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If deleted claim content still appears in retrieval, the delete may have removed only one layer of the pipeline—or retrieval may still be searching a separate copy. Define what “delete” means for your system, trace the source document to every chunk and index record, and test that the removed material no longer appears in returned context. A vector-store delete call alone does not establish that all source-derived data is gone.
What exactly are you deleting?
Start with the business object named in the request, then map it to the records your implementation persists. In an insurance intake system, the deletion subject might be a claim attachment, a source document, a particular document version, or an individual chunk. Those are not necessarily interchangeable: one source file may produce multiple chunks, and each chunk may have its own vector or index record.
Build a traceable identity map
Record how the domain identifier connects to source-file identifiers, chunk identifiers, vector IDs, and metadata records. Keep enough provenance to determine which records came from the source or version being removed. The ChromaDB Go client documentation shows upsert and deletion operations keyed by IDs, but that example does not establish a universal ID scheme, retry behavior, or transaction guarantee for other adapters. ChromaDB Go Client documentation
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- Source document or version: the imported file or a particular revision of it.
- Chunk: a text segment derived from a source document, potentially linked to its file and metadata.
- Vector or index record: the searchable representation used to find relevant content.
- Other persisted copies: any source text, linkage, metadata, or derived record held outside the vector index.
These names are a design map, not a claim that every RAG system stores each item separately. Inspect the actual implementation and its source-of-truth stores before defining the operation’s scope.
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Which delete operation does your backend support?
Deletion selectors and resource boundaries differ. The examples below describe only the documented APIs, not every Go adapter or a guarantee that one operation cleans up every related record.
| Behavior | ChromaDB Go client | Google Cloud RAG Engine |
|---|---|---|
| Documented deletion target and selectors | Records can be deleted by IDs, metadata filter, or document-content filter. The API examples also use IDs for upsert. ChromaDB Go Client documentation | The API documents deleting a named RagFile resource. The cited API reference does not establish Chroma-style ID, metadata, or content-filter selectors for that deletion operation. RAG Engine API reference |
| What associated stores are removed | Not established by the cited client documentation; verify whether your application separately persists chunk text, source metadata, or other linked records. ChromaDB Go Client documentation | Not established by the cited API reference; verify the resource’s effects on the records and stores your implementation depends on. RAG Engine API reference |
| Delete completion, consistency, and retry guarantees | Not established here. Check the exact client and server versions and your adapter’s behavior. ChromaDB Go Client documentation | Not established here. Check the exact API operation’s response and lifecycle semantics for the deployed version. RAG Engine API reference |
| Retrieval-time metadata filtering | Not established by the cited deletion examples; do not infer filter behavior from delete selectors. | Metadata-filtered retrieval considers only files matching the metadata expression; this is a retrieval constraint, not evidence that unmatched files were erased. Google Cloud metadata search documentation |
| Score interpretation | Not established by the cited client page; use the configured metric’s documented direction. | Score meaning depends on the underlying database and metric. For the documented cosine-distance example, scores run from 0 (most relevant) to 2 (least relevant). RagContexts reference |
“Not established” means the cited documentation does not support a general guarantee. It is not evidence that a backend lacks the behavior. Confirm it for the exact backend, adapter, and version in use.
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Does deleting a vector also delete its chunk?
Not necessarily. A Go package example describes a ChunkStore that holds text, file linkage, and metadata separately from a VectorStore that holds embeddings; it also documents a file-vector deletion operation. This illustrates why removing vectors and removing all source-derived records are different assertions. ragcore Go package documentation
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How is physical cleanup different from retrieval filtering?
Physical cleanup aims to remove records from the stores that persist them. Retrieval filtering changes which records a query considers. Google Cloud’s metadata-search documentation describes retrieval as considering only files whose metadata matches the expression. That can be useful for scoping a search, but it does not establish that nonmatching records have been erased. Google Cloud metadata search documentation
Keep these as separate paths in both design and tests: one for deletion and one for query-time constraints. A filter can conceal stale content from a particular query while leaving the underlying record available to other queries or processes. Treating filtering as deletion would make the system’s reported lifecycle state stronger than the operation proves.
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How should a Go pipeline implement deletion?
- Resolve the domain request. Determine whether the request targets a claim attachment, a source document, a document version, or a chunk. Define the intended scope before translating it to backend selectors.
- Find every related record. Use the source-to-document-to-chunk-to-index identity map to enumerate the affected records across each persistence layer. If the implementation has separate chunk and vector stores, include both in the cleanup plan.
- Apply the backend’s documented selector. For example, the ChromaDB Go client documents deletion by IDs, metadata filter, or document-content filter. Choose a selector only when it matches the intended scope and the IDs or metadata are sufficiently specific.
- Clean up other stores explicitly where needed. If chunk text, file links, or metadata are stored outside the vector backend, confirm that the application’s deletion workflow addresses them. Do not infer cross-store cleanup from a successful vector delete.
- Define retry and replacement behavior. Decide how the application handles a retry, a partial failure, or a replacement document with new chunks. The cited API examples do not establish universal idempotency, atomicity, or cross-store transaction semantics; verify these properties in the exact implementation.
- Verify retrieval results. Run queries likely to match the deleted material and inspect returned context text and provenance, not only the deletion call’s returned error. Check the selected backend’s documented completion and consistency behavior before deciding when verification is meaningful.
This is implementation guidance, not a framework-specific Go recipe: the title does not identify a particular RAG framework, vector database, source-of-truth store, or deployment version, so a universal code sample would imply guarantees the documented examples do not establish.
How do you test that deleted content is no longer retrievable?
- Choose queries that would have matched the removed source, including distinctive wording from its content where appropriate.
- Inspect the returned context text and provenance to determine whether any result still points to the removed document or its chunks.
- Test the intended deletion scope: for example, a single version versus every chunk associated with a source document.
- Test filtering separately. Confirm that retrieval constraints change the candidate set without treating that result as evidence of physical erasure.
- Exercise retries and partial failures according to the selected adapter’s documented behavior, and check for stale records in every relevant store.
- Interpret score thresholds using the configured metric. Google’s RagContexts reference notes that a score can represent distance or similarity depending on the database and metric; in its cosine-distance example, larger values mean less relevance. RagContexts reference
A deletion response is one signal in this test, not the entire acceptance criterion. The stronger operational check is that the system no longer returns the removed source-derived context through the retrieval path your application actually uses.
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What does this mean for insurance claims data?
Technical deletion needs to follow the organization’s approved records lifecycle. The API examples described here do not establish insurance-specific retention duties, privacy requirements, claims workflows, or legal deletion rules. Those depend on the applicable organization and jurisdiction and require separate authoritative guidance. Keep the technical operation traceable to the approved policy rather than treating a vector-store API as the policy itself.
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