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dspy.Refine

dspy.Refine(module: Module, N: int, reward_fn: Callable[[dict, Prediction], float], threshold: float, fail_count: Optional[int] = None)

Bases: Module

Refines a module by running it up to N times with different temperatures and returns the best prediction.

This module runs the provided module multiple times with varying temperature settings and selects either the first prediction that exceeds the specified threshold or the one with the highest reward. If no prediction meets the threshold, it automatically generates feedback to improve future predictions.

Parameters:

Name Type Description Default
module Module

The module to refine.

 required
N int

The number of times to run the module. must

 required
reward_fn Callable

The reward function.

 required
threshold float

The threshold for the reward function.

 required
fail_count Optional[int]

The number of times the module can fail before raising an error

 None
Example 
import dspy

dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))

# Define a QA module with chain of thought
qa = dspy.ChainOfThought("question -> answer")

# Define a reward function that checks for one-word answers
def one_word_answer(args, pred):
    return 1.0 if len(pred.answer.split()) == 1 else 0.0

# Create a refined module that tries up to 3 times
best_of_3 = dspy.Refine(module=qa, N=3, reward_fn=one_word_answer, threshold=1.0)

# Use the refined module
result = best_of_3(question="What is the capital of Belgium?").answer
# Returns: Brussels
Source code in dspy/predict/refine.py 
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def __init__(
    self,
    module: Module,
    N: int,
    reward_fn: Callable[[dict, Prediction], float],
    threshold: float,
    fail_count: Optional[int] = None,
):
    """
    Refines a module by running it up to N times with different temperatures and returns the best prediction.

    This module runs the provided module multiple times with varying temperature settings and selects
    either the first prediction that exceeds the specified threshold or the one with the highest reward.
    If no prediction meets the threshold, it automatically generates feedback to improve future predictions.


    Args:
        module (Module): The module to refine.
        N (int): The number of times to run the module. must
        reward_fn (Callable): The reward function.
        threshold (float): The threshold for the reward function.
        fail_count (Optional[int], optional): The number of times the module can fail before raising an error

    Example:
        ```python
        import dspy

        dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))

        # Define a QA module with chain of thought
        qa = dspy.ChainOfThought("question -> answer")

        # Define a reward function that checks for one-word answers
        def one_word_answer(args, pred):
            return 1.0 if len(pred.answer.split()) == 1 else 0.0

        # Create a refined module that tries up to 3 times
        best_of_3 = dspy.Refine(module=qa, N=3, reward_fn=one_word_answer, threshold=1.0)

        # Use the refined module
        result = best_of_3(question="What is the capital of Belgium?").answer
        # Returns: Brussels
        ```
    """
    self.module = module
    self.reward_fn = lambda *args: reward_fn(*args)  # to prevent this from becoming a parameter
    self.threshold = threshold
    self.N = N
    self.fail_count = fail_count or N  # default to N if fail_count is not provided
    self.module_code = inspect.getsource(module.__class__)
    try:
        self.reward_fn_code = inspect.getsource(reward_fn)
    except TypeError:
        self.reward_fn_code = inspect.getsource(reward_fn.__class__)

Functions

__call__(*args, **kwargs)

Source code in dspy/primitives/program.py 
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@with_callbacks
def __call__(self, *args, **kwargs):
    if settings.track_usage and settings.usage_tracker is None:
        with track_usage() as usage_tracker:
            output = self.forward(*args, **kwargs)
            output.set_lm_usage(usage_tracker.get_total_tokens())
            return output

    return self.forward(*args, **kwargs)

batch(examples, num_threads: Optional[int] = None, max_errors: int = 10, return_failed_examples: bool = False, provide_traceback: Optional[bool] = None, disable_progress_bar: bool = False)

Processes a list of dspy.Example instances in parallel using the Parallel module.

