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Documentation Index

Fetch the complete documentation index at: https://docs.jeanmemory.com/llms.txt

Use this file to discover all available pages before exploring further.

Five primitives. Every API call works on one of them. See Models for the three-tier compatibility-model architecture that powers /match.

User

A row keyed by user_id, scoped to a domain. Structured fields plus dense vectors.

Schema

The typed shape of a user in a domain. Hard, soft, and derived fields.

Match

Pipeline that takes a user plus filters and returns ranked candidates.

Feedback

Outcome labels that train the ranker.

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Users

A user_id plus domain uniquely identifies a record. Two ways to write:
  • POST /users is an upsert. You send the full payload (raw context, pre-built fields, or a memory_ref to an external store) and we replace or create. Right for initial intake and one-shot uploads.
  • POST /context is an incremental append. You send one new piece of context (a chat message, a voice transcript chunk, a behavior event) and we merge it into the existing representation. Right for anything that streams.
Reading is GET /users/{user_id} and returns the full representation: structured fields, derived attributes, dense vectors per domain, and provenance.
The same physical person can have one row per domain. A user in your hiring product and a user in your dating product are different rows, with different schemas and different embeddings.

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Schemas

A schema declares what to extract from incoming context for a given domain. Three kinds of field:
Strict types. Used as WHERE clauses in the SQL filter stage. Examples: age, gender, location, salary_min_usd.
Free-text fields that feed the domain-adapted embedding head. Examples: values_text, motivation_text, thesis_text.
Computed at ingest from raw input. Examples: attachment_style from intake transcript, tenure_predicted from CV trajectory, photo_aesthetic_vector from vision model.
Schemas are co-designed with you at onboarding, then versioned. You can inspect the current schema for a domain with GET /schema?domain=....

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Matches

POST /match runs the pipeline: Each stage can be toggled or tuned via the stages object on the request: 
{
  "user_id": "u_alice",
  "domain": "dating",
  "stages": {
    "llm_judge": true,
    "cross_encoder": { "enabled": true, "top_k": 25 },
    "light_ml": { "min_score": 0.3 }
  },
  "explain": true
}
Pass true or false as a shortcut, or pass { "enabled": ..., "top_k": ..., "min_score": ..., "model": ... } for finer control. Set explain: true to get per-candidate stage_scores and human-readable reasons so you can attribute any decision back to the stage that made it.
StagePer-candidate costTypical survivors
SQL filtermicroseconds10,000
Light MLmilliseconds500
Dense embeddingsub-ms (ANN)50
Cross-encoder~10 ms10
LLM judgeseconds5

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Feedback

POST /feedback accepts outcome labels (accepted, rejected, converted, expired, or a tenant-registered label) plus an optional weight. Outcomes accumulate into training triplets. Past a per-domain threshold, the ranker auto-fine-tunes. Use POST /train with force=true to skip the threshold.
Latency of the loop. Fast signals (like a click) update the reranker continuously. Slow signals (like a 12-month hire tenure) update the embedding head on a longer cadence and weigh more heavily when they land.

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Where context comes from

You decide.

Raw text on intake

Pass context to POST /users. The platform extracts fields against your domain schema.

Structured fields

Pass fields directly. Skips extraction.

Memory reference

Pass memory_ref pointing at an external store you operate (Jean Memory, Mem0, your own vector DB).

Streaming context

POST /context for chat messages, transcripts, behavior events. The platform merges incrementally without you re-sending the full user.

Voice or chat intake

Optionally, we run a short intake (LLM-driven call or web widget) on your behalf and ingest the transcript.