---

name: vector-indexing description: "Build and troubleshoot DocArray Document Index workflows for local exact search, filters, hybrid queries, persistence, nested subindexes, and optional vector databases." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DocArray vector indexing

Use this sub-skill when a task needs to index typed documents, search embeddings, filter metadata, combine queries, persist a local index, or select an optional vector database backend.

Route here for

• Defining a dimensioned vector field for InMemoryExactNNIndex or another DocumentIndex.
• Indexing DocList data and using find(), find_batched(), filter(), filter_batched(), ID access, deletion, or re-index updates.
• Building local hybrid queries with build_query() and execute_query().
• Searching nested DocList fields with a subindex.
• Choosing HNSWLib, Qdrant, Weaviate, Elasticsearch, Redis, Milvus, MongoDB Atlas, or Epsilla integrations and diagnosing missing extras or services.

Route elsewhere

• Base schemas, predefined documents, tensor typing, or DocList/DocVec modeling before indexing: use sibling document-modeling.
• JSON/protobuf/bytes/CSV/DataFrame serialization or file:///S3 storage: use sibling serialization-storage.

Read these bundled references

1. Start with API reference for verified index signatures, schema requirements, and configuration fields.
2. Use Index workflows for local indexing, search, filters, query builders, persistence, and nested subindexes.
3. Use Optional backends before installing an external backend or starting a service.
4. Use Troubleshooting for schema, dimension, query-builder, dependency, and service failures.

Safe bundled helper

Run inmemory_index_smoke.py for deterministic CPU-only local checks. It uses only NumPy and the in-memory index; it does not start a database, download data, or require credentials:

Run these commands from this sub-skill root (the directory containing this SKILL.md):

python scripts/inmemory_index_smoke.py --help
python scripts/inmemory_index_smoke.py
python scripts/inmemory_index_smoke.py --exercise-persist

The verified minimum scope is CPU with the in-memory backend. External services, HNSWLib, and accelerator-specific tensor paths remain optional and unverified until a task prepares their dependencies and service/hardware plan.