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fenic.api.session

Session module for managing query execution context and state.

Classes:

AnthropicLanguageModel

Bases: BaseModel

Configuration for Anthropic language models.

This class defines the configuration settings for Anthropic language models, including model selection and separate rate limiting parameters for input and output tokens.

Attributes:

  • model_name (AnthropicLanguageModelName) –

    The name of the Anthropic model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • input_tpm (int) –

    Input tokens per minute limit; must be greater than 0.

  • output_tpm (int) –

    Output tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring an Anthropic model with separate input/output rate limits:

config = AnthropicLanguageModel(
    model_name="claude-3-5-haiku-latest",
    rpm=100,
    input_tpm=100,
    output_tpm=100
)

Configuring an Anthropic model with profiles:

config = SessionConfig(
    semantic=SemanticConfig(
        language_models={
            "claude": AnthropicLanguageModel(
                model_name="claude-opus-4-0",
                rpm=100,
                input_tpm=100,
                output_tpm=100,
                profiles={
                    "thinking_disabled": AnthropicLanguageModel.Profile(),
                    "fast": AnthropicLanguageModel.Profile(thinking_token_budget=1024),
                    "thorough": AnthropicLanguageModel.Profile(thinking_token_budget=4096)
                },
                default_profile="fast"
            )
        },
        default_language_model="claude"
)

# Using the default "fast" profile for the "claude" model
semantic.map(instruction="Construct a formal proof of the {hypothesis}.", model_alias="claude")

# Using the "thorough" profile for the "claude" model
semantic.map(instruction="Construct a formal proof of the {hypothesis}.", model_alias=ModelAlias(name="claude", profile="thorough"))

Classes:

  • Profile –

    Anthropic-specific profile configurations.

Profile

Bases: BaseModel

Anthropic-specific profile configurations.

This class defines profile configurations for Anthropic models, allowing different thinking token budget settings to be applied to the same model.

Attributes:

  • thinking_token_budget (Optional[int]) –

    If configuring a model that supports reasoning, provide a default thinking budget in tokens. If not provided, thinking will be disabled for the profile. The minimum token budget supported by Anthropic is 1024 tokens.

Note

If thinking_token_budget is set, temperature cannot be customized -- any changes to temperature will be ignored.

Example

Configuring a profile with a thinking budget:

profile = AnthropicLanguageModel.Profile(thinking_token_budget=2048)

Configuring a profile with a large thinking budget:

profile = AnthropicLanguageModel.Profile(thinking_token_budget=8192)

CloudConfig

Bases: BaseModel

Configuration for cloud-based execution.

This class defines settings for running operations in a cloud environment, allowing for scalable and distributed processing of language model operations.

Attributes:

  • size (Optional[CloudExecutorSize]) –

    Size of the cloud executor instance. If None, the default size will be used.

Example

Configuring cloud execution with a specific size:

config = CloudConfig(size=CloudExecutorSize.MEDIUM)

Using default cloud configuration:

config = CloudConfig()

CloudExecutorSize

Bases: str, Enum

Enum defining available cloud executor sizes.

This enum represents the different size options available for cloud-based execution environments.

Attributes:

  • SMALL –

    Small instance size.

  • MEDIUM –

    Medium instance size.

  • LARGE –

    Large instance size.

  • XLARGE –

    Extra large instance size.

CohereEmbeddingModel

Bases: BaseModel

Configuration for Cohere embedding models.

This class defines the configuration settings for Cohere embedding models, including model selection and rate limiting parameters.

Attributes:

  • model_name (CohereEmbeddingModelName) –

    The name of the Cohere model to use.

  • rpm (int) –

    Requests per minute limit for the model.

  • tpm (int) –

    Tokens per minute limit for the model.

  • profiles (Optional[dict[str, Profile]]) –

    Optional dictionary of profile configurations.

  • default_profile (Optional[str]) –

    Default profile name to use if none specified.

Example

Configuring a Cohere embedding model with profiles:

cohere_config = CohereEmbeddingModel(
    model_name="embed-v4.0",
    rpm=100,
    tpm=50_000,
    profiles={
        "high_dim": CohereEmbeddingModel.Profile(
            embedding_dimensionality=1536,
            embedding_task_type="search_document"
        ),
        "classification": CohereEmbeddingModel.Profile(
            embedding_dimensionality=1024,
            embedding_task_type="classification"
        )
    },
    default_profile="high_dim"
)

Classes:

  • Profile –

    Profile configurations for Cohere embedding models.

Profile

Bases: BaseModel

Profile configurations for Cohere embedding models.

This class defines profile configurations for Cohere embedding models, allowing different output dimensionality and task type settings to be applied to the same model.

Attributes:

  • output_dimensionality (Optional[int]) –

    The dimensionality of the embedding created by this model. If not provided, the model will use its default dimensionality.

