Initial Dockerhub image tag

• First steps towards publishing on tagged releases

Initial Public Release

Release v1.0.0 – Initial Public Release

Highlights

• Launch of same-same: a lightweight RESTful microservice for vector storage and semantic search
• In-memory vector database with thread safety
• Pluggable embedding support (Google Gemini, HuggingFace)
• Quote-specific endpoints for easy text vectorization
• Cosine similarity search for fast, relevant results
• Health check endpoint for monitoring

API Endpoints

• POST /api/v1/vectors/embed – Create vector from quote text (auto-generates embedding)
• POST /api/v1/vectors – Create a new vector manually
• GET /api/v1/vectors – List all vectors
• GET /api/v1/vectors/{id} – Get a specific vector
• PUT /api/v1/vectors/{id} – Update a vector
• DELETE /api/v1/vectors/{id} – Delete a vector
• POST /api/v1/vectors/search – Search vectors by similarity
• POST /api/v1/search – Search vectors by text (auto-embedding and similarity search)
• GET /api/v1/vectors/count – Get total number of vectors
• GET /health – Health check

Getting Started

• Quickstart and usage guides included.
• Sample dataset for immediate testing.
• Docker and local development support.

Notes

• This is a foundation for further vector database and semantic search experimentation.
• Contributions welcome!

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