API Overview

`function` `init`

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 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` `publish`

publish(obj: 'Any', name: '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. Args:

obj: The object to save and version.
name: The name to save the object under. Returns: A Weave Ref to the saved object.

`function` `ref`

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` `get`

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:

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` `require_current_call`

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.

@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.

client = weave.init("<project>")
mycall = client.get_call("<call_id>")

Alternately, after defining your Op you can use its call method. For example:

@weave.op
def add(a: int, b: int) -> int:
     return a + b

result, call = add.call(1, 2)
print(call.id)

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` `get_current_call`

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` `finish`

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` `op`

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
) → 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.

`function` `attributes`

attributes(attributes: 'dict[str, Any]') → Iterator

Context manager for setting attributes on a call. Example:

with weave.attributes({'env': 'production'}):
     print(my_function.call("World"))

`function` `thread`

thread(
    thread_id: 'str | None | object' = <object object at 0x7f126ec3ce50>
) → Iterator[ThreadContext]

Context manager for setting thread_id on calls within the context. Examples:

# Auto-generate thread_id
with weave.thread() as t:
     print(f"Thread ID: {t.thread_id}")
     result = my_function("input")  # This call will have the auto-generated thread_id
     print(f"Current turn: {t.turn_id}")

# Explicit thread_id
with weave.thread("custom_thread") as t:
     result = my_function("input")  # This call will have thread_id="custom_thread"

# Disable threading
with weave.thread(None) as t:
     result = my_function("input")  # This call will have thread_id=None

Args:

thread_id: The thread identifier to associate with calls in this context. If not provided, a UUID v7 will be auto-generated. If None, thread tracking will be disabled. Yields:
ThreadContext: An object providing access to thread_id and current turn_id.

`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:

# 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: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

`classmethod` `from_uri`

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:

obj = MyObject.from_uri("weave:///entity/project/object:digest")

`classmethod` `handle_relocatable_object`

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)

---

<a href="https://github.com/wandb/weave/blob/0.52.10/weave/dataset/dataset.py#L26"><img align="right" src="https://img.shields.io/badge/-source-cccccc?style=flat-square" /></a>

