> ## Documentation Index > Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt > Use this file to discover all available pages before exploring further. # weave > Python SDK reference for weave export const SourceLink = ({url}) => Source ; # API Overview *** ## class `Agent` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `model_name`: `` * `temperature`: `` * `system_message`: `` * `tools`: `list[typing.Any]` ### method `step` ```python theme={null} step(state: AgentState) → AgentState ``` Run a step of the agent. **Args:** * `state`: The current state of the environment. * `action`: The action to take. **Returns:** The new state of the environment. *** ## class `AgentState` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `history`: `list[typing.Any]` *** ## class `AnnotationSpec` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `field_schema`: `dict[str, typing.Any]` * `unique_among_creators`: `` * `op_scope`: `list[str] | None` ### classmethod `preprocess_field_schema` ```python theme={null} preprocess_field_schema(data: dict[str, Any]) → dict[str, Any] ``` *** ### classmethod `validate_field_schema` ```python theme={null} validate_field_schema(schema: dict[str, Any]) → dict[str, Any] ``` *** ### method `value_is_valid` ```python theme={null} value_is_valid(payload: Any) → bool ``` Validates a payload against this annotation spec's schema. **Args:** * `payload`: The data to validate against the schema **Returns:** * `bool`: True if validation succeeds, False otherwise *** ## class `Audio` A class representing audio data in a supported format (wav or mp3). This class handles audio data storage and provides methods for loading from different sources and exporting to files. **Attributes:** * `format`: The audio format (currently supports 'wav' or 'mp3') * `data`: The raw audio data as bytes **Args:** * `data`: The audio data (bytes or base64 encoded string) * `format`: The audio format ('wav' or 'mp3') * `validate_base64`: Whether to attempt base64 decoding of the input data **Raises:** * `ValueError`: If audio data is empty or format is not supported ### method `__init__` ```python theme={null} __init__( data: 'bytes', format: 'SUPPORTED_FORMATS_TYPE', validate_base64: 'bool' = True ) → None ``` *** ### method `export` ```python theme={null} export(path: 'str | bytes | Path | PathLike') → None ``` Export audio data to a file. **Args:** *** ### classmethod `from_data` ```python theme={null} from_data(data: 'str | bytes', format: 'str') → Self ``` Create an Audio object from raw data and specified format. * `path`: Path where the audio file should be written **Args:** * `data`: Audio data as bytes or base64 encoded string * `format`: Audio format ('wav' or 'mp3') **Returns:** * `Audio`: A new Audio instance **Raises:** * `ValueError`: If format is not supported *** ### classmethod `from_path` ```python theme={null} from_path(path: 'str | bytes | Path | PathLike') → Self ``` Create an Audio object from a file path. **Args:** * `path`: Path to an audio file (must have .wav or .mp3 extension) **Returns:** * `Audio`: A new Audio instance loaded from the file **Raises:** * `ValueError`: If file doesn't exist or has unsupported extension *** ## class `Content` A class to represent content from various sources, resolving them to a unified byte-oriented representation with associated metadata. This class must be instantiated using one of its classmethods: * from\_path() * from\_bytes() * from\_text() * from\_url() * from\_base64() * from\_data\_url() ### method `__init__` ```python theme={null} __init__(*args: 'Any', **kwargs: 'Any') → None ``` Direct initialization is disabled. Please use a classmethod like `Content.from_path()` to create an instance. **Pydantic Fields:** * `data`: `` * `size`: `` * `mimetype`: `` * `digest`: `` * `filename`: `` * `content_type`: `typing.Literal['bytes', 'text', 'base64', 'file', 'url', 'data_url', 'data_url:base64', 'data_url:encoding', 'data_url:encoding:base64']` * `input_type`: `` * `encoding`: `` * `metadata`: `dict[str, typing.Any] | None` * `extension`: `str | None` *** #### property art #### property ref *** ### method `as_string` ```python theme={null} as_string() → str ``` Display the data as a string. Bytes are decoded using the `encoding` attribute If base64, the data will be re-encoded to base64 bytes then decoded to an ASCII string **Returns:** str. *** ### classmethod `from_base64` ```python theme={null} from_base64( b64_data: 'str | bytes', extension: 'str | None' = None, mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None ) → Self ``` Initializes Content from a base64 encoded string or bytes. *** ### classmethod `from_bytes` ```python theme={null} from_bytes( data: 'bytes', extension: 'str | None' = None, mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None, encoding: 'str' = 'utf-8' ) → Self ``` Initializes Content from raw bytes. *** ### classmethod `from_data_url` ```python theme={null} from_data_url(url: 'str', metadata: 'dict[str, Any] | None' = None) → Self ``` Initializes Content from a data URL. *** ### classmethod `from_path` ```python theme={null} from_path( path: 'str | Path', encoding: 'str' = 'utf-8', mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None ) → Self ``` Initializes Content from a local file path. *** ### classmethod `from_text` ```python theme={null} from_text( text: 'str', extension: 'str | None' = None, mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None, encoding: 'str' = 'utf-8' ) → Self ``` Initializes Content from a string of text. *** ### classmethod `from_url` ```python theme={null} from_url( url: 'str', headers: 'dict[str, Any] | None' = None, timeout: 'int | None' = 30, metadata: 'dict[str, Any] | None' = None ) → Self ``` Initializes Content by fetching bytes from an HTTP(S) URL. Downloads the content, infers mimetype/extension from headers, URL path, and data, and constructs a Content object from the resulting bytes. *** ### classmethod `model_validate` ```python theme={null} model_validate( obj: 'Any', strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'dict[str, Any] | None' = None, extra: 'str | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None ) → Self ``` Override model\_validate to handle Content reconstruction from dict. *** ### classmethod `model_validate_json` ```python theme={null} model_validate_json( json_data: 'str | bytes | bytearray', strict: 'bool | None' = None, context: 'dict[str, Any] | None' = None, extra: 'str | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None ) → Self ``` Override model\_validate\_json to handle Content reconstruction from JSON. *** ### method `open` ```python theme={null} open() → bool ``` Open the file using the operating system's default application. This method uses the