diff --git a/benchmark/data/benchmark_api_calling_data.yaml b/benchmark/data/benchmark_api_calling_data.yaml
index 85340a81..8405259d 100644
--- a/benchmark/data/benchmark_api_calling_data.yaml
+++ b/benchmark/data/benchmark_api_calling_data.yaml
@@ -73,3 +73,24 @@ api_calling:
     expected:
       parts_of_query:
         ["https://bio.tools/api/t/", "\\?topic=", "[mM]etabolomics"]
+  - case: scanpy:pl:scatter
+    input:
+      prompt:
+        exact_variable_names: "Make a scatter plot of n_genes_by_counts vs total_counts."
+    expected:
+      parts_of_query:
+        ["sc.pl.scatter\\(", "n_genes_by_counts", "total_counts", "\\)"]
+  - case: scanpy:pl:pca
+    input:
+      prompt:
+        fuzzy_search: "plot the PCA of the data colored by n_genes_by_counts and total_counts."
+    expected:
+      parts_of_query:
+        ["sc.pl.pca\\(", ["n_genes_by_counts", "total_counts"], "\\)"]
+  - case: scanpy:pl:tsne
+    input:
+      prompt:
+        fuzzy_search: "plot the tsne embeddding of the data colored by n_genes_by_counts."
+    expected:
+      parts_of_query:
+        ["sc.pl.tsne\\(", "n_genes_by_counts", "\\)"]
diff --git a/benchmark/test_api_calling.py b/benchmark/test_api_calling.py
index 67c51789..749dbedc 100644
--- a/benchmark/test_api_calling.py
+++ b/benchmark/test_api_calling.py
@@ -5,7 +5,7 @@
 import pytest
 
 from biochatter._misc import ensure_iterable
-from biochatter.api_agent import BioToolsQueryBuilder, OncoKBQueryBuilder
+from biochatter.api_agent import BioToolsQueryBuilder, OncoKBQueryBuilder, ScanpyPlQueryBuilder, format_as_rest_call, format_as_python_call
 
 from .benchmark_utils import (
     get_result_file_path,
@@ -15,7 +15,7 @@
 from .conftest import calculate_bool_vector_score
 
 
-def test_api_calling(
+def test_web_api_calling(
     model_name,
     test_data_api_calling,
     conversation,
@@ -32,6 +32,10 @@ def test_api_calling(
         pytest.skip(
             f"model {model_name} does not support API calling for {task} benchmark",
         )
+    if "scanpy" in yaml_data["case"]:
+        pytest.skip(
+            "scanpy is not a web API",
+        )
 
     def run_test():
         conversation.reset()  # needs to be reset for each test
@@ -39,17 +43,14 @@ def run_test():
             builder = OncoKBQueryBuilder()
         elif "biotools" in yaml_data["case"]:
             builder = BioToolsQueryBuilder()
+        elif "scanpy:pl" in yaml_data["case"]:
+            builder = ScanpyPlQueryBuilder()
         parameters = builder.parameterise_query(
             question=yaml_data["input"]["prompt"],
             conversation=conversation,
         )
 
-        params = parameters.dict(exclude_none=True)
-        endpoint = params.pop("endpoint")
-        base_url = params.pop("base_url")
-        params.pop("question_uuid")
-        full_url = f"{base_url.rstrip('/')}/{endpoint.lstrip('/')}"
-        api_query = f"{full_url}?{urlencode(params)}"
+        api_query = format_as_rest_call(parameters)
 
         score = []
         for expected_part in ensure_iterable(
@@ -72,3 +73,57 @@ def run_test():
         yaml_data["hash"],
         get_result_file_path(task),
     )
+
+def test_python_api_calling(
+    model_name,
+    test_data_api_calling,
+    conversation,
+    multiple_testing,
+):
+    """Test the Python API calling capability."""
+    task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}"
+    yaml_data = test_data_api_calling
+
+    skip_if_already_run(
+        model_name=model_name,
+        task=task,
+        md5_hash=yaml_data["hash"],
+    )
+
+    if "scanpy" not in yaml_data["case"]:
+        pytest.skip(
+            "Function to be tested is not a Python API",
+        )
+
+    def run_test():
+        conversation.reset()  # needs to be reset for each test
+        if "scanpy:pl" in yaml_data["case"]:
+            builder = ScanpyPlQueryBuilder()
+        parameters = builder.parameterise_query(
+            question=yaml_data["input"]["prompt"],
+            conversation=conversation,
+        )
+
+        method_call = format_as_python_call(parameters)
+
+        score = []
+        for expected_part in ensure_iterable(
+            yaml_data["expected"]["parts_of_query"],
+        ):
+            if re.search(expected_part, method_call):
+                score.append(True)
+            else:
+                score.append(False)
+
+        return calculate_bool_vector_score(score)
+
+    mean_score, max, n_iterations = multiple_testing(run_test)
+
+    write_results_to_file(
+        model_name,
+        yaml_data["case"],
+        f"{mean_score}/{max}",
+        f"{n_iterations}",
+        yaml_data["hash"],
+        get_result_file_path(task),
+    )
diff --git a/biochatter/api_agent/__init__.py b/biochatter/api_agent/__init__.py
index 21f0fca9..2de0343d 100644
--- a/biochatter/api_agent/__init__.py
+++ b/biochatter/api_agent/__init__.py
@@ -12,6 +12,8 @@
     BlastQueryParameters,
 )
 from .oncokb import OncoKBFetcher, OncoKBInterpreter, OncoKBQueryBuilder
+from .scanpy_pl import ScanpyPlQueryBuilder
+from .formatters import format_as_rest_call, format_as_python_call
 
