Pandas Extension¶

openaivec.pandas_ext ¶

Pandas Series / DataFrame extension for OpenAI.

Setup¶

from openai import OpenAI
from openaivec import pandas_ext

# Set up the OpenAI client to use with pandas_ext
# Option 1: Use an existing client instance
# pandas_ext.use(OpenAI())

# Option 2: Use environment variables (OPENAI_API_KEY or Azure variables)
# (No explicit setup needed if variables are set)

# Option 3: Provide API key directly
pandas_ext.use_openai("YOUR_API_KEY")

# Option 4: Use Azure OpenAI credentials
# pandas_ext.use_azure_openai(
#     api_key="YOUR_AZURE_KEY",
#     endpoint="YOUR_AZURE_ENDPOINT",
#     api_version="YOUR_API_VERSION"
# )

# Set up the model_name for responses and embeddings (optional, defaults shown)
pandas_ext.responses_model("gpt-4o-mini")
pandas_ext.embeddings_model("text-embedding-3-small")

This module provides .ai and .aio accessors for pandas Series and DataFrames to easily interact with OpenAI APIs for tasks like generating responses or embeddings.

OpenAIVecSeriesAccessor ¶

pandas Series accessor (.ai) that adds OpenAI helpers.

Source code in src/openaivec/pandas_ext.py

@pd.api.extensions.register_series_accessor("ai")
class OpenAIVecSeriesAccessor:
    """pandas Series accessor (``.ai``) that adds OpenAI helpers."""

    def __init__(self, series_obj: pd.Series):
        self._obj = series_obj

    def responses(
        self,
        instructions: str,
        response_format: Type[T] = str,
        batch_size: int = 128,
        temperature: float = 0.0,
        top_p: float = 1.0,
    ) -> pd.Series:
        """Call an LLM once for every Series element.

        Example:
            ```python
            animals = pd.Series(["cat", "dog", "elephant"])
            animals.ai.responses("translate to French")
            ```
            This method returns a Series of strings, each containing the
            assistant's response to the corresponding input.
            The model used is set by the `responses_model` function.
            The default model is `gpt-4o-mini`.

        Args:
            instructions (str): System prompt prepended to every user message.
            response_format (Type[T], optional): Pydantic model or built‑in
                type the assistant should return. Defaults to ``str``.
            batch_size (int, optional): Number of prompts grouped into a single
                request. Defaults to ``128``.
            temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
            top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.

        Returns:
            pandas.Series: Series whose values are instances of ``response_format``.
        """
        client: BatchResponses = BatchResponses(
            client=_get_openai_client(),
            model_name=_RESPONSES_MODEL_NAME,
            system_message=instructions,
            response_format=response_format,
            temperature=temperature,
            top_p=top_p,
        )

        return pd.Series(
            client.parse(self._obj.tolist(), batch_size=batch_size),
            index=self._obj.index,
            name=self._obj.name,
        )

    def task(self, task: PreparedTask, batch_size: int = 128) -> pd.Series:
        """Execute a prepared task on every Series element.

        This method applies a pre-configured task to each element in the Series,
        using the task's instructions and response format to generate structured
        responses from the language model.

        Example:
            ```python
            from openaivec.task.model import PreparedTask

            # Assume you have a prepared task for sentiment analysis
            sentiment_task = PreparedTask(...)

            reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
            results = reviews.ai.task(sentiment_task)
            ```
            This method returns a Series containing the task results for each
            corresponding input element, following the task's defined structure.

        Args:
            task (PreparedTask): A pre-configured task containing instructions,
                response format, and other parameters for processing the inputs.
            batch_size (int, optional): Number of prompts grouped into a single
                request to optimize API usage. Defaults to 128.

        Returns:
            pandas.Series: Series whose values are instances of the task's
                response format, aligned with the original Series index.
        """
        client = BatchResponses.of_task(
            client=_get_openai_client(),
            model_name=_RESPONSES_MODEL_NAME,
            task=task
        )

        return pd.Series(
            client.parse(self._obj.tolist(), batch_size=batch_size),
            index=self._obj.index,
            name=self._obj.name,
        )

    def embeddings(self, batch_size: int = 128) -> pd.Series:
        """Compute OpenAI embeddings for every Series element.

        Example:
            ```python
            animals = pd.Series(["cat", "dog", "elephant"])
            animals.ai.embeddings()
            ```
            This method returns a Series of numpy arrays, each containing the
            embedding vector for the corresponding input.
            The embedding model is set by the `embeddings_model` function.
            The default embedding model is `text-embedding-3-small`.

        Args:
            batch_size (int, optional): Number of inputs grouped into a
                single request. Defaults to ``128``.

        Returns:
            pandas.Series: Series whose values are ``np.ndarray`` objects
                (dtype ``float32``).
        """
        client: BatchEmbeddings = BatchEmbeddings(
            client=_get_openai_client(),
            model_name=_EMBEDDINGS_MODEL_NAME,
        )

        return pd.Series(
            client.create(self._obj.tolist(), batch_size=batch_size),
            index=self._obj.index,
            name=self._obj.name,
        )

    def count_tokens(self) -> pd.Series:
        """Count `tiktoken` tokens per row.

        Example:
            ```python
            animals = pd.Series(["cat", "dog", "elephant"])
            animals.ai.count_tokens()
            ```
            This method uses the `tiktoken` library to count tokens based on the
            model name set by `responses_model`.

        Returns:
            pandas.Series: Token counts for each element.
        """
        return self._obj.map(_TIKTOKEN_ENCODING.encode).map(len).rename("num_tokens")

    def extract(self) -> pd.DataFrame:
        """Expand a Series of Pydantic models/dicts into columns.

        Example:
            ```python
            animals = pd.Series([
                {"name": "cat", "legs": 4},
                {"name": "dog", "legs": 4},
                {"name": "elephant", "legs": 4},
            ])
            animals.ai.extract()
            ```
            This method returns a DataFrame with the same index as the Series,
            where each column corresponds to a key in the dictionaries.
            If the Series has a name, extracted columns are prefixed with it.

        Returns:
            pandas.DataFrame: Expanded representation.
        """
        extracted = pd.DataFrame(
            self._obj.map(lambda x: _extract_value(x, self._obj.name)).tolist(),
            index=self._obj.index,
        )

        if self._obj.name:
            # If the Series has a name and all elements are dict or BaseModel, use it as the prefix for the columns
            extracted.columns = [f"{self._obj.name}_{col}" for col in extracted.columns]
        return extracted

responses ¶

responses(
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
) -> pd.Series

Call an LLM once for every Series element.

Example

animals = pd.Series(["cat", "dog", "elephant"])
animals.ai.responses("translate to French")

This method returns a Series of strings, each containing the assistant's response to the corresponding input. The model used is set by the responses_model function. The default model is gpt-4o-mini.

Parameters:

Name	Type	Description	Default
`instructions`	`str`	System prompt prepended to every user message.	required
`response_format`	`Type[T]`	Pydantic model or built‑in type the assistant should return. Defaults to `str`.	`str`
`batch_size`	`int`	Number of prompts grouped into a single request. Defaults to `128`.	`128`
`temperature`	`float`	Sampling temperature. Defaults to `0.0`.	`0.0`
`top_p`	`float`	Nucleus sampling parameter. Defaults to `1.0`.	`1.0`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of `response_format`.

Source code in src/openaivec/pandas_ext.py

def responses(
    self,
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
) -> pd.Series:
    """Call an LLM once for every Series element.

    Example:
        ```python
        animals = pd.Series(["cat", "dog", "elephant"])
        animals.ai.responses("translate to French")
        ```
        This method returns a Series of strings, each containing the
        assistant's response to the corresponding input.
        The model used is set by the `responses_model` function.
        The default model is `gpt-4o-mini`.

