fastembed-vectorstore 0.5.3

In-memory vector store with fastembed

FastEmbed VectorStore

A high-performance, in-memory vector store with FastEmbed integration for Python applications.

Supported Embedding Models

The library supports a wide variety of embedding models:

• BGE Models: BGEBaseENV15, BGELargeENV15, BGESmallENV15 (with quantized variants)
• Nomic Models: NomicEmbedTextV1, NomicEmbedTextV15 (with quantized variants)
• GTE Models: GTEBaseENV15, GTELargeENV15 (with quantized variants)
• Multilingual Models: MultilingualE5Small, MultilingualE5Base, MultilingualE5Large
• Specialized Models: ClipVitB32, JinaEmbeddingsV2BaseCode, ModernBertEmbedLarge
• And many more...

Installation

Prerequisites

• Python 3.8 or higher

Install from PyPI

pip install fastembed-vectorstore

From Source

1. Clone the repository:

git clone https://github.com/sauravniraula/fastembed_vectorstore.git
cd fastembed_vectorstore

2. Install the package:

pip install -e .

Quick Start

from fastembed_vectorstore import FastembedVectorstore, FastembedEmbeddingModel

# Initialize with a model
model = FastembedEmbeddingModel.BGESmallENV15
vectorstore = FastembedVectorstore(model)

# Optional Configurations
# vectorstore = FastembedVectorstore(
#     model,
#     show_download_progress=False,           # default: True
#     cache_directory="fastembed_cache",      # default: fastembed_cache
# )

# Add documents
documents = [
    "The quick brown fox jumps over the lazy dog",
    "A quick brown dog jumps over the lazy fox",
    "The lazy fox sleeps while the quick brown dog watches",
    "Python is a programming language",
    "Rust is a systems programming language"
]

# Embed and store documents
success = vectorstore.embed_documents(documents)
print(f"Documents embedded: {success}")

# Search for similar documents
query = "What is Python?"
results = vectorstore.search(query, n=3)

for doc, similarity in results:
    print(f"Document: {doc}")
    print(f"Similarity: {similarity:.4f}")
    print("---")

# Save the vector store
vectorstore.save("my_vectorstore.json")

# Load the vector store later
loaded_vectorstore = FastembedVectorstore.load(model, "my_vectorstore.json")

# Optional Configurations
# loaded_vectorstore = FastembedVectorstore.load(
#     model,
#     "my_vectorstore.json",
#     show_download_progress=False,           # default: True
#     cache_directory="fastembed_cache",      # default: fastembed_cache
# )

API Reference

FastembedEmbeddingModel

Enum containing all supported embedding models. Choose based on your use case:

• Small models: Faster, lower memory usage (e.g., BGESmallENV15)
• Base models: Balanced performance (e.g., BGEBaseENV15)
• Large models: Higher quality embeddings (e.g., BGELargeENV15)
• Quantized models: Reduced memory usage (e.g., BGESmallENV15Q)

FastembedVectorstore

Constructor

vectorstore = FastembedVectorstore(
    model: FastembedEmbeddingModel,
    show_download_progress: bool | None = ...,
    cache_directory: str | os.PathLike[str] | None = ...,
)

Args:

• model: Embedding model to use.
• show_download_progress: Whether to show model download progress. Defaults to True.
• cache_directory: Directory to cache/download model files. Defaults to ./fastembed.

Methods

embed_documents(documents: List[str]) -> bool

Embeds a list of documents and stores them in the vector store.

search(query: str, n: int) -> List[Tuple[str, float]]

Searches for the most similar documents to the query. Returns a list of tuples containing (document, similarity_score).

save(path: str) -> bool

Saves the vector store to a JSON file.

load(model: FastembedEmbeddingModel, path: str) -> FastembedVectorstore

Loads a vector store from a JSON file.

Performance Considerations

• Memory Usage: All embeddings are stored in memory, so consider the size of your document collection
• Model Selection: Smaller models are faster but may have lower quality embeddings
• Batch Processing: The embed_documents method processes documents in batches for efficiency

Use Cases

• Semantic Search: Find documents similar to a query
• Document Clustering: Group similar documents together
• Recommendation Systems: Find similar items or content
• Question Answering: Retrieve relevant context for Q&A systems
• Content Discovery: Help users find related content

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Author

• Saurav Niraula - sauravniraula
• Email: [e-mail]

Acknowledgments

• Built with FastEmbed for efficient text embeddings