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venv/lib/python3.9/site-packages/mlflow/prompt/promptlab_model.py

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+import os
+import re
+
+import yaml
+
+from mlflow.exceptions import MlflowException
+from mlflow.version import VERSION as __version__
+
+
+class _PromptlabModel:
+    import pandas as pd
+
+    def __init__(self, prompt_template, prompt_parameters, model_parameters, model_route):
+        self.prompt_parameters = prompt_parameters
+        self.model_parameters = model_parameters
+        self.model_route = model_route
+        self.prompt_template = prompt_template
+
+    def predict(self, inputs: pd.DataFrame) -> list[str]:
+        from mlflow.gateway import query
+
+        results = []
+        for idx in inputs.index:
+            prompt_parameters_as_dict = {
+                param.key: inputs[param.key][idx] for param in self.prompt_parameters
+            }
+
+            # copy replacement logic from PromptEngineering.utils.ts for consistency
+            prompt = self.prompt_template
+            for key, value in prompt_parameters_as_dict.items():
+                prompt = re.sub(r"\{\{\s*" + key + r"\s*\}\}", value, prompt)
+
+            model_parameters_as_dict = {param.key: param.value for param in self.model_parameters}
+            query_data = self._construct_query_data(prompt)
+
+            response = query(
+                route=self.model_route, data={**query_data, **model_parameters_as_dict}
+            )
+            results.append(self._parse_gateway_response(response))
+
+        return results
+
+    def _construct_query_data(self, prompt):
+        from mlflow.gateway import get_route
+
+        route_type = get_route(self.model_route).route_type
+
+        if route_type == "llm/v1/completions":
+            return {"prompt": prompt}
+        elif route_type == "llm/v1/chat":
+            return {"messages": [{"content": prompt, "role": "user"}]}
+        else:
+            raise MlflowException(
+                "Error when constructing gateway query: "
+                f"Unsupported route type for _PromptlabModel: {route_type}"
+            )
+
+    def _parse_gateway_response(self, response):
+        from mlflow.gateway import get_route
+
+        route_type = get_route(self.model_route).route_type
+
+        if route_type == "llm/v1/completions":
+            return response["choices"][0]["text"]
+        elif route_type == "llm/v1/chat":
+            return response["choices"][0]["message"]["content"]
+        else:
+            raise MlflowException(
+                "Error when parsing gateway response: "
+                f"Unsupported route type for _PromptlabModel: {route_type}"
+            )
+
+
+def _load_pyfunc(path):
+    from mlflow import pyfunc
+    from mlflow.entities.param import Param
+    from mlflow.utils.model_utils import (
+        _get_flavor_configuration,
+    )
+
+    pyfunc_flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=pyfunc.FLAVOR_NAME)
+    parameters_path = os.path.join(path, pyfunc_flavor_conf["parameters_path"])
+    with open(parameters_path) as f:
+        parameters = yaml.safe_load(f)
+
+        prompt_parameters_as_params = [
+            Param(key=key, value=value) for key, value in parameters["prompt_parameters"].items()
+        ]
+        model_parameters_as_params = [
+            Param(key=key, value=value) for key, value in parameters["model_parameters"].items()
+        ]
+
+        return _PromptlabModel(
+            prompt_template=parameters["prompt_template"],
+            prompt_parameters=prompt_parameters_as_params,
+            model_parameters=model_parameters_as_params,
+            model_route=parameters["model_route"],
+        )
+
+
+def save_model(
+    path,
+    conda_env=None,
+    code_paths=None,
+    mlflow_model=None,
+    signature=None,
+    input_example=None,
+    pip_requirements=None,
+    prompt_template=None,
+    prompt_parameters=None,
+    model_parameters=None,
+    model_route=None,
+):
+    from mlflow import pyfunc
+    from mlflow.models import Model
+    from mlflow.models.model import MLMODEL_FILE_NAME, Model
+    from mlflow.models.utils import _save_example
+    from mlflow.utils.environment import (
+        _CONDA_ENV_FILE_NAME,
+        _CONSTRAINTS_FILE_NAME,
+        _PYTHON_ENV_FILE_NAME,
+        _REQUIREMENTS_FILE_NAME,
+        _process_conda_env,
+        _process_pip_requirements,
+        _PythonEnv,
+        _validate_env_arguments,
+        infer_pip_requirements,
+    )
+    from mlflow.utils.file_utils import write_to
+    from mlflow.utils.model_utils import (
+        _validate_and_copy_code_paths,
+        _validate_and_prepare_target_save_path,
+    )
+
+    _validate_env_arguments(conda_env, pip_requirements, None)
+
+    _validate_and_prepare_target_save_path(path)
+    code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
+
+    if mlflow_model is None:
+        mlflow_model = Model()
+    if signature is not None:
+        mlflow_model.signature = signature
+    if input_example is not None:
+        _save_example(mlflow_model, input_example, path)
+
+    parameters_sub_path = "parameters.yaml"
+    parameters_path = os.path.join(path, parameters_sub_path)
+    # dump prompt_template, prompt_parameters, model_parameters, model_route to parameters_path
+
+    parameters = {
+        "prompt_template": prompt_template,
+        "prompt_parameters": {param.key: param.value for param in prompt_parameters},
+        "model_parameters": {param.key: param.value for param in model_parameters},
+        "model_route": model_route,
+    }
+    with open(parameters_path, "w") as f:
+        yaml.safe_dump(parameters, stream=f, default_flow_style=False)
+
+    pyfunc.add_to_model(
+        mlflow_model,
+        loader_module="mlflow.prompt.promptlab_model",
+        parameters_path=parameters_sub_path,
+        conda_env=_CONDA_ENV_FILE_NAME,
+        python_env=_PYTHON_ENV_FILE_NAME,
+        code=code_dir_subpath,
+    )
+
+    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
+
+    if conda_env is None:
+        if pip_requirements is None:
+            inferred_reqs = infer_pip_requirements(
+                path, "mlflow._promptlab", [f"mlflow[gateway]=={__version__}"]
+            )
+            default_reqs = sorted(inferred_reqs)
+        else:
+            default_reqs = None
+        conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
+            default_reqs, pip_requirements, None
+        )
+    else:
+        conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env)
+
+    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
+
+    if pip_constraints:
+        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+
+    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+
+    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))