    Args:
        instructions (str): System prompt prepended to every user message.
        response_format (Type[T], optional): Pydantic model or built‑in
            type the assistant should return. Defaults to ``str``.
        batch_size (int, optional): Number of prompts grouped into a single
            request. Defaults to ``128``.
        temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
        top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.

    Returns:
        pandas.Series: Series whose values are instances of ``response_format``.
    """
    client: BatchResponses = BatchResponses(
        client=_get_openai_client(),
        model_name=_RESPONSES_MODEL_NAME,
        system_message=instructions,
        response_format=response_format,
        temperature=temperature,
        top_p=top_p,
    )

    return pd.Series(
        client.parse(self._obj.tolist(), batch_size=batch_size),
        index=self._obj.index,
        name=self._obj.name,
    )

task ¶

task(
    task: PreparedTask, batch_size: int = 128
) -> pd.Series

Execute a prepared task on every Series element.

This method applies a pre-configured task to each element in the Series, using the task's instructions and response format to generate structured responses from the language model.

Example

from openaivec.task.model import PreparedTask

# Assume you have a prepared task for sentiment analysis
sentiment_task = PreparedTask(...)

reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
results = reviews.ai.task(sentiment_task)

This method returns a Series containing the task results for each corresponding input element, following the task's defined structure.

Parameters:

Name	Type	Description	Default
`task`	`PreparedTask`	A pre-configured task containing instructions, response format, and other parameters for processing the inputs.	required
`batch_size`	`int`	Number of prompts grouped into a single request to optimize API usage. Defaults to 128.	`128`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of the task's response format, aligned with the original Series index.

Source code in src/openaivec/pandas_ext.py

def task(self, task: PreparedTask, batch_size: int = 128) -> pd.Series:
    """Execute a prepared task on every Series element.

    This method applies a pre-configured task to each element in the Series,
    using the task's instructions and response format to generate structured
    responses from the language model.

    Example:
        ```python
        from openaivec.task.model import PreparedTask

        # Assume you have a prepared task for sentiment analysis
        sentiment_task = PreparedTask(...)

        reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
        results = reviews.ai.task(sentiment_task)
        ```
        This method returns a Series containing the task results for each
        corresponding input element, following the task's defined structure.

    Args:
        task (PreparedTask): A pre-configured task containing instructions,
            response format, and other parameters for processing the inputs.
        batch_size (int, optional): Number of prompts grouped into a single
            request to optimize API usage. Defaults to 128.

    Returns:
        pandas.Series: Series whose values are instances of the task's
            response format, aligned with the original Series index.
    """
    client = BatchResponses.of_task(
        client=_get_openai_client(),
        model_name=_RESPONSES_MODEL_NAME,
        task=task
    )

    return pd.Series(
        client.parse(self._obj.tolist(), batch_size=batch_size),
        index=self._obj.index,
        name=self._obj.name,
    )

embeddings ¶

embeddings(batch_size: int = 128) -> pd.Series

Compute OpenAI embeddings for every Series element.

Example

animals = pd.Series(["cat", "dog", "elephant"])
animals.ai.embeddings()

This method returns a Series of numpy arrays, each containing the embedding vector for the corresponding input. The embedding model is set by the embeddings_model function. The default embedding model is text-embedding-3-small.

Parameters:

Name	Type	Description	Default
`batch_size`	`int`	Number of inputs grouped into a single request. Defaults to `128`.	`128`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are `np.ndarray` objects (dtype `float32`).

Source code in src/openaivec/pandas_ext.py

def embeddings(self, batch_size: int = 128) -> pd.Series:
    """Compute OpenAI embeddings for every Series element.

    Example:
        ```python
        animals = pd.Series(["cat", "dog", "elephant"])
        animals.ai.embeddings()
        ```
        This method returns a Series of numpy arrays, each containing the
        embedding vector for the corresponding input.
        The embedding model is set by the `embeddings_model` function.
        The default embedding model is `text-embedding-3-small`.

    Args:
        batch_size (int, optional): Number of inputs grouped into a
            single request. Defaults to ``128``.

    Returns:
        pandas.Series: Series whose values are ``np.ndarray`` objects
            (dtype ``float32``).
    """
    client: BatchEmbeddings = BatchEmbeddings(
        client=_get_openai_client(),
        model_name=_EMBEDDINGS_MODEL_NAME,
    )

    return pd.Series(
        client.create(self._obj.tolist(), batch_size=batch_size),
        index=self._obj.index,
        name=self._obj.name,
    )

count_tokens ¶

count_tokens() -> pd.Series

Count tiktoken tokens per row.

Example

animals = pd.Series(["cat", "dog", "elephant"])
animals.ai.count_tokens()

This method uses the tiktoken library to count tokens based on the model name set by responses_model.

Returns:

Type	Description
`Series`	pandas.Series: Token counts for each element.

Source code in src/openaivec/pandas_ext.py

def count_tokens(self) -> pd.Series:
    """Count `tiktoken` tokens per row.

    Example:
        ```python
        animals = pd.Series(["cat", "dog", "elephant"])
        animals.ai.count_tokens()
        ```
        This method uses the `tiktoken` library to count tokens based on the
        model name set by `responses_model`.

    Returns:
        pandas.Series: Token counts for each element.
    """
    return self._obj.map(_TIKTOKEN_ENCODING.encode).map(len).rename("num_tokens")

extract ¶

extract() -> pd.DataFrame

Expand a Series of Pydantic models/dicts into columns.

Example

animals = pd.Series([
    {"name": "cat", "legs": 4},
    {"name": "dog", "legs": 4},
    {"name": "elephant", "legs": 4},
])
animals.ai.extract()

This method returns a DataFrame with the same index as the Series, where each column corresponds to a key in the dictionaries. If the Series has a name, extracted columns are prefixed with it.

Returns:

Type	Description
`DataFrame`	pandas.DataFrame: Expanded representation.

Source code in src/openaivec/pandas_ext.py

def extract(self) -> pd.DataFrame:
    """Expand a Series of Pydantic models/dicts into columns.

    Example:
        ```python
        animals = pd.Series([
            {"name": "cat", "legs": 4},
            {"name": "dog", "legs": 4},
            {"name": "elephant", "legs": 4},
        ])
        animals.ai.extract()
        ```
        This method returns a DataFrame with the same index as the Series,
        where each column corresponds to a key in the dictionaries.
        If the Series has a name, extracted columns are prefixed with it.

    Returns:
        pandas.DataFrame: Expanded representation.
    """
    extracted = pd.DataFrame(
        self._obj.map(lambda x: _extract_value(x, self._obj.name)).tolist(),
        index=self._obj.index,
    )

    if self._obj.name:
        # If the Series has a name and all elements are dict or BaseModel, use it as the prefix for the columns
        extracted.columns = [f"{self._obj.name}_{col}" for col in extracted.columns]
    return extracted

OpenAIVecDataFrameAccessor ¶

pandas DataFrame accessor (.ai) that adds OpenAI helpers.

Source code in src/openaivec/pandas_ext.py

@pd.api.extensions.register_dataframe_accessor("ai")
class OpenAIVecDataFrameAccessor:
    """pandas DataFrame accessor (``.ai``) that adds OpenAI helpers."""

    def __init__(self, df_obj: pd.DataFrame):
        self._obj = df_obj

    def extract(self, column: str) -> pd.DataFrame:
        """Flatten one column of Pydantic models/dicts into top‑level columns.