  • input_type (CohereEmbeddingTaskType) –

    The type of input text (search_query, search_document, classification, clustering)

Example

Configuring a profile with custom dimensionality:

profile = CohereEmbeddingModel.Profile(output_dimensionality=1536)

Configuring a profile with default settings:

profile = CohereEmbeddingModel.Profile()

GoogleDeveloperEmbeddingModel

Bases: BaseModel

Configuration for Google Developer embedding models.

This class defines the configuration settings for Google embedding models available in Google Developer AI Studio, including model selection and rate limiting parameters. These models are accessible using a GOOGLE_API_KEY environment variable.

Attributes:

  • model_name (GoogleDeveloperEmbeddingModelName) –

    The name of the Google Developer embedding model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring a Google Developer embedding model with rate limits:

config = GoogleDeveloperEmbeddingModelConfig(
    model_name="gemini-embedding-001",
    rpm=100,
    tpm=1000
)

Configuring a Google Developer embedding model with profiles:

config = GoogleDeveloperEmbeddingModelConfig(
    model_name="gemini-embedding-001",
    rpm=100,
    tpm=1000,
    profiles={
        "default": GoogleDeveloperEmbeddingModelConfig.Profile(),
        "high_dim": GoogleDeveloperEmbeddingModelConfig.Profile(output_dimensionality=3072)
    },
    default_profile="default"
)

Classes:

  • Profile –

    Profile configurations for Google Developer embedding models.

Profile

Bases: BaseModel

Profile configurations for Google Developer embedding models.

This class defines profile configurations for Google embedding models, allowing different output dimensionality and task type settings to be applied to the same model.

Attributes:

  • output_dimensionality (Optional[int]) –

    The dimensionality of the embedding created by this model. If not provided, the model will use its default dimensionality.

  • task_type (GoogleEmbeddingTaskType) –

    The type of task for the embedding model.

Example

Configuring a profile with custom dimensionality:

profile = GoogleDeveloperEmbeddingModelConfig.Profile(output_dimensionality=3072)

Configuring a profile with default settings:

profile = GoogleDeveloperEmbeddingModelConfig.Profile()

GoogleDeveloperLanguageModel

Bases: BaseModel

Configuration for Gemini models accessible through Google Developer AI Studio.

This class defines the configuration settings for Google Gemini models available in Google Developer AI Studio, including model selection and rate limiting parameters. These models are accessible using a GOOGLE_API_KEY environment variable.

Attributes:

  • model_name (GoogleDeveloperLanguageModelName) –

    The name of the Google Developer model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring a Google Developer model with rate limits:

config = GoogleDeveloperLanguageModel(
    model_name="gemini-2.0-flash",
    rpm=100,
    tpm=1000
)

Configuring a reasoning Google Developer model with profiles:

config = GoogleDeveloperLanguageModel(
    model_name="gemini-2.5-flash",
    rpm=100,
    tpm=1000,
    profiles={
        "thinking_disabled": GoogleDeveloperLanguageModel.Profile(),
        "fast": GoogleDeveloperLanguageModel.Profile(thinking_token_budget=1024),
        "thorough": GoogleDeveloperLanguageModel.Profile(thinking_token_budget=8192)
    },
    default_profile="fast"
)

Classes:

  • Profile –

    Profile configurations for Google Developer models.

Profile

Bases: BaseModel

Profile configurations for Google Developer models.

This class defines profile configurations for Google Gemini models, allowing different thinking/reasoning settings to be applied to the same model.

Attributes:

  • thinking_token_budget (Optional[int]) –

    If configuring a reasoning model, provide a thinking budget in tokens. If not provided, or if set to 0, thinking will be disabled for the profile (not supported on gemini-2.5-pro). To have the model automatically determine a thinking budget based on the complexity of the prompt, set this to -1. Note that Gemini models take this as a suggestion -- and not a hard limit. It is very possible for the model to generate far more thinking tokens than the suggested budget, and for the model to generate reasoning tokens even if thinking is disabled.

Example

Configuring a profile with a fixed thinking budget:

profile = GoogleDeveloperLanguageModel.Profile(thinking_token_budget=4096)

Configuring a profile with automatic thinking budget:

profile = GoogleDeveloperLanguageModel.Profile(thinking_token_budget=-1)

Configuring a profile with thinking disabled:

profile = GoogleDeveloperLanguageModel.Profile(thinking_token_budget=0)

GoogleVertexEmbeddingModel

Bases: BaseModel

Configuration for Google Vertex AI embedding models.

This class defines the configuration settings for Google embedding models available in Google Vertex AI, including model selection and rate limiting parameters. These models are accessible using Google Cloud credentials.

