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nomic-embed-text-v1.5
sentence-similarity model from nomic-ai. Find semantically related passages for retrieval.
Why learn nomic-embed-text-v1.5?
Provides downloadable weights for sentence-similarity experimentation, subject to the model card and license.
The problem it solves
Find semantically related passages for retrieval.
Where it came from
nomic-embed-text-v1.5: sentence-similarity model from nomic-ai. Find semantically related passages for retrieval. License: apache-2.0. Hugging Face downloads at snapshot: 12909216. This is a task-stratified popularity sample, not a performance ranking. Runtime compatibility must be checked for the specific architecture.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what nomic-embed-text-v1.5 does.
- Identify a use case for nomic-embed-text-v1.5.
- Describe a limitation of nomic-embed-text-v1.5.
- Name a compatible tool for nomic-embed-text-v1.5.
- Compare nomic-embed-text-v1.5 against an alternative.
Documentation and sources
- Primary source / model card (huggingface.co)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Optimizing Legal Document Retrieval for Boutique Firms
Boutique law firms managing thousands of case files and historical precedents.
The business problem
High latency and storage costs when performing semantic searches across massive, unstructured document repositories using standard high-dimensional embeddings.
How this topic solves it
Utilizing nomic-embed-text-v1.5 with Matryoshka Representation Learning to compress embedding vectors while maintaining semantic retrieval accuracy for RAG applications.
- Prepend the search_document prefix to all historical legal case files before embedding.
- Generate embeddings for the document corpus using the Sentence Transformers library.
- Apply layer normalization and slice the resulting vectors to a smaller dimensionality, such as 256, to reduce storage footprint.
- Prepend the search_query prefix to incoming attorney questions before performing vector similarity searches.
The flow from raw legal text to optimized vector storage and query retrieval.
nomic-embed-text-v1.5
Compressed 256-dim vectors
Natural language question
- Legal Corpus → apply search_document prefix → Embedding Model
- Embedding Model → normalize and slice → Vector Database
- Attorney Query → apply search_query prefix → Embedding Model
- Embedding Model → calculate similarity → Vector Database
Implementation example
This code demonstrates how to prepare a document for the index using the required prefix and how to compress the resulting embedding to 256 dimensions to save space.
from sentence_transformers import SentenceTransformer
import torch.nn.functional as F
# Initialize model
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")
# Embed document with prefix
doc = ['search_document: Case precedent regarding liability...']
embeddings = model.encode(doc, convert_to_tensor=True)
# Apply Matryoshka compression to 256 dimensions
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
compressed_embeddings = embeddings[:, :256]
compressed_embeddings = F.normalize(compressed_embeddings, p=2, dim=1)Expected business outcome
Reduced memory overhead for the vector database and faster retrieval times with minimal impact on the relevance of retrieved legal precedents.
Limitations and checks
Reducing the embedding dimensionality via Matryoshka Representation Learning involves a tradeoff where smaller vector sizes lead to a slight reduction in overall retrieval performance; developers should validate accuracy against their specific dataset after compression.