## <kbd>class</kbd> `Dataset`
Dataset object with easy saving and automatic versioning. 

**Examples:**
```python
# 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: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
rows: typing.Union[trace.table.Table, trace.vals.WeaveTable]

`method` `add_rows`

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`

convert_to_table(rows: Any) → Union[Table, WeaveTable]

`classmethod` `from_calls`

from_calls(calls: Iterable[Call]) → Self

`classmethod` `from_hf`

from_hf(
    hf_dataset: Union[ForwardRef('HFDataset'), ForwardRef('HFDatasetDict')]
) → Self

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`classmethod` `from_pandas`

from_pandas(df: 'DataFrame') → Self

`method` `select`

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`

to_hf() → HFDataset

`method` `to_pandas`

to_pandas() → DataFrame

`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:

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: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

`method` `get_infer_method`

get_infer_method() → Callable

`class` `Prompt`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]

`method` `format`

format(**kwargs: Any) → Any

`class` `StringPrompt`

`method` `init`

__init__(content: str)

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
content: <class 'str'>

`method` `format`

format(**kwargs: Any) → str

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`class` `MessagesPrompt`

`method` `init`

__init__(messages: list[dict])

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
messages: list[dict]

`method` `format`

format(**kwargs: Any) → list

`method` `format_message`

format_message(message: dict, **kwargs: Any) → dict

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`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:

# 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: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
dataset: <class 'dataset.dataset.Dataset'>
scorers: typing.Optional[list[typing.Annotated[typing.Union[trace.op_protocol.Op, flow.scorer.Scorer], BeforeValidator(func=<function cast_to_scorer at 0x7f126eaa0040>, json_schema_input_type=PydanticUndefined)]]]
preprocess_model_input: typing.Optional[typing.Callable[[dict], dict]]
trials: <class 'int'>
metadata: typing.Optional[dict[str, typing.Any]]
evaluation_name: typing.Union[str, typing.Callable[[trace.call.Call], str], NoneType]

`method` `evaluate`

evaluate(model: Union[Op, Model]) → dict

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`method` `get_eval_results`

get_eval_results(model: Union[Op, Model]) → EvaluationResults

`method` `get_evaluate_calls`

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:

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`

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:

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`

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:

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`

model_post_init(_Evaluation__context: Any) → None

`method` `predict_and_score`

predict_and_score(model: Union[Op, Model], example: dict) → dict

`method` `summarize`

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:

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:

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})

Pydantic Fields:

name: str | None
model: flow.model.Model | dict | str
dataset: dataset.dataset.Dataset | list[dict] | str
eval_attributes: dict[str, typing.Any]
scorers: list[str] | None

`property` attributes

`property` ui_url

`method` `fail`

fail(exception: 'BaseException') → None

Convenience method to fail the evaluation with an exception.

`method` `finish`

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_prediction`

log_prediction(
    inputs: 'dict[str, Any]',
    output: 'Any | _NotSetType' = <NOT_SET>
) → ScoreLogger | _LogPredictionContext

Log a prediction to the Evaluation. If output is provided, returns a ScoreLogger for direct use. If output is not provided, returns a PredictionContext for use as a context manager. Args:

inputs: The input data for the prediction
output: The output value. If not provided, use as context manager to set later. Returns: ScoreLogger if output provided, PredictionContext if not.

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)

`method` `log_summary`

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` `model_post_init`

model_post_init(_EvaluationLogger__context: 'Any') → None

Initialize the pseudo evaluation with the dataset from the model.

`method` `set_view`

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.<name> 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` `Scorer`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
column_map: typing.Optional[dict[str, str]]

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`method` `model_post_init`

model_post_init(_Scorer__context: Any) → None

`method` `score`

score(output: Any, **kwargs: Any) → Any

`method` `summarize`

summarize(score_rows: list) → Optional[dict]

`class` `AnnotationSpec`

Pydantic Fields:

name: typing.Optional[str]
description: typing.Optional[str]
