Core Types

The core logic custom types.

Type Aliases

• DomainKey = Literal["usability", "io", "description", "execution", "parametric", "error"]
• OptionKey = Literal[ "loader", "chunking_config", "embedding_model", "vector_store", "similarity_top_k", "llm", "mode"]

GitFilter

Bases: Enum

Enum delineating between the directory and file extension filters.

Attributes:

Name	Type	Description
DIRECTORY	int	A git directory filter, represented by the value 1.
FILE_EXTENSION	int	A file extension filter, represented by the value 2.

Source code in bcorag/custom_types/core_types.py

class GitFilter(Enum):
    """Enum delineating between the directory and file extension filters.

    Attributes
    ----------
    DIRECTORY : int
        A git directory filter, represented by the value 1.
    FILE_EXTENSION : int
        A file extension filter, represented by the value 2.
    """

    DIRECTORY = 1
    FILE_EXTENSION = 2

GitFilters

Bases: TypedDict

Typed dict for github loader filters.

Attributes:

Name	Type	Description
filter_type	FilterType	The type of github filter (whether it is an include or exclude filter).
filter	GitFilter	The filter enum specification.
value	list[str]	The values to filter on.

Source code in bcorag/custom_types/core_types.py

class GitFilters(TypedDict):
    """Typed dict for github loader filters.

    Attributes
    ----------
    filter_type : GithubRepositoryReader.FilterType
        The type of github filter (whether it is an include or exclude filter).
    filter : GitFilter
        The filter enum specification.
    value : list[str]
        The values to filter on.
    """

    filter_type: GithubRepositoryReader.FilterType
    filter: GitFilter
    value: list[str]

GitData

Bases: TypedDict

Typed dict for the optional git repo information.

Attributes:

Name	Type	Description
user	str	The github repo owner.
repo	str	The repo name.
branch	str	The repo branch to index.
filters	list[GitFilters]	The list of filters to apply.

Source code in bcorag/custom_types/core_types.py

class GitData(TypedDict):
    """Typed dict for the optional git repo information.

    Attributes
    ----------
    user : str
        The github repo owner.
    repo : str
        The repo name.
    branch : str
        The repo branch to index.
    filters : list[GitFilters]
        The list of filters to apply.
    """

    user: str
    repo: str
    branch: str
    # TODO : can we refactor this for a tuple?
    filters: list[GitFilters]

UserSelections

Bases: TypedDict

Types dict for the user selections.

Attributes:

Name	Type	Description
llm	str	The LLM to use.
embedding_model	str	The embedding model to use.
filename	str	The file name of the paper being processed.
filepath	str	The file path to the paper being processed.
vector_store	str	The vector store to use.
loader	str	The data loader to ingest the paper with.
mode	str	The run mode.
similarity_top_k	int	The base integer used to calculate the similarity_top_k and top_n values.
chunking_config	str	The chunking configuration to use during node parsing.
git_data	Optional[GitData]	The optional github repository information to include in the documents.
other_docs	Optional[list[str]]	The file path to any additional documentation to include in the documents.

Source code in bcorag/custom_types/core_types.py

class UserSelections(TypedDict):
    """Types dict for the user selections.

    Attributes
    ----------
    llm : str
        The LLM to use.
    embedding_model : str
        The embedding model to use.
    filename : str
        The file name of the paper being processed.
    filepath : str
        The file path to the paper being processed.
    vector_store : str
        The vector store to use.
    loader : str
        The data loader to ingest the paper with.
    mode : str
        The run mode.
    similarity_top_k : int
        The base integer used to calculate the similarity_top_k and top_n values.
    chunking_config : str
        The chunking configuration to use during node parsing.
    git_data : Optional[GitData]
        The optional github repository information to include in the documents.
    other_docs : Optional[list[str]]
        The file path to any additional documentation to include in the documents.
    """

    llm: str
    embedding_model: str
    filename: str
    filepath: str
    vector_store: str
    loader: str
    mode: str
    similarity_top_k: int
    chunking_config: str
    git_data: Optional[GitData]
    other_docs: Optional[list[str]]

SourceNode

Bases: TypedDict

Holds the source node information for one node.