Attributes:

  • model_name (GoogleVertexEmbeddingModelName) –

    The name of the Google Vertex embedding model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring a Google Vertex embedding model with rate limits:

embedding_model = GoogleVertexEmbeddingModel(
    model_name="gemini-embedding-001",
    rpm=100,
    tpm=1000
)

Configuring a Google Vertex embedding model with profiles:

embedding_model = GoogleVertexEmbeddingModel(
    model_name="gemini-embedding-001",
    rpm=100,
    tpm=1000,
    profiles={
        "default": GoogleVertexEmbeddingModel.Profile(),
        "high_dim": GoogleVertexEmbeddingModel.Profile(output_dimensionality=3072)
    },
    default_profile="default"
)

Classes:

  • Profile –

    Profile configurations for Google Vertex embedding models.

Profile

Bases: BaseModel

Profile configurations for Google Vertex embedding models.

This class defines profile configurations for Google embedding models, allowing different output dimensionality and task type settings to be applied to the same model.

Attributes:

  • output_dimensionality (Optional[int]) –

    The dimensionality of the embedding created by this model. If not provided, the model will use its default dimensionality.

  • task_type (GoogleEmbeddingTaskType) –

    The type of task for the embedding model.

Example

Configuring a profile with custom dimensionality:

profile = GoogleVertexEmbeddingModelConfig.Profile(output_dimensionality=3072)

Configuring a profile with default settings:

profile = GoogleVertexEmbeddingModelConfig.Profile()

GoogleVertexLanguageModel

Bases: BaseModel

Configuration for Google Vertex AI models.

This class defines the configuration settings for Google Gemini models available in Google Vertex AI, including model selection and rate limiting parameters. These models are accessible using Google Cloud credentials.

Attributes:

  • model_name (GoogleVertexLanguageModelName) –

    The name of the Google Vertex model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring a Google Vertex model with rate limits:

config = GoogleVertexLanguageModel(
    model_name="gemini-2.0-flash",
    rpm=100,
    tpm=1000
)

Configuring a reasoning Google Vertex model with profiles:

config = GoogleVertexLanguageModel(
    model_name="gemini-2.5-flash",
    rpm=100,
    tpm=1000,
    profiles={
        "thinking_disabled": GoogleVertexLanguageModel.Profile(),
        "fast": GoogleVertexLanguageModel.Profile(thinking_token_budget=1024),
        "thorough": GoogleVertexLanguageModel.Profile(thinking_token_budget=8192)
    },
    default_profile="fast"
)

Classes:

  • Profile –

    Profile configurations for Google Vertex models.

Profile

Bases: BaseModel

Profile configurations for Google Vertex models.

This class defines profile configurations for Google Gemini models, allowing different thinking/reasoning settings to be applied to the same underlying model.

Attributes:

  • thinking_token_budget (Optional[int]) –

    If configuring a reasoning model, provide a thinking budget in tokens. If not provided, or if set to 0, thinking will be disabled for the profile (not supported on gemini-2.5-pro). To have the model automatically determine a thinking budget based on the complexity of the prompt, set this to -1. Note that Gemini models take this as a suggestion -- and not a hard limit. It is very possible for the model to generate far more thinking tokens than the suggested budget, and for the model to generate reasoning tokens even if thinking is disabled.

Example

Configuring a profile with a fixed thinking budget:

profile = GoogleVertexLanguageModel.Profile(thinking_token_budget=4096)

Configuring a profile with automatic thinking budget:

profile = GoogleVertexLanguageModel.Profile(thinking_token_budget=-1)

Configuring a profile with thinking disabled:

profile = GoogleVertexLanguageModel.Profile(thinking_token_budget=0)

OpenAIEmbeddingModel

Bases: BaseModel

Configuration for OpenAI embedding models.

This class defines the configuration settings for OpenAI embedding models, including model selection and rate limiting parameters.

Attributes:

  • model_name (OpenAIEmbeddingModelName) –

    The name of the OpenAI embedding model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

Example

Configuring an OpenAI embedding model with rate limits:

config = OpenAIEmbeddingModel(
    model_name="text-embedding-3-small",
    rpm=100,
    tpm=100
)

OpenAILanguageModel

Bases: BaseModel

Configuration for OpenAI language models.

This class defines the configuration settings for OpenAI language models, including model selection and rate limiting parameters.

Attributes:

  • model_name (OpenAILanguageModelName) –

    The name of the OpenAI model to use.

  • rpm (int) –

    Requests per minute limit; must be greater than 0.

  • tpm (int) –

    Tokens per minute limit; must be greater than 0.

  • profiles (Optional[dict[str, Profile]]) –

    Optional mapping of profile names to profile configurations.

  • default_profile (Optional[str]) –

    The name of the default profile to use if profiles are configured.

