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EmbeddingWithHuggingFace

Embedding class using HuggingFace model based on LangChain Embeddings class.

EmbeddingWithHuggingFace

Bases: Embeddings

Embedding class using HuggingFace model based on LangChain Embeddings class.

Source code in aiagents4pharma/talk2knowledgegraphs/utils/embeddings/huggingface.py 
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class EmbeddingWithHuggingFace(Embeddings):
    """
    Embedding class using HuggingFace model based on LangChain Embeddings class.
    """

    def __init__(
        self,
        model_name: str,
        model_cache_dir: str = None,
        truncation: bool = True,
        device: str = "cpu",
    ):
        """
        Initialize the EmbeddingWithHuggingFace class.

        Args:
            model_name: The name of the HuggingFace model to be used.
            model_cache_dir: The directory to cache the HuggingFace model.
            truncation: The truncation flag for the HuggingFace tokenizer.
            return_tensors: The return_tensors flag for the HuggingFace tokenizer.
            device: The device to run the model on.
        """

        # Set parameters
        self.model_name = model_name
        self.model_cache_dir = model_cache_dir
        self.truncation = truncation
        self.device = device

        # Try to load the model from HuggingFace Hub
        try:
            AutoConfig.from_pretrained(self.model_name)
        except OSError as e:
            raise ValueError(f"Model {self.model_name} is not available on HuggingFace Hub.") from e

        # Load HuggingFace tokenizer and model
        self.tokenizer = AutoTokenizer.from_pretrained(
            self.model_name, cache_dir=self.model_cache_dir
        )
        self.model = AutoModel.from_pretrained(self.model_name, cache_dir=self.model_cache_dir)

    def meanpooling(self, output, mask) -> torch.Tensor:
        """
        Mean Pooling - Take attention mask into account for correct averaging.
        According to the following documentation:
        https://huggingface.co/NeuML/pubmedbert-base-embeddings

        Args:
            output: The output of the model.
            mask: The mask of the model.
        """
        embeddings = output[0]  # First element of model_output contains all token embeddings
        mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
        return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)

    def embed_documents(self, texts: list[str]) -> list[float]:
        """
        Generate embedding for a list of input texts using HuggingFace model.

        Args:
            texts: The list of texts to be embedded.

        Returns:
            The list of embeddings for the given texts.
        """

        # Generate the embedding
        with torch.no_grad():
            inputs = self.tokenizer(
                texts,
                padding=True,
                truncation=self.truncation,
                return_tensors="pt",
            ).to(self.device)
            outputs = self.model.to(self.device)(**inputs)
            embeddings = self.meanpooling(outputs, inputs["attention_mask"]).cpu()

        return embeddings

    def embed_query(self, text: str) -> list[float]:
        """
        Generate embeddings for an input text using HuggingFace model.

        Args:
            text: A query to be embedded.
        Returns:
            The embeddings for the given query.
        """

        # Generate the embedding
        with torch.no_grad():
            inputs = self.tokenizer(
                text,
                padding=True,
                truncation=self.truncation,
                return_tensors="pt",
            ).to(self.device)
            outputs = self.model.to(self.device)(**inputs)
            embeddings = self.meanpooling(outputs, inputs["attention_mask"]).cpu()[0]

        return embeddings

__init__(model_name, model_cache_dir=None, truncation=True, device='cpu')

Initialize the EmbeddingWithHuggingFace class.

Parameters:

Name Type Description Default
model_name str

The name of the HuggingFace model to be used.

 required
model_cache_dir str

The directory to cache the HuggingFace model.

 None
truncation bool

The truncation flag for the HuggingFace tokenizer.

 True
return_tensors

The return_tensors flag for the HuggingFace tokenizer.

 required
device str

The device to run the model on.

 'cpu'
Source code in aiagents4pharma/talk2knowledgegraphs/utils/embeddings/huggingface.py 
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def __init__(
    self,
    model_name: str,
    model_cache_dir: str = None,
    truncation: bool = True,
    device: str = "cpu",
):
    """
    Initialize the EmbeddingWithHuggingFace class.

    Args:
        model_name: The name of the HuggingFace model to be used.
        model_cache_dir: The directory to cache the HuggingFace model.
        truncation: The truncation flag for the HuggingFace tokenizer.
        return_tensors: The return_tensors flag for the HuggingFace tokenizer.
        device: The device to run the model on.
    """

    # Set parameters
    self.model_name = model_name
    self.model_cache_dir = model_cache_dir
    self.truncation = truncation
    self.device = device

    # Try to load the model from HuggingFace Hub
    try:
        AutoConfig.from_pretrained(self.model_name)
    except OSError as e:
        raise ValueError(f"Model {self.model_name} is not available on HuggingFace Hub.") from e

    # Load HuggingFace tokenizer and model
    self.tokenizer = AutoTokenizer.from_pretrained(
        self.model_name, cache_dir=self.model_cache_dir
    )
    self.model = AutoModel.from_pretrained(self.model_name, cache_dir=self.model_cache_dir)

embed_documents(texts)

Generate embedding for a list of input texts using HuggingFace model.

Parameters:

Name Type Description Default
texts list[str]

The list of texts to be embedded.

 required

Returns:

Type Description
list[float]

The list of embeddings for the given texts.
Source code in aiagents4pharma/talk2knowledgegraphs/utils/embeddings/huggingface.py 
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def embed_documents(self, texts: list[str]) -> list[float]:
    """
    Generate embedding for a list of input texts using HuggingFace model.

    Args:
        texts: The list of texts to be embedded.

    Returns:
        The list of embeddings for the given texts.
    """

    # Generate the embedding
    with torch.no_grad():
        inputs = self.tokenizer(
            texts,
            padding=True,
            truncation=self.truncation,
            return_tensors="pt",
        ).to(self.device)
        outputs = self.model.to(self.device)(**inputs)
        embeddings = self.meanpooling(outputs, inputs["attention_mask"]).cpu()

    return embeddings

embed_query(text)

Generate embeddings for an input text using HuggingFace model.

Parameters:

Name Type Description Default
text str

A query to be embedded.

 required

Returns: The embeddings for the given query.

Source code in aiagents4pharma/talk2knowledgegraphs/utils/embeddings/huggingface.py 
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def embed_query(self, text: str) -> list[float]:
    """
    Generate embeddings for an input text using HuggingFace model.

    Args:
        text: A query to be embedded.
    Returns:
        The embeddings for the given query.
    """

    # Generate the embedding
    with torch.no_grad():
        inputs = self.tokenizer(
            text,
            padding=True,
            truncation=self.truncation,
            return_tensors="pt",
        ).to(self.device)
        outputs = self.model.to(self.device)(**inputs)
        embeddings = self.meanpooling(outputs, inputs["attention_mask"]).cpu()[0]

    return embeddings

meanpooling(output, mask)

Mean Pooling - Take attention mask into account for correct averaging. According to the following documentation: https://huggingface.co/NeuML/pubmedbert-base-embeddings

Parameters:

Name Type Description Default
output

The output of the model.

 required
mask

The mask of the model.

 required
Source code in aiagents4pharma/talk2knowledgegraphs/utils/embeddings/huggingface.py 
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def meanpooling(self, output, mask) -> torch.Tensor:
    """
    Mean Pooling - Take attention mask into account for correct averaging.
    According to the following documentation:
    https://huggingface.co/NeuML/pubmedbert-base-embeddings

    Args:
        output: The output of the model.
        mask: The mask of the model.
    """
    embeddings = output[0]  # First element of model_output contains all token embeddings
    mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
    return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)