Attributes:

Name	Type	Description
node_id	str
content	str
metdata	str
score	str

Source code in bcorag/custom_types/core_types.py

class SourceNode(TypedDict):
    """Holds the source node information for one node.

    Attributes
    ----------
    node_id : str
    content : str
    metdata : str
    score : str
    """

    node_id: str
    content: str
    metadata: str
    score: str

DomainContent

Bases: TypedDict

Holds the most recent generated domain for in memory storage.

Attributes:

Name	Type	Description
usability	Optional[str]
io	Optional[str]
description	Optional[str]
execution	Optional[str]
parametric	Optional[str]
error	Optional[list[str]]

Source code in bcorag/custom_types/core_types.py

class DomainContent(TypedDict):
    """Holds the most recent generated domain for in memory storage.

    Attributes
    ----------
    usability: Optional[str]
    io: Optional[str]
    description: Optional[str]
    execution: Optional[str]
    parametric: Optional[str]
    error: Optional[list[str]]
    """

    usability: Optional[str]
    io: Optional[str]
    description: Optional[str]
    execution: Optional[str]
    parametric: Optional[str]
    error: Optional[str]
    last_source_nodes: Optional[list[SourceNode]]

IndividualDomainMapEntry

Bases: TypedDict

Information for one domain to prompt and process the user domain choice.

Attributes:

Name	Type	Description
retrieval_prompt	str	The prompt to use for the RAG pipeline retrieval process.
llm_prompt	str	The prompt to use for the LLM.
top_level	bool	Whether the specified domain includes object's defined in the top level JSON schema.
user_prompt	str	The prompt string to display to the user.
code	str	The short hand code for choosing the domain.
dependencies	list[DomainKey]	The domain dependencies.

Source code in bcorag/custom_types/core_types.py

class IndividualDomainMapEntry(TypedDict):
    """Information for one domain to prompt and process the user domain choice.

    Attributes
    ----------
    retrieval_prompt : str
        The prompt to use for the RAG pipeline retrieval process.
    llm_prompt : str
        The prompt to use for the LLM.
    top_level : bool
        Whether the specified domain includes object's defined in the top level JSON schema.
    user_prompt : str
        The prompt string to display to the user.
    code : str
        The short hand code for choosing the domain.
    dependencies : list[DomainKey]
        The domain dependencies.
    """

    retrieval_prompt: str
    llm_prompt: str
    top_level: bool
    user_prompt: str
    code: str
    dependencies: list[DomainKey]

DomainMap

Bases: TypedDict

Domain map for processing user input. Maps the user input for the domain prompt to the prompt to use for querying the RAG pipeline.

Attributes:

Name	Type	Description
usability	IndividualDomainMapEntry
io	IndividualDomainMapEntry
description	IndividualDomainMapEntry
execution	IndividualDomainMapEntry
parametric	IndividualDomainMapEntry
error	IndividualDomainMapEntry

Source code in bcorag/custom_types/core_types.py

class DomainMap(TypedDict):
    """Domain map for processing user input. Maps the user input for
    the domain prompt to the prompt to use for querying the RAG pipeline.

    Attributes
    ----------
    usability : IndividualDomainMapEntry
    io: IndividualDomainMapEntry
    description: IndividualDomainMapEntry
    execution: IndividualDomainMapEntry
    parametric: IndividualDomainMapEntry
    error: IndividualDomainMapEntry
    """

    usability: IndividualDomainMapEntry
    io: IndividualDomainMapEntry
    description: IndividualDomainMapEntry
    execution: IndividualDomainMapEntry
    parametric: IndividualDomainMapEntry
    error: IndividualDomainMapEntry

OptionSchema

Bases: TypedDict

Schema for a config object option entry in the config JSON file.