Example

Configuring an OpenAI language model with rate limits:

config = OpenAILanguageModel(
    model_name="gpt-4.1-nano",
    rpm=100,
    tpm=100
)

Configuring an OpenAI model with profiles:

config = OpenAILanguageModel(
    model_name="o4-mini",
    rpm=100,
    tpm=100,
    profiles={
        "fast": OpenAILanguageModel.Profile(reasoning_effort="low"),
        "thorough": OpenAILanguageModel.Profile(reasoning_effort="high")
    },
    default_profile="fast"
)

Using a profile in a semantic operation:

config = SemanticConfig(
    language_models={
        "o4": OpenAILanguageModel(
            model_name="o4-mini",
            rpm=1_000,
            tpm=1_000_000,
            profiles={
                "fast": OpenAILanguageModel.Profile(reasoning_effort="low"),
                "thorough": OpenAILanguageModel.Profile(reasoning_effort="high")
            },
            default_profile="fast"
        )
    },
    default_language_model="o4"
)

# Will use the default "fast" profile for the "o4" model
semantic.map(instruction="Construct a formal proof of the {hypothesis}.", model_alias="o4")

# Will use the "thorough" profile for the "o4" model
semantic.map(instruction="Construct a formal proof of the {hypothesis}.", model_alias=ModelAlias(name="o4", profile="thorough"))

Classes:

  • Profile –

    OpenAI-specific profile configurations.

Profile

Bases: BaseModel

OpenAI-specific profile configurations.

This class defines profile configurations for OpenAI models, allowing a user to reference the same underlying model in semantic operations with different settings. For now, only the reasoning effort can be customized.

Attributes:

  • reasoning_effort (Optional[ReasoningEffort]) –

    If configuring a reasoning model, provide a reasoning effort. OpenAI has separate o-series reasoning models, for which thinking cannot be disabled. If an o-series model is specified, but no reasoning_effort is provided, the reasoning_effort will be set to low.

Note

When using an o-series reasoning model, the temperature cannot be customized -- any changes to temperature will be ignored.

Example

Configuring a profile with medium reasoning effort:

profile = OpenAILanguageModel.Profile(reasoning_effort="medium")

SemanticConfig

Bases: BaseModel

Configuration for semantic language and embedding models.

This class defines the configuration for both language models and optional embedding models used in semantic operations. It ensures that all configured models are valid and supported by their respective providers.

Attributes:

  • language_models (Optional[dict[str, LanguageModel]]) –

    Mapping of model aliases to language model configurations.

  • default_language_model (Optional[str]) –

    The alias of the default language model to use for semantic operations. Not required if only one language model is configured.

  • embedding_models (Optional[dict[str, EmbeddingModel]]) –

    Optional mapping of model aliases to embedding model configurations.

  • default_embedding_model (Optional[str]) –

    The alias of the default embedding model to use for semantic operations.

Note

The embedding model is optional and only required for operations that need semantic search or embedding capabilities.

Example

Configuring semantic models with a single language model:

config = SemanticConfig(
    language_models={
        "gpt4": OpenAILanguageModel(
            model_name="gpt-4.1-nano",
            rpm=100,
            tpm=100
        )
    }
)

Configuring semantic models with multiple language models and an embedding model:

config = SemanticConfig(
    language_models={
        "gpt4": OpenAILanguageModel(
            model_name="gpt-4.1-nano",
            rpm=100,
            tpm=100
        ),
        "claude": AnthropicLanguageModel(
            model_name="claude-3-5-haiku-latest",
            rpm=100,
            input_tpm=100,
            output_tpm=100
        ),
        "gemini": GoogleDeveloperLanguageModel(
            model_name="gemini-2.0-flash",
            rpm=100,
            tpm=1000
        )
    },
    default_language_model="gpt4",
    embedding_models={
        "openai_embeddings": OpenAIEmbeddingModel(
            model_name="text-embedding-3-small",
            rpm=100,
            tpm=100
        )
    },
    default_embedding_model="openai_embeddings"
)

Configuring models with profiles:

config = SemanticConfig(
    language_models={
        "gpt4": OpenAILanguageModel(
            model_name="gpt-4o-mini",
            rpm=100,
            tpm=100,
            profiles={
                "fast": OpenAILanguageModel.Profile(reasoning_effort="low"),
                "thorough": OpenAILanguageModel.Profile(reasoning_effort="high")
            },
            default_profile="fast"
        ),
        "claude": AnthropicLanguageModel(
            model_name="claude-3-5-haiku-latest",
            rpm=100,
            input_tpm=100,
            output_tpm=100,
            profiles={
                "fast": AnthropicLanguageModel.Profile(thinking_token_budget=1024),
                "thorough": AnthropicLanguageModel.Profile(thinking_token_budget=4096)
            },
            default_profile="fast"
        )
    },
    default_language_model="gpt4"
)

Methods:

model_post_init 

model_post_init(__context) -> None

Post initialization hook to set defaults.

This hook runs after the model is initialized and validated. It sets the default language and embedding models if they are not set and there is only one model available.

Source code in src/fenic/api/session/config.py 
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def model_post_init(self, __context) -> None:
    """Post initialization hook to set defaults.

