🌐 REST API Reference

Complete reference for all Spector REST endpoints. The API runs on an embedded Armeria server with virtual threads, accepting and returning JSON. Every request gets its own virtual thread — no connection limits to worry about.

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🔧 Base Configuration

Setting	Default	Description
Base URL	http://localhost:7070	Configurable port
Content-Type	application/json	All requests and responses
Auth Header	X-API-Key: <key>	Optional, configured at startup
CORS	Enabled	All origins by default

Note

When an API key is configured, requests without a valid key receive 401 Unauthorized.

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💚 Health & Status

GET /health

Quick health check for load balancers and monitoring.

curl http://localhost:7070/health

Response 200:

{"status": "UP"}

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GET /api/v1/status

Engine status including SIMD capabilities, GPU availability, and configuration.

curl http://localhost:7070/api/v1/status

Response 200:

{
  "status": "RUNNING",
  "simd": "AVX2 (256-bit, 8 lanes)",
  "gpuAvailable": false,
  "rerankerEnabled": false,
  "documentCount": 1250,
  "dimensions": 384,
  "capacity": 100000
}

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GET /api/v1/metrics

Request metrics including query counts, latencies, and throughput.

curl http://localhost:7070/api/v1/metrics

Response 200:

{
  "totalQueries": 4521,
  "totalIngestions": 1250,
  "avgLatencyMs": 0.34,
  "p99LatencyMs": 1.12,
  "queriesPerSecond": 8432.5
}

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📥 Ingest Endpoints

POST /api/v1/ingest

Ingest a single document with a pre-computed vector embedding.

curl -X POST http://localhost:7070/api/v1/ingest \
  -H "Content-Type: application/json" \
  -H "X-API-Key: my-secret-key" \
  -d '{
    "id": "doc-1",
    "title": "Java Vector API",
    "content": "SIMD-accelerated search engine on modern JVM",
    "vector": [0.1, 0.2, 0.3, 0.4, 0.5]
  }'

Request Schema:

Field	Type	Required	Description
id	string	✅	Unique document identifier
title	string	❌	Document title
content	string	✅	Text content for BM25 indexing
vector	float[]	✅	Embedding vector (must match configured dimensions)
metadata	object	❌	Arbitrary key-value metadata

Response 200:

{"id": "doc-1", "status": "indexed"}

---

POST /api/v1/ingest/auto

Ingest with automatic embedding generation. Requires a configured embedding provider (e.g., Ollama).

curl -X POST http://localhost:7070/api/v1/ingest/auto \
  -H "Content-Type: application/json" \
  -d '{
    "id": "doc-2",
    "title": "Panama FFM",
    "content": "Foreign Function and Memory API for zero-copy storage"
  }'

Field	Type	Required	Description
id	string	✅	Unique document identifier
title	string	❌	Document title
content	string	✅	Text content (used for both BM25 and embedding)
metadata	object	❌	Arbitrary key-value metadata

---

POST /api/v1/ingest/bulk

Ingest multiple documents in a single request.

curl -X POST http://localhost:7070/api/v1/ingest/bulk \
  -H "Content-Type: application/json" \
  -d '{
    "documents": [
      {"id": "d1", "content": "first document", "vector": [0.1, 0.2, 0.3]},
      {"id": "d2", "content": "second document", "vector": [0.4, 0.5, 0.6]}
    ]
  }'

Response 200:

{
  "indexed": 2,
  "failed": 0,
  "results": [
    {"id": "d1", "status": "indexed"},
    {"id": "d2", "status": "indexed"}
  ]
}

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🔍 Search Endpoints

POST /api/v1/search

Auto-detecting search endpoint. The mode is determined by which fields you provide:

Fields Provided	Mode	Engine Used
text only	📝 KEYWORD	BM25
vector only	🧠 VECTOR	HNSW
text + vector	🧬 HYBRID	RRF Fusion

curl -X POST http://localhost:7070/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "text": "vector search engine",
    "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
    "topK": 10
  }'

Request Schema:

Field	Type	Required	Description
text	string	❌*	Query text for keyword search
vector	float[]	❌*	Query vector for similarity search
topK	int	❌	Number of results (default: 10, max: 10000)

Important

*At least one of text or vector must be provided.