platform-specific mechanism to open the file with the default application associated with the file's type. **Returns:** * `bool`: True if the file was successfully opened, False otherwise. *** ### method `save` ```python theme={null} save(dest: 'str | Path') → None ``` Copy the file to the specified destination path. Updates the filename and the path of the content to reflect the last saved copy. **Args:** *** ### method `serialize_data` ```python theme={null} serialize_data(data: 'bytes') → str ``` When dumping model in json mode *** ### method `to_data_url` ```python theme={null} to_data_url(use_base64: 'bool' = True) → str ``` Constructs a data URL from the content. * `dest`: Destination path where the file will be copied to (string or pathlib.Path) The destination path can be a file or a directory. If dest has no file extension (e.g. .txt), destination will be considered a directory. **Args:** * `use_base64`: If True, the data will be base64 encoded. Otherwise, it will be percent-encoded. Defaults to True. **Returns:** A data URL string. *** ## class `Dataset` Dataset object with easy saving and automatic versioning. **Examples:** ```python theme={null} # Create a dataset dataset = Dataset(name='grammar', rows=[ {'id': '0', 'sentence': "He no likes ice cream.", 'correction': "He doesn't like ice cream."}, {'id': '1', 'sentence': "She goed to the store.", 'correction': "She went to the store."}, {'id': '2', 'sentence': "They plays video games all day.", 'correction': "They play video games all day."} ]) # Publish the dataset weave.publish(dataset) # Retrieve the dataset dataset_ref = weave.ref('grammar').get() # Access a specific example example_label = dataset_ref.rows[2]['sentence'] ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `rows`: `trace.table.Table | trace.vals.WeaveTable` ### method `add_rows` ```python theme={null} add_rows(rows: Iterable[dict]) → Dataset ``` Create a new dataset version by appending rows to the existing dataset. This is useful for adding examples to large datasets without having to load the entire dataset into memory. **Args:** * `rows`: The rows to add to the dataset. **Returns:** The updated dataset. *** ### classmethod `convert_to_table` ```python theme={null} convert_to_table(rows: Any) → Table | WeaveTable ``` *** ### classmethod `from_calls` ```python theme={null} from_calls(calls: Iterable[Call]) → Self ``` *** ### classmethod `from_hf` ```python theme={null} from_hf( hf_dataset: Union[ForwardRef('HFDataset'), ForwardRef('HFDatasetDict')] ) → Self ``` *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ### classmethod `from_pandas` ```python theme={null} from_pandas(df: 'DataFrame') → Self ``` *** ### method `select` ```python theme={null} select(indices: Iterable[int]) → Self ``` Select rows from the dataset based on the provided indices. **Args:** * `indices`: An iterable of integer indices specifying which rows to select. **Returns:** A new Dataset object containing only the selected rows. *** ### method `to_hf` ```python theme={null} to_hf() → HFDataset ``` *** ### method `to_pandas` ```python theme={null} to_pandas() → DataFrame ``` *** ## class `EasyPrompt` ### method `__init__` ```python theme={null} __init__( content: str | dict | list | None = None, role: str | None = None, dedent: bool = False, **kwargs: Any ) → None ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `data`: `` * `config`: `` * `requirements`: `` *** #### property as\_str Join all messages into a single string. *** #### property is\_bound *** #### property messages #### property placeholders *** #### property system\_message Join all messages into a system prompt message. *** #### property system\_prompt Join all messages into a system prompt object. *** #### property unbound\_placeholders *** ### method `append` ```python theme={null} append(item: Any, role: str | None = None, dedent: bool = False) → None ``` *** ### method `as_dict` ```python theme={null} as_dict() → dict[str, Any] ``` *** ### method `as_pydantic_dict` ```python theme={null} as_pydantic_dict() → dict[str, Any] ``` *** ### method `bind` ```python theme={null} bind(*args: Any, **kwargs: Any) → Prompt ``` *** ### method `bind_rows` ```python theme={null} bind_rows(dataset: list[dict] | Any) → list['Prompt'] ``` *** ### method `config_table` ```python theme={null} config_table(title: str | None = None) → Table ``` *** ### method `configure` ```python theme={null} configure(config: dict | None = None, **kwargs: Any) → Prompt ``` *** ### method `dump` ```python theme={null} dump(fp: ) → None ``` *** ### method `dump_file` ```python theme={null} dump_file(filepath: str | Path) → None ``` *** ### method `format` ```python theme={null} format(**kwargs: Any) → Any ``` *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ### classmethod `load` ```python theme={null} load(fp: ) → Self ``` *** ### classmethod `load_file` ```python theme={null} load_file(filepath: str | Path) → Self ``` *** ### method `messages_table` ```python theme={null} messages_table(title: str | None = None) → Table ``` *** ### method `print` ```python theme={null} print() → str ``` *** ### method `publish` ```python theme={null} publish( name: str | None = None, tags: list[str] | None = None, aliases: list[str] | None = None ) → ObjectRef ``` *** ### method `require` ```python theme={null} require(param_name: str, **kwargs: Any) → Prompt ``` *** ### method `run` ```python theme={null} run() → Any ``` *** ### method `validate_requirement` ```python theme={null} validate_requirement(key: str, value: Any) → list ``` *** ### method `validate_requirements` ```python theme={null} validate_requirements(values: dict[str, Any]) → list ``` *** ### method `values_table` ```python theme={null} values_table(title: str | None = None) → Table ``` *** ## class `Evaluation` Sets up an evaluation which includes a set of scorers and a dataset. Calling evaluation.evaluate(model) will pass in rows from a dataset into a model matching the names of the columns of the dataset to the argument names in model.predict. Then it will call all of the scorers and save the results in weave. If you want to preprocess the rows from the dataset you can pass in a function to preprocess\_model\_input. **Examples:** ```python theme={null} # Collect your examples examples = [ {"question": "What is the capital of France?", "expected": "Paris"}, {"question": "Who wrote 'To Kill a Mockingbird'?", "expected": "Harper Lee"}, {"question": "What is the square root of 64?", "expected": "8"}, ] # Define any custom scoring function @weave.op def match_score1(expected: str, model_output: dict) -> dict: # Here is where you'd define the logic to score the model output return {'match': expected == model_output['generated_text']} @weave.op def function_to_evaluate(question: str): # here's where you would add your LLM call and return the output return {'generated_text': 'Paris'} # Score your examples using scoring functions evaluation = Evaluation( dataset=examples, scorers=[match_score1] ) # Start tracking the evaluation weave.init('intro-example') # Run the evaluation asyncio.run(evaluation.evaluate(function_to_evaluate)) ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `dataset`: `` * `scorers`: `list[typing.Annotated[trace.op_protocol.Op | flow.scorer.Scorer, BeforeValidator(func=, json_schema_input_type=PydanticUndefined)]] | None` * `preprocess_model_input`: `collections.abc.Callable[[dict], dict] | None` * `trials`: `` * `metadata`: `dict[str, typing.Any] | None` * `evaluation_name`: `str | collections.abc.Callable[trace.call.Call, str] | None` ### method `evaluate` ```python theme={null} evaluate(model: Op | Model) → dict ``` *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ### method `get_eval_results` ```python theme={null} get_eval_results(model: Op | Model) → EvaluationResults ``` *** ### method `get_evaluate_calls` ```python theme={null} get_evaluate_calls() → PaginatedIterator[CallSchema, WeaveObject] ``` Retrieve all evaluation calls that used this Evaluation object. Note that this returns a CallsIter instead of a single call because it's possible to have multiple evaluation calls for a single evaluation (e.g. if you run the same evaluation multiple times). **Returns:** * `CallsIter`: An iterator over Call objects representing evaluation runs. **Raises:** * `ValueError`: If the evaluation has no ref (hasn't been saved/run yet). **Examples:** ```python theme={null} evaluation = Evaluation(dataset=examples, scorers=[scorer]) await evaluation.evaluate(model) # Run evaluation first calls = evaluation.get_evaluate_calls() for call in calls: print(f"Evaluation run: {call.id} at {call.started_at}") ``` *** ### method `get_score_calls` ```python theme={null} get_score_calls() → dict[str, list[Call]] ``` Retrieve scorer calls for each evaluation run, grouped by trace ID. **Returns:** * `dict[str, list[Call]]`: A dictionary mapping trace IDs to lists of scorer Call objects. Each trace ID represents one evaluation run, and the list contains all scorer calls executed during that run. **Examples:** ```python theme={null} evaluation = Evaluation(dataset=examples, scorers=[accuracy_scorer, f1_scorer]) await evaluation.evaluate(model) score_calls = evaluation.get_score_calls() for trace_id, calls in score_calls.items(): print(f"Trace {trace_id}: {len(calls)} scorer calls") for call in calls: scorer_name = call.summary.get("weave", {}).get("trace_name") print(f" Scorer: {scorer_name}, Output: {call.output}") ``` *** ### method `get_scores` ```python theme={null} get_scores() → dict[str, dict[str, list[Any]]] ``` Extract and organize scorer outputs from evaluation runs. **Returns:** * `dict[str, dict[str, list[Any]]]`: A nested dictionary structure where: * First level keys are trace IDs (evaluation runs) * Second level keys are scorer names * Values are lists of scorer outputs for that run and scorer **Examples:** ```python theme={null} evaluation = Evaluation(dataset=examples, scorers=[accuracy_scorer, f1_scorer]) await evaluation.evaluate(model) scores = evaluation.get_scores() # Access scores by trace and scorer for trace_id, trace_scores in scores.items(): print(f"Evaluation run {trace_id}:") for scorer_name, outputs in trace_scores.items(): print(f" {scorer_name}: {outputs}") ``` Expected output: ``` { "trace_123": { "accuracy_scorer": [{"accuracy": 0.85}], "f1_scorer": [{"f1": 0.78}] } } ``` *** ### method `model_post_init` ```python theme={null} model_post_init(_Evaluation__context: Any) → None ``` *** ### method `predict_and_score` ```python theme={null} predict_and_score(model: Op | Model, example: dict) → dict ``` *** ### method `summarize` ```python theme={null} summarize(eval_table: EvaluationResults) → dict ``` *** ## class `EvaluationLogger` This class provides an imperative interface for logging evaluations. An evaluation is started automatically when the first prediction is logged using the `log_prediction` method, and finished when the `log_summary` method is called. Each time you log a prediction, you will get back a `ScoreLogger` object. You can use this object to log scores and metadata for that specific prediction. For more information, see the `ScoreLogger` class. Basic usage - log predictions with inputs and outputs directly: ```python theme={null} ev = EvaluationLogger() # Log predictions with known inputs/outputs pred = ev.log_prediction(inputs={'q': 'Hello'}, outputs={'a': 'Hi there!'}) pred.log_score("correctness", 0.9) # Finish the evaluation ev.log_summary({"avg_score": 0.9}) ``` Advanced usage - use context manager for dynamic outputs and nested operations: ```python theme={null} ev = EvaluationLogger() # Use context manager when you need to capture nested operations with ev.log_prediction(inputs={'q': 'Hello'}) as pred: # Any operations here (like LLM calls) automatically become # children of the predict call response = your_llm_call(...) pred.output = response.content pred.log_score("correctness", 0.9) # Finish the evaluation ev.log_summary({"avg_score": 0.9}) ``` ### method `__init__` ```python theme={null} __init__( name: 'str | None' = None, model: 'Model | dict | str | None' = None, dataset: 'Dataset | list[dict] | str | None' = None, eval_attributes: 'dict[str, Any] | None' = None, scorers: 'list[str] | None' = None ) → None ``` *** #### property attributes *** #### property ui\_url *** ### method `fail` ```python theme={null} fail(exception: 'BaseException') → None ``` Convenience method to fail the evaluation with an exception. *** ### method `finish` ```python theme={null} finish(exception: 'BaseException | None' = None) → None ``` Clean up the evaluation resources explicitly without logging a summary. Ensures all prediction calls and the main evaluation call are finalized. This is automatically called if the logger is used as a context manager. *** ### method `log_example` ```python theme={null} log_example( inputs: 'dict[str, Any]', output: 'Any', scores: 'dict[str, ScoreType]' ) → None ``` Log a complete example with inputs, output, and scores. This is a convenience method that combines log\_prediction and log\_score for when you have all the data upfront. **Args:** * `inputs`: The input data for the prediction * `output`: The output value * `scores`: Dictionary mapping scorer names to score values **Example:** ```python theme={null} ev = EvaluationLogger() ev.log_example( inputs={'q': 'What is 2+2?'