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fix: improve memory footprint of recall (#423)

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Nicolò Boschi 2026-02-23 11:30:16 +01:00 committed by GitHub
parent 8b1a46585d
commit 9f0c031df7
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5 changed files with 20 additions and 23 deletions
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4
hindsight-api/hindsight_api/engine/search/graph_retrieval.py
View file

@ -162,7 +162,7 @@ class BFSGraphRetriever(GraphRetriever):
entry_points = await conn.fetch(
f"""
SELECT id, text, context, event_date, occurred_start, occurred_end,
mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
mentioned_at, fact_type, document_id, chunk_id, tags,
1 - (embedding <=> $1::vector) AS similarity
FROM {fq_table("memory_units")}
WHERE bank_id = $2
@ -216,7 +216,7 @@ class BFSGraphRetriever(GraphRetriever):
neighbors = await conn.fetch(
f"""
SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
mu.mentioned_at, mu.embedding, mu.fact_type,
mu.mentioned_at, mu.fact_type,
mu.document_id, mu.chunk_id, mu.tags,
ml.weight, ml.link_type, ml.from_unit_id
FROM {fq_table("memory_links")} ml

16
hindsight-api/hindsight_api/engine/search/link_expansion_retrieval.py
View file

@ -45,7 +45,7 @@ async def _find_semantic_seeds(
rows = await conn.fetch(
f"""
SELECT id, text, context, event_date, occurred_start, occurred_end,
mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
mentioned_at, fact_type, document_id, chunk_id, tags,
1 - (embedding <=> $1::vector) AS similarity
FROM {fq_table("memory_units")}
WHERE bank_id = $2
@ -216,7 +216,7 @@ class LinkExpansionRetriever(GraphRetriever):
-- Only exclude the actual seed observations
SELECT
mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
mu.occurred_end, mu.mentioned_at, mu.embedding,
mu.occurred_end, mu.mentioned_at,
mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
COUNT(DISTINCT cs.source_id)::float AS score
FROM all_connected_sources cs
@ -239,7 +239,7 @@ class LinkExpansionRetriever(GraphRetriever):
f"""
SELECT
mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
mu.occurred_end, mu.mentioned_at, mu.embedding,
mu.occurred_end, mu.mentioned_at,
mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
COUNT(*)::float AS score
FROM {fq_table("unit_entities")} seed_ue
@ -264,7 +264,7 @@ class LinkExpansionRetriever(GraphRetriever):
f"""
SELECT DISTINCT ON (mu.id)
mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
mu.occurred_end, mu.mentioned_at, mu.embedding,
mu.occurred_end, mu.mentioned_at,
mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
ml.weight + 1.0 AS score
FROM {fq_table("memory_links")} ml
@ -291,7 +291,7 @@ class LinkExpansionRetriever(GraphRetriever):
WITH outgoing AS (
-- Links FROM seeds TO other facts
SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
mu.occurred_end, mu.mentioned_at, mu.embedding,
mu.occurred_end, mu.mentioned_at,
mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
ml.weight
FROM {fq_table("memory_links")} ml
@ -305,7 +305,7 @@ class LinkExpansionRetriever(GraphRetriever):
incoming AS (
-- Links FROM other facts TO seeds (reverse direction)
SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
mu.occurred_end, mu.mentioned_at, mu.embedding,
mu.occurred_end, mu.mentioned_at,
mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
ml.weight
FROM {fq_table("memory_links")} ml
@ -323,12 +323,12 @@ class LinkExpansionRetriever(GraphRetriever):
)
SELECT DISTINCT ON (id)
id, text, context, event_date, occurred_start,
occurred_end, mentioned_at, embedding,
occurred_end, mentioned_at,
fact_type, document_id, chunk_id, tags,
(MAX(weight) * 0.5) AS score
FROM combined
GROUP BY id, text, context, event_date, occurred_start,
occurred_end, mentioned_at, embedding,
occurred_end, mentioned_at,
fact_type, document_id, chunk_id, tags
ORDER BY id, score DESC
LIMIT $4

