end = self.raw_decode(s, idx=_w(s, 0).end())
[2025-04-03T16:08:35Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] self = <json.decoder.JSONDecoder object at 0x7f32ee3035c0>, s = '', idx = 0
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] def raw_decode(self, s, idx=0):
[2025-04-03T16:08:35Z] """Decode a JSON document from ``s`` (a ``str`` beginning with
[2025-04-03T16:08:35Z] a JSON document) and return a 2-tuple of the Python
[2025-04-03T16:08:35Z] representation and the index in ``s`` where the document ended.
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] This can be used to decode a JSON document from a string that may
[2025-04-03T16:08:35Z] have extraneous data at the end.
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] """
[2025-04-03T16:08:35Z] try:
[2025-04-03T16:08:35Z] obj, end = self.scan_once(s, idx)
[2025-04-03T16:08:35Z] except StopIteration as err:
[2025-04-03T16:08:35Z] > raise JSONDecodeError("Expecting value", s, err.value) from None
[2025-04-03T16:08:35Z] E json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
[2025-04-03T16:08:35Z]
[2025-04-03T16:08:35Z] /usr/lib/python3.12/json/decoder.py:356: JSONDecodeError
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