Module answer

ExtractedAnswer

ExtractedAnswer.to_dict

def to_dict() -> Dict[str, Any]

Serialize the object to a dictionary.

Returns:

Serialized dictionary representation of the object.

ExtractedAnswer.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "ExtractedAnswer"

Deserialize the object from a dictionary.

Arguments:

• data: Dictionary representation of the object.

Returns:

Deserialized object.

ExtractedTableAnswer

ExtractedTableAnswer.to_dict

def to_dict() -> Dict[str, Any]

Serialize the object to a dictionary.

Returns:

Serialized dictionary representation of the object.

ExtractedTableAnswer.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "ExtractedTableAnswer"

Deserialize the object from a dictionary.

Arguments:

• data: Dictionary representation of the object.

Returns:

Deserialized object.

GeneratedAnswer

GeneratedAnswer.to_dict

def to_dict() -> Dict[str, Any]

Serialize the object to a dictionary.

Returns:

Serialized dictionary representation of the object.

GeneratedAnswer.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "GeneratedAnswer"

Deserialize the object from a dictionary.

Arguments:

• data: Dictionary representation of the object.

Returns:

Deserialized object.

Module byte_stream

ByteStream

Base data class representing a binary object in the Haystack API.

ByteStream.to_file

def to_file(destination_path: Path)

Write the ByteStream to a file. Note: the metadata will be lost.

Arguments:

• destination_path: The path to write the ByteStream to.

ByteStream.from_file_path

@classmethod
def from_file_path(cls,
                   filepath: Path,
                   mime_type: Optional[str] = None,
                   meta: Optional[Dict[str, Any]] = None) -> "ByteStream"

Create a ByteStream from the contents read from a file.

Arguments:

• filepath: A valid path to a file.
• mime_type: The mime type of the file.
• meta: Additional metadata to be stored with the ByteStream.

ByteStream.from_string

@classmethod
def from_string(cls,
                text: str,
                encoding: str = "utf-8",
                mime_type: Optional[str] = None,
                meta: Optional[Dict[str, Any]] = None) -> "ByteStream"

Create a ByteStream encoding a string.

Arguments:

• text: The string to encode
• encoding: The encoding used to convert the string into bytes
• mime_type: The mime type of the file.
• meta: Additional metadata to be stored with the ByteStream.

ByteStream.to_string

def to_string(encoding: str = "utf-8") -> str

Convert the ByteStream to a string, metadata will not be included.

Arguments:

• encoding: The encoding used to convert the bytes to a string. Defaults to "utf-8".

Raises:

• None: UnicodeDecodeError: If the ByteStream data cannot be decoded with the specified encoding.

Returns:

The string representation of the ByteStream.

Module chat_message

ChatRole

Enumeration representing the roles within a chat.

ChatMessage

Represents a message in a LLM chat conversation.

Arguments:

• content: The text content of the message.
• role: The role of the entity sending the message.
• name: The name of the function being called (only applicable for role FUNCTION).
• meta: Additional metadata associated with the message.

ChatMessage.to_openai_format

def to_openai_format() -> Dict[str, Any]

Convert the message to the format expected by OpenAI's Chat API.

See the API reference for details.

Returns:

A dictionary with the following key:

• role
• content
• name (optional)

ChatMessage.is_from

def is_from(role: ChatRole) -> bool

Check if the message is from a specific role.

Arguments:

• role: The role to check against.

Returns:

True if the message is from the specified role, False otherwise.

ChatMessage.from_assistant

@classmethod
def from_assistant(cls,
                   content: str,
                   meta: Optional[Dict[str, Any]] = None) -> "ChatMessage"

Create a message from the assistant.

Arguments:

• content: The text content of the message.
• meta: Additional metadata associated with the message.

Returns:

A new ChatMessage instance.

ChatMessage.from_user

@classmethod
def from_user(cls, content: str) -> "ChatMessage"

Create a message from the user.

Arguments:

• content: The text content of the message.

Returns:

A new ChatMessage instance.

ChatMessage.from_system

@classmethod
def from_system(cls, content: str) -> "ChatMessage"

Create a message from the system.

Arguments:

• content: The text content of the message.

Returns:

A new ChatMessage instance.

ChatMessage.from_function

@classmethod
def from_function(cls, content: str, name: str) -> "ChatMessage"

Create a message from a function call.

Arguments:

• content: The text content of the message.
• name: The name of the function being called.

Returns:

A new ChatMessage instance.

Module document

_BackwardCompatible

Metaclass that handles Document backward compatibility.

_BackwardCompatible.__call__

def __call__(cls, *args, **kwargs)

Called before Document.init, will remap legacy fields to new ones.

Also handles building a Document from a flattened dictionary.

Document

Base data class containing some data to be queried.

Can contain text snippets, tables, and file paths to images or audios. Documents can be sorted by score and saved to/from dictionary and JSON.

Arguments:

• id: Unique identifier for the document. When not set, it's generated based on the Document fields' values.
• content: Text of the document, if the document contains text.
• dataframe: Pandas dataframe with the document's content, if the document contains tabular data.
• blob: Binary data associated with the document, if the document has any binary data associated with it.
• meta: Additional custom metadata for the document. Must be JSON-serializable.
• score: Score of the document. Used for ranking, usually assigned by retrievers.
• embedding: dense vector representation of the document.
• sparse_embedding: sparse vector representation of the document.

Document.__eq__

def __eq__(other)

Compares Documents for equality.

Two Documents are considered equals if their dictionary representation is identical.

Document.__post_init__

def __post_init__()

Generate the ID based on the init parameters.

Document.to_dict

def to_dict(flatten=True) -> Dict[str, Any]

Converts Document into a dictionary.

dataframe and blob fields are converted to JSON-serializable types.

Arguments:

• flatten: Whether to flatten meta field or not. Defaults to True to be backward-compatible with Haystack 1.x.

Document.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "Document"

Creates a new Document object from a dictionary.

The dataframe and blob fields are converted to their original types.

Document.content_type

@property
def content_type()

Returns the type of the content for the document.

This is necessary to keep backward compatibility with 1.x.

Raises:

• ValueError: If both text and dataframe fields are set or both are missing.

Module streaming_chunk

StreamingChunk

The StreamingChunk class encapsulates a segment of streamed content along with associated metadata.

This structure facilitates the handling and processing of streamed data in a systematic manner.

Arguments:

• content: The content of the message chunk as a string.
• meta: A dictionary containing metadata related to the message chunk.

Module sparse_embedding

SparseEmbedding

Class representing a sparse embedding.

Arguments:

• indices: List of indices of non-zero elements in the embedding.
• values: List of values of non-zero elements in the embedding.

SparseEmbedding.__post_init__

def __post_init__()

Checks if the indices and values lists are of the same length.

Raises a ValueError if they are not.

SparseEmbedding.to_dict

def to_dict() -> Dict[str, Any]

Convert the SparseEmbedding object to a dictionary.

Returns:

Serialized sparse embedding.

SparseEmbedding.from_dict

@classmethod
def from_dict(cls, sparse_embedding_dict: Dict[str, Any]) -> "SparseEmbedding"

Deserializes the sparse embedding from a dictionary.

Arguments:

• sparse_embedding_dict: Dictionary to deserialize from.

Returns:

Deserialized sparse embedding.