Attributes:

Name	Type	Description
list	list[str]	The list of options to choose from.
default	str	The option to use as the default.
documentation	str	The link to the documentation for the option.

Source code in bcorag/custom_types/core_types.py

class OptionSchema(TypedDict):
    """Schema for a config object option entry in the config JSON file.

    Attributes
    ----------
    list : list[str]
        The list of options to choose from.
    default : str
        The option to use as the default.
    documentation : str
        The link to the documentation for the option.
    """

    list: list[str]
    default: str
    documentation: str

ConfigObjectOptions

Bases: TypedDict

Schema for the supported options.

Attributes:

Name	Type	Description
loader	OptionSchema
chunking_config	OptionSchema
embedding_model	OptionSchema
vector_store	OptionSchema
similarity_top_k	OptionSchema
llm	OptionSchema
mode	OptionSchema

Source code in bcorag/custom_types/core_types.py

class ConfigObjectOptions(TypedDict):
    """Schema for the supported options.

    Attributes
    ----------
    loader : OptionSchema
    chunking_config : OptionSchema
    embedding_model: OptionSchema
    vector_store: OptionSchema
    similarity_top_k: OptionSchema
    llm: OptionSchema
    mode: OptionSchema
    """

    loader: OptionSchema
    chunking_config: OptionSchema
    embedding_model: OptionSchema
    vector_store: OptionSchema
    similarity_top_k: OptionSchema
    llm: OptionSchema
    mode: OptionSchema

ConfigObject

Bases: TypedDict

Config JSON schema.

Attributes:

Name	Type	Description
paper_directory	str	The file path to the paper's directory.
options	ConfigObjectOptions	The supported configuration options.

Source code in bcorag/custom_types/core_types.py

class ConfigObject(TypedDict):
    """Config JSON schema.

    Attributes
    ----------
    paper_directory : str
        The file path to the paper's directory.
    options : ConfigObjectOptions
        The supported configuration options.
    """

    paper_directory: str
    options: ConfigObjectOptions

create_git_filters(filter_type, filter, value)

Constructor for the GitFilters TypedDict.

Parameters:

Name	Type	Description	Default
filter_type	FilterType	The type of github filter (whether it is an include or exclude filter).	required
filter	GitFilter	The filter enum specification.	required
value	list[str]	The values to filter on.	required

Returns:

Type	Description
GitFilters

Source code in bcorag/custom_types/core_types.py

def create_git_filters(
    filter_type: GithubRepositoryReader.FilterType, filter: GitFilter, value: list[str]
) -> GitFilters:
    """Constructor for the `GitFilters` TypedDict.

    Parameters
    ----------
    filter_type : GithubRepositoryReader.FilterType
        The type of github filter (whether it is an include or exclude filter).
    filter : GitFilter
        The filter enum specification.
    value : list[str]
        The values to filter on.

    Returns
    -------
    GitFilters
    """
    sorted_values = sorted(value)
    return_data: GitFilters = {
        "filter_type": filter_type,
        "filter": filter,
        "value": sorted_values,
    }
    return return_data

create_git_data(user, repo, branch, filters=[])

Constructor for the GitData TypedDict.

Parameters:

Name	Type	Description	Default
user	str	The github repo owner.	required
repo	str	The repo name.	required
branch	str	The repo branch to index.	required
filters	list[GitFilters]	The list of filters to apply.	[]

Returns:

Type	Description
GitData

Source code in bcorag/custom_types/core_types.py

def create_git_data(
    user: str, repo: str, branch: str, filters: list[GitFilters] = []
) -> GitData:
    """Constructor for the `GitData` TypedDict.

    Parameters
    ----------
    user : str
        The github repo owner.
    repo : str
        The repo name.
    branch : str
        The repo branch to index.
    filters : list[GitFilters]
        The list of filters to apply.

    Returns
    -------
    GitData
    """
    return_data: GitData = {
        "user": user,
        "repo": repo,
        "branch": branch,
        "filters": filters,
    }
    return return_data

create_user_selections(llm, embedding_model, filename, filepath, vector_store, loader, mode, similarity_top_k, chunking_config, git_data, other_docs)

Constructor for the UserSelections TypedDict.

