Practical overview
When it fits
Use VecDB when an application needs a small, documented HTTP interface for keyed vector storage and cosine-similarity retrieval, either with vectors generated elsewhere or with its optional Gemini-backed text embedding endpoints.
Who it is for
- Developers adding vector similarity lookup to an application or service.
- Teams with an existing embedding pipeline that need keyed vector storage and search.
- Builders of text-retrieval components who want the optional Gemini embedding layer.
Documented uses
- Store externally generated vectors against unique record keys.
- Search a bucket for similar vectors and limit results by minimum cosine similarity or result count.
- Create and store embeddings from text, then search by text through the configured Gemini integration.
- Retrieve matching record keys for use in an application’s separate data store.
Requirements & limits
The public repository identifies the project as MIT-licensed. The README lists binary and Docker installation options.
- The raw-vector layer expects callers to provide vectors; the optional text layer generates them through Gemini.
- The documented storage configuration uses Bolt and a configured database file path.
- Search requests support a minimum cosine similarity and maximum result count.
- Writes use a unique key representing an item in the caller’s main data store.
- Text endpoints require the embedder to be enabled and configured with a Gemini API key and model.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Configure VecDB with Bolt and optional Gemini embeddings
Prepare the documented server configuration for raw-vector search and, if needed, text embedding.
Before you start
- A VecDB binary or Docker installation option from the project README.
- A configuration file; by default VecDB looks for config.yml in the current working directory.
- For text embeddings, a Gemini API key and a configured text-embedding model.
Steps
Set the server listen address in the YAML configuration.
Set the storage driver to bolt and specify the database file path.
If using text endpoints, enable the embedder, choose the gemini driver, and configure api_key and text_embedding_model; the example reads the key from GEMINI_API_KEY.
Save the configuration as config.yml or provide a custom path with the --config flag.
Expected outcome
A configuration describing the HTTP listener, Bolt database path, and optional Gemini text-embedding integration.
Guide limitations
- This guide covers configuration only; it does not establish that the service has been started or that requests have succeeded.
- The README lists Bolt as the currently supported storage driver and Gemini as the currently supported embedder.
Compare options for Vector search
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Vecdb — alash3al | Store externally generated vectors against unique record keys. | Docker, Server | The public repository identifies the project as MIT-licensed. The README lists binary and Docker installation options. |
| Chatbot with PDF for Semantic Search | Prepare document content as embeddings and save it to a supported vector database. | Web | The repository is identified as open source and publicly visible on GitHub. |
| AI LLM Playground | Ask questions about PDF documents through an assistant interface. | Web |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- alash3al/vecdb · Official homepage title · retained in its source language