:param examples: List of dspy.Example instances to process. :param num_threads: Number of threads to use for parallel processing. :param max_errors: Maximum number of errors allowed before stopping execution. :param return_failed_examples: Whether to return failed examples and exceptions. :param provide_traceback: Whether to include traceback information in error logs. :return: List of results, and optionally failed examples and exceptions.

Source code in dspy/primitives/program.py 
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def batch(
    self,
    examples,
    num_threads: Optional[int] = None,
    max_errors: int = 10,
    return_failed_examples: bool = False,
    provide_traceback: Optional[bool] = None,
    disable_progress_bar: bool = False,
):
    """
    Processes a list of dspy.Example instances in parallel using the Parallel module.

    :param examples: List of dspy.Example instances to process.
    :param num_threads: Number of threads to use for parallel processing.
    :param max_errors: Maximum number of errors allowed before stopping execution.
    :param return_failed_examples: Whether to return failed examples and exceptions.
    :param provide_traceback: Whether to include traceback information in error logs.
    :return: List of results, and optionally failed examples and exceptions.
    """
    # Create a list of execution pairs (self, example)
    exec_pairs = [(self, example.inputs()) for example in examples]

    # Create an instance of Parallel
    parallel_executor = Parallel(
        num_threads=num_threads,
        max_errors=max_errors,
        return_failed_examples=return_failed_examples,
        provide_traceback=provide_traceback,
        disable_progress_bar=disable_progress_bar,
    )

    # Execute the forward method of Parallel
    if return_failed_examples:
        results, failed_examples, exceptions = parallel_executor.forward(exec_pairs)
        return results, failed_examples, exceptions
    else:
        results = parallel_executor.forward(exec_pairs)
        return results

deepcopy()

Deep copy the module.

This is a tweak to the default python deepcopy that only deep copies self.parameters(), and for other attributes, we just do the shallow copy.

Source code in dspy/primitives/module.py 
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def deepcopy(self):
    """Deep copy the module.

    This is a tweak to the default python deepcopy that only deep copies `self.parameters()`, and for other
    attributes, we just do the shallow copy.
    """
    try:
        # If the instance itself is copyable, we can just deep copy it.
        # Otherwise we will have to create a new instance and copy over the attributes one by one.
        return copy.deepcopy(self)
    except Exception:
        pass

    # Create an empty instance.
    new_instance = self.__class__.__new__(self.__class__)
    # Set attribuetes of the copied instance.
    for attr, value in self.__dict__.items():
        if isinstance(value, BaseModule):
            setattr(new_instance, attr, value.deepcopy())
        else:
            try:
                # Try to deep copy the attribute
                setattr(new_instance, attr, copy.deepcopy(value))
            except Exception:
                logging.warning(
                    f"Failed to deep copy attribute '{attr}' of {self.__class__.__name__}, "
                    "falling back to shallow copy or reference copy."
                )
                try:
                    # Fallback to shallow copy if deep copy fails
                    setattr(new_instance, attr, copy.copy(value))
                except Exception:
                    # If even the shallow copy fails, we just copy over the reference.
                    setattr(new_instance, attr, value)

    return new_instance

dump_state()

Source code in dspy/primitives/module.py 
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def dump_state(self):
    return {name: param.dump_state() for name, param in self.named_parameters()}

forward(**kwargs)

Source code in dspy/predict/refine.py 
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def forward(self, **kwargs):
    lm = self.module.get_lm() or dspy.settings.lm
    temps = [lm.kwargs["temperature"]] + [0.5 + i * (0.5 / self.N) for i in range(self.N)]
    temps = list(dict.fromkeys(temps))[: self.N]
    best_pred, best_trace, best_reward = None, None, -float("inf")
    advice = None
    adapter = dspy.settings.adapter or dspy.ChatAdapter()

    for idx, t in enumerate(temps):
        lm_ = lm.copy(temperature=t)
        mod = self.module.deepcopy()
        mod.set_lm(lm_)