 __all__ = [
     "BaseFetcher",
@@ -28,4 +30,7 @@
     "BioToolsInterpreter",
     "BioToolsQueryBuilder",
     "APIAgent",
-]
+    "ScanpyPlQueryBuilder",
+    "format_as_rest_call",
+    "format_as_python_call",
+]
\ No newline at end of file
diff --git a/biochatter/api_agent/formatters.py b/biochatter/api_agent/formatters.py
new file mode 100644
index 00000000..f2009224
--- /dev/null
+++ b/biochatter/api_agent/formatters.py
@@ -0,0 +1,41 @@
+"""Formatters for API calls (Pydantic models to strings)."""
+
+from urllib.parse import urlencode
+
+from pydantic import BaseModel
+
+def format_as_rest_call(model: BaseModel) -> str:
+    """Convert a parameter model (BaseModel) into a REST API call string.
+
+    Args:
+        model: Pydantic model containing API call parameters
+
+    Returns:
+        String representation of the REST API call
+
+    """
+    params = model.dict(exclude_none=True)
+    endpoint = params.pop("endpoint")
+    base_url = params.pop("base_url")
+    params.pop("question_uuid", None)
+
+    full_url = f"{base_url.rstrip('/')}/{endpoint.strip('/')}"
+    return f"{full_url}?{urlencode(params)}"
+
+def format_as_python_call(model: BaseModel) -> str:
+    """Convert a parameter model into a Python method call string.
+
+    Args:
+        model: Pydantic model containing method parameters
+
+    Returns:
+        String representation of the Python method call
+
+    """
+    params = model.dict(exclude_none=True)
+    method_name = params.pop("method_name", None)
+    params.pop("question_uuid", None)
+
+    param_str = ", ".join(f"{k}={repr(v)}" for k, v in params.items())
+
+    return f"{method_name}({param_str})"
diff --git a/biochatter/api_agent/scanpy_pl.py b/biochatter/api_agent/scanpy_pl.py
new file mode 100644
index 00000000..d288048c
--- /dev/null
+++ b/biochatter/api_agent/scanpy_pl.py
@@ -0,0 +1,514 @@
+"""Module for interacting with the `scanpy` API for plotting (`pl`)."""
+
+import uuid
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from langchain.chains.openai_functions import create_structured_output_runnable
+from langchain_core.pydantic_v1 import BaseModel, Field
+
+if TYPE_CHECKING:
+    from biochatter.llm_connect import Conversation
+
+from .abc import BaseQueryBuilder
+
+SCANPY_PL_QUERY_PROMPT = """
+You are a world class algorithm for creating queries in structured formats.
+Your task is to use the Python API of `scanpy` to answer questions about the `scanpy` plotting module.
+You should prefix the function calls with `scanpy.pl.`, for instance, for a scatter plot, you should use
+`scanpy.pl.scatter`.
+
+Extract information from the following documentation to answer the user's question:
+
+The plotting module scanpy.pl largely parallels the tl.* and a few of the pp.* functions. For most tools and for some
+preprocessing functions, you’ll find a plotting function with the same name.
+
+Generic
+pl.scatter
+Scatter plot along observations or variables axes.
+pl.heatmap
+Heatmap of the expression values of genes.
+pl.dotplot
+Makes a dot plot of the expression values of var_names.
+pl.tracksplot
+In this type of plot each var_name is plotted as a filled line plot where the y values correspond to the var_name values
+and x is each of the cells.
+pl.violin
+Violin plot.
+pl.stacked_violin
+Stacked violin plots.
+pl.matrixplot
+Creates a heatmap of the mean expression values per group of each var_names.
+pl.clustermap
+Hierarchically-clustered heatmap.
+pl.ranking
+Plot rankings.
+pl.dendrogram
+Plots a dendrogram of the categories defined in groupby.
+Classes
+These classes allow fine tuning of visual parameters.
+
+pl.DotPlot
+Allows the visualization of two values that are encoded as dot size and color.
+pl.MatrixPlot
+Allows the visualization of values using a color map.
+pl.StackedViolin
+Stacked violin plots.
+Preprocessing
+Methods for visualizing quality control and results of preprocessing functions.
+
+pl.highest_expr_genes
+Fraction of counts assigned to each gene over all cells.
+pl.filter_genes_dispersion
+Plot dispersions versus means for genes.
+pl.highly_variable_genes
+Plot dispersions or normalized variance versus means for genes.
+pl.scrublet_score_distribution
+Plot histogram of doublet scores for observed transcriptomes and simulated doublets.
+Tools
+Methods that extract and visualize tool-specific annotation in an AnnData object. For any method in module tl, there is a method with the same name in pl.
+
+PCA
+
+pl.pca
+Scatter plot in PCA coordinates.
+pl.pca_loadings
+Rank genes according to contributions to PCs.
+pl.pca_variance_ratio
+Plot the variance ratio.
+pl.pca_overview
+Plot PCA results.
+Embeddings
+
+pl.tsne
+Scatter plot in tSNE basis.
+pl.umap
+Scatter plot in UMAP basis.
+pl.diffmap
+Scatter plot in Diffusion Map basis.
+pl.draw_graph
+Scatter plot in graph-drawing basis.
+pl.spatial
+Scatter plot in spatial coordinates.
+pl.embedding
+Scatter plot for user specified embedding basis (e.g. umap, pca, etc).
+Compute densities on embeddings.
+
+pl.embedding_density
+Plot the density of cells in an embedding (per condition).
+Branching trajectories and pseudotime, clustering
+
+Visualize clusters using one of the embedding methods passing color='louvain'.
+
+pl.dpt_groups_pseudotime
+Plot groups and pseudotime.
+pl.dpt_timeseries
+Heatmap of pseudotime series.
+pl.paga
+Plot the PAGA graph through thresholding low-connectivity edges.
+pl.paga_path
+Gene expression and annotation changes along paths in the abstracted graph.
+pl.paga_compare
+Scatter and PAGA graph side-by-side.
+Marker genes
+
+pl.rank_genes_groups
+Plot ranking of genes.
+pl.rank_genes_groups_violin
+Plot ranking of genes for all tested comparisons.
+pl.rank_genes_groups_stacked_violin
+Plot ranking of genes using stacked_violin plot (see stacked_violin())
+pl.rank_genes_groups_heatmap
+Plot ranking of genes using heatmap plot (see heatmap())
+pl.rank_genes_groups_dotplot
+Plot ranking of genes using dotplot plot (see dotplot())
+pl.rank_genes_groups_matrixplot
+Plot ranking of genes using matrixplot plot (see matrixplot())
+pl.rank_genes_groups_tracksplot
+Plot ranking of genes using heatmap plot (see heatmap())
+Simulations
+
+pl.sim
+Plot results of simulation.
+"""
+
+
+class ScanpyPlScatterQueryParameters(BaseModel):
+    """Parameters for querying the scanpy `pl.scatter` API."""
+
+    method_name: str = Field(
+        default="sc.pl.scatter",
+        description="The name of the method to call.",
+    )
+    question_uuid: str | None = Field(
+        default_factory=lambda: str(uuid.uuid4()),
+        description="Unique identifier for the question.",
+    )
+    adata: str = Field(description="Annotated data matrix.",)
+    x: str | None = Field(default=None, description="x coordinate.",)
+    y: str | None = Field(default=None, description="y coordinate.",)
+    color: str | tuple[float, ...] | list[str | tuple[float, ...]] | None = Field(
+        default=None,
+        description="Keys for annotations of observations/cells or variables/genes, or a hex color specification.",
+    )
+    use_raw: bool | None = Field(
+        default=None,
+        description="Whether to use raw attribute of adata. Defaults to True if .raw is present.",
+    )
+    layers: str | list[str] | None = Field(
+        default=None,
+        description="Layer(s) to use from adata's layers attribute.",
+    )
+    basis: str | None = Field(
+        default=None,
+        description="String that denotes a plotting tool that computed coordinates (e.g., 'pca', 'tsne', 'umap').",
+    )
+    sort_order: bool = Field(
+        default=True,
+        description="For continuous annotations used as color parameter, plot data points with higher values on top.",
+    )
+    groups: str | list[str] | None = Field(
+        default=None,
+        description="Restrict to specific categories in categorical observation annotation.",
+    )
+    projection: str = Field(
+        default="2d",
+        description="Projection of plot ('2d' or '3d').",
+    )
+    legend_loc: str | None = Field(
+        default="right margin",
+        description="Location of legend ('none', 'right margin', 'on data', etc.).",
+    )
+    size: int | float | None = Field(
+        default=None,
+        description="Point size. If None, automatically computed as 120000 / n_cells.",
+    )
+    color_map: str | None = Field(
+        default=None,
+        description="Color map to use for continuous variables (e.g., 'magma', 'viridis').",
+    )
+    title: str | list[str] | None = Field(
+        default=None,
+        description="Title for panels either as string or list of strings.",
+    )
+    show: bool | None = Field(
+        default=None,
+        description="Show the plot, do not return axis.",
+    )
+    save: str | bool | None = Field(
+        default=None,
+        description="If True or a str, save the figure. String is appended to default filename.",
+    )
+
+### Embeddings
+class ScanpyPlPcaQueryParameters(BaseModel):
+    """Parameters for querying the scanpy `pl.pca` API."""
+
+    question_uuid: str | None = Field(
+        default=None,
+        description="Unique identifier for the question.",
+    )
+    adata: str = Field(
+        ...,
+        description="Annotated data matrix.",
+    )
+    color: str | list[str] | None = Field(
+        default=None,
+        description="Keys for annotations of observations/cells or variables/genes.",
+    )
+    components: str | list[str] = Field(
+        default="1,2",
+        description="For example, ['1,2', '2,3']. To plot all available components use 'all'.",
+    )
+    projection: str = Field(
+        default="2d",
+        description="Projection of plot.",
+    )
+    legend_loc: str = Field(
+        default="right margin",
+        description="Location of legend.",
+    )
+    legend_fontsize: int | float | str | None = Field(
+        default=None,
+        description="Font size for legend.",
+    )
+    legend_fontweight: int | str | None = Field(
+        default=None,
+        description="Font weight for legend.",