Parameters:

Name	Type	Description	Default
llm	str	The LLM to use.	required
embedding_model	str	The embedding model to use.	required
filename	str	The file name of the paper being processed.	required
filepath	str	The file path to the paper being processed.	required
vector_store	str	The vector store to use.	required
loader	str	The data loader to ingest the paper with.	required
mode	str	The run mode.	required
similarity_top_k	int	The base integer used to calculate the similarity_top_k and top_n values.	required
chunking_config	str	The chunking configuration to use during node parsing.	required
git_data	Optional[GitData]	The optional github repository information to include in the documents.	required
other_docs	Optional[list[str]]	The file path to any additional documentation to include in the documents.	required

Returns:

Type	Description
UserSelections

Source code in bcorag/custom_types/core_types.py

def create_user_selections(
    llm: str,
    embedding_model: str,
    filename: str,
    filepath: str,
    vector_store: str,
    loader: str,
    mode: str,
    similarity_top_k: int,
    chunking_config: str,
    git_data: Optional[GitData],
    other_docs: Optional[list[str]],
) -> UserSelections:
    """Constructor for the `UserSelections` TypedDict.

    Parameters
    ----------
    llm : str
        The LLM to use.
    embedding_model : str
        The embedding model to use.
    filename : str
        The file name of the paper being processed.
    filepath : str
        The file path to the paper being processed.
    vector_store : str
        The vector store to use.
    loader : str
        The data loader to ingest the paper with.
    mode : str
        The run mode.
    similarity_top_k : int
        The base integer used to calculate the similarity_top_k and top_n values.
    chunking_config : str
        The chunking configuration to use during node parsing.
    git_data : Optional[GitData]
        The optional github repository information to include in the documents.
    other_docs : Optional[list[str]]
        The file path to any additional documentation to include in the documents.

    Returns
    -------
    UserSelections
    """
    return_data: UserSelections = {
        "llm": llm,
        "embedding_model": embedding_model,
        "filename": filename,
        "filepath": filepath,
        "vector_store": vector_store,
        "loader": loader,
        "mode": mode,
        "similarity_top_k": similarity_top_k,
        "chunking_config": chunking_config,
        "git_data": git_data,
        "other_docs": other_docs,
    }
    return return_data

default_domain_content()

Creates an empty, default DomainContent TypedDict.

Returns:

Type	Description
DomainContent

Source code in bcorag/custom_types/core_types.py

def default_domain_content() -> DomainContent:
    """Creates an empty, default DomainContent TypedDict.

    Returns
    -------
    DomainContent
    """
    return_data: DomainContent = {
        "usability": None,
        "io": None,
        "description": None,
        "execution": None,
        "parametric": None,
        "error": None,
        "last_source_nodes": None,
    }
    return return_data

add_source_nodes(domain_content, nodes)

Adds source node data to the domain content.

Parameters:

Name	Type	Description	Default
domain_content	DomainContent	The domain content instance to add source node data to.	required
nodes	list[NodeWithScore]	List of nodes with score data.	required

Returns:

Type	Description
DomainContent	The updated domain content object.

Source code in bcorag/custom_types/core_types.py

def add_source_nodes(
    domain_content: DomainContent, nodes: list[NodeWithScore]
) -> DomainContent:
    """Adds source node data to the domain content.

    Parameters
    ----------
    domain_content : DomainContent
        The domain content instance to add source node data to.
    nodes : list[NodeWithScore]
        List of nodes with score data.

    Returns
    -------
    DomainContent
        The updated domain content object.
    """
    node_list: list[SourceNode] = []
    for node in nodes:
        node_list.append(
            {
                "node_id": node.node.node_id,
                "content": node.node.get_content(),
                "metadata": node.node.get_metadata_str(),
                "score": str(node.score),
            }
        )
    domain_content["last_source_nodes"] = node_list
    return domain_content