        Example:
            ```python
            df = pd.DataFrame([
                {"animal": {"name": "cat", "legs": 4}},
                {"animal": {"name": "dog", "legs": 4}},
                {"animal": {"name": "elephant", "legs": 4}},
            ])
            df.ai.extract("animal")
            ```
            This method returns a DataFrame with the same index as the original,
            where each column corresponds to a key in the dictionaries.
            The source column is dropped.

        Args:
            column (str): Column to expand.

        Returns:
            pandas.DataFrame: Original DataFrame with the extracted columns; the source column is dropped.
        """
        if column not in self._obj.columns:
            raise ValueError(f"Column '{column}' does not exist in the DataFrame.")

        return (
            self._obj.pipe(lambda df: df.reset_index(drop=True))
            .pipe(lambda df: df.join(df[column].ai.extract()))
            .pipe(lambda df: df.set_index(self._obj.index))
            .pipe(lambda df: df.drop(columns=[column], axis=1))
        )

    def responses(
        self,
        instructions: str,
        response_format: Type[T] = str,
        batch_size: int = 128,
        temperature: float = 0.0,
        top_p: float = 1.0,
    ) -> pd.Series:
        """Generate a response for each row after serialising it to JSON.

        Example:
            ```python
            df = pd.DataFrame([
                {"name": "cat", "legs": 4},
                {"name": "dog", "legs": 4},
                {"name": "elephant", "legs": 4},
            ])
            df.ai.responses("what is the animal's name?")
            ```
            This method returns a Series of strings, each containing the
            assistant's response to the corresponding input.
            Each row is serialised to JSON before being sent to the assistant.
            The model used is set by the `responses_model` function.
            The default model is `gpt-4o-mini`.

        Args:
            instructions (str): System prompt for the assistant.
            response_format (Type[T], optional): Desired Python type of the
                responses. Defaults to ``str``.
            batch_size (int, optional): Number of requests sent in one batch.
                Defaults to ``128``.
            temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
            top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.

        Returns:
            pandas.Series: Responses aligned with the DataFrame’s original index.
        """
        return self._obj.pipe(
            lambda df: (
                df.pipe(lambda df: pd.Series(df.to_dict(orient="records"), index=df.index, name="record"))
                .map(lambda x: json.dumps(x, ensure_ascii=False))
                .ai.responses(
                    instructions=instructions,
                    response_format=response_format,
                    batch_size=batch_size,
                    temperature=temperature,
                    top_p=top_p,
                )
            )
        )

    def task(self, task: PreparedTask, batch_size: int = 128) -> pd.Series:
        """Execute a prepared task on each DataFrame row after serialising it to JSON.

        This method applies a pre-configured task to each row in the DataFrame,
        using the task's instructions and response format to generate structured
        responses from the language model. Each row is serialised to JSON before
        being processed by the task.

        Example:
            ```python
            from openaivec.task.model import PreparedTask

            # Assume you have a prepared task for data analysis
            analysis_task = PreparedTask(...)

            df = pd.DataFrame([
                {"name": "cat", "legs": 4},
                {"name": "dog", "legs": 4},
                {"name": "elephant", "legs": 4},
            ])
            results = df.ai.task(analysis_task)
            ```
            This method returns a Series containing the task results for each
            corresponding row, following the task's defined structure.

        Args:
            task (PreparedTask): A pre-configured task containing instructions,
                response format, and other parameters for processing the inputs.
            batch_size (int, optional): Number of requests sent in one batch
                to optimize API usage. Defaults to 128.

        Returns:
            pandas.Series: Series whose values are instances of the task's
                response format, aligned with the DataFrame's original index.
        """
        return self._obj.pipe(
            lambda df: (
                df.pipe(lambda df: pd.Series(df.to_dict(orient="records"), index=df.index, name="record"))
                .map(lambda x: json.dumps(x, ensure_ascii=False))
                .ai.task(task=task, batch_size=batch_size)
            )
        )

    def similarity(self, col1: str, col2: str) -> pd.Series:
        return self._obj.apply(
            lambda row: np.dot(row[col1], row[col2]) / (np.linalg.norm(row[col1]) * np.linalg.norm(row[col2])),
            axis=1,
        ).rename("similarity")

extract ¶

extract(column: str) -> pd.DataFrame

Flatten one column of Pydantic models/dicts into top‑level columns.

Example

df = pd.DataFrame([
    {"animal": {"name": "cat", "legs": 4}},
    {"animal": {"name": "dog", "legs": 4}},
    {"animal": {"name": "elephant", "legs": 4}},
])
df.ai.extract("animal")

This method returns a DataFrame with the same index as the original, where each column corresponds to a key in the dictionaries. The source column is dropped.

Parameters:

Name	Type	Description	Default
`column`	`str`	Column to expand.	required

Returns:

Type	Description
`DataFrame`	pandas.DataFrame: Original DataFrame with the extracted columns; the source column is dropped.

Source code in src/openaivec/pandas_ext.py

def extract(self, column: str) -> pd.DataFrame:
    """Flatten one column of Pydantic models/dicts into top‑level columns.

    Example:
        ```python
        df = pd.DataFrame([
            {"animal": {"name": "cat", "legs": 4}},
            {"animal": {"name": "dog", "legs": 4}},
            {"animal": {"name": "elephant", "legs": 4}},
        ])
        df.ai.extract("animal")
        ```
        This method returns a DataFrame with the same index as the original,
        where each column corresponds to a key in the dictionaries.
        The source column is dropped.

    Args:
        column (str): Column to expand.

    Returns:
        pandas.DataFrame: Original DataFrame with the extracted columns; the source column is dropped.
    """
    if column not in self._obj.columns:
        raise ValueError(f"Column '{column}' does not exist in the DataFrame.")

    return (
        self._obj.pipe(lambda df: df.reset_index(drop=True))
        .pipe(lambda df: df.join(df[column].ai.extract()))
        .pipe(lambda df: df.set_index(self._obj.index))
        .pipe(lambda df: df.drop(columns=[column], axis=1))
    )

responses ¶

responses(
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
) -> pd.Series

Generate a response for each row after serialising it to JSON.

Example

df = pd.DataFrame([
    {"name": "cat", "legs": 4},
    {"name": "dog", "legs": 4},
    {"name": "elephant", "legs": 4},
])
df.ai.responses("what is the animal's name?")

This method returns a Series of strings, each containing the assistant's response to the corresponding input. Each row is serialised to JSON before being sent to the assistant. The model used is set by the responses_model function. The default model is gpt-4o-mini.

Parameters:

Name	Type	Description	Default
`instructions`	`str`	System prompt for the assistant.	required
`response_format`	`Type[T]`	Desired Python type of the responses. Defaults to `str`.	`str`
`batch_size`	`int`	Number of requests sent in one batch. Defaults to `128`.	`128`
`temperature`	`float`	Sampling temperature. Defaults to `0.0`.	`0.0`
`top_p`	`float`	Nucleus sampling parameter. Defaults to `1.0`.	`1.0`

Returns:

Type	Description
`Series`	pandas.Series: Responses aligned with the DataFrame’s original index.

Source code in src/openaivec/pandas_ext.py

def responses(
    self,
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
) -> pd.Series:
    """Generate a response for each row after serialising it to JSON.

    Example:
        ```python
        df = pd.DataFrame([
            {"name": "cat", "legs": 4},
            {"name": "dog", "legs": 4},
            {"name": "elephant", "legs": 4},
        ])
        df.ai.responses("what is the animal's name?")
        ```
        This method returns a Series of strings, each containing the
        assistant's response to the corresponding input.
        Each row is serialised to JSON before being sent to the assistant.
        The model used is set by the `responses_model` function.
        The default model is `gpt-4o-mini`.

