Caching and retention limits for licensed RAG content

This page covers storage during the license term. Purging everything when a license ends is a different trigger with different engineering, covered in removing licensed content from vector indexes at termination.

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Why caching is a licensed right, not an engineering detail

Caching needs its own grant because persistent stored copies are reproductions, and grounding licenses list storage and retention as negotiated terms alongside display and attribution [1]. A training license does not authorize live grounding, and a grounding license does not authorize training on the cached corpus [1]. Engineers who treat a Redis layer or a parsed-text bucket as "just performance" are creating copies the licensor may never have granted.

The commercial shift makes this sharper. Publishers are moving from one-time training payments toward ongoing, usage-based grounding deals [2], and query-time access layers that meter each fetch are now a visible market practice [3]. A long-lived cache can bypass the metering the licensor priced on, which is why licensors write cache limits into the deal, and why usage reporting and metering and caching terms should be negotiated together.

The stores a retention clause has to reach

A compliant retention rule names every place licensed text, or something derived from it, persists. Erasure guidance for RAG systems treats caches and derived artifacts such as extracted text as separate stores that a removal process must cover explicitly [4]. In a typical pipeline that list is longer than teams expect:

• Raw fetch store: original HTML, PDF or XML in object storage (S3, GCS, Azure Blob), often versioned so "deleted" objects survive as noncurrent versions.
• Parsed and chunked text: output of the extraction step (Unstructured, Apache Tika, custom parsers), usually JSONL or Parquet keyed by document ID.
• Vector index: embeddings plus payload metadata in pgvector, OpenSearch k-NN, Pinecone, Weaviate or S3 Vectors; payloads frequently hold the chunk text itself. See embedding and vector index rights.
• Keyword index: BM25 inverted indexes in Elasticsearch or OpenSearch that store source fields by default (_source).
• Response and semantic caches: Redis or GPTCache entries keyed on query embeddings that store generated answers quoting licensed passages.
• Observability logs: prompt and completion traces in LangSmith, Langfuse, Datadog or plain request logs, which capture retrieved context verbatim.
• Evaluation snapshots and backups: frozen test collections and database snapshots that outlive the live index.

Vector stores make per-record deletion possible by key, for example deleting vectors from an index in Amazon S3 Vectors [5], but only if you kept a stable mapping from licensor document ID to every vector key you created. Without that mapping, enforcing a TTL means rebuilding the index.

Three architectures and what each lets you promise

Choose the index architecture from the license, not the other way round: a pointer-only index is the safest default, a TTL cache is the common middle ground, and a full-text index needs an express storage grant. The table compares them on the terms a licensor will ask about.

Illustrative example: invented to show structure; it does not describe an available dataset.

Retrieval quality usually favors stored text, because you can chunk, rerank and run hybrid search over it. Before paying for a storage grant, run a pilot to measure retrieval lift for the cached versus live-fetch variants on your own queries.

Terms to pin down in a grounding license

A usable caching clause states what may be stored, for how long, in which systems, and what happens when content changes. Most disputes come from vague phrases like "temporary copies" or "reasonable caching." Ask the licensor to define each term below in writing, and map each to a configuration value your platform team owns. The broader clause list lives in RAG content license terms and the general guide to AI data license terms.

Illustrative example: invented to show structure; it does not describe an available dataset.

Your internal retention policy should then reference these contract values instead of a single company-wide default, because licensed content usually needs a shorter clock than your own records.

Logging which content version answered which query

An answer log that records the document ID, version and fetch time behind each response is the evidence that your cache honored the license. Where a licensor asks for usage reports, a per-answer record lets you show that no expired or withdrawn version was served. It also supports debugging when a licensor issues a correction.

Illustrative example: invented to show structure; it does not describe an available dataset.

{
  "answer_id": "ans_7f3c",
  "query_ts": "2026-10-09T14:02:11Z",
  "license_id": "lic_grounding_001",
  "retrieved": [
    {
      "doc_id": "pub-48812",
      "version": "v3",
      "source_etag": "\"a91e\"",
      "fetched_at": "2026-10-08T22:00:04Z",
      "expires_at": "2026-10-10T22:00:04Z",
      "store": "fulltext_ttl",
      "displayed_chars": 412
    }
  ],
  "cache_hit": false
}

Keep this log free of the retrieved text itself. Store identifiers and lengths, not passages, so the audit trail does not become another copy the retention clause has to reach.

EU and other regulatory overlays

Regulation sets a floor under the contract but rarely gives cache rights on its own. As of October 2026, providers of general-purpose AI models that sign the EU GPAI Code of Practice commit, in its copyright chapter, to reproduce and extract only lawfully accessible content when crawling the web, as part of their Article 53(1)(c) copyright policy [6]. The Code governs model providers rather than a deployer's RAG cache, so for most retrieval teams an over-retained cache entry is primarily a contract and copyright question, not an AI Act one. If licensed content includes personal data, privacy retention limits can be shorter than the license allows; see retrieval-time privacy controls and regulatory retention versus license deletion duties.

Sourcing operational documents for a cached RAG index

If you need operational content such as support histories, engineering records or internal documents for a RAG index, SourceX sources these datasets on request from US companies, and every release is approved by the supplying company. Each dataset is rights-reviewed and delivered under a license defining records, uses, term and delivery, and nothing is contracted until a supplier agrees. You can describe the data your index needs, and see the retrieval and RAG buyer's guide or the AI data hub for related topics.

Request licensed content for your RAG index

SourceX finds US businesses that hold the data you describe, assesses data and licensing permissions, and agrees pricing and allowed uses in a license before any transaction. Requests are sourced on demand, and a request may not produce a match. Start a buyer request.