Feedback

import weave
client = weave.init('intro-example')

# Get all feedback in a project
all_feedback = client.get_feedback()

# Fetch a specific feedback object by id.
# The API returns a collection, which is expected to contain at most one item.
one_feedback = client.get_feedback("<feedback_uuid>")[0]

# Find all feedback objects with a specific reaction. You can specify offset and limit.
thumbs_up = client.get_feedback(reaction="👍", limit=10)

# After retrieval, view the details of individual feedback objects.
for f in client.get_feedback():
    print(f.id)
    print(f.created_at)
    print(f.feedback_type)
    print(f.payload)

import weave
client = weave.init('intro-example')

call = client.get_call("<call_uuid>")

# Adding an emoji reaction
call.feedback.add_reaction("👍")

# Adding a note
call.feedback.add_note("this is a note")

# Adding custom key/value pairs.
# The first argument is a user-defined "type" string.
# Feedback must be JSON serializable and less than 1 KB when serialized.
call.feedback.add("correctness", { "value": 5 })

import weave
weave.init("uuid")

@weave.op()
def simple_operation(input_value):
    # Perform some simple operation
    output = f"Processed {input_value}"
    # Get the current call ID
    current_call = weave.require_current_call()
    call_id = current_call.id
    return output, call_id

import weave
weave.init("uuid")

@weave.op()
def simple_operation(input_value):
    return f"Processed {input_value}"

# Execute the operation and retrieve the result and call ID
result, call = simple_operation.call("example input")
call_id = call.id

call.feedback.purge("<feedback_uuid>")

import weave
from weave.flow.annotation_spec import AnnotationSpec

client = weave.init("feedback-example")

spec1 = AnnotationSpec(
  name="Temperature",
  description="The perceived temperature of the llm call",
  field_schema={
    "type": "number",
    "minimum": -1,
    "maximum": 1,
  }
)
spec2 = AnnotationSpec(
  name="Tone",
  description="The tone of the llm response",
  field_schema={
    "type": "string",
    "enum": ["Aggressive", "Neutral", "Polite", "N/A"],
  },
)
weave.publish(spec1, "temperature-scorer")
weave.publish(spec2, "tone-scorer")

import weave
from weave.flow.annotation_spec import AnnotationSpec

client = weave.init("feedback-example")

# create a new version of the scorer
spec1 = AnnotationSpec(
  name="Temperature",
  description="The perceived temperature of the llm call",
  field_schema={
    "type": "integer",  # <<- change type to integer
    "minimum": -1,
    "maximum": 1,
  }
)
weave.publish(spec1, "temperature-scorer")

import weave

client = weave.init("feedback-example")

call = client.get_call("<call_id>")
annotation_spec = weave.ref("<annotation_spec_ref_uri>")

call.feedback.add(
  feedback_type="wandb.annotation." + annotation_spec.name,
  payload={"value": 1},
  annotation_ref=annotation_spec.uri(),
)