Module haystack_integrations.components.embedders.fastembed.fastembed_document_embedder

FastembedDocumentEmbedder

FastembedDocumentEmbedder computes Document embeddings using Fastembed embedding models. The embedding of each Document is stored in the embedding field of the Document.

Usage example:

# To use this component, install the "fastembed-haystack" package.
# pip install fastembed-haystack

from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder
from haystack.dataclasses import Document

doc_embedder = FastembedDocumentEmbedder(
    model="BAAI/bge-small-en-v1.5",
    batch_size=256,
)

doc_embedder.warm_up()

# Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
document_list = [
    Document(
        content="Oxidative stress generated within inflammatory joints can produce autoimmune phenomena and joint destruction. Radical species with oxidative activity, including reactive nitrogen species, represent mediators of inflammation and cartilage damage.",
        meta={
            "pubid": "25,445,628",
            "long_answer": "yes",
        },
    ),
    Document(
        content="Plasma levels of pancreatic polypeptide (PP) rise upon food intake. Although other pancreatic islet hormones, such as insulin and glucagon, have been extensively investigated, PP secretion and actions are still poorly understood.",
        meta={
            "pubid": "25,445,712",
            "long_answer": "yes",
        },
    ),
]

result = doc_embedder.run(document_list)
print(f"Document Text: {result['documents'][0].content}")
print(f"Document Embedding: {result['documents'][0].embedding}")
print(f"Embedding Dimension: {len(result['documents'][0].embedding)}")

FastembedDocumentEmbedder.__init__

def __init__(model: str = "BAAI/bge-small-en-v1.5",
             cache_dir: Optional[str] = None,
             threads: Optional[int] = None,
             prefix: str = "",
             suffix: str = "",
             batch_size: int = 256,
             progress_bar: bool = True,
             parallel: Optional[int] = None,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n")

Create an FastembedDocumentEmbedder component.

Arguments:

• model: Local path or name of the model in Hugging Face's model hub, such as BAAI/bge-small-en-v1.5.
• cache_dir: The path to the cache directory. Can be set using the FASTEMBED_CACHE_PATH env variable. Defaults to fastembed_cache in the system's temp directory.
• threads: The number of threads single onnxruntime session can use. Defaults to None.
• prefix: A string to add to the beginning of each text.
• suffix: A string to add to the end of each text.
• batch_size: Number of strings to encode at once.
• progress_bar: If true, displays progress bar during embedding.
• parallel: If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.
• meta_fields_to_embed: List of meta fields that should be embedded along with the Document content.
• embedding_separator: Separator used to concatenate the meta fields to the Document content.

FastembedDocumentEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

FastembedDocumentEmbedder.warm_up

def warm_up()

Initializes the component.

FastembedDocumentEmbedder.run

@component.output_types(documents=List[Document])
def run(documents: List[Document])

Embeds a list of Documents.

Arguments:

• documents: List of Documents to embed.

Returns:

A dictionary with the following keys:

• documents: List of Documents with each Document's embedding field set to the computed embeddings.

Module haystack_integrations.components.embedders.fastembed.fastembed_text_embedder

FastembedTextEmbedder

FastembedTextEmbedder computes string embedding using fastembed embedding models.

Usage example:

# To use this component, install the "fastembed-haystack" package.
# pip install fastembed-haystack

from haystack_integrations.components.embedders.fastembed import FastembedTextEmbedder

text = "It clearly says online this will work on a Mac OS system. The disk comes and it does not, only Windows. Do Not order this if you have a Mac!!"

text_embedder = FastembedTextEmbedder(
    model="BAAI/bge-small-en-v1.5"
)
text_embedder.warm_up()

embedding = text_embedder.run(text)["embedding"]

FastembedTextEmbedder.__init__

def __init__(model: str = "BAAI/bge-small-en-v1.5",
             cache_dir: Optional[str] = None,
             threads: Optional[int] = None,
             prefix: str = "",
             suffix: str = "",
             progress_bar: bool = True,
             parallel: Optional[int] = None)

Create a FastembedTextEmbedder component.

Arguments:

• model: Local path or name of the model in Fastembed's model hub, such as BAAI/bge-small-en-v1.5
• cache_dir: The path to the cache directory. Can be set using the FASTEMBED_CACHE_PATH env variable. Defaults to fastembed_cache in the system's temp directory.
• threads: The number of threads single onnxruntime session can use. Defaults to None.
• prefix: A string to add to the beginning of each text.
• suffix: A string to add to the end of each text.
• progress_bar: If true, displays progress bar during embedding.
• parallel: If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.

FastembedTextEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

FastembedTextEmbedder.warm_up

def warm_up()

Initializes the component.

FastembedTextEmbedder.run

@component.output_types(embedding=List[float])
def run(text: str)

Embeds text using the Fastembed model.

Arguments:

• text: A string to embed.

Raises:

• TypeError: If the input is not a string.
• RuntimeError: If the embedding model has not been loaded.

Returns:

A dictionary with the following keys:

• embedding: A list of floats representing the embedding of the input text.