field_schema: dict[str, typing.Any]
unique_among_creators: <class 'bool'>
op_scope: typing.Optional[list[str]]

`classmethod` `preprocess_field_schema`

preprocess_field_schema(data: dict[str, Any]) → dict[str, Any]

`classmethod` `validate_field_schema`

validate_field_schema(schema: dict[str, Any]) → dict[str, Any]

`method` `value_is_valid`

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` `File`

A class representing a file with path, mimetype, and size information.

`method` `init`

__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`

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`

save(dest: 'str | Path') → None

Copy the file to the specified destination path. Args:

`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`

__init__(*args: 'Any', **kwargs: 'Any') → None

Direct initialization is disabled. Please use a classmethod like Content.from_path() to create an instance.

dest: Destination path where the file will be copied to (string or pathlib.Path) The destination path can be a file or a directory. Pydantic Fields:
data: <class 'bytes'>
size: <class 'int'>
mimetype: <class 'str'>
digest: <class 'str'>
filename: <class 'str'>
content_type: typing.Literal['bytes', 'text', 'base64', 'file', 'url', 'data_url', 'data_url:base64', 'data_url:encoding', 'data_url:encoding:base64']
input_type: <class 'str'>
encoding: <class 'str'>
metadata: dict[str, typing.Any] | None
extension: str | None

`property` art

`property` ref

`method` `as_string`

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`

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`

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`

from_data_url(url: 'str', metadata: 'dict[str, Any] | None' = None) → Self

Initializes Content from a data URL.

`classmethod` `from_path`

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`

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`

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`

model_validate(
    obj: 'Any',
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'dict[str, Any] | None' = None
) → Self

Override model_validate to handle Content reconstruction from dict.

`classmethod` `model_validate_json`

model_validate_json(
    json_data: 'str | bytes | bytearray',
    strict: 'bool | None' = None,
    context: 'dict[str, Any] | None' = None
) → Self

Override model_validate_json to handle Content reconstruction from JSON.

`method` `open`

open() → bool

bool: True if the file was successfully opened, False otherwise.

`method` `save`

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`

serialize_data(data: 'bytes') → str

When dumping model in json mode

`method` `to_data_url`

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` `Markdown`

A Markdown renderable. Args:

markup (str): A string containing markdown.
code_theme (str, optional): Pygments theme for code blocks. Defaults to “monokai”. See 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`

__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` `Monitor`

Sets up a monitor to score incoming calls automatically.

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. Examples:

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: typing.Optional[str]
description: typing.Optional[str]
ref: typing.Optional[trace.refs.ObjectRef]
sampling_rate: <class 'float'>
scorers: list[flow.scorer.Scorer]
op_names: list[str]
query: typing.Optional[trace_server.interface.query.Query]
active: <class 'bool'>

`method` `activate`

activate() → ObjectRef

Activates the monitor. Returns: The ref to the monitor.

`method` `deactivate`

deactivate() → ObjectRef

Deactivates the monitor. Returns: The ref to the monitor.

`classmethod` `from_obj`

from_obj(obj: WeaveObject) → Self

`class` `SavedView`

A fluent-style class for working with SavedView objects.

`method` `init`

__init__(view_type: 'str' = 'traces', label: 'str' = 'SavedView') → None

`property` entity

`property` label

`property` project

`property` view_type

`method` `add_column`

add_column(path: 'str | ObjectPath', label: 'str | None' = None) → SavedView

`method` `add_columns`

add_columns(*columns: 'str') → SavedView

Convenience method for adding multiple columns to the grid.

`method` `add_filter`

add_filter(
    field: 'str',
    operator: 'str',
    value: 'Any | None' = None
) → SavedView

`method` `add_sort`

add_sort(field: 'str', direction: 'SortDirection') → SavedView

`method` `column_index`

column_index(path: 'int | str | ObjectPath') → int

`method` `filter_op`

filter_op(op_name: 'str | None') → SavedView

`method` `get_calls`

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`

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`

get_table_columns() → list[TableColumn]

`method` `hide_column`

hide_column(col_name: 'str') → SavedView

`method` `insert_column`

insert_column(
    idx: 'int',
    path: 'str | ObjectPath',
    label: 'str | None' = None
) → SavedView

`classmethod` `load`

load(ref: 'str') → SavedView

`method` `page_size`

page_size(page_size: 'int') → SavedView

`method` `pin_column_left`

pin_column_left(col_name: 'str') → SavedView

`method` `pin_column_right`