    Args:
        instructions (str): System prompt for the assistant.
        response_format (Type[T], optional): Desired Python type of the
            responses. Defaults to ``str``.
        batch_size (int, optional): Number of requests sent in one batch.
            Defaults to ``128``.
        temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
        top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.

    Returns:
        pandas.Series: Responses aligned with the DataFrame’s original index.
    """
    return self._obj.pipe(
        lambda df: (
            df.pipe(lambda df: pd.Series(df.to_dict(orient="records"), index=df.index, name="record"))
            .map(lambda x: json.dumps(x, ensure_ascii=False))
            .ai.responses(
                instructions=instructions,
                response_format=response_format,
                batch_size=batch_size,
                temperature=temperature,
                top_p=top_p,
            )
        )
    )

task ¶

task(
    task: PreparedTask, batch_size: int = 128
) -> pd.Series

Execute a prepared task on each DataFrame row after serialising it to JSON.

This method applies a pre-configured task to each row in the DataFrame, using the task's instructions and response format to generate structured responses from the language model. Each row is serialised to JSON before being processed by the task.

Example

from openaivec.task.model import PreparedTask

# Assume you have a prepared task for data analysis
analysis_task = PreparedTask(...)

df = pd.DataFrame([
    {"name": "cat", "legs": 4},
    {"name": "dog", "legs": 4},
    {"name": "elephant", "legs": 4},
])
results = df.ai.task(analysis_task)

This method returns a Series containing the task results for each corresponding row, following the task's defined structure.

Parameters:

Name	Type	Description	Default
`task`	`PreparedTask`	A pre-configured task containing instructions, response format, and other parameters for processing the inputs.	required
`batch_size`	`int`	Number of requests sent in one batch to optimize API usage. Defaults to 128.	`128`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of the task's response format, aligned with the DataFrame's original index.

Source code in src/openaivec/pandas_ext.py

def task(self, task: PreparedTask, batch_size: int = 128) -> pd.Series:
    """Execute a prepared task on each DataFrame row after serialising it to JSON.

    This method applies a pre-configured task to each row in the DataFrame,
    using the task's instructions and response format to generate structured
    responses from the language model. Each row is serialised to JSON before
    being processed by the task.

    Example:
        ```python
        from openaivec.task.model import PreparedTask

        # Assume you have a prepared task for data analysis
        analysis_task = PreparedTask(...)

        df = pd.DataFrame([
            {"name": "cat", "legs": 4},
            {"name": "dog", "legs": 4},
            {"name": "elephant", "legs": 4},
        ])
        results = df.ai.task(analysis_task)
        ```
        This method returns a Series containing the task results for each
        corresponding row, following the task's defined structure.

    Args:
        task (PreparedTask): A pre-configured task containing instructions,
            response format, and other parameters for processing the inputs.
        batch_size (int, optional): Number of requests sent in one batch
            to optimize API usage. Defaults to 128.

    Returns:
        pandas.Series: Series whose values are instances of the task's
            response format, aligned with the DataFrame's original index.
    """
    return self._obj.pipe(
        lambda df: (
            df.pipe(lambda df: pd.Series(df.to_dict(orient="records"), index=df.index, name="record"))
            .map(lambda x: json.dumps(x, ensure_ascii=False))
            .ai.task(task=task, batch_size=batch_size)
        )
    )

AsyncOpenAIVecSeriesAccessor ¶

pandas Series accessor (.aio) that adds OpenAI helpers.

Source code in src/openaivec/pandas_ext.py

@pd.api.extensions.register_series_accessor("aio")
class AsyncOpenAIVecSeriesAccessor:
    """pandas Series accessor (``.aio``) that adds OpenAI helpers."""

    def __init__(self, series_obj: pd.Series):
        self._obj = series_obj

    async def responses(
        self,
        instructions: str,
        response_format: Type[T] = str,
        batch_size: int = 128,
        temperature: float = 0.0,
        top_p: float = 1.0,
        max_concurrency: int = 8,
    ) -> pd.Series:
        """Call an LLM once for every Series element (asynchronously).

        Example:
            ```python
            animals = pd.Series(["cat", "dog", "elephant"])
            # Must be awaited
            results = await animals.aio.responses("translate to French")
            ```
            This method returns a Series of strings, each containing the
            assistant's response to the corresponding input.
            The model used is set by the `responses_model` function.
            The default model is `gpt-4o-mini`.

        Args:
            instructions (str): System prompt prepended to every user message.
            response_format (Type[T], optional): Pydantic model or built‑in
                type the assistant should return. Defaults to ``str``.
            batch_size (int, optional): Number of prompts grouped into a single
                request. Defaults to ``128``.
            temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
            top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.
            max_concurrency (int, optional): Maximum number of concurrent
                requests. Defaults to ``8``.

        Returns:
            pandas.Series: Series whose values are instances of ``response_format``.

        Note:
            This is an asynchronous method and must be awaited.
        """
        client: AsyncBatchResponses = AsyncBatchResponses(
            client=_get_async_openai_client(),
            model_name=_RESPONSES_MODEL_NAME,
            system_message=instructions,
            response_format=response_format,
            temperature=temperature,
            top_p=top_p,
            max_concurrency=max_concurrency,
        )

        # Await the async operation
        results = await client.parse(self._obj.tolist(), batch_size=batch_size)

        return pd.Series(
            results,
            index=self._obj.index,
            name=self._obj.name,
        )

    async def embeddings(self, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
        """Compute OpenAI embeddings for every Series element (asynchronously).

        Example:
            ```python
            animals = pd.Series(["cat", "dog", "elephant"])
            # Must be awaited
            embeddings = await animals.aio.embeddings()
            ```
            This method returns a Series of numpy arrays, each containing the
            embedding vector for the corresponding input.
            The embedding model is set by the `embeddings_model` function.
            The default embedding model is `text-embedding-3-small`.

        Args:
            batch_size (int, optional): Number of inputs grouped into a
                single request. Defaults to ``128``.
            max_concurrency (int, optional): Maximum number of concurrent
                requests. Defaults to ``8``.

        Returns:
            pandas.Series: Series whose values are ``np.ndarray`` objects
                (dtype ``float32``).

        Note:
            This is an asynchronous method and must be awaited.
        """
        client: AsyncBatchEmbeddings = AsyncBatchEmbeddings(
            client=_get_async_openai_client(),
            model_name=_EMBEDDINGS_MODEL_NAME,
            max_concurrency=max_concurrency,
        )

        # Await the async operation
        results = await client.create(self._obj.tolist(), batch_size=batch_size)

        return pd.Series(
            results,
            index=self._obj.index,
            name=self._obj.name,
        )

    async def task(self, task: PreparedTask, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
        """Execute a prepared task on every Series element (asynchronously).

        This method applies a pre-configured task to each element in the Series,
        using the task's instructions and response format to generate structured
        responses from the language model.

        Example:
            ```python
            from openaivec.task.model import PreparedTask

            # Assume you have a prepared task for sentiment analysis
            sentiment_task = PreparedTask(...)

            reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
            # Must be awaited
            results = await reviews.aio.task(sentiment_task)
            ```
            This method returns a Series containing the task results for each
            corresponding input element, following the task's defined structure.

        Args:
            task (PreparedTask): A pre-configured task containing instructions,
                response format, and other parameters for processing the inputs.
            batch_size (int, optional): Number of prompts grouped into a single
                request to optimize API usage. Defaults to 128.
            max_concurrency (int, optional): Maximum number of concurrent
                requests. Defaults to 8.

        Returns:
            pandas.Series: Series whose values are instances of the task's
                response format, aligned with the original Series index.