Response 200:

{
  "results": [
    {
      "id": "doc-1",
      "score": 0.9523,
      "title": "Java Vector API",
      "content": "SIMD-accelerated search engine on modern JVM"
    }
  ],
  "searchMode": "HYBRID",
  "latencyMs": 0.47,
  "totalResults": 1
}

---

POST /api/v1/vector-search

Explicit vector-only similarity search.

curl -X POST http://localhost:7070/api/v1/vector-search \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.1, 0.2, 0.3, 0.4, 0.5], "topK": 10}'

POST /api/v1/bm25

Explicit keyword-only BM25 search.

curl -X POST http://localhost:7070/api/v1/bm25 \
  -H "Content-Type: application/json" \
  -d '{"text": "SIMD acceleration", "topK": 10}'

POST /api/v1/hybrid

Explicit hybrid search combining vector + keyword via RRF.

curl -X POST http://localhost:7070/api/v1/hybrid \
  -H "Content-Type: application/json" \
  -d '{"text": "vector search", "vector": [0.1, 0.2, 0.3, 0.4, 0.5], "topK": 10}'

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GET /api/v1/search/stream (SSE)

Streaming search via Server-Sent Events. Results are emitted one-by-one as they become available, enabling progressive display in UIs.

curl -N "http://localhost:7070/api/v1/search/stream?text=vector+search&topK=5&mode=HYBRID"

Query Parameters:

Param	Type	Required	Default	Description
text	string	❌*	—	Query text for keyword/hybrid search
vector	string	❌*	—	Comma-separated floats (e.g., 0.1,0.2,0.3)
topK	int	❌	10	Number of results
mode	string	❌	auto-detect	KEYWORD, VECTOR, or HYBRID

Important

*At least one of text or vector must be provided.

Event Stream:

event: result
data: {"id":"doc-1","score":0.9523,"rank":1}

event: result
data: {"id":"doc-3","score":0.8741,"rank":2}

event: result
data: {"id":"doc-7","score":0.8102,"rank":3}

event: done
data: {"totalHits":3,"queryTimeMs":12,"mode":"HYBRID"}

Event Types:

Event	Description
result	A single search result with id, score, and rank
done	Search complete — includes timing and metadata
error	An error occurred during search

Tip

Use the EventSource API in browsers or any SSE client library. Results stream immediately as they are scored, giving users instant feedback.

JavaScript Example:

const source = new EventSource('/api/v1/search/stream?text=HNSW+algorithm&topK=5');

source.addEventListener('result', (event) => {
  const result = JSON.parse(event.data);
  console.log(`#${result.rank}: ${result.id} (score: ${result.score})`);
});

source.addEventListener('done', (event) => {
  const meta = JSON.parse(event.data);
  console.log(`Search complete in ${meta.queryTimeMs}ms`);
  source.close();
});

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🤖 RAG (Retrieval-Augmented Generation)

POST /api/v1/rag

Retrieve relevant context for LLM prompting. Performs search, then assembles a context window from matching chunks.

curl -X POST http://localhost:7070/api/v1/rag \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How does HNSW indexing work?",
    "topK": 5,
    "tokenLimit": 4096,
    "searchMode": "hybrid"
  }'

Request Schema:

Field	Type	Required	Default	Description
query	string	✅	—	Query text (1–2000 chars)
topK	int	❌	5	Results to retrieve (1–100)
tokenLimit	int	❌	4096	Max context tokens (1–8192)
searchMode	string	❌	"vector"	"vector" or "hybrid"

Response 200:

{
  "context": "Assembled context text from relevant document chunks...",
  "attributions": [
    {"documentId": "doc-1", "chunkOffset": 0},
    {"documentId": "doc-3", "chunkOffset": 2}
  ],
  "isEmpty": false
}

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🗑️ Document Management

DELETE /api/v1/documents/{id}

Delete a document by its ID.

curl -X DELETE http://localhost:7070/api/v1/documents/doc-1

Response 200:

{"id": "doc-1", "deleted": true}

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📊 Index Management

POST /api/v1/index

Create or manage indexes.

curl -X POST http://localhost:7070/api/v1/index \
  -H "Content-Type: application/json" \
  -d '{"action": "create", "name": "my-index", "dimensions": 384}'

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❌ Error Responses

Status	Meaning
200	✅ Success
400	Bad request (validation error, dimension mismatch)
401	Unauthorized (invalid or missing API key)
404	Resource not found
503	Service unavailable (embedding provider down)

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🔗 See Also

• Getting Started — Quick start with curl examples

• Java SDK Guide — Type-safe programmatic access

• CLI Reference — Command-line access to the API

• Configuration Guide — Server and auth configuration