fix: improve memory footprint of recall

hindsight-api/hindsight_api/engine/search/graph_retrieval.py

@@ -162,7 +162,7 @@ async def _retrieve_with_conn(
         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 @@ async def _retrieve_with_conn(
                 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

hindsight-api/hindsight_api/engine/search/link_expansion_retrieval.py

@@ -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 @@ async def retrieve(
                     -- 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 @@ async def retrieve(
                     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 @@ async def retrieve(
                 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 @@ async def retrieve(
                 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 @@ async def retrieve(
                 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 @@ async def retrieve(
                 )
                 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

hindsight-api/hindsight_api/engine/search/mpfp_retrieval.py

@@ -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

hindsight-api/hindsight_api/engine/search/retrieval.py

@@ -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

hindsight-api/hindsight_api/engine/search/types.py

@@ -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 @@ def from_db_row(cls, row: dict[str, Any]) -> "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 @@ def to_dict(self) -> dict[str, Any]:
             "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,