---

name: integrations-and-storage description: "Choose and verify LlamaIndex integration packages, namespace imports, storage/vector-store/readers/LLM/embed providers, and optional dependency or service boundaries." disable-model-invocation: true metadata: disco-role: operating license: MIT

Integrations and Storage

Use this sub-skill when a task is blocked on selecting, installing, importing, or troubleshooting a LlamaIndex provider package or storage backend.

Route Here For

• Deciding between the llama-index starter package and llama-index-core plus specific integration packages.
• Mapping a distribution such as llama-index-llms-openai or llama-index-vector-stores-qdrant to imports such as llama_index.llms.openai or llama_index.vector_stores.qdrant.
• Choosing categories of integrations: LLMs, embeddings, vector stores, readers, tools, postprocessors, callbacks, storage/docstores/index stores, or llama-index-utils helpers.
• Determining whether a storage choice is local/in-memory, local persistent, remote filesystem-backed, self-hosted service-backed, or cloud service-backed.
• Diagnosing missing optional packages, provider credentials, service/network availability, import namespace mismatches, and plugin/core version compatibility.

Do Not Use For

• End-to-end RAG code after the provider/storage choice is made; use ../indexing-and-querying/SKILL.md.
• File loading, parsing, chunking, SimpleDirectoryReader, SentenceSplitter, or IngestionPipeline; use ../ingestion-and-loading/SKILL.md.
• Prompt templates, structured outputs, Pydantic output models, and provider-specific generation parameters; use ../customization-and-structured-outputs/SKILL.md.
• Agent/workflow composition with tools and memory; use ../agents-and-workflows/SKILL.md.

Fast Triage

1. Identify the requested capability and category: LLM, embedding, vector store, reader, tool, utility, or core storage.
2. If the code imports llama_index.core..., check llama-index-core; if it imports llama_index.<category>.<provider>, check the matching integration distribution.
3. Prefer installing one narrow package such as llama-index-llms-ollama, llama-index-embeddings-huggingface, or llama-index-vector-stores-qdrant instead of broad optional extras.
4. Verify import and distribution metadata before changing code:

python sub-skills/integrations-and-storage/scripts/check_integration_imports.py \
  --dist llama-index-core \
  --module llama_index.core \
  --dist llama-index-llms-openai \
  --module llama_index.llms.openai

5. Separate local persistence from backend persistence: StorageContext.persist(persist_dir=...) applies to core/simple stores, while many vector-store integrations persist in their own database or cloud service.
6. If a provider call fails after import succeeds, inspect credentials, endpoint/base URL, region, local service process, collection/index existence, and network policy before blaming LlamaIndex APIs.

Required References

• references/package-selection.md: package split, namespace rules, category-to-distribution naming, and how to verify imports.
• references/storage-and-provider-patterns.md: storage decision tree, vector-store persistence assumptions, and provider wiring patterns.
• references/troubleshooting.md: install/import, credential, service, persistence, and compatibility failure modes.

Bundled Helper

check_integration_imports.py is a safe offline checker for importability and installed distribution metadata. It does not call provider APIs, open network connections, or require credentials; it only imports requested modules and reads package metadata.