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In the Hindsight-based legal-memory system described by developer rosa, tags narrow recall, metadata connects recalled facts to their sources, and a separate SQLite registry resolves case and person identities. Keeping those jobs distinct helps an application retrieve relevant records without asking a language model to invent citation details.
What the system is designed to do
In a September 29, 2026, first-person account, rosa describes Tareekh, a legal-practice application built around Hindsight. A lawyer uploads material such as diary photos, certified order sheets, deeds, and typed notes. The application divides uploads into hearing entries, resolves each entry to a case, and retains memories in a Hindsight bank. An agent can then use project tools the author calls find_case, recall_memories, reflect_on_memories, and case_timeline. These are details of the author’s implementation, not independently verified product capabilities or evidence of legal reliability. Read the author’s account on DEV Community.
The useful design idea is a division of labor: put each kind of lookup information where it can do its job. Retrieval filters, source details, and exact identity resolution are related, but they are not interchangeable.
Which information belongs in tags, metadata, or a registry?
| Store | Job in the reported design | Use it for |
|---|---|---|
| Hindsight tags | Filter and scope recall | Dimensions that should narrow a search, such as case, judge, counsel, client, record type, or author. Example tag forms reported by the author include case:C5, judge:J1, counsel:<id>, client:<id>, type:<doc_type>, and author:<name-or-id>. |
| Hindsight metadata | Carry source-tracking and display details with a memory | Fields such as source_file, upload_id, doc_type, case_id, hearing_date, and author, which application code can use to show details or link to a record. |
| Separate SQLite registry | Resolve identifiers consistently | Exact case numbers and aliases, along with identities for cases, judges, counsel, and clients that must be matched deterministically. |
This split matches Hindsight’s documented distinction: tags can be filtered, while metadata accompanies memories for source tracking and is not itself filterable. The official best-practices page puts it plainly: “Metadata is not filterable — use tags for anything you’ll filter on”. Hindsight best practices and the retain API documentation describe the roles of tags and metadata.
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Use tags for retrieval dimensions
The author says the application tags hearing memories with case, judge, counsel, client, record type, and author identifiers. In this scheme, short opaque IDs stand in for names so variations in spelling do not change the filter value. The design is specific to Tareekh; the broader rule is to tag dimensions that callers need to use to narrow recall, not every descriptive fact about a record.
Use metadata to preserve provenance
Metadata supplies context after a memory has been retrieved: which file or upload it came from, what kind of record it is, and which case or hearing it concerns. Because metadata is not a Hindsight filter, it should not be treated as a substitute for a tag when a query must be scoped by that value. The application can still use returned metadata to render a source label or link.
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Use a registry for exact identity
Semantic recall is not the right sole authority for resolving an exact case number or alias. The author reports keeping cases, aliases, judges, counsel, and clients in SQLite, checking exact case numbers and aliases before attempting fuzzy matching, and using the registry as the source of tag IDs. The reported matching tiers and thresholds are implementation details; the account does not establish that they are tested or suitable for other systems.
How Hindsight tag matching changes recall
Hindsight’s Recall API offers five tags_match modes. Choosing among them controls whether requested tags act like an OR or an AND, whether untagged memories can still appear, and whether extra tags on a memory are acceptable. Consult the Recall API reference for the current semantics.
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| Mode | Requested-tag rule | Can untagged memories remain eligible? | Are extra tags allowed? |
|---|---|---|---|
any |
At least one requested tag must match | Yes | Yes |
any_strict |
At least one requested tag must match | No | Yes |
all |
Every requested tag must match | Yes | Yes |
all_strict |
Every requested tag must match | No | Yes |
exact |
The memory’s complete tag set must equal the requested set | No; an empty requested set selects the untagged/global scope | No |
For example, if a caller requests case:C5 and judge:J1, all_strict requires both tags and excludes untagged memories; any_strict requires either requested tag and also excludes untagged memories. In contrast, all allows untagged/global memories alongside memories with both requested tags. These differences matter if the bank contains general memories that are not assigned case tags.
Empty tags and global-only recall
With most modes, a missing or empty tags list means no tag filter. With exact, an empty list instead requests memories whose tag set is empty. The documentation says MCP callers should send tags: [] together with tags_match: "exact" to request that global-only scope; an empty list alone does not mean the same thing under every mode.
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Combining tag conditions
For more complex conditions, the Recall API documents recursive tag_groups expressions using and, or, and not. Groups are AND-ed at the top level. The public request models treat tag_groups and flat tags as mutually exclusive, so an application should construct one form or the other rather than assume both can be combined in a single request. The Recall API reference describes the supported request structure.
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How the reported citation flow separates facts from source identity
In the author’s account, tools return facts with numbered references. The model is asked to place those numbers in its answer; application code then maps valid markers to the returned facts and their metadata. The rendered citation can expose the source image and related details. This places a useful boundary between model output and application-held provenance: the model selects a reference, while code supplies source identity from records already returned by the tools.
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The author also says invalid citation numbers are dropped. This is a report about Tareekh’s implementation, not a guarantee from Hindsight that citations are correct, complete, or legally sufficient. A source link is only as trustworthy as the underlying record, the retrieval flow, and the code that maps the marker.
Keeping records distinct from conversational memory
The account says Tareekh labels chat-derived memories in their content, tags, and metadata, then ranks them after records and notes. If chat memory conflicts with record material, the reported application gives priority to the records. That is an application-level evidence hierarchy, not a Hindsight default. Making the distinction visible helps readers understand whether an answer rests on an uploaded record or a prior conversation.
Tag design also affects consolidation
Tags influence more than query-time filtering. The author reports that complete tag sets affected how observations were grouped in this project. Hindsight’s retain documentation also discusses tag behavior and consolidation, so a tag scheme should be considered at write time as well as at recall time. Check the current retain documentation before carrying over a scheme: grouping and filter semantics are part of the system’s behavior, not merely labels added for display.
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