        Note:
            This is an asynchronous method and must be awaited.
        """
        client = AsyncBatchResponses.of_task(
            client=_get_async_openai_client(),
            model_name=_RESPONSES_MODEL_NAME,
            task=task,
            max_concurrency=max_concurrency,
        )

        # Await the async operation
        results = await client.parse(self._obj.tolist(), batch_size=batch_size)

        return pd.Series(
            results,
            index=self._obj.index,
            name=self._obj.name,
        )

responses `async` ¶

responses(
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
    max_concurrency: int = 8,
) -> pd.Series

Call an LLM once for every Series element (asynchronously).

Example

animals = pd.Series(["cat", "dog", "elephant"])
# Must be awaited
results = await animals.aio.responses("translate to French")

This method returns a Series of strings, each containing the assistant's response to the corresponding input. The model used is set by the responses_model function. The default model is gpt-4o-mini.

Parameters:

Name	Type	Description	Default
`instructions`	`str`	System prompt prepended to every user message.	required
`response_format`	`Type[T]`	Pydantic model or built‑in type the assistant should return. Defaults to `str`.	`str`
`batch_size`	`int`	Number of prompts grouped into a single request. Defaults to `128`.	`128`
`temperature`	`float`	Sampling temperature. Defaults to `0.0`.	`0.0`
`top_p`	`float`	Nucleus sampling parameter. Defaults to `1.0`.	`1.0`
`max_concurrency`	`int`	Maximum number of concurrent requests. Defaults to `8`.	`8`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of `response_format`.

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def responses(
    self,
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
    max_concurrency: int = 8,
) -> pd.Series:
    """Call an LLM once for every Series element (asynchronously).

    Example:
        ```python
        animals = pd.Series(["cat", "dog", "elephant"])
        # Must be awaited
        results = await animals.aio.responses("translate to French")
        ```
        This method returns a Series of strings, each containing the
        assistant's response to the corresponding input.
        The model used is set by the `responses_model` function.
        The default model is `gpt-4o-mini`.

    Args:
        instructions (str): System prompt prepended to every user message.
        response_format (Type[T], optional): Pydantic model or built‑in
            type the assistant should return. Defaults to ``str``.
        batch_size (int, optional): Number of prompts grouped into a single
            request. Defaults to ``128``.
        temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
        top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.
        max_concurrency (int, optional): Maximum number of concurrent
            requests. Defaults to ``8``.

    Returns:
        pandas.Series: Series whose values are instances of ``response_format``.

    Note:
        This is an asynchronous method and must be awaited.
    """
    client: AsyncBatchResponses = AsyncBatchResponses(
        client=_get_async_openai_client(),
        model_name=_RESPONSES_MODEL_NAME,
        system_message=instructions,
        response_format=response_format,
        temperature=temperature,
        top_p=top_p,
        max_concurrency=max_concurrency,
    )

    # Await the async operation
    results = await client.parse(self._obj.tolist(), batch_size=batch_size)

    return pd.Series(
        results,
        index=self._obj.index,
        name=self._obj.name,
    )

embeddings `async` ¶

embeddings(
    batch_size: int = 128, max_concurrency: int = 8
) -> pd.Series

Compute OpenAI embeddings for every Series element (asynchronously).

Example

animals = pd.Series(["cat", "dog", "elephant"])
# Must be awaited
embeddings = await animals.aio.embeddings()

This method returns a Series of numpy arrays, each containing the embedding vector for the corresponding input. The embedding model is set by the embeddings_model function. The default embedding model is text-embedding-3-small.

Parameters:

Name	Type	Description	Default
`batch_size`	`int`	Number of inputs grouped into a single request. Defaults to `128`.	`128`
`max_concurrency`	`int`	Maximum number of concurrent requests. Defaults to `8`.	`8`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are `np.ndarray` objects (dtype `float32`).

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def embeddings(self, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
    """Compute OpenAI embeddings for every Series element (asynchronously).

    Example:
        ```python
        animals = pd.Series(["cat", "dog", "elephant"])
        # Must be awaited
        embeddings = await animals.aio.embeddings()
        ```
        This method returns a Series of numpy arrays, each containing the
        embedding vector for the corresponding input.
        The embedding model is set by the `embeddings_model` function.
        The default embedding model is `text-embedding-3-small`.

    Args:
        batch_size (int, optional): Number of inputs grouped into a
            single request. Defaults to ``128``.
        max_concurrency (int, optional): Maximum number of concurrent
            requests. Defaults to ``8``.

    Returns:
        pandas.Series: Series whose values are ``np.ndarray`` objects
            (dtype ``float32``).

    Note:
        This is an asynchronous method and must be awaited.
    """
    client: AsyncBatchEmbeddings = AsyncBatchEmbeddings(
        client=_get_async_openai_client(),
        model_name=_EMBEDDINGS_MODEL_NAME,
        max_concurrency=max_concurrency,
    )

    # Await the async operation
    results = await client.create(self._obj.tolist(), batch_size=batch_size)

    return pd.Series(
        results,
        index=self._obj.index,
        name=self._obj.name,
    )

task `async` ¶

task(
    task: PreparedTask,
    batch_size: int = 128,
    max_concurrency: int = 8,
) -> pd.Series

Execute a prepared task on every Series element (asynchronously).

This method applies a pre-configured task to each element in the Series, using the task's instructions and response format to generate structured responses from the language model.

Example

from openaivec.task.model import PreparedTask

# Assume you have a prepared task for sentiment analysis
sentiment_task = PreparedTask(...)

reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
# Must be awaited
results = await reviews.aio.task(sentiment_task)

This method returns a Series containing the task results for each corresponding input element, following the task's defined structure.

Parameters:

Name	Type	Description	Default
`task`	`PreparedTask`	A pre-configured task containing instructions, response format, and other parameters for processing the inputs.	required
`batch_size`	`int`	Number of prompts grouped into a single request to optimize API usage. Defaults to 128.	`128`
`max_concurrency`	`int`	Maximum number of concurrent requests. Defaults to 8.	`8`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of the task's response format, aligned with the original Series index.

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def task(self, task: PreparedTask, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
    """Execute a prepared task on every Series element (asynchronously).

    This method applies a pre-configured task to each element in the Series,
    using the task's instructions and response format to generate structured
    responses from the language model.

    Example:
        ```python
        from openaivec.task.model import PreparedTask

        # Assume you have a prepared task for sentiment analysis
        sentiment_task = PreparedTask(...)

        reviews = pd.Series(["Great product!", "Not satisfied", "Amazing quality"])
        # Must be awaited
        results = await reviews.aio.task(sentiment_task)
        ```
        This method returns a Series containing the task results for each
        corresponding input element, following the task's defined structure.

    Args:
        task (PreparedTask): A pre-configured task containing instructions,
            response format, and other parameters for processing the inputs.
        batch_size (int, optional): Number of prompts grouped into a single
            request to optimize API usage. Defaults to 128.
        max_concurrency (int, optional): Maximum number of concurrent
            requests. Defaults to 8.

    Returns:
        pandas.Series: Series whose values are instances of the task's
            response format, aligned with the original Series index.

    Note:
        This is an asynchronous method and must be awaited.
    """
    client = AsyncBatchResponses.of_task(
        client=_get_async_openai_client(),
        model_name=_RESPONSES_MODEL_NAME,
        task=task,
        max_concurrency=max_concurrency,
    )

    # Await the async operation
    results = await client.parse(self._obj.tolist(), batch_size=batch_size)

    return pd.Series(
        results,
        index=self._obj.index,
        name=self._obj.name,
    )

AsyncOpenAIVecDataFrameAccessor ¶

pandas DataFrame accessor (.aio) that adds OpenAI helpers.