        predictor2name = {predictor: name for name, predictor in mod.named_predictors()}
        signature2name = {predictor.signature: name for name, predictor in mod.named_predictors()}
        module_names = [name for name, _ in mod.named_predictors()]

        try:
            with dspy.context(trace=[]):
                if not advice:
                    outputs = mod(**kwargs)
                else:

                    class WrapperAdapter(adapter.__class__):
                        def __call__(self, lm, lm_kwargs, signature, demos, inputs):
                            inputs["hint_"] = advice.get(signature2name[signature], "N/A")
                            signature = signature.append(
                                "hint_", InputField(desc="A hint to the module from an earlier run")
                            )
                            return adapter(lm, lm_kwargs, signature, demos, inputs)

                    with dspy.context(adapter=WrapperAdapter()):
                        outputs = mod(**kwargs)

                trace = dspy.settings.trace.copy()

                # TODO: Remove the hint from the trace, if it's there.

                # NOTE: Not including the trace of reward_fn.
                reward = self.reward_fn(kwargs, outputs)

            if reward > best_reward:
                best_reward, best_pred, best_trace = reward, outputs, trace

            if self.threshold is not None and reward >= self.threshold:
                break

            if idx == self.N - 1:
                break

            modules = dict(program_code=self.module_code, modules_defn=inspect_modules(mod))
            trajectory = [dict(module_name=predictor2name[p], inputs=i, outputs=dict(o)) for p, i, o in trace]
            trajectory = dict(program_inputs=kwargs, program_trajectory=trajectory, program_outputs=dict(outputs))
            reward = dict(reward_code=self.reward_fn_code, target_threshold=self.threshold, reward_value=reward)

            advise_kwargs = dict(**modules, **trajectory, **reward, module_names=module_names)
            # advise_kwargs = {k: ujson.dumps(recursive_mask(v), indent=2) for k, v in advise_kwargs.items()}
            # only dumps if it's a list or dict
            advise_kwargs = {
                k: v if isinstance(v, str) else ujson.dumps(recursive_mask(v), indent=2)
                for k, v in advise_kwargs.items()
            }
            advice = dspy.Predict(OfferFeedback)(**advise_kwargs).advice
            # print(f"Advice for each module: {advice}")

        except Exception as e:
            print(f"Refine: Attempt failed with temperature {t}: {e}")
            if idx > self.fail_count:
                raise e
            self.fail_count -= 1
    if best_trace:
        dspy.settings.trace.extend(best_trace)
    return best_pred

get_lm()

Source code in dspy/primitives/program.py 
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def get_lm(self):
    all_used_lms = [param.lm for _, param in self.named_predictors()]

    if len(set(all_used_lms)) == 1:
        return all_used_lms[0]

    raise ValueError("Multiple LMs are being used in the module. There's no unique LM to return.")

load(path)

Load the saved module. You may also want to check out dspy.load, if you want to load an entire program, not just the state for an existing program.

Parameters:

Name Type Description Default
path str

Path to the saved state file, which should be a .json or a .pkl file

 required
Source code in dspy/primitives/module.py 
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def load(self, path):
    """Load the saved module. You may also want to check out dspy.load, if you want to
    load an entire program, not just the state for an existing program.

    Args:
        path (str): Path to the saved state file, which should be a .json or a .pkl file
    """
    path = Path(path)

    if path.suffix == ".json":
        with open(path, "r") as f:
            state = ujson.loads(f.read())
    elif path.suffix == ".pkl":
        with open(path, "rb") as f:
            state = cloudpickle.load(f)
    else:
        raise ValueError(f"`path` must end with `.json` or `.pkl`, but received: {path}")

    dependency_versions = get_dependency_versions()
    saved_dependency_versions = state["metadata"]["dependency_versions"]
    for key, saved_version in saved_dependency_versions.items():
        if dependency_versions[key] != saved_version:
            logger.warning(
                f"There is a mismatch of {key} version between saved model and current environment. "
                f"You saved with `{key}=={saved_version}`, but now you have "
                f"`{key}=={dependency_versions[key]}`. This might cause errors or performance downgrade "
                "on the loaded model, please consider loading the model in the same environment as the "
                "saving environment."
            )
    self.load_state(state)

load_state(state)

Source code in dspy/primitives/module.py 
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def load_state(self, state):
    for name, param in self.named_parameters():
        param.load_state(state[name])

map_named_predictors(func)

Applies a function to all named predictors.

