Create Embedding Model that adds additional context

 1. Conceptual Understanding

Core Idea: You want to create embeddings that not only represent the input text but also incorporate external knowledge or context. This can significantly improve the quality of similarity searches and downstream tasks.

Example: Imagine you're embedding product descriptions. Adding context like brand, category, or even user purchase history can lead to more relevant recommendations.

2. Methods

Concatenation:

Approach:

Obtain Context Embeddings: Generate embeddings for the context information (e.g., brand, category) using the same or a different embedding model.

Concatenate: Concatenate the context embeddings with the input text embeddings.

Example:

Input: "Comfortable shoes"

Context: "Brand: Nike, Category: Running"

Embedding: embed("Comfortable shoes") + embed("Brand: Nike") + embed("Category: Running")

Weighted Sum:

Approach:

Obtain embeddings as in concatenation.

Assign weights to each embedding based on its importance.

Calculate a weighted sum of the embeddings.

Example:

weighted_embedding = 0.7 * embed("Comfortable shoes") + 0.2 * embed("Brand: Nike") + 0.1 * embed("Category: Running")

Contextualized Embeddings:

Approach:

Use a language model (like BERT or GPT) to generate embeddings.

Feed the input text and context to the model simultaneously.

The model will generate embeddings that capture the interaction between the text and the context.

Implementation: Utilize Hugging Face Transformers library for easy access to pre-trained models.

3. Implementation Example (Concatenation with Sentence-Transformers)

Python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2') 

def embed_with_context(text, context):

    """

    Generates embeddings for the input text with added context.

    Args:

        text: The input text.

        context: A dictionary containing context information.

    Returns:

        The concatenated embedding.

    """

    text_embedding = model.encode(text)

    context_embeddings = [model.encode(f"{key}: {value}") for key, value in context.items()]

    return np.concatenate([text_embedding] + context_embeddings)

# Example Usage

input_text = "Comfortable shoes"

context = {"Brand": "Nike", "Category": "Running"}

embedding = embed_with_context(input_text, context)

4. Key Considerations

Context Representation: Choose a suitable format for representing context (dictionaries, lists, etc.).

Embedding Model: Select an embedding model that aligns with your context and task.

Weighting: Experiment with different weighting schemes for optimal results.

Evaluation: Thoroughly evaluate the performance of your custom embeddings on your specific task.

Remember: The effectiveness of your custom embeddings will depend on the quality and relevance of the context information you provide. Experiment with different approaches and carefully evaluate the results to find the best solution for your use case.