2
hindsight-api/hindsight_api/engine/search/mpfp_retrieval.py
View file

@ -449,7 +449,7 @@ async def fetch_memory_units_by_ids(
rows = await conn.fetch(
f"""
SELECT id, text, context, event_date, occurred_start, occurred_end,
mentioned_at, embedding, fact_type, document_id, chunk_id, tags
mentioned_at, fact_type, document_id, chunk_id, tags
FROM {fq_table("memory_units")}
WHERE id = ANY($1::uuid[])
AND fact_type = $2

18
hindsight-api/hindsight_api/engine/search/retrieval.py
View file

@ -127,7 +127,7 @@ async def retrieve_semantic_bm25_combined(
results = await conn.fetch(
f"""
WITH semantic_ranked AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
1 - (embedding <=> $1::vector) AS similarity,
NULL::float AS bm25_score,
'semantic' AS source,
@ -139,7 +139,7 @@ async def retrieve_semantic_bm25_combined(
AND (1 - (embedding <=> $1::vector)) >= 0.3
{tags_clause}
)
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
similarity, bm25_score, source
FROM semantic_ranked
WHERE rn <= $4
@ -194,7 +194,7 @@ async def retrieve_semantic_bm25_combined(
# Single query template with backend-specific parts injected
query = f"""
WITH semantic_ranked AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
1 - (embedding <=> $1::vector) AS similarity,
NULL::float AS bm25_score,
'semantic' AS source,
@ -207,7 +207,7 @@ async def retrieve_semantic_bm25_combined(
{tags_clause}
),
bm25_ranked AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
NULL::float AS similarity,
{bm25_score_expr} AS bm25_score,
'bm25' AS source,
@ -219,12 +219,12 @@ async def retrieve_semantic_bm25_combined(
{tags_clause}
),
semantic AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
similarity, bm25_score, source
FROM semantic_ranked WHERE rn <= $4
),
bm25 AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
similarity, bm25_score, source
FROM bm25_ranked WHERE rn <= $4
)
@ -301,7 +301,7 @@ async def retrieve_temporal_combined(
entry_points = await conn.fetch(
f"""
WITH ranked_entries AS (
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
1 - (embedding <=> $1::vector) AS similarity,
ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, embedding <=> $1::vector) AS rn
FROM {fq_table("memory_units")}
@ -321,7 +321,7 @@ async def retrieve_temporal_combined(
AND (1 - (embedding <=> $1::vector)) >= $6
{tags_clause}
)
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags, similarity
SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, similarity
FROM ranked_entries
WHERE rn <= 10
""",
@ -401,7 +401,7 @@ async def retrieve_temporal_combined(
neighbors = await conn.fetch(
f"""
SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
ml.weight, ml.link_type, ml.from_unit_id,
1 - (mu.embedding <=> $1::vector) AS similarity
FROM {fq_table("memory_links")} ml

3
hindsight-api/hindsight_api/engine/search/types.py
View file

@ -46,7 +46,6 @@ class RetrievalResult:
mentioned_at: datetime | None = None
document_id: str | None = None
chunk_id: str | None = None
embedding: list[float] | None = None
tags: list[str] | None = None # Visibility scope tags
# Retrieval-specific scores (only one will be set depending on retrieval method)
@ -70,7 +69,6 @@ class RetrievalResult:
mentioned_at=row.get("mentioned_at"),
document_id=row.get("document_id"),
chunk_id=row.get("chunk_id"),
embedding=row.get("embedding"),
tags=row.get("tags"),
similarity=row.get("similarity"),
bm25_score=row.get("bm25_score"),
@ -154,7 +152,6 @@ class ScoredResult:
"mentioned_at": self.retrieval.mentioned_at,
"document_id": self.retrieval.document_id,
"chunk_id": self.retrieval.chunk_id,
"embedding": self.retrieval.embedding,
"tags": self.retrieval.tags,
"semantic_similarity": self.retrieval.similarity,
"bm25_score": self.retrieval.bm25_score,