"More like this"

Creating a “more like this” feature using a sentence transformer involves several steps that aim to find sentences or documents that are semantically similar to a given input. Sentence transformers are models trained specifically for generating sentence embeddings that can be compared using similarity metrics.

  1. Preparation and Embedding Generation:
  • Model Selection: Choose a suitable sentence transformer model for embedding generation. Popular choices include models from the sentence-transformers library, such as all-MiniLM-L6-v2 for general purposes or domain-specific models if available.
  • Data Embedding: Convert your corpus (the collection of sentences or documents you want to search through) into embeddings. Each sentence/document is transformed into a high-dimensional vector that represents its semantic meaning.
  1. Indexing Embeddings:
  • Efficiency Consideration: For large corpora, it’s crucial to use an efficient indexing mechanism to search through embeddings quickly. Faiss, Annoy, and HNSW are popular libraries that support fast nearest neighbor searches in high-dimensional spaces.
  • Index Construction: Build an index of your embeddings using one of these libraries. This step is essential for enabling quick retrieval of similar sentences/documents.
  1. Similarity Search:
  • Query Embedding: When a user inputs a query sentence for the “more like this” feature, transform this sentence into an embedding using the same sentence transformer model.
  • Retrieval: Use the indexed embeddings to find the nearest neighbors to the query embedding. The similarity metric used (e.g., cosine similarity) will determine how “closeness” is measured in the embedding space.
  1. Results Ranking and Presentation:
  • Ranking: The nearest neighbor search will return a list of embeddings (and their corresponding sentences/documents) that are most similar to the query. These results can be ranked based on the similarity score.
  • Post-Processing: Depending on the application, further filtering or ranking adjustments can be applied based on other criteria (e.g., date, relevance to a specific topic).
  1. Feedback Loop (Optional):
  • Incorporate user feedback to refine the model’s performance. Users can provide explicit feedback on the relevance of the results, which can be used to fine-tune the sentence transformer or adjust the similarity threshold.

Implementation Example in Python:

Here’s a simplified example in Python, using the sentence-transformers library for generating embeddings and faiss for efficient similarity search:

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load a pre-trained sentence transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Assume `documents` is a list of sentences/documents in your corpus
embeddings = model.encode(documents)

# Create a FAISS index
dimension = embeddings.shape[1]  # Get the dimension of embeddings
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)  # Add the document embeddings to the index

# To search for similar documents
query = "Example query sentence"
query_embedding = model.encode([query])
distances, indices = index.search(query_embedding, k=5)  # Find the 5 nearest neighbors

similar_documents = [documents[i] for i in indices[0]]

For alternatives to faiss, see Vector databases.

Page last modified: 2024-02-13 10:02:46