}, output='4', scores={'correctness': 1.0, 'fluency': 0.9} ) ``` *** ### method `log_prediction` ```python theme={null} log_prediction(inputs: 'dict[str, Any]', output: 'Any' = None) → ScoreLogger ``` Log a prediction to the Evaluation. Returns a ScoreLogger that can be used directly or as a context manager. **Args:** * `inputs`: The input data for the prediction * `output`: The output value. Defaults to None. Can be set later using pred.output. **Returns:** ScoreLogger for logging scores and optionally finishing the prediction. Example (direct): * `pred = ev.log_prediction({'q'`: '...'}, output="answer") pred.log\_score("correctness", 0.9) pred.finish() Example (context manager): * `with ev.log_prediction({'q'`: '...'}) as pred: response = model(...) pred.output = response pred.log\_score("correctness", 0.9) # Automatically calls finish() on exit *** ### method `log_summary` ```python theme={null} log_summary(summary: 'dict | None' = None, auto_summarize: 'bool' = True) → None ``` Log a summary dict to the Evaluation. This will calculate the summary, call the summarize op, and then finalize the evaluation, meaning no more predictions or scores can be logged. *** ### method `set_view` ```python theme={null} set_view( name: 'str', content: 'Content | str', extension: 'str | None' = None, mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None, encoding: 'str' = 'utf-8' ) → None ``` Attach a view to the evaluation's main call summary under `weave.views`. Saves the provided content as an object in the project and writes its reference URI under `summary.weave.views.` for the evaluation's `evaluate` call. String inputs are wrapped as text content using `Content.from_text` with the provided extension or mimetype. **Args:** * `name`: The view name to display, used as the key under `summary.weave.views`. * `content`: A `weave.Content` instance or string to serialize. * `extension`: Optional file extension for string content inputs. * `mimetype`: Optional MIME type for string content inputs. * `metadata`: Optional metadata attached to newly created `Content`. * `encoding`: Text encoding for string content inputs. **Returns:** None **Examples:** ` import weave` > > > ev = weave.EvaluationLogger() > > > ev.set\_view("report", "# Report", extension="md") *** ## class `File` A class representing a file with path, mimetype, and size information. ### method `__init__` ```python theme={null} __init__(path: 'str | Path', mimetype: 'str | None' = None) ``` Initialize a File object. **Args:** *** #### property filename Get the filename of the file. * `path`: Path to the file (string or pathlib.Path) * `mimetype`: Optional MIME type of the file - will be inferred from extension if not provided **Returns:** * `str`: The name of the file without the directory path. *** ### method `open` ```python theme={null} open() → bool ``` Open the file using the operating system's default application. This method uses the platform-specific mechanism to open the file with the default application associated with the file's type. **Returns:** * `bool`: True if the file was successfully opened, False otherwise. *** ### method `save` ```python theme={null} save(dest: 'str | Path') → None ``` Copy the file to the specified destination path. **Args:** *** ## class `Markdown` A Markdown renderable. * `dest`: Destination path where the file will be copied to (string or pathlib.Path) The destination path can be a file or a directory. **Args:** * `markup` (str): A string containing markdown. * `code_theme` (str, optional): Pygments theme for code blocks. Defaults to "monokai". See [https://pygments.org/styles/](https://pygments.org/styles/) for code themes. * `justify` (JustifyMethod, optional): Justify value for paragraphs. Defaults to None. * `style` (Union\[str, Style], optional): Optional style to apply to markdown. * `hyperlinks` (bool, optional): Enable hyperlinks. Defaults to `True`. ### method `__init__` ```python theme={null} __init__( markup: 'str', code_theme: 'str' = 'monokai', justify: 'JustifyMethod | None' = None, style: 'str | Style' = 'none', hyperlinks: 'bool' = True, inline_code_lexer: 'str | None' = None, inline_code_theme: 'str | None' = None ) → None ``` *** ## class `MessagesPrompt` ### method `__init__` ```python theme={null} __init__(messages: list[dict]) ``` * `inline_code_lexer`: (str, optional): Lexer to use if inline code highlighting is enabled. Defaults to None. * `inline_code_theme`: (Optional\[str], optional): Pygments theme for inline code highlighting, or None for no highlighting. Defaults to None. **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `messages`: `list[dict]` ### method `format` ```python theme={null} format(**kwargs: Any) → list ``` *** ### method `format_message` ```python theme={null} format_message(message: dict, **kwargs: Any) → dict ``` Format a single message by replacing template variables. This method delegates to the standalone format\_message\_with\_template\_vars function for the actual formatting logic. *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ## class `Model` Intended to capture a combination of code and data the operates on an input. For example it might call an LLM with a prompt to make a prediction or generate text. When you change the attributes or the code that defines your model, these changes will be logged and the version will be updated. This ensures that you can compare the predictions across different versions of your model. Use this to iterate on prompts or to try the latest LLM and compare predictions across different settings **Examples:** ```python theme={null} class YourModel(Model): attribute1: str attribute2: int @weave.op def predict(self, input_data: str) -> dict: # Model logic goes here prediction = self.attribute1 + ' ' + input_data return {'pred': prediction} ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` ### method `get_infer_method` ```python theme={null} get_infer_method() → Callable ``` *** ## class `Monitor` Sets up a monitor to score incoming calls automatically. **Examples:** ```python theme={null} import weave from weave.scorers import ValidJSONScorer json_scorer = ValidJSONScorer() my_monitor = weave.Monitor( name="my-monitor", description="This is a test monitor", sampling_rate=0.5, op_names=["my_op"], query={ "$expr": { "$gt": [ { "$getField": "started_at" }, { "$literal": 1742540400 } ] } } }, scorers=[json_scorer], ) my_monitor.activate() ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `sampling_rate`: `` * `scorers`: `list[flow.scorer.Scorer]` * `op_names`: `list[str]` * `query`: `trace_server.interface.query.Query | None` * `is_traced`: `` * `active`: `` * `scorer_debounce_config`: `flow.monitor.ScorerDebounceConfig | None` ### method `activate` ```python theme={null} activate() → ObjectRef ``` Activates the monitor. **Returns:** The ref to the monitor. *** ### method `deactivate` ```python theme={null} deactivate() → ObjectRef ``` Deactivates