Source code in src/openaivec/pandas_ext.py

@pd.api.extensions.register_dataframe_accessor("aio")
class AsyncOpenAIVecDataFrameAccessor:
    """pandas DataFrame accessor (``.aio``) that adds OpenAI helpers."""

    def __init__(self, df_obj: pd.DataFrame):
        self._obj = df_obj

    async def responses(
        self,
        instructions: str,
        response_format: Type[T] = str,
        batch_size: int = 128,
        temperature: float = 0.0,
        top_p: float = 1.0,
        max_concurrency: int = 8,
    ) -> pd.Series:
        """Generate a response for each row after serialising it to JSON (asynchronously).

        Example:
            ```python
            df = pd.DataFrame([
                {\"name\": \"cat\", \"legs\": 4},
                {\"name\": \"dog\", \"legs\": 4},
                {\"name\": \"elephant\", \"legs\": 4},
            ])
            # Must be awaited
            results = await df.aio.responses(\"what is the animal\'s name?\")
            ```
            This method returns a Series of strings, each containing the
            assistant's response to the corresponding input.
            Each row is serialised to JSON before being sent to the assistant.
            The model used is set by the `responses_model` function.
            The default model is `gpt-4o-mini`.

        Args:
            instructions (str): System prompt for the assistant.
            response_format (Type[T], optional): Desired Python type of the
                responses. Defaults to ``str``.
            batch_size (int, optional): Number of requests sent in one batch.
                Defaults to ``128``.
            temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
            top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.
            max_concurrency (int, optional): Maximum number of concurrent
                requests. Defaults to ``8``.

        Returns:
            pandas.Series: Responses aligned with the DataFrame’s original index.

        Note:
            This is an asynchronous method and must be awaited.
        """
        series_of_json = self._obj.pipe(
            lambda df: (
                pd.Series(df.to_dict(orient="records"), index=df.index, name="record").map(
                    lambda x: json.dumps(x, ensure_ascii=False)
                )
            )
        )
        # Await the call to the async Series method using .aio
        return await series_of_json.aio.responses(
            instructions=instructions,
            response_format=response_format,
            batch_size=batch_size,
            temperature=temperature,
            top_p=top_p,
            max_concurrency=max_concurrency,
        )

    async def task(self, task: PreparedTask, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
        """Execute a prepared task on each DataFrame row after serialising it to JSON (asynchronously).

        This method applies a pre-configured task to each row in the DataFrame,
        using the task's instructions and response format to generate structured
        responses from the language model. Each row is serialised to JSON before
        being processed by the task.

        Example:
            ```python
            from openaivec.task.model import PreparedTask

            # Assume you have a prepared task for data analysis
            analysis_task = PreparedTask(...)

            df = pd.DataFrame([
                {"name": "cat", "legs": 4},
                {"name": "dog", "legs": 4},
                {"name": "elephant", "legs": 4},
            ])
            # Must be awaited
            results = await df.aio.task(analysis_task)
            ```
            This method returns a Series containing the task results for each
            corresponding row, following the task's defined structure.

        Args:
            task (PreparedTask): A pre-configured task containing instructions,
                response format, and other parameters for processing the inputs.
            batch_size (int, optional): Number of requests sent in one batch
                to optimize API usage. Defaults to 128.
            max_concurrency (int, optional): Maximum number of concurrent
                requests. Defaults to 8.

        Returns:
            pandas.Series: Series whose values are instances of the task's
                response format, aligned with the DataFrame's original index.

        Note:
            This is an asynchronous method and must be awaited.
        """
        series_of_json = self._obj.pipe(
            lambda df: (
                pd.Series(df.to_dict(orient="records"), index=df.index, name="record").map(
                    lambda x: json.dumps(x, ensure_ascii=False)
                )
            )
        )
        # Await the call to the async Series method using .aio
        return await series_of_json.aio.task(
            task=task,
            batch_size=batch_size,
            max_concurrency=max_concurrency,
        )

    async def pipe(self, func: Callable[[pd.DataFrame], Awaitable[T] | T]) -> T:
        """
        Apply a function to the DataFrame, supporting both synchronous and asynchronous functions.

        This method allows chaining operations on the DataFrame, similar to pandas' `pipe` method,
        but with support for asynchronous functions.

        Args:
            func (Callable[[pd.DataFrame], Awaitable[T] | T]): A function that takes a DataFrame
                as input and returns either a result or an awaitable result.

        Returns:
            T: The result of applying the function, either directly or after awaiting it.

        Note:
            This is an asynchronous method and must be awaited if the function returns an awaitable.
        """
        result = func(self._obj)
        if inspect.isawaitable(result):
            return await result
        else:
            return result

    async def assign(self, **kwargs: Any) -> pd.DataFrame:
        """Asynchronously assign new columns to the DataFrame, evaluating sequentially.

        This method extends pandas' `assign` method by supporting asynchronous
        functions as column values and evaluating assignments sequentially, allowing
        later assignments to refer to columns created earlier in the same call.

        For each key-value pair in `kwargs`:
        - If the value is a callable, it is invoked with the current state of the DataFrame
          (including columns created in previous steps of this `assign` call).
          If the result is awaitable, it is awaited; otherwise, it is used directly.
        - If the value is not callable, it is assigned directly to the new column.

        Example:
            ```python
            async def compute_column(df):
                # Simulate an asynchronous computation
                await asyncio.sleep(1)
                return df["existing_column"] * 2

            async def use_new_column(df):
                # Access the column created in the previous step
                await asyncio.sleep(1)
                return df["new_column"] + 5


            df = pd.DataFrame({"existing_column": [1, 2, 3]})
            # Must be awaited
            df = await df.aio.assign(
                new_column=compute_column,
                another_column=use_new_column
            )
            ```

        Args:
            **kwargs: Any. Column names as keys and either static values or callables
                (synchronous or asynchronous) as values.

        Returns:
            pandas.DataFrame: A new DataFrame with the assigned columns.

        Note:
            This is an asynchronous method and must be awaited.
        """
        df_current = self._obj.copy()
        for key, value in kwargs.items():
            if callable(value):
                result = value(df_current)
                if inspect.isawaitable(result):
                    column_data = await result
                else:
                    column_data = result
            else:
                column_data = value

            df_current[key] = column_data

        return df_current

responses `async` ¶

responses(
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
    max_concurrency: int = 8,
) -> pd.Series

Generate a response for each row after serialising it to JSON (asynchronously).

Example

df = pd.DataFrame([
    {"name": "cat", "legs": 4},
    {"name": "dog", "legs": 4},
    {"name": "elephant", "legs": 4},
])
# Must be awaited
results = await df.aio.responses("what is the animal's name?")

This method returns a Series of strings, each containing the assistant's response to the corresponding input. Each row is serialised to JSON before being sent to the assistant. The model used is set by the responses_model function. The default model is gpt-4o-mini.

Parameters:

Name	Type	Description	Default
`instructions`	`str`	System prompt for the assistant.	required
`response_format`	`Type[T]`	Desired Python type of the responses. Defaults to `str`.	`str`
`batch_size`	`int`	Number of requests sent in one batch. Defaults to `128`.	`128`
`temperature`	`float`	Sampling temperature. Defaults to `0.0`.	`0.0`
`top_p`	`float`	Nucleus sampling parameter. Defaults to `1.0`.	`1.0`
`max_concurrency`	`int`	Maximum number of concurrent requests. Defaults to `8`.	`8`

Returns:

Type	Description
`Series`	pandas.Series: Responses aligned with the DataFrame’s original index.