+    )
+    color_map: str | None = Field(
+        default=None,
+        description="String denoting matplotlib color map.",
+    )
+    palette: str | list[str] | dict | None = Field(
+        default=None,
+        description="Colors to use for plotting categorical annotation groups.",
+    )
+    frameon: bool | None = Field(
+        default=None,
+        description="Draw a frame around the scatter plot.",
+    )
+    size: int | float | None = Field(
+        default=None,
+        description="Point size. If `None`, is automatically computed as 120000 / n_cells.",
+    )
+    title: str | list[str] | None = Field(
+        default=None,
+        description="Provide title for panels either as string or list of strings.",
+    )
+    show: bool | None = Field(
+        default=None,
+        description="Show the plot, do not return axis.",
+    )
+    save: str | bool | None = Field(
+        default=None,
+        description="If `True` or a `str`, save the figure.",
+    )
+    ax: str | None = Field(
+        default=None,
+        description="A matplotlib axes object.",
+    )
+    return_fig: bool = Field(
+        default=False,
+        description="Return the matplotlib figure object.",
+    )
+    marker: str | None = Field(
+        default=".",
+        description="Marker symbol.",
+    )
+    annotate_var_explained: bool = Field(
+        default=False,
+        description="Annotate the percentage of explained variance.",
+    )
+
+class ScanpyPlTsneQueryParameters(BaseModel):
+    """Parameters for querying the Scanpy `pl.tsne` API."""
+
+    adata: str = Field(
+        ...,
+        description="Annotated data matrix.",
+    )
+    color: str | list[str] | None = Field(
+        default=None,
+        description="Keys for annotations of observations/cells or variables/genes.",
+    )
+    gene_symbols: str | None = Field(
+        default=None,
+        description="Column name in `.var` DataFrame that stores gene symbols.",
+    )
+    use_raw: bool | None = Field(
+        default=None,
+        description="Use `.raw` attribute of `adata` for coloring with gene expression.",
+    )
+    sort_order: bool = Field(
+        default=True,
+        description="Plot data points with higher values on top for continuous annotations.",
+    )
+    edges: bool = Field(
+        default=False,
+        description="Show edges.",
+    )
+    edges_width: float = Field(
+        default=0.1,
+        description="Width of edges.",
+    )
+    edges_color: str | list[float] | list[str] = Field(
+        default="grey",
+        description="Color of edges.",
+    )
+    neighbors_key: str | None = Field(
+        default=None,
+        description="Key for neighbors connectivities.",
+    )
+    arrows: bool = Field(
+        default=False,
+        description="Show arrows (deprecated in favor of `scvelo.pl.velocity_embedding`).",
+    )
+    arrows_kwds: dict[str, Any] | None = Field(
+        default=None,
+        description="Arguments passed to `quiver()`.",
+    )
+    groups: str | None = Field(
+        default=None,
+        description="Restrict to specific categories in categorical observation annotation.",
+    )
+    components: str | list[str] | None = Field(
+        default=None,
+        description="Components to plot, e.g., ['1,2', '2,3']. Use 'all' to plot all available components.",
+    )
+    projection: str = Field(
+        default="2d",
+        description="Projection of plot ('2d' or '3d').",
+    )
+    legend_loc: str = Field(
+        default="right margin",
+        description="Location of legend.",
+    )
+    legend_fontsize: int | float | str | None = Field(
+        default=None,
+        description="Font size for legend.",
+    )
+    legend_fontweight: int | str = Field(
+        default="bold",
+        description="Font weight for legend.",
+    )
+    legend_fontoutline: int | None = Field(
+        default=None,
+        description="Line width of the legend font outline in pt.",
+    )
+    size: float | list[float] | None = Field(
+        default=None,
+        description="Point size. If `None`, computed as 120000 / n_cells.",
+    )
+    color_map: str | Any | None = Field(
+        default=None,
+        description="Color map for continuous variables.",
+    )
+    palette: str | list[str] | Any | None = Field(
+        default=None,
+        description="Colors for plotting categorical annotation groups.",
+    )
+    na_color: str | tuple[float, ...] = Field(
+        default="lightgray",
+        description="Color for null or masked values.",
+    )
+    na_in_legend: bool = Field(
+        default=True,
+        description="Include missing values in the legend.",
+    )
+    frameon: bool | None = Field(
+        default=None,
+        description="Draw a frame around the scatter plot.",
+    )
+    title: str | list[str] | None = Field(
+        default=None,
+        description="Title for panels, either as string or list of strings.",
+    )
+    vmin: str | float | Any | list[str | float | Any] | None = Field(
+        default=None,
+        description="Lower limit of the color scale.",
+    )
+    vmax: str | float | Any | list[str | float | Any] | None = Field(
+        default=None,
+        description="Upper limit of the color scale.",
+    )
+    vcenter: str | float | Any | list[str | float | Any] | None = Field(
+        default=None,
+        description="Center of the color scale, useful for diverging colormaps.",
+    )
+    norm: Any | None = Field(
+        default=None,
+        description="Normalization for the colormap.",
+    )
+    add_outline: bool = Field(
+        default=False,
+        description="Add a thin border around groups of dots.",
+    )
+    outline_width: tuple[float, float] = Field(
+        default=(0.3, 0.05),
+        description="Width of the outline as a fraction of the scatter dot size.",
+    )
+    outline_color: tuple[str, str] = Field(
+        default=("black", "white"),
+        description="Colors for the outline: border color and gap color.",
+    )
+    ncols: int = Field(
+        default=4,
+        description="Number of panels per row.",
+    )
+    hspace: float = Field(
+        default=0.25,
+        description="Height of the space between multiple panels.",
+    )
+    wspace: float | None = Field(
+        default=None,
+        description="Width of the space between multiple panels.",
+    )
+    return_fig: bool | None = Field(
+        default=None,
+        description="Return the matplotlib figure.",
+    )
+    show: bool | None = Field(
+        default=None,
+        description="Show the plot; do not return axis.",
+    )
+    save: str | bool | None = Field(
+        default=None,
+        description="If `True` or a `str`, save the figure.",
+    )
+    ax: Any | None = Field(
+        default=None,
+        description="A matplotlib axes object.",
+    )
+    kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description="Additional arguments passed to `matplotlib.pyplot.scatter()`.",
+    )
+
+class ScanpyPlQueryBuilder(BaseQueryBuilder):
+    """A class for building an ScanpyPlQuery object."""
+
+    def create_runnable(
+        self,
+        query_parameters: "ScanpyPlScatterQueryParameters",
+        conversation: "Conversation",
+    ) -> Callable:
+        """Create a runnable object for executing queries.
+
+        Create runnable using the LangChain `create_structured_output_runnable`
+        method.
+
+        Args:
+        ----
+            query_parameters: A Pydantic data model that specifies the fields of
+                the API that should be queried.
+
+            conversation: A BioChatter conversation object.
+
+        Returns:
+        -------
+            A Callable object that can execute the query.
+
+        """
+        return create_structured_output_runnable(
+            output_schema=query_parameters,
+            llm=conversation.chat,
+            prompt=self.structured_output_prompt,
+        )
+
+    def parameterise_query(
+        self,
+        question: str,
+        conversation: "Conversation",
+    ) -> ScanpyPlScatterQueryParameters:
+        """Generate an ScanpyPlQuery object.
+
+        Generate a ScanpyPlQuery object based on the given question, prompt,
+        and BioChatter conversation. Uses a Pydantic model to define the API
+        fields.  Creates a runnable that can be invoked on LLMs that are
+        qualified to parameterise functions.
+
+        Args:
+        ----
+            question (str): The question to be answered.
+
+            conversation: The conversation object used for parameterising the
+                ScanpyPlQuery.
+
+        Returns:
+        -------
+            ScanpyPlQueryParameters: the parameterised query object (Pydantic
+                model)
+
+        """
+        runnable = self.create_runnable(
+            query_parameters=ScanpyPlScatterQueryParameters,
+            conversation=conversation,
+        )
+        scanpy_pl_call_obj = runnable.invoke(
+            {
+                "input": f"Answer:\n{question} based on:\n {SCANPY_PL_QUERY_PROMPT}",
+            },
+        )
+        scanpy_pl_call_obj.question_uuid = str(uuid.uuid4())
+        return scanpy_pl_call_obj
diff --git a/poetry.lock b/poetry.lock
index 7489f058..7c652422 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "accelerate"
-version = "1.1.1"
+version = "1.2.0"
 description = "Accelerate"
 optional = true
 python-versions = ">=3.9.0"
 files = [
-    {file = "accelerate-1.1.1-py3-none-any.whl", hash = "sha256:61edd81762131b8d4bede008643fa1e1f3bf59bec710ebda9771443e24feae02"},
-    {file = "accelerate-1.1.1.tar.gz", hash = "sha256:0d39dfac557052bc735eb2703a0e87742879e1e40b88af8a2f9a93233d4cd7db"},
+    {file = "accelerate-1.2.0-py3-none-any.whl", hash = "sha256:815708367eb83cd682e120da634b2cf318729bff95f30e2f046c086aae20201e"},
+    {file = "accelerate-1.2.0.tar.gz", hash = "sha256:4187a9dea21101255ada3a1974532585feeb6ca8b681693dba362d6d25e85ef2"},
 ]
 