the monitor. **Returns:** The ref to the monitor. *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ## class `Object` Base class for Weave objects that can be tracked and versioned. This class extends Pydantic's BaseModel to provide Weave-specific functionality for object tracking, referencing, and serialization. Objects can have names, descriptions, and references that allow them to be stored and retrieved from the Weave system. **Attributes:** * `name` (Optional\[str]): A human-readable name for the object. * `description` (Optional\[str]): A description of what the object represents. * `ref` (Optional\[ObjectRef]): A reference to the object in the Weave system. **Examples:** ```python theme={null} # Create a simple object obj = Object(name="my_object", description="A test object") # Create an object from a URI obj = Object.from_uri("weave:///entity/project/object:digest") ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` ### classmethod `from_uri` ```python theme={null} from_uri(uri: str, objectify: bool = True) → Self ``` Create an object instance from a Weave URI. **Args:** * `uri` (str): The Weave URI pointing to the object. * `objectify` (bool): Whether to objectify the result. Defaults to True. **Returns:** * `Self`: An instance of the class created from the URI. **Raises:** * `NotImplementedError`: If the class doesn't implement the required methods for deserialization. **Examples:** ```python theme={null} obj = MyObject.from_uri("weave:///entity/project/object:digest") ``` *** ### classmethod `handle_relocatable_object` ```python theme={null} handle_relocatable_object( v: Any, handler: ValidatorFunctionWrapHandler, info: ValidationInfo ) → Any ``` Handle validation of relocatable objects including ObjectRef and WeaveObject. This validator handles special cases where the input is an ObjectRef or WeaveObject that needs to be properly converted to a standard Object instance. It ensures that references are preserved and that ignored types are handled correctly during the validation process. **Args:** * `v` (Any): The value to validate. * `handler` (ValidatorFunctionWrapHandler): The standard pydantic validation handler. * `info` (ValidationInfo): Validation context information. **Returns:** * `Any`: The validated object instance. **Examples:** This method is called automatically during object creation and validation. It handles cases like: \`\`\`python # When an ObjectRef is passed obj = MyObject(some\_object\_ref) # When a WeaveObject is passed obj = MyObject(some\_weave\_object) ```` --- ## class `ObjectRef` ObjectRef(entity: 'str', project: 'str', name: 'str', _digest: 'str | Future[str]', _extra: 'tuple[str | Future[str], ...]' = ()) ### method `__init__` ```python __init__( entity: 'str', project: 'str', name: 'str', _digest: 'str | Future[str]', _extra: 'tuple[str | Future[str], ]' = () ) → None ```` *** #### property digest *** #### property extra *** ### method `as_param_dict` ```python theme={null} as_param_dict() → dict ``` *** ### method `delete` ```python theme={null} delete() → None ``` *** ### method `get` ```python theme={null} get(objectify: 'bool' = True) → Any ``` *** ### method `is_descended_from` ```python theme={null} is_descended_from(potential_ancestor: 'ObjectRef') → bool ``` *** ### method `maybe_parse_uri` ```python theme={null} maybe_parse_uri(s: 'str') → AnyRef | None ``` *** ### method `parse_uri` ```python theme={null} parse_uri(uri: 'str') → ObjectRef ``` *** ### method `with_attr` ```python theme={null} with_attr(attr: 'str') → Self ``` *** ### method `with_extra` ```python theme={null} with_extra(extra: 'tuple[str | Future[str], ]') → Self ``` *** ### method `with_index` ```python theme={null} with_index(index: 'int') → Self ``` *** ### method `with_item` ```python theme={null} with_item(item_digest: 'str | Future[str]') → Self ``` *** ### method `with_key` ```python theme={null} with_key(key: 'str') → Self ``` *** ## class `Prompt` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` ### method `format` ```python theme={null} format(**kwargs: Any) → Any ``` *** ## class `SavedView` A fluent-style class for working with SavedView objects. ### method `__init__` ```python theme={null} __init__(view_type: 'str' = 'traces', label: 'str' = 'SavedView') → None ``` *** #### property entity *** #### property label *** #### property project *** #### property view\_type *** ### method `add_column` ```python theme={null} add_column(path: 'str | ObjectPath', label: 'str | None' = None) → SavedView ``` *** ### method `add_columns` ```python theme={null} add_columns(*columns: 'str') → SavedView ``` Convenience method for adding multiple columns to the grid. *** ### method `add_filter` ```python theme={null} add_filter( field: 'str', operator: 'str', value: 'Any | None' = None ) → SavedView ``` *** ### method `add_sort` ```python theme={null} add_sort(field: 'str', direction: 'SortDirection') → SavedView ``` *** ### method `column_index` ```python theme={null} column_index(path: 'int | str | ObjectPath') → int ``` *** ### method `filter_op` ```python theme={null} filter_op(op_name: 'str | None') → SavedView ``` *** ### method `get_calls` ```python theme={null} get_calls( limit: 'int | None' = None, offset: 'int | None' = None, include_costs: 'bool' = False, include_feedback: 'bool' = False, all_columns: 'bool' = False ) → CallsIter ``` Get calls matching this saved view's filters and settings. *** ### method `get_known_columns` ```python theme={null} get_known_columns(num_calls_to_query: 'int | None' = None) → list[str] ``` Get the set of columns that are known to exist. *** ### method `get_table_columns` ```python theme={null} get_table_columns() → list[TableColumn] ``` *** ### method `hide_column` ```python theme={null} hide_column(col_name: 'str') → SavedView ``` *** ### method `insert_column` ```python theme={null} insert_column( idx: 'int', path: 'str | ObjectPath', label: 'str | None' = None ) → SavedView ``` *** ### classmethod `load` ```python theme={null} load(ref: 'str') → Self ``` *** ### method `page_size` ```python theme={null} page_size(page_size: 'int') → SavedView ``` *** ### method `pin_column_left` ```python theme={null} pin_column_left(col_name: 'str') → SavedView ``` *** ### method `pin_column_right` ```python theme={null} pin_column_right(col_name: 'str') → SavedView ``` *** ### method `remove_column` ```python theme={null} remove_column(path: 'int | str | ObjectPath') → SavedView ``` *** ### method `remove_columns` ```python theme={null} remove_columns(*columns: 'str') → SavedView ``` Remove columns from the saved view. *** ### method `remove_filter` ```python theme={null} remove_filter(index_or_field: 'int | str') → SavedView ``` *** ### method `remove_filters` ```python theme={null} remove_filters() → SavedView ``` Remove all filters from the saved view. *** ### method `rename` ```python theme={null} rename(label: 'str') → SavedView ``` *** ### method `rename_column` ```python theme={null} rename_column(path: 'int | str | ObjectPath', label: 'str') → SavedView ``` *** ### method `save` ```python theme={null} save() → SavedView ``` Publish the saved view to the server. *** ### method `set_columns` ```python theme={null} set_columns(*columns: 'str') → SavedView ``` Set the columns to be displayed in the grid. *** ### method `show_column` ```python theme={null} show_column(col_name: 'str') → SavedView ``` *** ### method `sort_by` ```python theme={null} sort_by(field: 'str', direction: 'SortDirection') → SavedView ``` *** ### method `to_grid` ```python theme={null} to_grid(limit: 'int | None' = None) → Grid ``` *** ### method `to_rich_table_str` ```python theme={null} to_rich_table_str() → str ``` *** ### method `ui_url` ```python theme={null} ui_url() → str | None ``` URL to show this saved view in the UI. Note this is the "result" page with traces etc, not the URL for the view object. *** ### method `unpin_column` ```python theme={null} unpin_column(col_name: 'str') → SavedView ``` *** ## class `Scorer` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `column_map`: `dict[str, str] | None` *** #### property display\_name ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ### method `model_post_init` ```python theme={null} model_post_init(_Scorer__context: Any) → None ``` *** ### method `score` ```python theme={null} score(output: Any, **kwargs: Any) → Any ``` *** ### method `summarize` ```python theme={null} summarize(score_rows: list) → dict | None ``` *** ## class `StringPrompt` ### method `__init__` ```python theme={null} __init__(content: str) ``` **Pydantic Fields:** * `name`: `str | None` * `description`: `str | None` * `ref`: `trace.refs.ObjectRef | None` * `content`: `` ### method `format` ```python theme={null} format(**kwargs: Any) → str ``` *** ### classmethod `from_obj` ```python theme={null} from_obj(obj: WeaveObject) → Self ``` *** ## class `Table` ### method `__init__` ```python theme={null} __init__(rows: 'list[dict]') → None ``` *** #### property rows *** ### method `append` ```python theme={null} append(row: 'dict') → None ``` Add a row to the table. *** ### method `pop` ```python theme={null} pop(index: 'int') → None ``` Remove a row at the given index from the table. *** ## class `ContextAwareThread` A Thread that runs functions with the context of the caller. This is a drop-in replacement for threading.Thread that ensures calls behave as expected inside the thread. Weave requires certain contextvars to be set (see call\_context.py), but new threads do not automatically copy context from the parent, which can cause the call context to be lost -- not good! This class automates contextvar copying so using this thread "just works" as the user probably expects. You can achieve the same effect without this class by instead writing: ```python theme={null} def run_with_context(func, *args, **kwargs): context = copy_context() def wrapper(): context.run(func, *args, **kwargs) return wrapper thread = threading.Thread(target=run_with_context(your_func, *args, **kwargs)) thread.start() ``` ### method `__init__` ```python theme={null} __init__(*args: 'Any', **kwargs: 'Any') → None ``` *** #### property daemon A boolean value indicating whether this thread is a daemon thread. This must be set before start() is called, otherwise RuntimeError is raised. Its initial value is inherited from the creating thread; the main thread is not a daemon thread and therefore all threads created in the main thread default to daemon = False. The entire Python program exits when only daemon threads are left. *** #### property ident Thread identifier of this thread or None if it has not been started. This is a nonzero integer. See the get\_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited. *** #### property name A string used for identification purposes only. It has no semantics. Multiple threads may be given the same name. The initial name is set by the constructor. *** #### property native\_id Native integral thread ID of this thread, or None if it has not been started. This is a non-negative integer. See the get\_native\_id() function. This represents the Thread ID as reported by the kernel. *** ### method `run` ```python theme={null} run() → None ``` *** ## class `ThreadContext` Context object providing access to current thread and turn information. ### method `__init__` ```python theme={null} __init__(thread_id: 'str | None') ``` Initialize ThreadContext with the specified thread\_id. **Args:** *** #### property thread\_id Get the thread\_id for this context. * `thread_id`: The thread identifier for this context, or None if disabled. **Returns:** The thread identifier, or None if thread tracking is disabled. *** #### property turn\_id Get the current turn\_id from the active context. **Returns:** The current turn\_id if set, None otherwise. *** ## class `ContextAwareThreadPoolExecutor` A ThreadPoolExecutor that runs functions with the context of the caller. This is a drop-in replacement for concurrent.futures.ThreadPoolExecutor that ensures weave calls behave as expected inside the executor. Weave requires certain contextvars to be set (see call\_context.py), but new threads do not automatically copy context from the parent, which can cause the call context to be lost -- not good! This class automates contextvar copying so using this executor "just works" as the user probably expects. You can achieve the same effect without this class by instead writing: ```python theme={null} with concurrent.futures.ThreadPoolExecutor() as executor: contexts = [copy_context() for _ in range(len(vals))] def _wrapped_fn(*args): return contexts.pop().run(fn, *args) executor.map(_wrapped_fn, vals) ``` ### method `__init__` ```python theme={null} __init__(*args: 'Any', **kwargs: 'Any') → None ``` *** ### method `map` ```python theme={null} map( fn: 'Callable', *iterables: 'Iterable[Any]', timeout: 'float | None' = None, chunksize: 'int' = 1 ) → Iterator ``` *** ### method `submit` ```python theme={null} submit(fn: 'Callable', *args: 'Any', **kwargs: 'Any') → Any ``` *** ### function `add_tags` ```python theme={null} add_tags(obj_ref: 'ObjectRef | str', tags: 'list[str]') → None ``` Add tags to an object version. **Args:** *** ### function `as_op` ```python theme={null} as_op(fn: 'Callable[P, R]') → Op[P, R] ``` Given a @weave.op decorated function, return its Op. @weave.op decorated functions are instances of Op already, so this function should be a no-op at runtime. But you can use it to satisfy type checkers if you need to access OpDef attributes in a typesafe