pin_column_right(col_name: 'str') → SavedView

`method` `remove_column`

remove_column(path: 'int | str | ObjectPath') → SavedView

`method` `remove_columns`

remove_columns(*columns: 'str') → SavedView

Remove columns from the saved view.

`method` `remove_filter`

remove_filter(index_or_field: 'int | str') → SavedView

`method` `remove_filters`

remove_filters() → SavedView

Remove all filters from the saved view.

`method` `rename`

rename(label: 'str') → SavedView

`method` `rename_column`

rename_column(path: 'int | str | ObjectPath', label: 'str') → SavedView

`method` `save`

save() → SavedView

Publish the saved view to the server.

`method` `set_columns`

set_columns(*columns: 'str') → SavedView

Set the columns to be displayed in the grid.

`method` `show_column`

show_column(col_name: 'str') → SavedView

`method` `sort_by`

sort_by(field: 'str', direction: 'SortDirection') → SavedView

`method` `to_grid`

to_grid(limit: 'int | None' = None) → Grid

`method` `to_rich_table_str`

to_rich_table_str() → str

`method` `ui_url`

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`

unpin_column(col_name: 'str') → SavedView

`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`

__init__(
    data: 'bytes',
    format: 'SUPPORTED_FORMATS_TYPE',
    validate_base64: 'bool' = True
) → None

`method` `export`

export(path: 'str | bytes | Path | PathLike') → None

Export audio data to a file. Args:

`classmethod` `from_data`

from_data(data: 'str | bytes', format: 'str') → Audio

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`

from_path(path: 'str | bytes | Path | PathLike') → Audio

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

Guides

Cookbooks

Reference

Open Source

Community

​API Overview

​function init

​function publish

​function ref

​function get

​function require_current_call

​function get_current_call

​function finish

​function op

​function attributes

​function thread

​class Object

​classmethod from_uri

​classmethod handle_relocatable_object

​When an ObjectRef is passed

​When a WeaveObject is passed

​method add_rows

​classmethod convert_to_table

​classmethod from_calls

​classmethod from_hf

​classmethod from_obj

​classmethod from_pandas

​method select

​method to_hf

​method to_pandas

​class Model

​method get_infer_method

​class Prompt

​method format

​class StringPrompt

​method __init__

​method format

​classmethod from_obj

​class MessagesPrompt

​method __init__

​method format

​method format_message

​classmethod from_obj

​class Evaluation

​method evaluate

​classmethod from_obj

​method get_eval_results

​method get_evaluate_calls

​method get_score_calls

​method get_scores

​method model_post_init

​method predict_and_score

​method summarize

​class EvaluationLogger

​property attributes

​property ui_url

​method fail

​method finish

​method log_prediction

​method log_summary

​method model_post_init

​method set_view

​class Scorer

​classmethod from_obj

​method model_post_init

​method score

​method summarize

​class AnnotationSpec

​classmethod preprocess_field_schema

​classmethod validate_field_schema

​method value_is_valid

​class File

​method __init__

​property filename

​method open

​method save

​class Content

​method __init__

​property art

​property ref

API Overview

`function` `init`

`function` `publish`

`function` `ref`

`function` `get`

`function` `require_current_call`

`function` `get_current_call`

`function` `finish`

`function` `op`

`function` `attributes`

`function` `thread`

`class` `Object`

`classmethod` `from_uri`

`classmethod` `handle_relocatable_object`

When an ObjectRef is passed

When a WeaveObject is passed

`method` `add_rows`

`classmethod` `convert_to_table`

`classmethod` `from_calls`

`classmethod` `from_hf`

`classmethod` `from_obj`

`classmethod` `from_pandas`

`method` `select`

`method` `to_hf`

`method` `to_pandas`

`class` `Model`

`method` `get_infer_method`

`class` `Prompt`

`method` `format`

`class` `StringPrompt`

`method` `init`

`method` `format`

`classmethod` `from_obj`

`class` `MessagesPrompt`

`method` `init`

`method` `format`

`method` `format_message`

`classmethod` `from_obj`

`class` `Evaluation`

`method` `evaluate`

`classmethod` `from_obj`

`method` `get_eval_results`

`method` `get_evaluate_calls`

`method` `get_score_calls`

`method` `get_scores`

`method` `model_post_init`

`method` `predict_and_score`

`method` `summarize`

`class` `EvaluationLogger`

`property` attributes

`property` ui_url

`method` `fail`

`method` `finish`

`method` `log_prediction`

`method` `log_summary`

`method` `model_post_init`

`method` `set_view`

`class` `Scorer`

`classmethod` `from_obj`

`method` `model_post_init`

`method` `score`

`method` `summarize`

`class` `AnnotationSpec`

`classmethod` `preprocess_field_schema`

`classmethod` `validate_field_schema`

`method` `value_is_valid`

`class` `File`

`method` `init`

`property` filename

`method` `open`

`method` `save`

`class` `Content`

`method` `init`

`property` art

`property` ref

`method` `as_string`

`classmethod` `from_base64`

`classmethod` `from_bytes`

`classmethod` `from_data_url`

`classmethod` `from_path`