Module haystack_integrations.components.embedders.fastembed.fastembed_sparse_document_embedder

FastembedSparseDocumentEmbedder

FastembedSparseDocumentEmbedder computes Document embeddings using Fastembed sparse models.

Usage example:

# To use this component, install the "fastembed-haystack" package.
# pip install fastembed-haystack

from haystack_integrations.components.embedders.fastembed import FastembedSparseDocumentEmbedder
from haystack.dataclasses import Document

doc_embedder = FastembedSparseDocumentEmbedder(
    model="prithvida/Splade_PP_en_v1",
    batch_size=32,
)

doc_embedder.warm_up()

# Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
document_list = [
    Document(
        content="Oxidative stress generated within inflammatory joints can produce autoimmune phenomena and joint destruction. Radical species with oxidative activity, including reactive nitrogen species, represent mediators of inflammation and cartilage damage.",
        meta={
            "pubid": "25,445,628",
            "long_answer": "yes",
        },
    ),
    Document(
        content="Plasma levels of pancreatic polypeptide (PP) rise upon food intake. Although other pancreatic islet hormones, such as insulin and glucagon, have been extensively investigated, PP secretion and actions are still poorly understood.",
        meta={
            "pubid": "25,445,712",
            "long_answer": "yes",
        },
    ),
]

result = doc_embedder.run(document_list)
print(f"Document Text: {result['documents'][0].content}")
print(f"Document Embedding: {result['documents'][0].sparse_embedding}")
print(f"Embedding Dimension: {len(result['documents'][0].sparse_embedding)}")

FastembedSparseDocumentEmbedder.__init__

def __init__(model: str = "prithvida/Splade_PP_en_v1",
             cache_dir: Optional[str] = None,
             threads: Optional[int] = None,
             batch_size: int = 32,
             progress_bar: bool = True,
             parallel: Optional[int] = None,
             meta_fields_to_embed: Optional[List[str]] = None,
             embedding_separator: str = "\n")

Create an FastembedDocumentEmbedder component.

Arguments:

• model: Local path or name of the model in Hugging Face's model hub, such as prithvida/Splade_PP_en_v1.
• cache_dir: The path to the cache directory. Can be set using the FASTEMBED_CACHE_PATH env variable. Defaults to fastembed_cache in the system's temp directory.
• threads: The number of threads single onnxruntime session can use.
• batch_size: Number of strings to encode at once.
• progress_bar: If True, displays progress bar during embedding.
• parallel: If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.
• meta_fields_to_embed: List of meta fields that should be embedded along with the Document content.
• embedding_separator: Separator used to concatenate the meta fields to the Document content.

FastembedSparseDocumentEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

FastembedSparseDocumentEmbedder.warm_up

def warm_up()

Initializes the component.

FastembedSparseDocumentEmbedder.run

@component.output_types(documents=List[Document])
def run(documents: List[Document])

Embeds a list of Documents.

Arguments:

• documents: List of Documents to embed.

Returns:

A dictionary with the following keys:

• documents: List of Documents with each Document's sparse_embedding field set to the computed embeddings.

Module haystack_integrations.components.embedders.fastembed.fastembed_sparse_text_embedder

FastembedSparseTextEmbedder

FastembedSparseTextEmbedder computes string embedding using fastembed sparse models.

Usage example:

# To use this component, install the "fastembed-haystack" package.
# pip install fastembed-haystack

from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder

text = "It clearly says online this will work on a Mac OS system. The disk comes and it does not, only Windows. Do Not order this if you have a Mac!!"

text_embedder = FastembedSparseTextEmbedder(
    model="prithvida/Splade_PP_en_v1"
)
text_embedder.warm_up()

embedding = text_embedder.run(text)["embedding"]

FastembedSparseTextEmbedder.__init__

def __init__(model: str = "prithvida/Splade_PP_en_v1",
             cache_dir: Optional[str] = None,
             threads: Optional[int] = None,
             batch_size: int = 32,
             progress_bar: bool = True,
             parallel: Optional[int] = None)

Create a FastembedSparseTextEmbedder component.

Arguments:

• model: Local path or name of the model in Fastembed's model hub, such as prithvida/Splade_PP_en_v1
• cache_dir: The path to the cache directory. Can be set using the FASTEMBED_CACHE_PATH env variable. Defaults to fastembed_cache in the system's temp directory.
• threads: The number of threads single onnxruntime session can use. Defaults to None.
• batch_size: Number of strings to encode at once.
• progress_bar: If true, displays progress bar during embedding.
• parallel: If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.

FastembedSparseTextEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

FastembedSparseTextEmbedder.warm_up

def warm_up()

Initializes the component.

FastembedSparseTextEmbedder.run

@component.output_types(sparse_embedding=SparseEmbedding)
def run(text: str)

Embeds text using the Fastembed model.

Arguments:

• text: A string to embed.

Raises:

• TypeError: If the input is not a string.
• RuntimeError: If the embedding model has not been loaded.

Returns:

A dictionary with the following keys:

• embedding: A list of floats representing the embedding of the input text.