Source code in dspy/primitives/program.py 
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def map_named_predictors(self, func):
    """Applies a function to all named predictors."""
    for name, predictor in self.named_predictors():
        set_attribute_by_name(self, name, func(predictor))
    return self

named_parameters()

Unlike PyTorch, handles (non-recursive) lists of parameters too.

Source code in dspy/primitives/module.py 
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def named_parameters(self):
    """
    Unlike PyTorch, handles (non-recursive) lists of parameters too.
    """

    import dspy
    from dspy.predict.parameter import Parameter

    visited = set()
    named_parameters = []

    def add_parameter(param_name, param_value):
        if isinstance(param_value, Parameter):
            if id(param_value) not in visited:
                visited.add(id(param_value))
                param_name = postprocess_parameter_name(param_name, param_value)
                named_parameters.append((param_name, param_value))

        elif isinstance(param_value, dspy.Module):
            # When a sub-module is pre-compiled, keep it frozen.
            if not getattr(param_value, "_compiled", False):
                for sub_name, param in param_value.named_parameters():
                    add_parameter(f"{param_name}.{sub_name}", param)

    if isinstance(self, Parameter):
        add_parameter("self", self)

    for name, value in self.__dict__.items():
        if isinstance(value, Parameter):
            add_parameter(name, value)

        elif isinstance(value, dspy.Module):
            # When a sub-module is pre-compiled, keep it frozen.
            if not getattr(value, "_compiled", False):
                for sub_name, param in value.named_parameters():
                    add_parameter(f"{name}.{sub_name}", param)

        elif isinstance(value, (list, tuple)):
            for idx, item in enumerate(value):
                add_parameter(f"{name}[{idx}]", item)

        elif isinstance(value, dict):
            for key, item in value.items():
                add_parameter(f"{name}['{key}']", item)

    return named_parameters

named_predictors()

Source code in dspy/primitives/program.py 
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def named_predictors(self):
    from dspy.predict.predict import Predict

    return [(name, param) for name, param in self.named_parameters() if isinstance(param, Predict)]

named_sub_modules(type_=None, skip_compiled=False) -> Generator[tuple[str, BaseModule], None, None]

Find all sub-modules in the module, as well as their names.

Say self.children[4]['key'].sub_module is a sub-module. Then the name will be 'children[4][key].sub_module'. But if the sub-module is accessible at different paths, only one of the paths will be returned.

Source code in dspy/primitives/module.py 
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def named_sub_modules(self, type_=None, skip_compiled=False) -> Generator[tuple[str, "BaseModule"], None, None]:
    """Find all sub-modules in the module, as well as their names.

    Say self.children[4]['key'].sub_module is a sub-module. Then the name will be
    'children[4][key].sub_module'. But if the sub-module is accessible at different
    paths, only one of the paths will be returned.
    """
    if type_ is None:
        type_ = BaseModule

    queue = deque([("self", self)])
    seen = {id(self)}

    def add_to_queue(name, item):
        name = postprocess_parameter_name(name, item)

        if id(item) not in seen:
            seen.add(id(item))
            queue.append((name, item))

    while queue:
        name, item = queue.popleft()

        if isinstance(item, type_):
            yield name, item

        if isinstance(item, BaseModule):
            if skip_compiled and getattr(item, "_compiled", False):
                continue
            for sub_name, sub_item in item.__dict__.items():
                add_to_queue(f"{name}.{sub_name}", sub_item)

        elif isinstance(item, (list, tuple)):
            for i, sub_item in enumerate(item):
                add_to_queue(f"{name}[{i}]", sub_item)

        elif isinstance(item, dict):
            for key, sub_item in item.items():
                add_to_queue(f"{name}[{key}]", sub_item)

parameters()

Source code in dspy/primitives/module.py 
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def parameters(self):
    return [param for _, param in self.named_parameters()]

predictors()

Source code in dspy/primitives/program.py 
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def predictors(self):
    return [param for _, param in self.named_predictors()]

reset_copy()

Deep copy the module and reset all parameters.