    This hook runs after the model is initialized and validated.
    It sets the default language and embedding models if they are not set
    and there is only one model available.
    """
    if self.language_models:
        # Set default language model if not set and only one model exists
        if self.default_language_model is None and len(self.language_models) == 1:
            self.default_language_model = list(self.language_models.keys())[0]

        # Set default profile for each model if not set and only one profile exists
        for model_config in self.language_models.values():
            if model_config.profiles is not None:
                profile_names = list(model_config.profiles.keys())
                if model_config.default_profile is None and len(profile_names) == 1:
                    model_config.default_profile = profile_names[0]

    # Set default embedding model if not set and only one model exists
    if self.embedding_models:
        if self.default_embedding_model is None and len(self.embedding_models) == 1:
            self.default_embedding_model = list(self.embedding_models.keys())[0]
        # Set default profile for each model if not set and only one preset exists
        for model_config in self.embedding_models.values():
            if hasattr(model_config, "profiles") and model_config.profiles is not None:
                preset_names = list(model_config.profiles.keys())
                if (
                    model_config.default_profile is None
                    and len(preset_names) == 1
                ):
                    model_config.default_profile = preset_names[0]

validate_models 

validate_models() -> SemanticConfig

Validates that the selected models are supported by the system.

This validator checks that both the language model and embedding model (if provided) are valid and supported by their respective providers.

Returns:

Raises:

Source code in src/fenic/api/session/config.py 
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@model_validator(mode="after")
def validate_models(self) -> SemanticConfig:
    """Validates that the selected models are supported by the system.

    This validator checks that both the language model and embedding model (if provided)
    are valid and supported by their respective providers.

    Returns:
        The validated SemanticConfig instance.

    Raises:
        ConfigurationError: If any of the models are not supported.
    """
    # Skip validation if no models configured (embedding-only or empty config)
    if not self.language_models and not self.embedding_models:
        return self

    # Validate language models if provided
    if self.language_models:
        available_language_model_aliases = list(self.language_models.keys())
        if self.default_language_model is None and len(self.language_models) > 1:
            raise ConfigurationError(
                f"default_language_model is not set, and multiple language models are configured. Please specify one of: {available_language_model_aliases} as a default_language_model.")

        if self.default_language_model is not None and self.default_language_model not in self.language_models:
            raise ConfigurationError(
                f"default_language_model {self.default_language_model} is not in configured map of language models. Available models: {available_language_model_aliases} .")

        for model_alias, language_model in self.language_models.items():
            language_model_name = language_model.model_name
            language_model_provider = _get_model_provider_for_model_config(language_model)

            if language_model.profiles is not None:
                profile_names = list(language_model.profiles.keys())
                if language_model.default_profile is None and len(profile_names) > 0:
                    raise ConfigurationError(
                        f"default_profile is not set for model {model_alias}, but multiple profiles are configured. Please specify one of: {profile_names} as a default_profile.")
                if language_model.default_profile is not None and language_model.default_profile not in profile_names:
                    raise ConfigurationError(
                        f"default_profile {language_model.default_profile} is not in configured profiles for model {model_alias}. Available profiles: {profile_names}")

            completion_model = model_catalog.get_completion_model_parameters(language_model_provider,
                                                                             language_model_name)
            if completion_model is None:
                raise ConfigurationError(
                    model_catalog.generate_unsupported_completion_model_error_message(
                        language_model_provider,
                        language_model_name
                    )
                )
    if self.embedding_models is not None:
        available_embedding_model_aliases = list(self.embedding_models.keys())
        if self.default_embedding_model is None and len(self.embedding_models) > 1:
            raise ConfigurationError(
                f"default_embedding_model is not set, and multiple embedding models are configured. Please specify one of: {available_embedding_model_aliases} as a default_embedding_model.")

        if self.default_embedding_model is not None and self.default_embedding_model not in self.embedding_models:
            raise ConfigurationError(
                f"default_embedding_model {self.default_embedding_model} is not in configured map of embedding models. Available models: {available_embedding_model_aliases} .")
        for model_alias, embedding_model in self.embedding_models.items():
            embedding_model_provider = _get_model_provider_for_model_config(embedding_model)
            embedding_model_name = embedding_model.model_name
            embedding_model_parameters = model_catalog.get_embedding_model_parameters(embedding_model_provider,
                                                                                      embedding_model_name)
            if embedding_model_parameters is None:
                raise ConfigurationError(model_catalog.generate_unsupported_embedding_model_error_message(
                    embedding_model_provider,
                    embedding_model_name
                ))
            if hasattr(embedding_model, "profiles") and embedding_model.profiles:
                profile_names = list(embedding_model.profiles.keys())
                if embedding_model.default_profile is None and len(profile_names) > 0:
                    raise ConfigurationError(
                        f"default_profile is not set for model {model_alias}, but multiple profiles are configured. Please specify one of: {profile_names} as a default_profile.")
                if embedding_model.default_profile is not None and embedding_model.default_profile not in profile_names:
                    raise ConfigurationError(
                        f"default_profile {embedding_model.default_profile} is not in configured profiles for model {model_alias}. Available profiles: {profile_names}")

                for profile_alias, profile in embedding_model.profiles.items():
                    _validate_embedding_profile(embedding_model_parameters, profile_alias, profile)


    return self

Session

The entry point to programming with the DataFrame API. Similar to PySpark's SparkSession.