 [package.dependencies]
@@ -66,87 +66,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.9"
+version = "3.11.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -258,24 +258,24 @@ vertex = ["google-auth (>=2,<3)"]
 
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 [package.dependencies]
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 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
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-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
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 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -796,73 +796,73 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
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 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
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 brotli = ["brotli", "brotlicffi"]
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+    {file = "jiter-0.8.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:859e8eb3507894093d01929e12e267f83b1d5f6221099d3ec976f0c995cb6bd9"},
+    {file = "jiter-0.8.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaa58399c01db555346647a907b4ef6d4f584b123943be6ed5588c3f2359c9f4"},
+    {file = "jiter-0.8.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8f2d5ed877f089862f4c7aacf3a542627c1496f972a34d0474ce85ee7d939c27"},
+    {file = "jiter-0.8.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:03c9df035d4f8d647f8c210ddc2ae0728387275340668fb30d2421e17d9a0841"},
+    {file = "jiter-0.8.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8bd2a824d08d8977bb2794ea2682f898ad3d8837932e3a74937e93d62ecbb637"},
+    {file = "jiter-0.8.2-cp39-cp39-win32.whl", hash = "sha256:ca29b6371ebc40e496995c94b988a101b9fbbed48a51190a4461fcb0a68b4a36"},
+    {file = "jiter-0.8.2-cp39-cp39-win_amd64.whl", hash = "sha256:1c0dfbd1be3cbefc7510102370d86e35d1d53e5a93d48519688b1bf0f761160a"},
+    {file = "jiter-0.8.2.tar.gz", hash = "sha256:cd73d3e740666d0e639f678adb176fad25c1bcbdae88d8d7b857e1783bb4212d"},
 ]
 
 [[package]]
@@ -2633,13 +2635,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.47"
+version = "9.5.48"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.47-py3-none-any.whl", hash = "sha256:53fb9c9624e7865da6ec807d116cd7be24b3cb36ab31b1d1d1a9af58c56009a2"},
-    {file = "mkdocs_material-9.5.47.tar.gz", hash = "sha256:fc3b7a8e00ad896660bd3a5cc12ca0cb28bdc2bcbe2a946b5714c23ac91b0ede"},
+    {file = "mkdocs_material-9.5.48-py3-none-any.whl", hash = "sha256:b695c998f4b939ce748adbc0d3bff73fa886a670ece948cf27818fa115dc16f8"},
+    {file = "mkdocs_material-9.5.48.tar.gz", hash = "sha256:a582531e8b34f4c7ed38c29d5c44763053832cf2a32f7409567e0c74749a47db"},
 ]
 
