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weave

The top-level functions and classes for working with Weave.

API Overview

Classes​

Functions​

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:

  • project_name: The name of the Weights & Biases project to log to.
  • settings: Configuration for the Weave client generally.
  • autopatch_settings: Configuration for autopatch integrations, e.g. openai
  • 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.

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

Returns: A Weave client.

function publish​

publish(obj: 'Any', name: 'str | None' = None) → ObjectRef

Save and version a python object.

If an object with name already exists, and the content hash of obj does not match the latest version of that object, a new version will be created.

TODO: Need to document how name works with this change.

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

Construct a Ref to a Weave object.

TODO: what happens if obj does not exist

Args:

  • location: A fully-qualified weave ref URI, or if weave.init() has been called, "name:version" or just "name" ("latest" will be used for version in this case).

Returns: A weave Ref to the object.

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.

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
) → 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"))

class Object​

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

classmethod handle_relocatable_object​

handle_relocatable_object(
    v: Any,
    handler: ValidatorFunctionWrapHandler,
    info: ValidationInfo
) → Any

class Dataset​

Dataset object with easy saving and automatic versioning

Examples:

# 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_obj​

from_obj(obj: WeaveObject) → Self

classmethod from_pandas​

from_pandas(df: 'DataFrame') → Self

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 'flow.dataset.Dataset'>
  • scorers: typing.Optional[list[typing.Annotated[typing.Union[trace.op.Op, flow.scorer.Scorer], BeforeValidator(func=<function cast_to_scorer at 0x146df4f70>, json_schema_input_type=PydanticUndefined)]]]
  • preprocess_model_input: typing.Optional[typing.Callable[[dict], dict]]
  • trials: <class 'int'>
  • evaluation_name: typing.Union[str, typing.Callable[[trace.weave_client.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 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.

Example:

    ev = EvaluationLogger()
    pred = ev.log_prediction(inputs, output)
    pred.log_score(scorer_name, score)
    ev.log_summary(summary)

Pydantic Fields:

  • name: str | None
  • model: flow.model.Model | dict | str
  • dataset: flow.dataset.Dataset | list[dict] | str

property ui_url​

method finish​

finish() → 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', output: 'Any') → ScoreLogger

Log a prediction to the Evaluation, and return a reference.

The reference can be used to log scores which are attached to the specific prediction instance.

method log_summary​

log_summary(summary: 'dict | None' = None) → 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.

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]]

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 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.
  • 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.

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.

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