Tuning Recall Quality

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”It’s returning duplicates of the same fact”

curl -X POST https://api.anonalabs.com/v1/retrieve \
  -H "Authorization: Bearer anona_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "space_id": "support",
    "query": "What plan are they on?"
  }'

results = client.retrieve(
    space_id="support",
    query="What plan are they on?",
    # prefer_observations defaults to True, so omit it
)

const results = await anona.retrieve({
  spaceId: "support",
  query: "What plan are they on?",
  // preferObservations defaults to true, so omit it
});

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“It’s returning too little” / “missing an obvious memory”

1. Is min_score filtering it out? This is the first thing to check, because almost any floor is a high one. In the default accurate mode relevance_score is close to two-valued: a near-verbatim match scores around 1.1, and everything else — including a correct answer phrased differently from the query — scores around 0.0001 or less. Very little lands in between, so any floor between those two values keeps near-verbatim matches only. A floor also drops results without shrinking limit, so it returns fewer than you asked for with nothing to say it did. Omit it.
2. Is a scope key excluding it? A scoped query (user_id/agent_id/session_id) returns only memories written under that exact scope, never unscoped ones, never another scope’s. If the memory was written without a scope and you’re querying with one, it won’t come back. See Scoping.
3. Is limit too low? Default is 10. Raise it (limit / top_k, up to 100) if the memory exists but is being cut off by rank.
4. Is it filtered by memory_type or tags? Both narrow the result set; drop them to confirm the memory shows up unfiltered, then add them back deliberately.
5. Is the memory in a different space from the one you searched? No amount of tuning finds a memory that is somewhere else — the space you named is the whole search. If you are not sure which space holds the answer, use route: "auto" and read the searched array in the response to see where it looked. A question whose answer genuinely spans two spaces — the charge in one, the account it landed on in another — needs more than one space searched.

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”It’s returning too much” / “the wrong things are ranking high”

1. Lower limit. This is the lever for precision vs. breadth, because results come back ranked and the ones you do not want are at the bottom. Reach for limit (or top_k) rather than min_score: as above, the score is close to two-valued, so min_score is a switch for “near-verbatim only” rather than a dial you can trim with. Set it only when that switch is what you want — and never to a round number like 0.1 or 0.5 picked to shave off the weakest results, which drops every paraphrase match instead. See relevance_score.
2. Add memory_type or tags filters if you know which kind of memory you actually want (e.g. memory_type: ["fact"] to skip synthesized notes and summaries).
3. Check you’re on mode: "accurate", not "fast". fast skips the neural rerank pass and ranks on retrieval fusion alone: faster, but lower precision. It trades exactly the quality you’re trying to improve.

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”The proxy is injecting too much / too little context into the model”

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”I want the model to see the memories exactly, not rely on the proxy’s injection”

{
  "space_id": "support",
  "query": "what plan are they on?",
  "format": "block",
  "context_max_tokens": 500
}

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”Recall is too slow”

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Quick reference

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Next steps