Create a session with default configuration 
session = Session.get_or_create(SessionConfig(app_name="my_app"))
Create a session with cloud configuration 
config = SessionConfig(
    app_name="my_app",
    cloud=True,
    api_key="your_api_key"
)
session = Session.get_or_create(config)

Methods:

  • create_dataframe –

    Create a DataFrame from a variety of Python-native data formats.

  • get_or_create –

    Gets an existing Session or creates a new one with the configured settings.

  • sql –

    Execute a read-only SQL query against one or more DataFrames using named placeholders.

  • stop –

    Stops the session and closes all connections.

  • table –

    Returns the specified table as a DataFrame.

  • view –

    Returns the specified view as a DataFrame.

Attributes:

  • catalog (Catalog) –

    Interface for catalog operations on the Session.

  • read (DataFrameReader) –

    Returns a DataFrameReader that can be used to read data in as a DataFrame.

catalog property 

catalog: Catalog

Interface for catalog operations on the Session.

read property 

read: DataFrameReader

Returns a DataFrameReader that can be used to read data in as a DataFrame.

Returns:

  • DataFrameReader ( DataFrameReader ) –

    A reader interface to read data into DataFrame

Raises:

  • RuntimeError –

    If the session has been stopped

create_dataframe 

create_dataframe(data: DataLike) -> DataFrame

Create a DataFrame from a variety of Python-native data formats.

Parameters:

  • data (DataLike) –

    Input data. Must be one of: - Polars DataFrame - Pandas DataFrame - dict of column_name -> list of values - list of dicts (each dict representing a row) - pyarrow Table

Returns:

Raises:

  • ValueError –

    If the input format is unsupported or inconsistent with provided column names.

Create from Polars DataFrame 
import polars as pl
df = pl.DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
session.create_dataframe(df)
Create from Pandas DataFrame 
import pandas as pd
df = pd.DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
session.create_dataframe(df)
Create from dictionary 
session.create_dataframe({"col1": [1, 2], "col2": ["a", "b"]})
Create from list of dictionaries 
session.create_dataframe([
    {"col1": 1, "col2": "a"},
    {"col1": 2, "col2": "b"}
])
Create from pyarrow Table 
import pyarrow as pa
table = pa.Table.from_pydict({"col1": [1, 2], "col2": ["a", "b"]})
session.create_dataframe(table)
Source code in src/fenic/api/session/session.py 
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def create_dataframe(
    self,
    data: DataLike,
) -> DataFrame:
    """Create a DataFrame from a variety of Python-native data formats.

    Args:
        data: Input data. Must be one of:
            - Polars DataFrame
            - Pandas DataFrame
            - dict of column_name -> list of values
            - list of dicts (each dict representing a row)
            - pyarrow Table

    Returns:
        A new DataFrame instance

    Raises:
        ValueError: If the input format is unsupported or inconsistent with provided column names.

    Example: Create from Polars DataFrame
        ```python
        import polars as pl
        df = pl.DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
        session.create_dataframe(df)
        ```

    Example: Create from Pandas DataFrame
        ```python
        import pandas as pd
        df = pd.DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
        session.create_dataframe(df)
        ```

    Example: Create from dictionary
        ```python
        session.create_dataframe({"col1": [1, 2], "col2": ["a", "b"]})
        ```

    Example: Create from list of dictionaries
        ```python
        session.create_dataframe([
            {"col1": 1, "col2": "a"},
            {"col1": 2, "col2": "b"}
        ])
        ```

    Example: Create from pyarrow Table
        ```python
        import pyarrow as pa
        table = pa.Table.from_pydict({"col1": [1, 2], "col2": ["a", "b"]})
        session.create_dataframe(table)
        ```
    """
    try:
        if isinstance(data, pl.DataFrame):
            pl_df = data
        elif isinstance(data, pd.DataFrame):
            pl_df = pl.from_pandas(data)
        elif isinstance(data, dict):
            pl_df = pl.DataFrame(data)
        elif isinstance(data, list):
            if not data:
                raise ValidationError(
                    "Cannot create DataFrame from empty list. Provide a non-empty list of dictionaries, lists, or other supported data types."
                )

            if not isinstance(data[0], dict):
                raise ValidationError(
                    "Cannot create DataFrame from list of non-dict values. Provide a list of dictionaries."
                )
            pl_df = pl.DataFrame(data)
        elif isinstance(data, pa.Table):
            pl_df = pl.from_arrow(data)

        else:
            raise ValidationError(
                f"Unsupported data type: {type(data)}. Supported types are: Polars DataFrame, Pandas DataFrame, dict, or list."
            )

    except ValidationError:
        raise
    except Exception as e:
        raise PlanError(f"Failed to create DataFrame from {data}") from e

    return DataFrame._from_logical_plan(
        InMemorySource.from_session_state(pl_df, self._session_state),
        self._session_state,
    )

get_or_create classmethod 

get_or_create(config: SessionConfig) -> Session

Gets an existing Session or creates a new one with the configured settings.

