mlflow.langchain

The mlflow.langchain module provides an API for logging and loading LangChain models. This module exports multivariate LangChain models in the langchain flavor and univariate LangChain models in the pyfunc flavor:

LangChain (native) format: This is the main flavor that can be accessed with LangChain APIs.
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and for batch inference.

mlflow.langchain.get_default_conda_env()[source]

Returns: The default Conda environment for MLflow Models produced by calls to save_model() and log_model().

mlflow.langchain.get_default_pip_requirements()[source]

Returns: A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at a minimum, contains these requirements.

mlflow.langchain.load_model(model_uri, dst_path=None)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Load a LangChain model from a local file or a run.

Parameters

model_uri –
The location, in URI format, of the MLflow model. For example:
- /Users/me/path/to/local/model
- relative/path/to/local/model
- s3://my_bucket/path/to/model
- runs:/<mlflow_run_id>/run-relative/path/to/model
For more information about supported URI schemes, see Referencing Artifacts.
dst_path – The local filesystem path to which to download the model artifact. This directory must already exist. If unspecified, a local output path will be created.

Returns

A LangChain model instance

mlflow.langchain.log_model(lc_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None, loader_fn=None, persist_dir=None)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Log a LangChain model as an MLflow artifact for the current run.

Parameters

lc_model – A LangChain model, which could be a Chain, Agent, or retriever.
artifact_path – Run-relative artifact path.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "langchain==x.y.z"
            ],
        },
    ],
}
```
code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.
registered_model_name – This argument may change or be removed in a future release without warning. If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.
signature –
ModelSignature describes model input and output Schema. If not specified, the model signature would be set according to lc_model.input_keys and lc_model.output_keys as columns names, and DataType.string as the column type. Alternatively, you can explicitly specify the model signature. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:
```
from mlflow.models import infer_signature

chain = LLMChain(llm=llm, prompt=prompt)
prediction = chain.run(input_str)
input_columns = [
    {"type": "string", "name": input_key} for input_key in chain.input_keys
]
signature = infer_signature(input_columns, predictions)
```
input_example – Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["langchain", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata –
Custom metadata dictionary passed to the model and stored in the MLmodel file.

Note

Experimental: This parameter may change or be removed in a future release without warning.
loader_fn –
A function that’s required for models containing objects that aren’t natively serialized by LangChain. This function takes a string persist_dir as an argument and returns the specific object that the model needs. Depending on the model, this could be a retriever, vectorstore, requests_wrapper, embeddings, or database. For RetrievalQA Chain and retriever models, the object is a (retriever). For APIChain models, it’s a (requests_wrapper). For HypotheticalDocumentEmbedder models, it’s an (embeddings). For SQLDatabaseChain models, it’s a (database).

persist_dir –

The directory where the object is stored. The loader_fn takes this string as the argument to load the object. This is optional for models containing objects that aren’t natively serialized by LangChain. MLflow logs the content in this directory as artifacts in the subdirectory named persist_dir_data.

Here is the code snippet for logging a RetrievalQA chain with loader_fn and persist_dir:

qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=db.as_retriever())


def load_retriever(persist_directory):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.load_local(persist_directory, embeddings)
    return vectorstore.as_retriever()


with mlflow.start_run() as run:
    logged_model = mlflow.langchain.log_model(
        qa,
        artifact_path="retrieval_qa",
        loader_fn=load_retriever,
        persist_dir=persist_dir,
    )

See a complete example in examples/langchain/retrieval_qa_chain.py.

Returns

A ModelInfo instance that contains the metadata of the logged model.

mlflow.langchain.save_model(lc_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None, loader_fn=None, persist_dir=None)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Save a LangChain model to a path on the local file system.

Parameters

lc_model –
A LangChain model, which could be a Chain, Agent, or retriever.
path – Local path where the serialized model (as YAML) is to be saved.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "langchain==x.y.z"
            ],
        },
    ],
}
```
code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.
mlflow_model – mlflow.models.Model this flavor is being added to.
signature –
ModelSignature describes model input and output Schema. If not specified, the model signature would be set according to lc_model.input_keys and lc_model.output_keys as columns names, and DataType.string as the column type. Alternatively, you can explicitly specify the model signature. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:
```
from mlflow.models import infer_signature

chain = LLMChain(llm=llm, prompt=prompt)
prediction = chain.run(input_str)
input_columns = [
    {"type": "string", "name": input_key} for input_key in chain.input_keys
]
signature = infer_signature(input_columns, predictions)
```
input_example – Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["langchain", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata –
Custom metadata dictionary passed to the model and stored in the MLmodel file.

Note

Experimental: This parameter may change or be removed in a future release without warning.
loader_fn –
A function that’s required for models containing objects that aren’t natively serialized by LangChain. This function takes a string persist_dir as an argument and returns the specific object that the model needs. Depending on the model, this could be a retriever, vectorstore, requests_wrapper, embeddings, or database. For RetrievalQA Chain and retriever models, the object is a (retriever). For APIChain models, it’s a (requests_wrapper). For HypotheticalDocumentEmbedder models, it’s an (embeddings). For SQLDatabaseChain models, it’s a (database).

persist_dir –

The directory where the object is stored. The loader_fn takes this string as the argument to load the object. This is optional for models containing objects that aren’t natively serialized by LangChain. MLflow logs the content in this directory as artifacts in the subdirectory named persist_dir_data.

Here is the code snippet for logging a RetrievalQA chain with loader_fn and persist_dir:

qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=db.as_retriever())


def load_retriever(persist_directory):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.load_local(persist_directory, embeddings)
    return vectorstore.as_retriever()


with mlflow.start_run() as run:
    logged_model = mlflow.langchain.log_model(
        qa,
        artifact_path="retrieval_qa",
        loader_fn=load_retriever,
        persist_dir=persist_dir,
    )

See a complete example in examples/langchain/retrieval_qa_chain.py.