Query Vectors
Query documents using similarity search with optional reranking.
Primary endpoint for semantic search, question-answering, and RAG (Retrieval-Augmented Generation) applications. Returns documents ranked by relevance to the query text with similarity scores.
Query Types:
semantic(default): Approximate nearest-neighbor search using HNSW over cosine similarity of document embeddings. Optimal for question-answering, conceptual search, and finding semantically related content without requiring exact keyword matches.lexical: Keyword-based text search (BM25 algorithm). Optimal for exact phrase matching, proper nouns, and scenarios where keyword presence is more important than semantic similarity.hybrid: Combines semantic and lexical approaches with weighted scoring. Provides maximum recall by identifying documents matching either semantically or lexically.
Metadata Filtering: Narrow the search scope by applying metadata filters (e.g., search only documents
where category: "technical"). Only indexed fields can be used for filtering.
Filters are applied before similarity search for optimal efficiency.
Reranking (Advanced): Optionally enhance result quality using a cross-encoder reranking model. The reranker rescores the initial results using a more sophisticated model that evaluates the complete query-document pair (not solely embeddings). This adds 100-500ms latency but significantly improves precision for high-stakes applications.
Reranking Strategy: Set top_k higher than the desired final count (e.g., 50) to retrieve more
candidates from the initial search. Then configure rerank_top_n to the desired final count (e.g., 10)
to return only the most relevant documents after reranking. This two-stage approach maximizes both recall
and precision.
Performance Metrics: The response includes detailed timing breakdowns (embedding generation time, index query time, reranking time) to facilitate search pipeline optimization and latency analysis.
Similarity Scores: Each result includes a score field indicating relevance. Higher scores indicate
greater relevance. Score ranges and semantics vary by query type (semantic scores use cosine similarity,
lexical scores use BM25, hybrid scores combine both approaches).
Query Vectors
package main
import (
"context"
"fmt"
"github.com/scaleapi/sgp-dev-go"
"github.com/scaleapi/sgp-dev-go/option"
)
func main() {
client := sgpdev.NewClient(
option.WithAPIKey("My API Key"),
option.WithAccountID("My Account ID"),
)
response, err := client.VectorStores.Query(
context.TODO(),
"vector_store_name",
sgpdev.VectorStoreQueryParams{
Content: sgpdev.TextContentParam{
Text: "text",
},
},
)
if err != nil {
panic(err.Error())
}
fmt.Printf("%+v\n", response.Metadata)
}
{
"metadata": {
"search_type": "search_type",
"total_query_time_ms": 0,
"embedding_config": {
"model_deployment_id": "model_deployment_id",
"type": "models_api"
},
"embedding_time_ms": 0,
"index_query_time_ms": 0,
"reranking_model": "reranking_model",
"reranking_time_ms": 0
},
"vectors": [
{
"id": "id",
"score": 0,
"content": {
"text": "text",
"type": "text"
},
"metadata": {
"foo": "bar"
},
"vector": [
0
]
}
]
}Returns Examples
{
"metadata": {
"search_type": "search_type",
"total_query_time_ms": 0,
"embedding_config": {
"model_deployment_id": "model_deployment_id",
"type": "models_api"
},
"embedding_time_ms": 0,
"index_query_time_ms": 0,
"reranking_model": "reranking_model",
"reranking_time_ms": 0
},
"vectors": [
{
"id": "id",
"score": 0,
"content": {
"text": "text",
"type": "text"
},
"metadata": {
"foo": "bar"
},
"vector": [
0
]
}
]
}