Build document search layers for AI apps with Morphik
Ingest documents into Morphik, expose retrieval over AI-app knowledge, and tune document search quality before handing context to agents or RAG workflows.
npx skills add agentskillexchange/skills --skill build-document-search-layers-for-ai-apps-with-morphik
Use Morphik when an agent or operator needs a repeatable document-search layer for an AI application, not just another vector database listing. The workflow is to deploy or connect Morphik, ingest a bounded document set, configure parsing and retrieval behavior, query the indexed corpus from an app or agent, and review retrieval quality before production use.
What this skill actually does
Invoke this when uploaded knowledge needs accurate document understanding, multimodal retrieval, or cache-augmented retrieval behind an AI workflow. Keep the scope to document ingestion, indexing, query, and retrieval-quality tuning. Do not use this as a generic database, model framework, or broad RAG architecture placeholder unless Morphik is the actual retrieval service being configured and evaluated.
Inputs and prerequisites: Morphik server or Morphik Python client, Python 3.10+, document corpus, configured model/retrieval providers as needed.
Setup notes: Follow the upstream Morphik documentation for deployment. For local Python environments, install the Morphik client with `pip install morphik`; use the morphik-core repository when running or developing the full service.
Source and verification boundary: use https://morphik.ai/docs as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.