# FastEmbed VectorStore
[](https://github.com/sauravniraula/fastembed_vectorstore)
A high-performance, Rust-based in-memory vector store with FastEmbed integration for Python applications.
## Overview
FastEmbed VectorStore is a lightweight, fast vector database that leverages the power of Rust and the [FastEmbed library](https://github.com/Anush008/fastembed-rs) to provide efficient text embedding and similarity search capabilities. It's designed for applications that need quick semantic search without the overhead of external database systems.
## Features
- 🚀 **High Performance**: Built in Rust with Python bindings for optimal speed
- 🧠 **Multiple Embedding Models**: Support for 30+ pre-trained embedding models including BGE, Nomic, GTE, and more
- 💾 **In-Memory Storage**: Fast in-memory vector storage with persistence capabilities
- 🔍 **Similarity Search**: Cosine similarity-based search with customizable result limits
- 💾 **Save/Load**: Persist and restore vector stores to/from JSON files
- 🐍 **Python Integration**: Seamless Python API with PyO3 bindings
## 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
- Rust toolchain (to build from source)
### Install from PyPI
```bash
pip install fastembed-vectorstore
```
### From Source
1. Clone the repository:
```bash
git clone https://github.com/sauravniraula/fastembed_vectorstore.git
cd fastembed_vectorstore
```
2. Install the package:
```bash
maturin develop
```
## Quick Start
```python
from fastembed_vectorstore import FastembedVectorstore, FastembedEmbeddingModel
# Initialize with a model
model = FastembedEmbeddingModel.BGESmallENV15
vectorstore = FastembedVectorstore(model)
# 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")
```
## 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
```python
vectorstore = FastembedVectorstore(model: FastembedEmbeddingModel)
```
#### 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](https://github.com/sauravniraula/fastembed_vectorstore/blob/main/LICENSE) file for details.
## Author
- **Saurav Niraula** - [sauravniraula](https://github.com/sauravniraula)
- Email: developmentsaurav@gmail.com
## Acknowledgments
- Built with [FastEmbed](https://github.com/Anush008/fastembed-rs) for efficient text embeddings
- Uses [PyO3](https://github.com/PyO3/pyo3) for Python-Rust bindings
- Inspired by the need for fast, lightweight vector storage solutions
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"description": "# FastEmbed VectorStore\n\n[](https://github.com/sauravniraula/fastembed_vectorstore)\n\nA high-performance, Rust-based in-memory vector store with FastEmbed integration for Python applications.\n\n## Overview\n\nFastEmbed VectorStore is a lightweight, fast vector database that leverages the power of Rust and the [FastEmbed library](https://github.com/Anush008/fastembed-rs) to provide efficient text embedding and similarity search capabilities. It's designed for applications that need quick semantic search without the overhead of external database systems.\n\n## Features\n\n- \ud83d\ude80 **High Performance**: Built in Rust with Python bindings for optimal speed\n- \ud83e\udde0 **Multiple Embedding Models**: Support for 30+ pre-trained embedding models including BGE, Nomic, GTE, and more\n- \ud83d\udcbe **In-Memory Storage**: Fast in-memory vector storage with persistence capabilities\n- \ud83d\udd0d **Similarity Search**: Cosine similarity-based search with customizable result limits\n- \ud83d\udcbe **Save/Load**: Persist and restore vector stores to/from JSON files\n- \ud83d\udc0d **Python Integration**: Seamless Python API with PyO3 bindings\n\n## Supported Embedding Models\n\nThe library supports a wide variety of embedding models:\n\n- **BGE Models**: BGEBaseENV15, BGELargeENV15, BGESmallENV15 (with quantized variants)\n- **Nomic Models**: NomicEmbedTextV1, NomicEmbedTextV15 (with quantized variants)\n- **GTE Models**: GTEBaseENV15, GTELargeENV15 (with quantized variants)\n- **Multilingual Models**: MultilingualE5Small, MultilingualE5Base, MultilingualE5Large\n- **Specialized Models**: ClipVitB32, JinaEmbeddingsV2BaseCode, ModernBertEmbedLarge\n- **And many more...**\n\n## Installation\n\n### Prerequisites\n\n- Python 3.8 or higher\n- Rust toolchain (to build from source)\n\n\n### Install from PyPI\n\n```bash\npip install fastembed-vectorstore\n```\n\n### From Source\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/sauravniraula/fastembed_vectorstore.git\ncd fastembed_vectorstore\n```\n\n2. Install the package:\n```bash\nmaturin develop\n```\n\n## Quick Start\n\n```python\nfrom fastembed_vectorstore import FastembedVectorstore, FastembedEmbeddingModel\n\n# Initialize with a model\nmodel = FastembedEmbeddingModel.BGESmallENV15\nvectorstore = FastembedVectorstore(model)\n\n# Add documents\ndocuments = [\n \"The quick brown fox jumps over the lazy dog\",\n \"A quick brown dog jumps over the lazy fox\",\n \"The lazy fox sleeps while the quick brown dog watches\",\n \"Python is a programming language\",\n \"Rust is a systems programming language\"\n]\n\n# Embed and store documents\nsuccess = vectorstore.embed_documents(documents)\nprint(f\"Documents embedded: {success}\")\n\n# Search for similar documents\nquery = \"What is Python?\"\nresults = vectorstore.search(query, n=3)\n\nfor doc, similarity in results:\n print(f\"Document: {doc}\")\n print(f\"Similarity: {similarity:.4f}\")\n print(\"---\")\n\n# Save the vector store\nvectorstore.save(\"my_vectorstore.json\")\n\n# Load the vector store later\nloaded_vectorstore = FastembedVectorstore.load(model, \"my_vectorstore.json\")\n```\n\n## API Reference\n\n### FastembedEmbeddingModel\n\nEnum containing all supported embedding models. Choose based on your use case:\n\n- **Small models**: Faster, lower memory usage (e.g., `BGESmallENV15`)\n- **Base models**: Balanced performance (e.g., `BGEBaseENV15`)\n- **Large models**: Higher quality embeddings (e.g., `BGELargeENV15`)\n- **Quantized models**: Reduced memory usage (e.g., `BGESmallENV15Q`)\n\n### FastembedVectorstore\n\n#### Constructor\n```python\nvectorstore = FastembedVectorstore(model: FastembedEmbeddingModel)\n```\n\n#### Methods\n\n##### `embed_documents(documents: List[str]) -> bool`\nEmbeds a list of documents and stores them in the vector store.\n\n##### `search(query: str, n: int) -> List[Tuple[str, float]]`\nSearches for the most similar documents to the query. Returns a list of tuples containing (document, similarity_score).\n\n##### `save(path: str) -> bool`\nSaves the vector store to a JSON file.\n\n##### `load(model: FastembedEmbeddingModel, path: str) -> FastembedVectorstore`\nLoads a vector store from a JSON file.\n\n## Performance Considerations\n\n- **Memory Usage**: All embeddings are stored in memory, so consider the size of your document collection\n- **Model Selection**: Smaller models are faster but may have lower quality embeddings\n- **Batch Processing**: The `embed_documents` method processes documents in batches for efficiency\n\n## Use Cases\n\n- **Semantic Search**: Find documents similar to a query\n- **Document Clustering**: Group similar documents together\n- **Recommendation Systems**: Find similar items or content\n- **Question Answering**: Retrieve relevant context for Q&A systems\n- **Content Discovery**: Help users find related content\n\n## Contributing\n\nContributions are welcome! 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