 [package.dependencies]
@@ -2748,13 +2750,13 @@ mkdocstrings = ">=0.24.2"
 
 [[package]]
 name = "modelscope"
-version = "1.20.1"
+version = "1.21.0"
 description = "ModelScope: bring the notion of Model-as-a-Service to life."
 optional = true
 python-versions = "*"
 files = [
-    {file = "modelscope-1.20.1-py3-none-any.whl", hash = "sha256:168f4594f44943fb6364cca7385dc4a07ddb7958726111ef370607f8ac22d6ac"},
-    {file = "modelscope-1.20.1.tar.gz", hash = "sha256:2670e58cb71f745f78bee9b541cc9e6bdeca82430e2b79a9c3649e394adde95e"},
+    {file = "modelscope-1.21.0-py3-none-any.whl", hash = "sha256:26caa7f77739eac60313db1ffa318b0212eb844f4df5ba07be76752d982d1ace"},
+    {file = "modelscope-1.21.0.tar.gz", hash = "sha256:023fe95862cdaad1ab0d7f88799225f5a3d1263422612d7b1429ee3ea2707532"},
 ]
 
 [package.dependencies]
@@ -2763,13 +2765,13 @@ tqdm = ">=4.64.0"
 urllib3 = ">=1.26"
 
 [package.extras]
-all = ["Pillow", "Pillow (>=6.2.0)", "PyMCubes (<=0.1.4)", "accelerate", "addict", "albumentations (>=1.0.3)", "attrs", "av (>=9.2.0)", "biopython", "bmt-clipit (>=1.0)", "boto3", "chumpy", "clip (>=1.0)", "cloudpickle", "control-ldm", "datasets (>=3.0.0,<=3.0.1)", "ddpm-guided-diffusion", "decord (>=0.6.0)", "diffusers", "diffusers (>=0.25.0)", "easydict", "easyrobust", "edit-distance", "einops", "embeddings", "face-alignment (>=1.3.5)", "fairscale (>=0.4.1)", "fairseq (==0.12.2)", "fastai (>=1.0.51)", "fastapi", "ffmpeg (>=1.4)", "ffmpeg-python (>=0.2.0)", "filelock", "ftfy", "ftfy (>=6.0.3)", "fvcore", "imageio (>=2.9.0)", "imageio-ffmpeg (>=0.4.2)", "imgaug (>=0.4.0)", "iopath", "ipdb", "jieba (>=0.42.1)", "kornia (>=0.5.0)", "librosa (==0.10.1)", "lmdb", "lpips", "matplotlib", "matplotlib (>=3.8.0)", "megatron-util", "ml-collections", "mmcls (>=0.21.0)", "mmdet (>=2.25.0,<=2.28.2)", "mmdet3d (==1.0.0a1)", "mmsegmentation (<=0.30.0)", "moviepy (>=1.0.3)", "nerfacc (==0.2.2)", "networkx", "nltk", "numba", "omegaconf", "onnx", "onnxruntime (>=1.10)", "onnxsim", "open-clip-torch (>=2.7.0)", "opencv-python", "oss2", "paint-ldm", "pandas", "panopticapi", "plyfile (>=0.7.4)", "protobuf (>=3.19.0,<3.21.0)", "psutil", "pyclipper", "pycocoevalcap (>=1.2)", "pycocotools (>=2.0.4)", "pydot", "pythainlp", "python-dateutil (>=2.1)", "pytorch-lightning", "pytorch-lightning (<=1.7.7)", "pyvi", "rapidfuzz", "regex", "requests (>=2.25)", "rouge", "rouge-score (<=0.0.4)", "sacrebleu", "sacremoses (>=0.0.41)", "safetensors", "scikit-image", "scikit-learn", "scipy", "sentencepiece", "seqeval", "setuptools (==69.5.1)", "shapely", "shotdetect-scenedetect-lgss (>=0.0.4)", "simplejson (>=3.3.0)", "smplx", "sortedcontainers (>=1.5.9)", "soundfile", "spacy (>=2.3.5,<=3.7.0)", "sse-starlette", "stanza", "subword-nmt (>=0.3.8)", "taming-transformers-rom1504", "tensorboardX", "tensorflow-estimator (>=1.15.1)", "termcolor", "tf-slim", "thop", "timm", "timm (>=0.4.9)", "tokenizers", "torch-scatter", "torchmetrics (>=0.6.2)", "torchsummary (>=1.5.1)", "torchvision", "tqdm", "tqdm (>=4.64.0)", "transformers", "transformers (>=4.12.0)", "transformers (>=4.26.0)", "transformers (>=4.27.1)", "trimesh", "ujson", "unicodedata2", "urllib3 (>=1.26)", "utils", "uvicorn", "videofeatures-clipit (>=1.0)", "yacs", "zhconv"]
+all = ["Pillow", "Pillow (>=6.2.0)", "PyMCubes (<=0.1.4)", "accelerate", "addict", "albumentations (>=1.0.3)", "attrs", "av (>=9.2.0)", "biopython", "bmt-clipit (>=1.0)", "boto3", "chumpy", "clip (>=1.0)", "cloudpickle", "control-ldm", "datasets (>=3.0.0,<=3.0.1)", "ddpm-guided-diffusion", "decord (>=0.6.0)", "diffusers", "diffusers (>=0.25.0)", "easydict", "easyrobust", "edit-distance", "einops", "embeddings", "face-alignment (>=1.3.5)", "fairscale (>=0.4.1)", "fairseq (==0.12.2)", "fastai (>=1.0.51)", "fastapi", "ffmpeg (>=1.4)", "ffmpeg-python (>=0.2.0)", "filelock", "ftfy", "ftfy (>=6.0.3)", "fvcore", "imageio (>=2.9.0)", "imageio-ffmpeg (>=0.4.2)", "imgaug (>=0.4.0)", "iopath", "ipdb", "jieba (>=0.42.1)", "kornia (>=0.5.0)", "librosa (==0.10.1)", "lmdb", "lpips", "matplotlib", "matplotlib (>=3.8.0)", "megatron-util", "ml-collections", "mmcls (>=0.21.0)", "mmdet (>=2.25.0,<=2.28.2)", "mmdet3d (==1.0.0a1)", "mmsegmentation (<=0.30.0)", "moviepy (==1.0.3)", "nerfacc (==0.2.2)", "networkx", "nltk", "numba", "omegaconf", "onnx", "onnxruntime (>=1.10)", "onnxsim", "open-clip-torch (>=2.7.0)", "opencv-python", "oss2", "paint-ldm", "pandas", "panopticapi", "plyfile (>=0.7.4)", "protobuf (>=3.19.0,<3.21.0)", "psutil", "pyclipper", "pycocoevalcap (>=1.2)", "pycocotools (>=2.0.4)", "pydot", "pythainlp", "python-dateutil (>=2.1)", "pytorch-lightning", "pytorch-lightning (<=1.7.7)", "pyvi", "rapidfuzz", "regex", "requests (>=2.25)", "rouge", "rouge-score (<=0.0.4)", "sacrebleu", "sacremoses (>=0.0.41)", "safetensors", "scikit-image", "scikit-learn", "scipy", "sentencepiece", "seqeval", "setuptools (==69.5.1)", "shapely", "shotdetect-scenedetect-lgss (>=0.0.4)", "simplejson (>=3.3.0)", "smplx", "sortedcontainers (>=1.5.9)", "soundfile", "spacy (>=2.3.5,<=3.7.0)", "sse-starlette", "stanza", "subword-nmt (>=0.3.8)", "taming-transformers-rom1504", "tensorboardX", "tensorflow-estimator (>=1.15.1)", "termcolor", "tf-slim", "thop", "timm", "timm (>=0.4.9)", "tokenizers", "torch-scatter", "torchmetrics (>=0.6.2)", "torchsummary (>=1.5.1)", "torchvision", "tqdm", "tqdm (>=4.64.0)", "transformers", "transformers (>=4.12.0)", "transformers (>=4.26.0)", "transformers (>=4.27.1)", "trimesh", "ujson", "unicodedata2", "urllib3 (>=1.26)", "utils", "uvicorn", "videofeatures-clipit (>=1.0)", "yacs", "zhconv"]