Returns:

  • Session –

    A Session instance configured with the provided settings

Source code in src/fenic/api/session/session.py 
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@classmethod
def get_or_create(
    cls,
    config: SessionConfig,
) -> Session:
    """Gets an existing Session or creates a new one with the configured settings.

    Returns:
        A Session instance configured with the provided settings
    """
    if config.cloud:
        from fenic._backends.cloud.manager import CloudSessionManager

        cloud_session_manager = CloudSessionManager()
        if not cloud_session_manager.initialized:
            session_manager_dependencies = (
                CloudSessionManager.create_global_session_dependencies()
            )
            cloud_session_manager.configure(session_manager_dependencies)
        future = asyncio.run_coroutine_threadsafe(
            cloud_session_manager.get_or_create_session_state(config),
            cloud_session_manager._asyncio_loop,
        )
        cloud_session_state = future.result()
        return Session._create_cloud_session(cloud_session_state)

    local_session_state: LocalSessionState = LocalSessionManager().get_or_create_session_state(config._to_resolved_config())
    return Session._create_local_session(local_session_state)

sql 

sql(query: str, /, **tables: DataFrame) -> DataFrame

Execute a read-only SQL query against one or more DataFrames using named placeholders.

This allows you to execute ad hoc SQL queries using familiar syntax when it's more convenient than the DataFrame API. Placeholders in the SQL string (e.g. {df}) should correspond to keyword arguments (e.g. df=my_dataframe).

For supported SQL syntax and functions, refer to the DuckDB SQL documentation: https://duckdb.org/docs/sql/introduction.

Parameters:

  • query (str) –

    A SQL query string with placeholders like {df}

  • **tables (DataFrame, default: {} ) –

    Keyword arguments mapping placeholder names to DataFrames

Returns:

  • DataFrame –

    A lazy DataFrame representing the result of the SQL query

Raises:

  • ValidationError –

    If a placeholder is used in the query but not passed as a keyword argument

Simple join between two DataFrames 
df1 = session.create_dataframe({"id": [1, 2]})
df2 = session.create_dataframe({"id": [2, 3]})
result = session.sql(
    "SELECT * FROM {df1} JOIN {df2} USING (id)",
    df1=df1,
    df2=df2
)
Complex query with multiple DataFrames 
users = session.create_dataframe({"user_id": [1, 2], "name": ["Alice", "Bob"]})
orders = session.create_dataframe({"order_id": [1, 2], "user_id": [1, 2]})
products = session.create_dataframe({"product_id": [1, 2], "name": ["Widget", "Gadget"]})

result = session.sql("""
    SELECT u.name, p.name as product
    FROM {users} u
    JOIN {orders} o ON u.user_id = o.user_id
    JOIN {products} p ON o.product_id = p.product_id
""", users=users, orders=orders, products=products)
Source code in src/fenic/api/session/session.py 
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def sql(self, query: str, /, **tables: DataFrame) -> DataFrame:
    """Execute a read-only SQL query against one or more DataFrames using named placeholders.

    This allows you to execute ad hoc SQL queries using familiar syntax when it's more convenient than the DataFrame API.
    Placeholders in the SQL string (e.g. `{df}`) should correspond to keyword arguments (e.g. `df=my_dataframe`).

    For supported SQL syntax and functions, refer to the DuckDB SQL documentation:
    https://duckdb.org/docs/sql/introduction.

    Args:
        query: A SQL query string with placeholders like `{df}`
        **tables: Keyword arguments mapping placeholder names to DataFrames

    Returns:
        A lazy DataFrame representing the result of the SQL query

    Raises:
        ValidationError: If a placeholder is used in the query but not passed
            as a keyword argument

    Example: Simple join between two DataFrames
        ```python
        df1 = session.create_dataframe({"id": [1, 2]})
        df2 = session.create_dataframe({"id": [2, 3]})
        result = session.sql(
            "SELECT * FROM {df1} JOIN {df2} USING (id)",
            df1=df1,
            df2=df2
        )
        ```

