Quick Start¶
Get Memista up and running in 5 minutes.
1. Start the Server¶
You should see output indicating the server is running on http://127.0.0.1:8083.
2. Insert Your First Chunk¶
Insert a text chunk with an embedding vector:
curl -X POST http://localhost:8083/v1/insert \
-H "Content-Type: application/json" \
-d '{
"database_id": "quickstart_db",
"chunks": [{
"embedding": [0.1, 0.2],
"text": "Hello, this is my first chunk!",
"metadata": "{\"source\": \"quickstart\"}"
}]
}'
Response:
3. Add More Chunks¶
Insert multiple chunks at once:
curl -X POST http://localhost:8083/v1/insert \
-H "Content-Type: application/json" \
-d '{
"database_id": "quickstart_db",
"chunks": [
{
"embedding": [0.3, 0.4],
"text": "Second chunk with different embedding",
"metadata": "{\"source\": \"quickstart\"}"
},
{
"embedding": [0.15, 0.25],
"text": "Third chunk similar to the first",
"metadata": "{\"source\": \"quickstart\"}"
}
]
}'
4. Search for Similar Chunks¶
Find chunks similar to a query embedding:
curl -X POST http://localhost:8083/v1/search \
-H "Content-Type: application/json" \
-d '{
"database_id": "quickstart_db",
"embeddings": [[0.1, 0.2]],
"num_results": 5
}'
Response:
{
"results": [
[
{
"chunk_id": 1,
"text": "Hello, this is my first chunk!",
"metadata": "{\"source\": \"quickstart\"}",
"score": 0.0
},
{
"chunk_id": 3,
"text": "Third chunk similar to the first",
"metadata": "{\"source\": \"quickstart\"}",
"score": 0.0707
}
]
]
}
Lower scores indicate higher similarity (distance-based).
5. Clean Up¶
Drop the database when done:
curl -X DELETE http://localhost:8083/v1/drop \
-H "Content-Type: application/json" \
-d '{
"database_id": "quickstart_db"
}'
Next Steps¶
- API Endpoints - Full API documentation
- Configuration - Customize settings
- Examples - More usage examples