 audio = ["MinDAEC", "Pillow", "PyWavelets (>=1.0.0)", "SoundFile (>0.10)", "addict", "attrs", "bitstring", "datasets (>=3.0.0,<=3.0.1)", "einops", "funasr (>=1.0.0)", "funcodec (>=0.2.0)", "greenlet (>=1.1.2)", "hdbscan", "hyperpyyaml", "inflect", "jedi (>=0.18.1)", "kaldiio", "kantts", "kwsbp (>=0.0.6)", "librosa (==0.10.1)", "lxml", "matplotlib", "mir-eval (>=0.7)", "msgpack (>=1.0.4)", "oss2", "parso (>=0.8.3)", "pexpect (>=4.8.0)", "pickleshare (>=0.7.5)", "prompt-toolkit (>=3.0.30)", "protobuf", "ptflops", "ptyprocess (>=0.7.0)", "py-sound-connect (>=0.1)", "pygments (>=2.12.0)", "pysptk (>=0.1.15,<0.1.19)", "python-dateutil (>=2.1)", "pytorch-wavelets", "rotary-embedding-torch (>=0.1.5)", "scikit-learn", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "sox", "speechbrain (>=0.5.12)", "tensorboardX", "torchaudio", "tqdm", "traitlets (>=5.3.0)", "transformers", "ttsfrd (>=0.1.2)", "umap-learn", "unidecode", "urllib3 (>=1.26)", "wcwidth (>=0.2.5)"]
 audio-asr = ["Pillow", "addict", "attrs", "datasets (>=3.0.0,<=3.0.1)", "einops", "funasr (>=1.0.0)", "oss2", "python-dateutil (>=2.1)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "transformers", "urllib3 (>=1.26)"]
 audio-kws = ["Pillow", "SoundFile (>0.10)", "addict", "attrs", "datasets (>=3.0.0,<=3.0.1)", "einops", "kaldiio", "kwsbp (>=0.0.6)", "matplotlib", "oss2", "py-sound-connect (>=0.1)", "python-dateutil (>=2.1)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "tensorboardX", "transformers", "urllib3 (>=1.26)"]
 audio-signal = ["MinDAEC", "Pillow", "SoundFile (>0.10)", "addict", "attrs", "datasets (>=3.0.0,<=3.0.1)", "einops", "hdbscan", "hyperpyyaml", "librosa (==0.10.1)", "mir-eval (>=0.7)", "oss2", "python-dateutil (>=2.1)", "rotary-embedding-torch (>=0.1.5)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "speechbrain (>=0.5.12)", "torchaudio", "tqdm", "transformers", "umap-learn", "urllib3 (>=1.26)"]
 audio-tts = ["Pillow", "PyWavelets (>=1.0.0)", "addict", "attrs", "bitstring", "datasets (>=3.0.0,<=3.0.1)", "einops", "greenlet (>=1.1.2)", "inflect", "jedi (>=0.18.1)", "kantts", "librosa (==0.10.1)", "lxml", "matplotlib", "msgpack (>=1.0.4)", "oss2", "parso (>=0.8.3)", "pexpect (>=4.8.0)", "pickleshare (>=0.7.5)", "prompt-toolkit (>=3.0.30)", "protobuf", "ptflops", "ptyprocess (>=0.7.0)", "pygments (>=2.12.0)", "pysptk (>=0.1.15,<0.1.19)", "python-dateutil (>=2.1)", "pytorch-wavelets", "scikit-learn", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "sox", "tensorboardx", "tqdm", "traitlets (>=5.3.0)", "transformers", "ttsfrd (>=0.1.2)", "unidecode", "urllib3 (>=1.26)", "wcwidth (>=0.2.5)"]
-cv = ["Pillow", "Pillow (>=6.2.0)", "PyMCubes (<=0.1.4)", "accelerate", "addict", "albumentations (>=1.0.3)", "attrs", "av (>=9.2.0)", "bmt-clipit (>=1.0)", "chumpy", "clip (>=1.0)", "control-ldm", "datasets (>=3.0.0,<=3.0.1)", "ddpm-guided-diffusion", "diffusers", "easydict", "easyrobust", "edit-distance", "einops", "face-alignment (>=1.3.5)", "fairscale (>=0.4.1)", "fastai (>=1.0.51)", "ffmpeg (>=1.4)", "ffmpeg-python (>=0.2.0)", "ftfy", "fvcore", "imageio (>=2.9.0)", "imageio-ffmpeg (>=0.4.2)", "imgaug (>=0.4.0)", "kornia (>=0.5.0)", "lmdb", "lpips", "matplotlib (>=3.8.0)", "ml-collections", "mmcls (>=0.21.0)", "mmdet (>=2.25.0,<=2.28.2)", "mmdet3d (==1.0.0a1)", "mmsegmentation (<=0.30.0)", "moviepy (>=1.0.3)", "nerfacc (==0.2.2)", "networkx", "numba", "omegaconf", "onnx", "onnxruntime (>=1.10)", "onnxsim", "open-clip-torch (>=2.7.0)", "opencv-python", "oss2", "paint-ldm", "pandas", "panopticapi", "plyfile (>=0.7.4)", "psutil", "pyclipper", "python-dateutil (>=2.1)", "pytorch-lightning", "regex", "scikit-image", "scikit-learn", "scipy", "setuptools (==69.5.1)", "shapely", "shotdetect-scenedetect-lgss (>=0.0.4)", "simplejson (>=3.3.0)", "smplx", "sortedcontainers (>=1.5.9)", "tensorflow-estimator (>=1.15.1)", "tf-slim", "thop", "timm (>=0.4.9)", "torch-scatter", "torchmetrics (>=0.6.2)", "torchsummary (>=1.5.1)", "torchvision", "tqdm", "transformers", "transformers (>=4.26.0)", "trimesh", "ujson", "urllib3 (>=1.26)", "utils", "videofeatures-clipit (>=1.0)", "yacs"]
+cv = ["Pillow", "Pillow (>=6.2.0)", "PyMCubes (<=0.1.4)", "accelerate", "addict", "albumentations (>=1.0.3)", "attrs", "av (>=9.2.0)", "bmt-clipit (>=1.0)", "chumpy", "clip (>=1.0)", "control-ldm", "datasets (>=3.0.0,<=3.0.1)", "ddpm-guided-diffusion", "diffusers", "easydict", "easyrobust", "edit-distance", "einops", "face-alignment (>=1.3.5)", "fairscale (>=0.4.1)", "fastai (>=1.0.51)", "ffmpeg (>=1.4)", "ffmpeg-python (>=0.2.0)", "ftfy", "fvcore", "imageio (>=2.9.0)", "imageio-ffmpeg (>=0.4.2)", "imgaug (>=0.4.0)", "kornia (>=0.5.0)", "lmdb", "lpips", "matplotlib (>=3.8.0)", "ml-collections", "mmcls (>=0.21.0)", "mmdet (>=2.25.0,<=2.28.2)", "mmdet3d (==1.0.0a1)", "mmsegmentation (<=0.30.0)", "moviepy (==1.0.3)", "nerfacc (==0.2.2)", "networkx", "numba", "omegaconf", "onnx", "onnxruntime (>=1.10)", "onnxsim", "open-clip-torch (>=2.7.0)", "opencv-python", "oss2", "paint-ldm", "pandas", "panopticapi", "plyfile (>=0.7.4)", "psutil", "pyclipper", "python-dateutil (>=2.1)", "pytorch-lightning", "regex", "scikit-image", "scikit-learn", "scipy", "setuptools (==69.5.1)", "shapely", "shotdetect-scenedetect-lgss (>=0.0.4)", "simplejson (>=3.3.0)", "smplx", "sortedcontainers (>=1.5.9)", "tensorflow-estimator (>=1.15.1)", "tf-slim", "thop", "timm (>=0.4.9)", "torch-scatter", "torchmetrics (>=0.6.2)", "torchsummary (>=1.5.1)", "torchvision", "tqdm", "transformers", "transformers (>=4.26.0)", "trimesh", "ujson", "urllib3 (>=1.26)", "utils", "videofeatures-clipit (>=1.0)", "yacs"]
 datasets = ["Pillow", "addict", "attrs", "datasets (>=3.0.0,<=3.0.1)", "einops", "oss2", "python-dateutil (>=2.1)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "urllib3 (>=1.26)"]
 framework = ["Pillow", "addict", "attrs", "datasets (>=3.0.0,<=3.0.1)", "einops", "oss2", "python-dateutil (>=2.1)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "transformers", "urllib3 (>=1.26)"]
 hub = ["requests (>=2.25)", "tqdm (>=4.64.0)", "urllib3 (>=1.26)"]
@@ -2940,13 +2942,13 @@ files = [
 