way. * `obj_ref`: Reference to the object version, either an ObjectRef (returned by weave.publish()) or a weave /// URI string. * `tags`: List of tag strings to add. **Args:** * `fn`: A weave.op decorated function. **Returns:** The Op of the function. *** ### function `attributes` ```python theme={null} attributes(attributes: 'dict[str, Any]') → Iterator ``` Context manager for setting attributes on a call. **Example:** ```python theme={null} with weave.attributes({'env': 'production'}): print(my_function.call("World")) ``` *** ### function `finish` ```python theme={null} finish() → None ``` Stops logging to weave. Following finish, calls of weave.op decorated functions will no longer be logged. You will need to run weave.init() again to resume logging. *** ### function `get` ```python theme={null} get(uri: 'str | ObjectRef') → Any ``` A convenience function for getting an object from a URI. Many objects logged by Weave are automatically registered with the Weave server. This function allows you to retrieve those objects by their URI. **Args:** * `uri`: A fully-qualified weave ref URI. **Returns:** The object. **Example:** ```python theme={null} weave.init("weave_get_example") dataset = weave.Dataset(rows=[{"a": 1, "b": 2}]) ref = weave.publish(dataset) dataset2 = weave.get(ref) # same as dataset! ``` *** ### function `get_aliases` ```python theme={null} get_aliases(obj_ref: 'ObjectRef | str') → list[str] ``` Get aliases for an object version. **Args:** * `obj_ref`: Reference to the object version, either an ObjectRef or a weave /// URI string. **Returns:** List of alias strings. *** ### function `get_client` ```python theme={null} get_client() → WeaveClient | None ``` *** ### function `get_current_call` ```python theme={null} get_current_call() → Call | None ``` Get the Call object for the currently executing Op, within that Op. **Returns:** The Call object for the currently executing Op, or None if tracking has not been initialized or this method is invoked outside an Op. **Note:** > The returned Call's `attributes` dictionary becomes immutable once the call starts. Use :func:`weave.attributes` to set call metadata before invoking an Op. The `summary` field may be updated while the Op executes and will be merged with computed summary information when the call finishes. *** ### function `get_tags` ```python theme={null} get_tags(obj_ref: 'ObjectRef | str') → list[str] ``` Get tags for an object version. **Args:** * `obj_ref`: Reference to the object version, either an ObjectRef or a weave /// URI string. **Returns:** List of tag strings. *** ### function `get_tags_and_aliases` ```python theme={null} get_tags_and_aliases(obj_ref: 'ObjectRef | str') → tuple[list[str], list[str]] ``` Get both tags and aliases for an object version in a single call. **Args:** * `obj_ref`: Reference to the object version, either an ObjectRef or a weave /// URI string. **Returns:** A tuple of (tags, aliases). Each is a list of strings. *** ### function `init` ```python theme={null} init( project_name: 'str', settings: 'UserSettings | dict[str, Any] | None' = None, autopatch_settings: 'AutopatchSettings | None' = None, global_postprocess_inputs: 'PostprocessInputsFunc | None' = None, global_postprocess_output: 'PostprocessOutputFunc | None' = None, global_attributes: 'dict[str, Any] | None' = None ) → WeaveClient ``` Initialize weave tracking, logging to a wandb project. Logging is initialized globally, so you do not need to keep a reference to the return value of init. Following init, calls of weave.op decorated functions will be logged to the specified project. **Args:** NOTE: Global postprocessing settings are applied to all ops after each op's own postprocessing. The order is always: 1. Op-specific postprocessing 2. Global postprocessing * `project_name`: The name of the Weights & Biases team and project to log to. If you don't specify a team, your default entity is used. To find or update your default entity, refer to [User Settings](https://docs.wandb.ai/guides/models/app/settings-page/user-settings/#default-team) in the W\&B Models documentation. * `settings`: Configuration for the Weave client generally. * `autopatch_settings`: (Deprecated) Configuration for autopatch integrations. Use explicit patching instead. * `global_postprocess_inputs`: A function that will be applied to all inputs of all ops. * `global_postprocess_output`: A function that will be applied to all outputs of all ops. * `global_attributes`: A dictionary of attributes that will be applied to all traces. **Returns:** A Weave client. *** ### function `list_aliases` ```python theme={null} list_aliases() → list[str] ``` List all distinct aliases in the project. **Returns:** Sorted list of all alias strings in the project. *** ### function `list_tags` ```python theme={null} list_tags() → list[str] ``` List all distinct tags in the project. **Returns:** Sorted list of all tag strings in the project. *** ### function `log_call` ```python theme={null} log_call( op: 'str', inputs: 'dict[str, Any]', output: 'Any', parent: 'Call | None' = None, attributes: 'dict[str, Any] | None' = None, display_name: 'str | Callable[[Call], str] | None' = None, use_stack: 'bool' = True, exception: 'BaseException | None' = None ) → Call ``` Log a call directly to Weave without using the decorator pattern. This function provides an imperative API for logging operations to Weave, useful when you want to log calls after they've already been executed or when the decorator pattern isn't suitable for your use case. **Args:** * `op` (str): The operation name to log. This will be used as the op\_name for the call. Anonymous operations (strings not referring to published ops) are supported. * `inputs` (dict\[str, Any]): A dictionary of input parameters for the operation. * `output` (Any): The output/result of the operation. * `parent` (Call | None): Optional parent call to nest this call under. If not provided, the call will be a root-level call (or nested under the current call context if one exists). Defaults to None. * `attributes` (dict\[str, Any] | None): Optional metadata to attach to the call. These are frozen once the call is created. Defaults to None. * `display_name` (str | Callable\[\[Call], str] | None): Optional display name for the call in the UI. Can be a string or a callable that takes the call and returns a string. Defaults to None. * `use_stack` (bool): Whether to push the call onto the runtime stack. When True, the call will be available in the call context and can be accessed via weave.require\_current\_call(). When False, the call is logged but not added to the call stack. Defaults to True. * `exception` (BaseException | None): Optional exception to log if the operation failed. Defaults to None. **Returns:** * `Call`: The created and finished Call object with full trace information. **Examples:** Basic usage: ````python theme={null} import weave >>> weave.init('my-project') >>> call = weave.log_call( ... op="my_function", ... inputs={"x": 5, "y": 10}, ... output=15 ... ) Logging with attributes and display name: >>> call = weave.log_call( ... op="process_data", ... inputs={"data": [1, 2, 3]}, ... output={"mean": 2.0}, ... attributes={"version": "1.0", "env": "prod"}, ... display_name="Data Processing" ... ) Logging a failed operation: >>> try: ... result = risky_operation() ... except Exception as e: ... call = weave.log_call( ... op="risky_operation", ... inputs={}, ... output=None, ... exception=e ... ) Nesting calls: >>> parent_call = weave.log_call("parent", {"input": 1}, 2) >>> child_call = weave.log_call( ... "child", ... {"input": 2}, ... 