Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

​
Upsert Documents

Insert or update documents with your pre-computed vectors and JSON payload. Up to 1,000 documents per request. 
requests.post(f"{BASE}/namespaces/product-search/documents/direct", headers=headers, json={
    "documents": [
        {
            "document_id": "prod-001",
            "vectors": {"text_embedding": [0.12, -0.34, 0.56, ...]},
            "payload": {"title": "Noise-Canceling Headphones", "price": 149.99, "in_stock": True}
        },
        {
            "document_id": "prod-002",
            "vectors": {"text_embedding": [0.08, 0.22, -0.41, ...]},
            "payload": {"title": "Bluetooth Speaker", "price": 59.99, "in_stock": True}
        }
    ]
})
# → {"inserted": 2, "document_ids": ["prod-001", "prod-002"]}
Vector dimensions are validated against namespace config. If a document with the same ID exists, it is overwritten.

​
Bulk Import

For large datasets, stream NDJSON directly to shard WALs. Returns 202 Accepted with a batch ID to poll. 
curl -X POST "https://api.mixpeek.com/v1/namespaces/product-search/bulk-import" \
  -H "Authorization: Bearer $MIXPEEK_API_KEY" \
  -H "Content-Type: application/x-ndjson" \
  --data-binary @products.ndjson
products.ndjson
{"document_id": "prod-001", "vectors": {"text_embedding": [0.12, ...]}, "payload": {"title": "..."}}
{"document_id": "prod-002", "vectors": {"text_embedding": [0.08, ...]}, "payload": {"title": "..."}}

​
Dense (Vector)

requests.post(f"{BASE}/search", headers=headers, json={
    "namespace_id": "product-search",
    "queries": [{
        "vector_name": "text_embedding",
        "vector": query_embedding,
        "top_k": 10,
        "score_threshold": 0.7
    }]
})

​
BM25 (Keyword)

Requires a text index on the target field. 
requests.post(f"{BASE}/search", headers=headers, json={
    "namespace_id": "product-search",
    "queries": [{"text": "wireless noise canceling", "top_k": 10}]
})

​
Sparse

requests.post(f"{BASE}/search", headers=headers, json={
    "namespace_id": "product-search",
    "queries": [{"sparse_vector": {42: 0.8, 1337: 0.5, 9001: 0.3}, "top_k": 10}]
})

​
Hybrid

Combine multiple query types with RRF or DBSF fusion. 
requests.post(f"{BASE}/search", headers=headers, json={
    "namespace_id": "product-search",
    "queries": [
        {"vector_name": "text_embedding", "vector": query_embedding, "top_k": 20},
        {"text": "wireless headphones", "top_k": 20}
    ],
    "fusion": "rrf"
})

​
Filtered

Add payload filters to any query type. Filters narrow results before scoring. 
requests.post(f"{BASE}/search", headers=headers, json={
    "namespace_id": "product-search",
    "queries": [{
        "vector_name": "text_embedding",
        "vector": query_embedding,
        "top_k": 10,
        "filters": {"price": {"$lte": 100}, "in_stock": True}
    }]
})

​
Document Operations

OperationMethodEndpoint
Get by IDGET/v1/namespaces/{ns}/documents/{doc_id}
Update payloadPATCH/v1/namespaces/{ns}/documents/{doc_id}
DeleteDELETE/v1/namespaces/{ns}/documents/{doc_id}
Scroll (paginate)POST/v1/namespaces/{ns}/documents/scroll

​
Update Vectors

OperationMethodEndpoint
Replace vectors (single doc)PATCH/v1/namespaces/{ns}/documents/{doc_id}/vectors
Batch replace vectorsPOST/v1/namespaces/{ns}/documents/update-vectors
Add new vector indexPOST/v1/namespaces/{ns}/documents/add-vectors
Payload is untouched when updating vectors.