 [[package]]
 name = "narwhals"
-version = "1.15.2"
+version = "1.16.0"
 description = "Extremely lightweight compatibility layer between dataframe libraries"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "narwhals-1.15.2-py3-none-any.whl", hash = "sha256:00d16ed1c4466b43ba37ef6799142340981e4ac7dcd7686e461d4955abcd921d"},
-    {file = "narwhals-1.15.2.tar.gz", hash = "sha256:bf32c4afc3b4e562be51d50a3ff4fcacd1e0c1d89dc4da1b818af81d7c28e0c7"},
+    {file = "narwhals-1.16.0-py3-none-any.whl", hash = "sha256:e5b764b1b571e25b08bb87db6feb89ff824d76f5d7b7e12e6d837c1c503763ae"},
+    {file = "narwhals-1.16.0.tar.gz", hash = "sha256:1ea3ac269df8126f1f79c1ba7b8f78c73b032a86861b2cc2f08c48010ef9f6f8"},
 ]
 
 [package.extras]
@@ -2956,6 +2958,7 @@ modin = ["modin"]
 pandas = ["pandas (>=0.25.3)"]
 polars = ["polars (>=0.20.3)"]
 pyarrow = ["pyarrow (>=11.0.0)"]
+pyspark = ["pyspark (>=3.3.0)"]
 
 [[package]]
 name = "neo4j"
@@ -3518,18 +3521,18 @@ pillow = "*"
 
 [[package]]
 name = "peft"
-version = "0.13.2"
+version = "0.14.0"
 description = "Parameter-Efficient Fine-Tuning (PEFT)"
 optional = true
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 files = [
-    {file = "peft-0.13.2-py3-none-any.whl", hash = "sha256:d4e0951ec78eac11c45a051801c569913436888c578d48e5ce86996b715bc6ef"},
-    {file = "peft-0.13.2.tar.gz", hash = "sha256:0e0cbd40ebdf5fe4ea79f255880d02f96712d18899509369a2cc5768ad46d672"},
+    {file = "peft-0.14.0-py3-none-any.whl", hash = "sha256:2f04f3a870c3baf30f15e7dcaa5dd70d3e54cfdd146d3c6c187735d3ae0a0700"},
+    {file = "peft-0.14.0.tar.gz", hash = "sha256:546d69af7b42f5ef715a3d3261ed818bc917ae6055e5d7e187ed3f2c76ad72dc"},
 ]
 
 [package.dependencies]
 accelerate = ">=0.21.0"
-huggingface-hub = ">=0.17.0"
+huggingface-hub = ">=0.25.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 psutil = "*"
@@ -3543,7 +3546,7 @@ transformers = "*"
 dev = ["black", "hf-doc-builder", "ruff (>=0.6.1,<0.7.0)"]
 docs-specific = ["black", "hf-doc-builder"]
 quality = ["black", "hf-doc-builder", "ruff (>=0.6.1,<0.7.0)"]
-test = ["black", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.6.1,<0.7.0)", "scipy"]
+test = ["black", "datasets", "diffusers", "hf-doc-builder", "parameterized", "protobuf", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.6.1,<0.7.0)", "scipy", "sentencepiece"]
 
 [[package]]
 name = "pillow"
@@ -3784,22 +3787,22 @@ files = [
 
 [[package]]
 name = "protobuf"
-version = "5.29.0"
+version = "5.29.1"
 description = ""
 optional = false
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@@ -5666,39 +5574,39 @@ telegram = ["requests"]
 