Source code in dspy/primitives/module.py 
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def reset_copy(self):
    """Deep copy the module and reset all parameters."""
    new_instance = self.deepcopy()

    for param in new_instance.parameters():
        param.reset()

    return new_instance

save(path, save_program=False)

Save the module.

Save the module to a directory or a file. There are two modes: - save_program=False: Save only the state of the module to a json or pickle file, based on the value of the file extension. - save_program=True: Save the whole module to a directory via cloudpickle, which contains both the state and architecture of the model.

We also save the dependency versions, so that the loaded model can check if there is a version mismatch on critical dependencies or DSPy version.

Parameters:

Name Type Description Default
path str

Path to the saved state file, which should be a .json or .pkl file when save_program=False, and a directory when save_program=True.

 required
save_program bool

If True, save the whole module to a directory via cloudpickle, otherwise only save the state.

 False
Source code in dspy/primitives/module.py 
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def save(self, path, save_program=False):
    """Save the module.

    Save the module to a directory or a file. There are two modes:
    - `save_program=False`: Save only the state of the module to a json or pickle file, based on the value of
        the file extension.
    - `save_program=True`: Save the whole module to a directory via cloudpickle, which contains both the state and
        architecture of the model.

    We also save the dependency versions, so that the loaded model can check if there is a version mismatch on
    critical dependencies or DSPy version.

    Args:
        path (str): Path to the saved state file, which should be a .json or .pkl file when `save_program=False`,
            and a directory when `save_program=True`.
        save_program (bool): If True, save the whole module to a directory via cloudpickle, otherwise only save
            the state.
    """
    metadata = {}
    metadata["dependency_versions"] = get_dependency_versions()
    path = Path(path)

    if save_program:
        if path.suffix:
            raise ValueError(
                f"`path` must point to a directory without a suffix when `save_program=True`, but received: {path}"
            )
        if path.exists() and not path.is_dir():
            raise NotADirectoryError(f"The path '{path}' exists but is not a directory.")

        if not path.exists():
            # Create the directory (and any parent directories)
            path.mkdir(parents=True)

        try:
            with open(path / "program.pkl", "wb") as f:
                cloudpickle.dump(self, f)
        except Exception as e:
            raise RuntimeError(
                f"Saving failed with error: {e}. Please remove the non-picklable attributes from your DSPy program, "
                "or consider using state-only saving by setting `save_program=False`."
            )
        with open(path / "metadata.json", "w") as f:
            ujson.dump(metadata, f, indent=2)

        return

    state = self.dump_state()
    state["metadata"] = metadata
    if path.suffix == ".json":
        try:
            with open(path, "w") as f:
                f.write(ujson.dumps(state, indent=2))
        except Exception as e:
            raise RuntimeError(
                f"Failed to save state to {path} with error: {e}. Your DSPy program may contain non "
                "json-serializable objects, please consider saving the state in .pkl by using `path` ending "
                "with `.pkl`, or saving the whole program by setting `save_program=True`."
            )
    elif path.suffix == ".pkl":
        with open(path, "wb") as f:
            cloudpickle.dump(state, f)
    else:
        raise ValueError(f"`path` must end with `.json` or `.pkl` when `save_program=False`, but received: {path}")

set_lm(lm)

Source code in dspy/primitives/program.py 
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def set_lm(self, lm):
    for _, param in self.named_predictors():
        param.lm = lm