    Example: Complex query with multiple DataFrames
        ```python
        users = session.create_dataframe({"user_id": [1, 2], "name": ["Alice", "Bob"]})
        orders = session.create_dataframe({"order_id": [1, 2], "user_id": [1, 2]})
        products = session.create_dataframe({"product_id": [1, 2], "name": ["Widget", "Gadget"]})

        result = session.sql(\"\"\"
            SELECT u.name, p.name as product
            FROM {users} u
            JOIN {orders} o ON u.user_id = o.user_id
            JOIN {products} p ON o.product_id = p.product_id
        \"\"\", users=users, orders=orders, products=products)
        ```
    """
    query = query.strip()
    if not query:
        raise ValidationError("SQL query must not be empty.")

    placeholders = set(SQL_PLACEHOLDER_RE.findall(query))
    missing = placeholders - tables.keys()
    if missing:
        raise ValidationError(
            f"Missing DataFrames for placeholders in SQL query: {', '.join(sorted(missing))}. "
            f"Make sure to pass them as keyword arguments, e.g., sql(..., {next(iter(missing))}=df)."
        )

    logical_plans = []
    template_names = []
    input_session_states = []
    for name, table in tables.items():
        if name in placeholders:
            template_names.append(name)
            logical_plans.append(table._logical_plan)
            input_session_states.append(table._session_state)

    DataFrame._ensure_same_session(self._session_state, input_session_states)
    return DataFrame._from_logical_plan(
        SQL.from_session_state(logical_plans, template_names, query, self._session_state),
        self._session_state,
    )

stop 

stop()

Stops the session and closes all connections.

Source code in src/fenic/api/session/session.py 
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def stop(self):
    """Stops the session and closes all connections."""
    self._session_state.stop()

table 

table(table_name: str) -> DataFrame

Returns the specified table as a DataFrame.

Parameters:

  • table_name (str) –

    Name of the table

Returns:

Raises:

  • ValueError –

    If the table does not exist

Load an existing table 
df = session.table("my_table")
Source code in src/fenic/api/session/session.py 
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def table(self, table_name: str) -> DataFrame:
    """Returns the specified table as a DataFrame.

    Args:
        table_name: Name of the table

    Returns:
        Table as a DataFrame

    Raises:
        ValueError: If the table does not exist

    Example: Load an existing table
        ```python
        df = session.table("my_table")
        ```
    """
    if not self._session_state.catalog.does_table_exist(table_name):
        raise ValueError(f"Table {table_name} does not exist")
    return DataFrame._from_logical_plan(
        TableSource.from_session_state(table_name, self._session_state),
        self._session_state,
    )

view 

view(view_name: str) -> DataFrame

Returns the specified view as a DataFrame.

Parameters:

  • view_name (str) –

    Name of the view

Returns: DataFrame: Dataframe with the given view

Source code in src/fenic/api/session/session.py 
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def view(self, view_name: str) -> DataFrame:
    """Returns the specified view as a DataFrame.

    Args:
        view_name: Name of the view
    Returns:
        DataFrame: Dataframe with the given view
    """
    if not self._session_state.catalog.does_view_exist(view_name):
        raise CatalogError(f"View {view_name} does not exist")

    view_plan = self._session_state.catalog.describe_view(view_name)
    validate_view(view_name, view_plan, self._session_state)

    return DataFrame._from_logical_plan(
        view_plan,
        self._session_state,
    )

SessionConfig

Bases: BaseModel

Configuration for a user session.

This class defines the complete configuration for a user session, including application settings, model configurations, and optional cloud settings. It serves as the central configuration object for all language model operations.

Attributes:

  • app_name (str) –

    Name of the application using this session. Defaults to "default_app".

  • db_path (Optional[Path]) –

    Optional path to a local database file for persistent storage.

  • semantic (Optional[SemanticConfig]) –

    Configuration for semantic models (optional).

  • cloud (Optional[CloudConfig]) –

    Optional configuration for cloud execution.

Note

The semantic configuration is optional. When not provided, only non-semantic operations are available. The cloud configuration is optional and only needed for distributed processing.

Example

Configuring a basic session with a single language model:

config = SessionConfig(
    app_name="my_app",
    semantic=SemanticConfig(
        language_models={
            "gpt4": OpenAILanguageModel(
                model_name="gpt-4.1-nano",
                rpm=100,
                tpm=100
            )
        }
    )
)

Configuring a session with multiple models and cloud execution:

config = SessionConfig(
    app_name="production_app",
    db_path=Path("/path/to/database.db"),
    semantic=SemanticConfig(
        language_models={
            "gpt4": OpenAILanguageModel(
                model_name="gpt-4.1-nano",
                rpm=100,
                tpm=100
            ),
            "claude": AnthropicLanguageModel(
                model_name="claude-3-5-haiku-latest",
                rpm=100,
                input_tpm=100,
                output_tpm=100
            )
        },
        default_language_model="gpt4",
        embedding_models={
            "openai_embeddings": OpenAIEmbeddingModel(
                model_name="text-embedding-3-small",
                rpm=100,
                tpm=100
            )
        },
        default_embedding_model="openai_embeddings"
    ),
    cloud=CloudConfig(size=CloudExecutorSize.MEDIUM)
)