# \[ANN\] SemaDbAPI.jl - Julia Client for SemaDB Vector Database

**URL:** https://discourse.julialang.org/t/ann-semadbapi-jl-julia-client-for-semadb-vector-database/132372
**Category:** Package Announcements
**Tags:** api, database
**Created:** [September 14, 2025, 3:38pm UTC](https://discourse.julialang.org/t/ann-semadbapi-jl-julia-client-for-semadb-vector-database/132372 "2025-09-14T15:38:19Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![hamza\_souidi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/hamza_souidi/32/218956_2.png) [@hamza\_souidi](https://discourse.julialang.org/u/hamza_souidi)
#### Post date: [September 14, 2025, 3:38pm UTC](https://discourse.julialang.org/t/ann-semadbapi-jl-julia-client-for-semadb-vector-database/132372/1 "2025-09-14T15:38:19Z")

</div>

I’m excited to announce **SemaDbAPI.jl** , a new Julia client package for interacting with [SemaDB](https://github.com/SemaFind/SemaDB), a high-performance vector database designed for similarity search and semantic operations.

## Why SemaDbAPI.jl?

Vector databases are becoming increasingly important for AI applications, semantic search, and similarity-based operations. While several vector databases exist, there hasn’t been a well-integrated Julia solution—until now. SemaDbAPI.jl provides a **strongly-typed, idiomatic Julia interface** to SemaDB, making it easy to incorporate vector search capabilities into your Julia workflows.

## Key Features

✅ **Multiple Indexing Strategies** :

- Vamana (graph-based approximate nearest neighbor search)
- Flat (exact search)
- Text indexing for semantic search
- Binary and Product quantization options

✅ **Rich Query Capabilities** :

- Vector similarity search with multiple distance metrics
- Metadata filtering with complex AND/OR compositions
- Sorting, pagination, and field selection
- Hybrid search combining vector and text queries

✅ **Julia-First Experience** :

- Strongly-typed interface generated via OpenAPI.jl
- Compile-time parameter validation
- Comprehensive error handling
- Idiomatic Julia syntax

## Installation

```julia
using Pkg
Pkg.add("SemaDbAPI")

```

## Quick Start

```julia
using SemaDbAPI
using UUIDs

# Initialize client
client = SemaDBClient("http://localhost:8081/v2")

# Create a collection with Vamana indexing
ivvp = IndexVectorVamanaParameters(
    vectorSize = 2,
    distanceMetric = "euclidean",
    searchSize = 75,
    degreeBound = 64,
    alpha = 1.2
)
ischema = Dict("vector" => IndexSchemaValue(
    type = "vectorVamana",
    vectorVamana = ivvp
))
req = CreateCollectionRequest(id = "mycollection", indexSchema = ischema)
create_collection(client, "Julia", req)

# Insert points
points = [
    Dict("_id" => string(uuid4()), "vector" => [1.0, 2.0], "metadata" => "first point"),
    Dict("_id" => string(uuid4()), "vector" => [3.0, 4.0], "metadata" => "second point")
]
insert_req = InsertPointsRequest(points = points)
insert_point(client, "Julia", "mycollection", insert_req)

# Perform search
search_opts = SearchVectorVamanaOptions(vector = [1, 2], operator = "near")
query = Query(property = "vector", vectorVamana = search_opts)
search_req = SearchRequest(query = query, limit = 10)
search_res = search_point(client, "Julia", "mycollection", search_req)

# Clean up
delete_collection(client, "Julia", "mycollection")

```

## Advanced Usage

SemaDbAPI.jl supports sophisticated operations like hybrid search (combining vector and text search):

```julia
# Vector part of the search
vector_query = Query(
    property = "vector",
    vectorVamana = SearchVectorVamanaOptions(vector = [0.1, 0.2, 0.3], weight = 0.7)
)

# Text part of the search
text_query = Query(
    property = "description",
    text = SearchTextOptions(query = "programming language", weight = 0.3)
)

# Combine both queries
combined_query = Query(_and = [vector_query, text_query])
search_req = SearchRequest(query = combined_query, limit = 10)
results = search_point(client, "user1", "hybrid_collection", search_req)

```

## Documentation & Resources

- [Documentation (dev)](https://imohag9.github.io/SemaDbAPI.jl/dev/)
- [GitHub Repository](https://github.com/imohag9/SemaDbAPI.jl)
- [SemaDB Documentation](https://semadb.com/docs/deployment/)

## Why This Matters for Julia Users

This package bridges Julia’s high-performance computing capabilities with modern vector database technology. Whether you’re building:

- Recommendation systems
- Semantic search applications
- Similarity-based analysis tools
- AI-powered applications requiring efficient vector search

SemaDbAPI.jl provides the type-safe, efficient interface you need to integrate these capabilities directly into your Julia workflow.

## Try It Out!

SemaDbAPI.jl is ready for use and includes a comprehensive test suite. I’d love to hear your feedback, suggestions, and contributions!

Special thanks to the [OpenAPI.jl](https://github.com/JuliaComputing/OpenAPI.jl) team for providing the foundation that made this package possible with its strong typing and validation capabilities.