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def responses(
    self,
    instructions: str,
    response_format: Type[T] = str,
    batch_size: int = 128,
    temperature: float = 0.0,
    top_p: float = 1.0,
    max_concurrency: int = 8,
) -> pd.Series:
    """Generate a response for each row after serialising it to JSON (asynchronously).

    Example:
        ```python
        df = pd.DataFrame([
            {\"name\": \"cat\", \"legs\": 4},
            {\"name\": \"dog\", \"legs\": 4},
            {\"name\": \"elephant\", \"legs\": 4},
        ])
        # Must be awaited
        results = await df.aio.responses(\"what is the animal\'s name?\")
        ```
        This method returns a Series of strings, each containing the
        assistant's response to the corresponding input.
        Each row is serialised to JSON before being sent to the assistant.
        The model used is set by the `responses_model` function.
        The default model is `gpt-4o-mini`.

    Args:
        instructions (str): System prompt for the assistant.
        response_format (Type[T], optional): Desired Python type of the
            responses. Defaults to ``str``.
        batch_size (int, optional): Number of requests sent in one batch.
            Defaults to ``128``.
        temperature (float, optional): Sampling temperature. Defaults to ``0.0``.
        top_p (float, optional): Nucleus sampling parameter. Defaults to ``1.0``.
        max_concurrency (int, optional): Maximum number of concurrent
            requests. Defaults to ``8``.

    Returns:
        pandas.Series: Responses aligned with the DataFrame’s original index.

    Note:
        This is an asynchronous method and must be awaited.
    """
    series_of_json = self._obj.pipe(
        lambda df: (
            pd.Series(df.to_dict(orient="records"), index=df.index, name="record").map(
                lambda x: json.dumps(x, ensure_ascii=False)
            )
        )
    )
    # Await the call to the async Series method using .aio
    return await series_of_json.aio.responses(
        instructions=instructions,
        response_format=response_format,
        batch_size=batch_size,
        temperature=temperature,
        top_p=top_p,
        max_concurrency=max_concurrency,
    )

task `async` ¶

task(
    task: PreparedTask,
    batch_size: int = 128,
    max_concurrency: int = 8,
) -> pd.Series

Execute a prepared task on each DataFrame row after serialising it to JSON (asynchronously).

This method applies a pre-configured task to each row in the DataFrame, using the task's instructions and response format to generate structured responses from the language model. Each row is serialised to JSON before being processed by the task.

Example

from openaivec.task.model import PreparedTask

# Assume you have a prepared task for data analysis
analysis_task = PreparedTask(...)

df = pd.DataFrame([
    {"name": "cat", "legs": 4},
    {"name": "dog", "legs": 4},
    {"name": "elephant", "legs": 4},
])
# Must be awaited
results = await df.aio.task(analysis_task)

This method returns a Series containing the task results for each corresponding row, following the task's defined structure.

Parameters:

Name	Type	Description	Default
`task`	`PreparedTask`	A pre-configured task containing instructions, response format, and other parameters for processing the inputs.	required
`batch_size`	`int`	Number of requests sent in one batch to optimize API usage. Defaults to 128.	`128`
`max_concurrency`	`int`	Maximum number of concurrent requests. Defaults to 8.	`8`

Returns:

Type	Description
`Series`	pandas.Series: Series whose values are instances of the task's response format, aligned with the DataFrame's original index.

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def task(self, task: PreparedTask, batch_size: int = 128, max_concurrency: int = 8) -> pd.Series:
    """Execute a prepared task on each DataFrame row after serialising it to JSON (asynchronously).

    This method applies a pre-configured task to each row in the DataFrame,
    using the task's instructions and response format to generate structured
    responses from the language model. Each row is serialised to JSON before
    being processed by the task.

    Example:
        ```python
        from openaivec.task.model import PreparedTask

        # Assume you have a prepared task for data analysis
        analysis_task = PreparedTask(...)

        df = pd.DataFrame([
            {"name": "cat", "legs": 4},
            {"name": "dog", "legs": 4},
            {"name": "elephant", "legs": 4},
        ])
        # Must be awaited
        results = await df.aio.task(analysis_task)
        ```
        This method returns a Series containing the task results for each
        corresponding row, following the task's defined structure.

    Args:
        task (PreparedTask): A pre-configured task containing instructions,
            response format, and other parameters for processing the inputs.
        batch_size (int, optional): Number of requests sent in one batch
            to optimize API usage. Defaults to 128.
        max_concurrency (int, optional): Maximum number of concurrent
            requests. Defaults to 8.

    Returns:
        pandas.Series: Series whose values are instances of the task's
            response format, aligned with the DataFrame's original index.

    Note:
        This is an asynchronous method and must be awaited.
    """
    series_of_json = self._obj.pipe(
        lambda df: (
            pd.Series(df.to_dict(orient="records"), index=df.index, name="record").map(
                lambda x: json.dumps(x, ensure_ascii=False)
            )
        )
    )
    # Await the call to the async Series method using .aio
    return await series_of_json.aio.task(
        task=task,
        batch_size=batch_size,
        max_concurrency=max_concurrency,
    )

pipe `async` ¶

pipe(func: Callable[[DataFrame], Awaitable[T] | T]) -> T

Apply a function to the DataFrame, supporting both synchronous and asynchronous functions.

This method allows chaining operations on the DataFrame, similar to pandas' pipe method, but with support for asynchronous functions.

Parameters:

Name	Type	Description	Default
`func`	`Callable[[DataFrame], Awaitable[T] \| T]`	A function that takes a DataFrame as input and returns either a result or an awaitable result.	required

Returns:

Name	Type	Description
`T`	`T`	The result of applying the function, either directly or after awaiting it.

Note

This is an asynchronous method and must be awaited if the function returns an awaitable.

Source code in src/openaivec/pandas_ext.py

async def pipe(self, func: Callable[[pd.DataFrame], Awaitable[T] | T]) -> T:
    """
    Apply a function to the DataFrame, supporting both synchronous and asynchronous functions.

    This method allows chaining operations on the DataFrame, similar to pandas' `pipe` method,
    but with support for asynchronous functions.

    Args:
        func (Callable[[pd.DataFrame], Awaitable[T] | T]): A function that takes a DataFrame
            as input and returns either a result or an awaitable result.

    Returns:
        T: The result of applying the function, either directly or after awaiting it.

    Note:
        This is an asynchronous method and must be awaited if the function returns an awaitable.
    """
    result = func(self._obj)
    if inspect.isawaitable(result):
        return await result
    else:
        return result

assign `async` ¶

assign(**kwargs: Any) -> pd.DataFrame

Asynchronously assign new columns to the DataFrame, evaluating sequentially.

This method extends pandas' assign method by supporting asynchronous functions as column values and evaluating assignments sequentially, allowing later assignments to refer to columns created earlier in the same call.

For each key-value pair in kwargs: - If the value is a callable, it is invoked with the current state of the DataFrame (including columns created in previous steps of this assign call). If the result is awaitable, it is awaited; otherwise, it is used directly. - If the value is not callable, it is assigned directly to the new column.

Example

async def compute_column(df):
    # Simulate an asynchronous computation
    await asyncio.sleep(1)
    return df["existing_column"] * 2

async def use_new_column(df):
    # Access the column created in the previous step
    await asyncio.sleep(1)
    return df["new_column"] + 5


df = pd.DataFrame({"existing_column": [1, 2, 3]})
# Must be awaited
df = await df.aio.assign(
    new_column=compute_column,
    another_column=use_new_column
)

Parameters:

Name	Type	Description	Default
`**kwargs`	`Any`	Any. Column names as keys and either static values or callables (synchronous or asynchronous) as values.	`{}`

Returns:

Type	Description
`DataFrame`	pandas.DataFrame: A new DataFrame with the assigned columns.

