[!WARNING] Edda is in early development (v0.1). The API will change between versions.
Edda is a lightweight embedded vector search engine written in Zig with Python bindings. Unlike server-based vector databases (Qdrant, Weaviate, Milvus), Edda runs in-process. No daemon, no network, no infrastructure. Unlike pure-C++ libraries (FAISS, hnswlib), it ships with a clean Python API and zero compile-time dependencies for end users. Useful for prototyping RAG pipelines, embedding small-to-medium collections in applications, or learning how vector search works under the hood.
Named after the Poetic Edda.
Installation
pip install pyedda
Supported platforms (prebuilt wheels):
- Linux x86_64
- macOS arm64 (Apple Silicon)
- Windows x86_64
On other platforms pip install will build from source, which requires Zig 0.15.2+ to be installed.
Quickstart
import numpy as np
from edda import IndexFlat
# In a real pipeline these vectors come from an embedding model
# (OpenAI, sentence-transformers, BGE, etc.)
vectors = np.array([
[0.1, 0.2, 0.3],
[0.9, 0.1, 0.0],
[0.1, 0.3, 0.28],
], dtype=np.float32)
idx = IndexFlat(dim=3, metric="cosine")
idx.add(vectors, ids=np.array([10, 20, 30], dtype=np.int64))
print(len(idx)) # 3
query = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
result = idx.search(query, k=2)
print("Top ids:", result.ids[0])
print("Scores:", result.scores[0])
# Persistence
idx.save("my_index.edda")
loaded = IndexFlat.load("my_index.edda")
print(len(loaded)) # 3
See examples/ for more usage scenarios.
Roadmap
- v0.1 (current) brute-force flat index, persistence, Python bindings, save/load.
- v0.2 HNSW index for sub-linear search, benchmarks against hnswlib and FAISS on standard ANN datasets.
- v0.3 filtered search with metadata, batch deletions.
- Later MCP server (rebuild after API stabilizes), additional distance metrics, multi-threading.
Development
Requires Zig 0.15.2+, uv, and just.
Setup
git clone https://github.com/dmitryglhf/edda.git
cd edda
just build # compiles the Zig core into the Python package
uv pip install -e . # installs pyedda in editable mode
After modifying Zig code, run just build again to rebuild the native library.
Testing
just test # runs both Zig unit tests and Python tests
Or run them separately:
zig build test # Zig unit tests only
uv run pytest # Python tests only
Releasing
Releases are automated via GitHub Actions. Two flows are available:
Dev release to TestPyPI for iterating on a version before the final release:
just bump-dev 0.1.3 1 # creates version 0.1.3.dev1
just release # pushes commit and tag, triggers TestPyPI workflow
just test-install 0.1.3.dev1 # installs from TestPyPI in a fresh venv and smoke-tests
Production release to PyPI for the final stable version:
just bump 0.1.3 # creates version 0.1.3
just release # pushes commit and tag, triggers PyPI workflow
just test-install-prod 0.1.3
License
1 maintainer
Metadata
1 maintainer
Release files for pyedda 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|
| pyedda-0.1.2.tar.gz | 78.8 kB | Details |
Built distributions (wheels)
Total release size: 3.9 MB
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