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muliple dimemnsions for confidence values

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Daniel O'Connell 2025-06-03 12:18:20 +02:00
parent a40e0b50fa
commit e5da3714de
13 changed files with 499 additions and 362 deletions
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79
db/migrations/versions/20250603_115642_add_confidences.py Normal file
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@ -0,0 +1,79 @@
"""Add confidences
Revision ID: 152f8b4b52e8
Revises: ba301527a2eb
Create Date: 2025-06-03 11:56:42.302327
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "152f8b4b52e8"
down_revision: Union[str, None] = "ba301527a2eb"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.create_table(
"confidence_score",
sa.Column("id", sa.BigInteger(), nullable=False),
sa.Column("source_item_id", sa.BigInteger(), nullable=False),
sa.Column("confidence_type", sa.Text(), nullable=False),
sa.Column("score", sa.Numeric(precision=3, scale=2), nullable=False),
sa.CheckConstraint("score >= 0.0 AND score <= 1.0", name="score_range_check"),
sa.ForeignKeyConstraint(
["source_item_id"], ["source_item.id"], ondelete="CASCADE"
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint(
"source_item_id", "confidence_type", name="unique_source_confidence_type"
),
)
op.create_index("confidence_score_idx", "confidence_score", ["score"], unique=False)
op.create_index(
"confidence_source_idx", "confidence_score", ["source_item_id"], unique=False
)
op.create_index(
"confidence_type_idx", "confidence_score", ["confidence_type"], unique=False
)
op.drop_index("agent_obs_confidence_idx", table_name="agent_observation")
op.drop_column("agent_observation", "confidence")
op.drop_index("note_confidence_idx", table_name="notes")
op.drop_column("notes", "confidence")
def downgrade() -> None:
op.add_column(
"notes",
sa.Column(
"confidence",
sa.NUMERIC(precision=3, scale=2),
server_default=sa.text("0.5"),
autoincrement=False,
nullable=False,
),
)
op.create_index("note_confidence_idx", "notes", ["confidence"], unique=False)
op.add_column(
"agent_observation",
sa.Column(
"confidence",
sa.NUMERIC(precision=3, scale=2),
server_default=sa.text("0.5"),
autoincrement=False,
nullable=False,
),
)
op.create_index(
"agent_obs_confidence_idx", "agent_observation", ["confidence"], unique=False
)
op.drop_index("confidence_type_idx", table_name="confidence_score")
op.drop_index("confidence_source_idx", table_name="confidence_score")
op.drop_index("confidence_score_idx", table_name="confidence_score")
op.drop_table("confidence_score")

6
src/memory/api/MCP/tools.py
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@ -654,11 +654,9 @@ async def create_note(
"""
if filename:
path = pathlib.Path(filename)
if path.is_absolute():
path = path.relative_to(settings.NOTES_STORAGE_DIR)
else:
if not path.is_absolute():
path = pathlib.Path(settings.NOTES_STORAGE_DIR) / path
filename = path.as_posix()
filename = path.relative_to(settings.NOTES_STORAGE_DIR).as_posix()
try:
task = celery_app.send_task(

2
src/memory/common/db/models/__init__.py
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@ -2,6 +2,7 @@ from memory.common.db.models.base import Base
from memory.common.db.models.source_item import (
Chunk,
SourceItem,
ConfidenceScore,
clean_filename,
)
from memory.common.db.models.source_items import (
@ -37,6 +38,7 @@ __all__ = [
"Chunk",
"clean_filename",
"SourceItem",
"ConfidenceScore",
"MailMessage",
"EmailAttachment",
"AgentObservation",