Note

This is an asynchronous method and must be awaited.

Source code in src/openaivec/pandas_ext.py

async def assign(self, **kwargs: Any) -> pd.DataFrame:
    """Asynchronously assign new columns to the DataFrame, evaluating sequentially.

    This method extends pandas' `assign` method by supporting asynchronous
    functions as column values and evaluating assignments sequentially, allowing
    later assignments to refer to columns created earlier in the same call.

    For each key-value pair in `kwargs`:
    - If the value is a callable, it is invoked with the current state of the DataFrame
      (including columns created in previous steps of this `assign` call).
      If the result is awaitable, it is awaited; otherwise, it is used directly.
    - If the value is not callable, it is assigned directly to the new column.

    Example:
        ```python
        async def compute_column(df):
            # Simulate an asynchronous computation
            await asyncio.sleep(1)
            return df["existing_column"] * 2

        async def use_new_column(df):
            # Access the column created in the previous step
            await asyncio.sleep(1)
            return df["new_column"] + 5


        df = pd.DataFrame({"existing_column": [1, 2, 3]})
        # Must be awaited
        df = await df.aio.assign(
            new_column=compute_column,
            another_column=use_new_column
        )
        ```

    Args:
        **kwargs: Any. Column names as keys and either static values or callables
            (synchronous or asynchronous) as values.

    Returns:
        pandas.DataFrame: A new DataFrame with the assigned columns.

    Note:
        This is an asynchronous method and must be awaited.
    """
    df_current = self._obj.copy()
    for key, value in kwargs.items():
        if callable(value):
            result = value(df_current)
            if inspect.isawaitable(result):
                column_data = await result
            else:
                column_data = result
        else:
            column_data = value

        df_current[key] = column_data

    return df_current

use ¶

use(client: OpenAI) -> None

Register a custom OpenAI‑compatible client.

Parameters:

Name	Type	Description	Default
`client`	`OpenAI`	A pre‑configured `openai.OpenAI` or `openai.AzureOpenAI` instance. The same instance is reused by every helper in this module.	required

Source code in src/openaivec/pandas_ext.py

def use(client: OpenAI) -> None:
    """Register a custom OpenAI‑compatible client.

    Args:
        client (OpenAI): A pre‑configured `openai.OpenAI` or
            `openai.AzureOpenAI` instance.
            The same instance is reused by every helper in this module.
    """
    global _CLIENT
    _CLIENT = client

use_async ¶

use_async(client: AsyncOpenAI) -> None

Register a custom asynchronous OpenAI‑compatible client.

Parameters:

Name	Type	Description	Default
`client`	`AsyncOpenAI`	A pre‑configured `openai.AsyncOpenAI` or `openai.AsyncAzureOpenAI` instance. The same instance is reused by every helper in this module.	required

Source code in src/openaivec/pandas_ext.py

def use_async(client: AsyncOpenAI) -> None:
    """Register a custom asynchronous OpenAI‑compatible client.

    Args:
        client (AsyncOpenAI): A pre‑configured `openai.AsyncOpenAI` or
            `openai.AsyncAzureOpenAI` instance.
            The same instance is reused by every helper in this module.
    """
    global _ASYNC_CLIENT
    _ASYNC_CLIENT = client

use_openai ¶

use_openai(api_key: str) -> None

Create and register a default openai.OpenAI client.

Parameters:

Name	Type	Description	Default
`api_key`	`str`	Value forwarded to the `api_key` parameter of `openai.OpenAI`.	required

Source code in src/openaivec/pandas_ext.py

def use_openai(api_key: str) -> None:
    """Create and register a default `openai.OpenAI` client.

    Args:
        api_key (str): Value forwarded to the ``api_key`` parameter of
            `openai.OpenAI`.
    """
    global _CLIENT, _ASYNC_CLIENT
    _CLIENT = OpenAI(api_key=api_key)
    _ASYNC_CLIENT = AsyncOpenAI(api_key=api_key)

use_azure_openai ¶

use_azure_openai(
    api_key: str, endpoint: str, api_version: str
) -> None

Create and register an openai.AzureOpenAI client.

Parameters:

Name	Type	Description	Default
`api_key`	`str`	Azure OpenAI subscription key.	required
`endpoint`	`str`	Resource endpoint, e.g. `https://<resource>.openai.azure.com`.	required
`api_version`	`str`	REST API version such as `2024‑02‑15-preview`.	required

Source code in src/openaivec/pandas_ext.py

def use_azure_openai(api_key: str, endpoint: str, api_version: str) -> None:
    """Create and register an `openai.AzureOpenAI` client.

    Args:
        api_key (str): Azure OpenAI subscription key.
        endpoint (str): Resource endpoint, e.g.
            ``https://<resource>.openai.azure.com``.
        api_version (str): REST API version such as ``2024‑02‑15-preview``.
    """
    global _CLIENT, _ASYNC_CLIENT
    _CLIENT = AzureOpenAI(
        api_key=api_key,
        azure_endpoint=endpoint,
        api_version=api_version,
    )
    _ASYNC_CLIENT = AsyncAzureOpenAI(
        api_key=api_key,
        azure_endpoint=endpoint,
        api_version=api_version,
    )

responses_model ¶

responses_model(name: str) -> None

Override the model used for text responses.

Parameters:

Name	Type	Description	Default
`name`	`str`	Model name as listed in the OpenAI API (for example, `gpt-4o-mini`).	required

Source code in src/openaivec/pandas_ext.py

def responses_model(name: str) -> None:
    """Override the model used for text responses.

    Args:
        name (str): Model name as listed in the OpenAI API
            (for example, ``gpt-4o-mini``).
    """
    global _RESPONSES_MODEL_NAME, _TIKTOKEN_ENCODING
    _RESPONSES_MODEL_NAME = name

    try:
        _TIKTOKEN_ENCODING = tiktoken.encoding_for_model(name)

    except KeyError:
        _LOGGER.info(
            "The model name '%s' is not supported by tiktoken. Instead, using the 'o200k_base' encoding.",
            name,
        )
        _TIKTOKEN_ENCODING = tiktoken.get_encoding("o200k_base")

embeddings_model ¶

embeddings_model(name: str) -> None

Override the model used for text embeddings.

Parameters:

Name	Type	Description	Default
`name`	`str`	Embedding model name, e.g. `text-embedding-3-small`.	required

Source code in src/openaivec/pandas_ext.py

def embeddings_model(name: str) -> None:
    """Override the model used for text embeddings.

    Args:
        name (str): Embedding model name, e.g. ``text-embedding-3-small``.
    """
    global _EMBEDDINGS_MODEL_NAME
    _EMBEDDINGS_MODEL_NAME = name

Pandas Extension¶

openaivec.pandas_ext ¶

Setup¶

OpenAIVecSeriesAccessor ¶

responses ¶

task ¶

embeddings ¶

count_tokens ¶

extract ¶

OpenAIVecDataFrameAccessor ¶

extract ¶

responses ¶

task ¶

AsyncOpenAIVecSeriesAccessor ¶

responses async ¶

embeddings async ¶

task async ¶

AsyncOpenAIVecDataFrameAccessor ¶

responses async ¶

task async ¶

pipe async ¶

assign async ¶

use ¶

use_async ¶

use_openai ¶

use_azure_openai ¶

responses_model ¶

embeddings_model ¶

responses `async` ¶

embeddings `async` ¶

task `async` ¶

responses `async` ¶

task `async` ¶

pipe `async` ¶

assign `async` ¶