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+    {file = "xoscar-0.4.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fe326f83c16550fd3f739ac995b117ff3d86e45132c0648462121cdb09420477"},
+    {file = "xoscar-0.4.5-cp39-cp39-win_amd64.whl", hash = "sha256:fafdeaa75d435fcf3ca55d8be33a896ce83de3cbd3dd53871d1c5a294184db40"},
+    {file = "xoscar-0.4.5.tar.gz", hash = "sha256:6621d46157e24745c827624e7c5038e320dfcbc86f5d6d7755dba5f0bc78522d"},
 ]
 
 [package.dependencies]
@@ -6499,4 +6407,4 @@ xinference = ["xinference"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "7aa9bbd49d9d15034a2e3a3e3d186ed97c7d8df33044623e86deef4836a9f74c"
+content-hash = "e941ee4816aabccecf3481c5d1880eca5edd38cfd4f15d3b75dec3bece3e09fd"
diff --git a/pyproject.toml b/pyproject.toml
index 0230c6cf..b60e0088 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ Issues = "https://github.com/biocypher/biochatter/issues"
 python = ">=3.10,<3.13"
 langchain = "^0.2.5"
 openai = "^1.1.0"
+httpx = "0.27.2"
 pymupdf = "^1.22.3"
 pymilvus = "2.2.8"
 nltk = "^3.8.1"
@@ -59,9 +60,9 @@ tiktoken = "0.7.0"
 grandalf = "^0.8"
 importlib-metadata = "^8.0.0"
 colorcet = "^3.1.0"
-
 langchain-anthropic = "^0.1.22"
 anthropic = "^0.33.0"
+
 [tool.poetry.extras]
 streamlit = ["streamlit"]
 podcast = ["gTTS"]
diff --git a/test/test_api_agent.py b/test/test_api_agent.py
index 6c082658..cd9b96d1 100644
--- a/test/test_api_agent.py
+++ b/test/test_api_agent.py
@@ -28,6 +28,12 @@
     OncoKBQueryBuilder,
     OncoKBQueryParameters,
 )
+from biochatter.api_agent.scanpy_pl import (
+    SCANPY_PL_QUERY_PROMPT,
+    ScanpyPlQueryBuilder,
+    ScanpyPlQueryParameters,
+)
+
 from biochatter.llm_connect import Conversation, GptConversation
 
 
@@ -422,3 +428,54 @@ def test_summarise_results(mock_prompt, mock_conversation, mock_chain):
         mock_chain.invoke.assert_called_once_with(
             {"input": {expected_summary_prompt}},
         )
+
+class TestScanpyPlQueryBuilder:
+    @pytest.fixture()
+    def mock_create_runnable(self):
+        with patch(
+            "biochatter.api_agent.scanpy_pl.create_structured_output_runnable"
+        ) as mock:
+            mock_runnable = MagicMock()
+            mock.return_value = mock_runnable
+            yield mock_runnable
+
+    def test_create_runnable(self, mock_create_runnable):
+        # Arrange
+        query_builder = ScanpyPlQueryBuilder()
+        mock_conversation = MagicMock()
+
+        # Act
+        result = query_builder.create_runnable(
+            ScanpyPlQueryParameters,
+            mock_conversation,
+        )
+
+        # Assert
+        assert result == mock_create_runnable
+
+    def test_parameterise_query(self, mock_create_runnable):
+        # Arrange
+        query_builder = ScanpyPlQueryBuilder()
+        mock_conversation = MagicMock()
+        question = "Create a scatter plot of n_genes_by_counts vs total_counts."
+        expected_input = f"Answer:\n{question} based on:\n {SCANPY_PL_QUERY_PROMPT}"
+        mock_query_obj = MagicMock()
+        mock_create_runnable.invoke.return_value = mock_query_obj
+
+        # Act
+        result = query_builder.parameterise_query(question, mock_conversation)
+
+        # Assert
+        mock_create_runnable.invoke.assert_called_once_with({"input": expected_input})
+        assert hasattr(result, "question_uuid")
+        assert result == mock_query_obj
+
+
+
+
+class TestScanpyPlFetcher:
+    pass
+
+
+class TestScanpyPlInterpreter:
+    pass
diff --git a/test/test_formatters.py b/test/test_formatters.py
new file mode 100644
index 00000000..f6a9afaf
--- /dev/null
+++ b/test/test_formatters.py
@@ -0,0 +1,78 @@
+from pydantic.v1 import BaseModel
+from biochatter.api_agent.formatters import format_as_rest_call, format_as_python_call
+
+class TestParams(BaseModel):
+    """Test parameter model for REST calls."""
+    base_url: str
+    endpoint: str
+    param1: str | None = None
+    param2: int | None = None
+    question_uuid: str | None = None
+
+class TestMethodParams(BaseModel):
+    """Test parameter model for Python method_name calls."""
+    method_name: str
+    param1: str | None = None
+    param2: int | None = None
+    question_uuid: str | None = None
+
+def test_format_as_rest_call_basic():
+    params = TestParams(
+        base_url="http://api.example.com",
+        endpoint="/v1/test",
+        param1="value1",
+        param2=42
+    )
+    expected = "http://api.example.com/v1/test?param1=value1&param2=42"
+    assert format_as_rest_call(params) == expected
+
+def test_format_as_rest_call_with_trailing_slash():
+    params = TestParams(
+        base_url="http://api.example.com/",
+        endpoint="/v1/test/",
+        param1="value1"
+    )
+    expected = "http://api.example.com/v1/test?param1=value1"
+    assert format_as_rest_call(params) == expected
+
+def test_format_as_rest_call_no_params():
+    params = TestParams(
+        base_url="http://api.example.com",
+        endpoint="/v1/test"
+    )
+    expected = "http://api.example.com/v1/test?"
+    assert format_as_rest_call(params) == expected
+
+def test_format_as_python_call_with_module():
+    params = TestMethodParams(
+        method_name="sqrt",
+        param1="16",
+        param2=42
+    )
+    expected = "sqrt(param1='16', param2=42)"
+    assert format_as_python_call(params) == expected
+
+def test_format_as_python_call_without_module():
+    params = TestMethodParams(
+        method_name="calculate",
+        param1="test",
+        param2=123
+    )
+    expected = "calculate(param1='test', param2=123)"
+    assert format_as_python_call(params) == expected
+
+def test_format_as_python_call_no_params():
+    params = TestMethodParams(
+        method_name="empty_function"
+    )
+    expected = "empty_function()"
+    assert format_as_python_call(params) == expected
+
+def test_format_as_python_call_ignores_question_uuid():
+    params = TestMethodParams(
+        method_name="test",
+        param1="value",
+        question_uuid="123-456"
+    )
+    expected = "test(param1='value')"
+    assert format_as_python_call(params) == expected
\ No newline at end of file