4, ... parent=parent_call ... ) Logging without adding to call stack: >>> call = weave.log_call( ... op="background_task", ... inputs={"task_id": 123}, ... output="completed", ... use_stack=False # Don't push to call stack ... ) --- ### function `op` ```python op( func: 'Callable[P, R] | None' = None, name: 'str | None' = None, call_display_name: 'str | CallDisplayNameFunc | None' = None, postprocess_inputs: 'PostprocessInputsFunc | None' = None, postprocess_output: 'PostprocessOutputFunc | None' = None, tracing_sample_rate: 'float' = 1.0, enable_code_capture: 'bool' = True, accumulator: 'Callable[[Any | None, Any], Any] | None' = None, kind: 'OpKind | None' = None, color: 'OpColor | None' = None, eager_call_start: 'bool' = False ) → Callable[[Callable[P, R]], Op[P, R]] | Op[P, R] ```` A decorator to weave op-ify a function or method. Works for both sync and async. Automatically detects iterator functions and applies appropriate behavior. **Args:** *** ### function `publish` ```python theme={null} publish( obj: 'Any', name: 'str | None' = None, tags: 'list[str] | None' = None, aliases: 'list[str] | None' = None ) → ObjectRef ``` Save and version a Python object. Weave creates a new version of the object if the object's name already exists and its content hash does not match the latest version of that object. * `func`: The function to decorate. * `name`: Custom name for the op. Defaults to the function name. * `call_display_name`: Display name for calls, can be a string or callable. * `postprocess_inputs`: Function to transform inputs before logging. * `postprocess_output`: Function to transform output before logging. * `tracing_sample_rate`: Fraction of calls to trace (0.0 to 1.0). * `enable_code_capture`: Whether to capture source code for this op. * `accumulator`: Function to accumulate results for streaming ops. * `eager_call_start`: If True, call starts are sent immediately rather than batched. Useful for long-running operations like evaluations that should be visible in the UI immediately. **Args:** * `obj`: The object to save and version. * `name`: The name to save the object under. * `tags`: Optional list of tags to add to the published object version. * `aliases`: Optional list of aliases to set on the published object version. **Returns:** A Weave Ref to the saved object. *** ### function `ref` ```python theme={null} ref(location: 'str') → ObjectRef ``` Creates a Ref to an existing Weave object. This does not directly retrieve the object but allows you to pass it to other Weave API functions. **Args:** * `location`: A Weave Ref URI, or if `weave.init()` has been called, `name:version` or `name`. If no version is provided, `latest` is used. **Returns:** A Weave Ref to the object. *** ### function `remove_aliases` ```python theme={null} remove_aliases(obj_ref: 'ObjectRef | str', alias: 'str | list[str]') → None ``` Remove one or more aliases from an object. **Args:** *** ### function `remove_tags` ```python theme={null} remove_tags(obj_ref: 'ObjectRef | str', tags: 'list[str]') → None ``` Remove tags from an object version. * `obj_ref`: Reference to the object, either an ObjectRef or a weave /// URI string. * `alias`: An alias name or list of alias names to remove. **Args:** *** ### function `require_current_call` ```python theme={null} require_current_call() → Call ``` Get the Call object for the currently executing Op, within that Op. This allows you to access attributes of the Call such as its id or feedback while it is running. ```python theme={null} @weave.op def hello(name: str) -> None: print(f"Hello {name}!") current_call = weave.require_current_call() print(current_call.id) ``` It is also possible to access a Call after the Op has returned. If you have the Call's id, perhaps from the UI, you can use the `get_call` method on the `WeaveClient` returned from `weave.init` to retrieve the Call object. ```python theme={null} client = weave.init("") mycall = client.get_call("") ``` Alternately, after defining your Op you can use its `call` method. For example: ```python theme={null} @weave.op def add(a: int, b: int) -> int: return a + b result, call = add.call(1, 2) print(call.id) ``` * `obj_ref`: Reference to the object version, either an ObjectRef or a weave /// URI string. * `tags`: List of tag strings to remove. **Returns:** The Call object for the currently executing Op **Raises:** * `NoCurrentCallError`: If tracking has not been initialized or this method is invoked outside an Op. *** ### function `set_aliases` ```python theme={null} set_aliases(obj_ref: 'ObjectRef | str', alias: 'str | list[str]') → None ``` Set one or more aliases for an object version. **Args:** *** ### function `set_view` ```python theme={null} set_view( name: 'str', content: 'Content | str', extension: 'str | None' = None, mimetype: 'str | None' = None, metadata: 'dict[str, Any] | None' = None, encoding: 'str' = 'utf-8' ) → None ``` Attach a custom view to the current call summary at `_weave.views.`. * `obj_ref`: Reference to the object version, either an ObjectRef or a weave /// URI string. * `alias`: An alias name or list of alias names to set (e.g., "production"). **Args:** * `name`: The view name (key under `summary._weave.views`). * `content`: A `weave.Content` instance or raw string. Strings are wrapped via `Content.from_text` using the supplied extension or mimetype. * `extension`: Optional file extension to use when `content` is a string. * `mimetype`: Optional MIME type to use when `content` is a string. * `metadata`: Optional metadata to attach when creating `Content` from text. * `encoding`: Text encoding to apply when creating `Content` from text. **Returns:** None **Examples:** ` import weave` > > > weave.init("proj") > > > @weave.op > > > ... def foo(): > > > ... weave.set\_view("readme", "# Hello", extension="md") > > > ... return 1 > > > foo() *** ### function `thread` ```python theme={null} thread( thread_id: 'str | None | object' =