71
src/memory/common/db/models/source_item.py
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@ -22,9 +22,11 @@ from sqlalchemy import (
Text,
event,
func,
UniqueConstraint,
)
from sqlalchemy.dialects.postgresql import BYTEA
from sqlalchemy.orm import Session, relationship
from sqlalchemy.types import Numeric
from memory.common import settings
import memory.common.extract as extract
@ -191,6 +193,41 @@ class Chunk(Base):
return items
class ConfidenceScore(Base):
"""
Stores structured confidence scores for source items.
Provides detailed confidence dimensions instead of a single score.
"""
__tablename__ = "confidence_score"
id = Column(BigInteger, primary_key=True)
source_item_id = Column(
BigInteger, ForeignKey("source_item.id", ondelete="CASCADE"), nullable=False
)
confidence_type = Column(
Text, nullable=False
) # e.g., "observation_accuracy", "interpretation", "predictive_value"
score = Column(Numeric(3, 2), nullable=False) # 0.0-1.0
# Relationship back to source item
source_item = relationship("SourceItem", back_populates="confidence_scores")
__table_args__ = (
Index("confidence_source_idx", "source_item_id"),
Index("confidence_type_idx", "confidence_type"),
Index("confidence_score_idx", "score"),
CheckConstraint("score >= 0.0 AND score <= 1.0", name="score_range_check"),
# Ensure each source_item can only have one score per confidence_type
UniqueConstraint(
"source_item_id", "confidence_type", name="unique_source_confidence_type"
),
)
def __repr__(self) -> str:
return f"<ConfidenceScore(type={self.confidence_type}, score={self.score})>"
class SourceItem(Base):
"""Base class for all content in the system using SQLAlchemy's joined table inheritance."""
@ -216,6 +253,11 @@ class SourceItem(Base):
embed_status = Column(Text, nullable=False, server_default="RAW")
chunks = relationship("Chunk", backref="source", cascade="all, delete-orphan")
# Confidence scores relationship
confidence_scores = relationship(
"ConfidenceScore", back_populates="source_item", cascade="all, delete-orphan"
)
# Discriminator column for SQLAlchemy inheritance
type = Column(String(50))
@ -235,6 +277,35 @@ class SourceItem(Base):
"""Get vector IDs from associated chunks."""
return [chunk.id for chunk in self.chunks]
@property
def confidence_dict(self) -> dict[str, float]:
return {
score.confidence_type: float(score.score)
for score in self.confidence_scores
}
def update_confidences(self, confidence_updates: dict[str, float]) -> None:
"""
Update confidence scores for this source item.
Merges new scores with existing ones, overwriting duplicates.
Args:
confidence_updates: Dict mapping confidence_type to score (0.0-1.0)
"""
if not confidence_updates:
return
current = {s.confidence_type: s for s in self.confidence_scores}
for confidence_type, score in confidence_updates.items():
if current_score := current.get(confidence_type):
current_score.score = score
else:
new_score = ConfidenceScore(
source_item_id=self.id, confidence_type=confidence_type, score=score
)
self.confidence_scores.append(new_score)
def _chunk_contents(self) -> Sequence[extract.DataChunk]:
content = cast(str | None, self.content)
if content:

13
src/memory/common/db/models/source_items.py
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@ -505,7 +505,6 @@ class Note(SourceItem):
)
note_type = Column(Text, nullable=True)
subject = Column(Text, nullable=True)
confidence = Column(Numeric(3, 2), nullable=False, default=0.5) # 0.0-1.0
__mapper_args__ = {
"polymorphic_identity": "note",
@ -514,7 +513,6 @@ class Note(SourceItem):
__table_args__ = (
Index("note_type_idx", "note_type"),
Index("note_subject_idx", "subject"),
Index("note_confidence_idx", "confidence"),
)
def as_payload(self) -> dict:
@ -522,7 +520,7 @@ class Note(SourceItem):
**super().as_payload(),
"note_type": self.note_type,
"subject": self.subject,
"confidence": float(cast(Any, self.confidence)),
"confidence": self.confidence_dict,
}
@property
@ -531,7 +529,7 @@ class Note(SourceItem):
"subject": self.subject,
"content": self.content,
"note_type": self.note_type,
"confidence": self.confidence,
"confidence": self.confidence_dict,
"tags": self.tags,
}
@ -573,7 +571,6 @@ class AgentObservation(SourceItem):
Text, nullable=False
) # belief, preference, pattern, contradiction, behavior
subject = Column(Text, nullable=False) # What/who the observation is about
confidence = Column(Numeric(3, 2), nullable=False, default=0.8) # 0.0-1.0
evidence = Column(JSONB) # Supporting context, quotes, etc.
agent_model = Column(Text, nullable=False) # Which AI model made this observation
@ -599,7 +596,6 @@ class AgentObservation(SourceItem):
Index("agent_obs_session_idx", "session_id"),
Index("agent_obs_type_idx", "observation_type"),
Index("agent_obs_subject_idx", "subject"),
Index("agent_obs_confidence_idx", "confidence"),
Index("agent_obs_model_idx", "agent_model"),
)
@ -613,7 +609,7 @@ class AgentObservation(SourceItem):
**super().as_payload(),
"observation_type": self.observation_type,
"subject": self.subject,
"confidence": float(cast(Any, self.confidence)),
"confidence": self.confidence_dict,
"evidence": self.evidence,
"agent_model": self.agent_model,
}
@ -633,7 +629,7 @@ class AgentObservation(SourceItem):
"content": self.content,
"observation_type": self.observation_type,
"evidence": self.evidence,
"confidence": self.confidence,
"confidence": self.confidence_dict,
"agent_model": self.agent_model,
"tags": self.tags,
}
@ -664,7 +660,6 @@ class AgentObservation(SourceItem):
temporal_text = observation.generate_temporal_text(
cast(str, self.subject),
cast(str, self.content),
cast(float, self.confidence),
cast(datetime, self.inserted_at),
)
if temporal_text:

3
src/memory/common/formatters/observation.py
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@ -31,7 +31,6 @@ def generate_semantic_text(
def generate_temporal_text(
subject: str,
content: str,
confidence: float,
created_at: datetime,
) -> str:
"""Generate text with temporal context for time-pattern search."""
@ -55,8 +54,6 @@ def generate_temporal_text(
f"Subject: {subject}",
f"Observation: {content}",
]
if confidence is not None:
parts.append(f"Confidence: {confidence}")
return " | ".join(parts)

9
src/memory/workers/tasks/notes.py
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@ -1,7 +1,6 @@
import logging
import pathlib
from memory.common import settings
from memory.common.db.connection import make_session
from memory.common.db.models import Note
from memory.common.celery_app import app, SYNC_NOTE, SYNC_NOTES
@ -23,7 +22,7 @@ def sync_note(
content: str,
filename: str | None = None,
note_type: str | None = None,
confidence: float | None = None,
confidences: dict[str, float] = {},
tags: list[str] = [],
):
logger.info(f"Syncing note {subject}")
@ -32,6 +31,8 @@ def sync_note(
if filename:
filename = filename.lstrip("/")
if not filename.endswith(".md"):
filename = f"{filename}.md"
with make_session() as session:
existing_note = check_content_exists(session, Note, sha256=sha256)
@ -45,7 +46,6 @@ def sync_note(
note = Note(
modality="note",
mime_type="text/markdown",
confidence=confidence or 0.5,
)
else:
logger.info("Editing preexisting note")
@ -58,11 +58,10 @@ def sync_note(
if note_type:
note.note_type = note_type # type: ignore
if confidence:
note.confidence = confidence # type: ignore
if tags:
note.tags = tags # type: ignore
note.update_confidences(confidences)
note.save_to_file()
return process_content_item(note, session)

4
src/memory/workers/tasks/observations.py
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@ -21,7 +21,7 @@ def sync_observation(
content: str,
observation_type: str,
evidence: dict | None = None,
confidence: float = 0.5,
confidences: dict[str, float] = {},
session_id: str | None = None,
agent_model: str = "unknown",
tags: list[str] = [],
@ -33,7 +33,6 @@ def sync_observation(
content=content,
subject=subject,
observation_type=observation_type,
confidence=confidence,
evidence=evidence,
tags=tags or [],
session_id=session_id,
@ -43,6 +42,7 @@ def sync_observation(
sha256=sha256,
modality="observation",
)
observation.update_confidences(confidences)
with make_session() as session:
existing_observation = check_content_exists(

600
tests/integration/test_real_queries.py
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6
tests/memory/common/db/models/test_source_item_embeddings.py
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@ -583,7 +583,6 @@ def test_agent_observation_embeddings(mock_voyage_client):
tags=["bla"],
observation_type="belief",
subject="humans",
confidence=0.8,
evidence={
"quote": "All humans are mortal.",
"source": "https://en.wikipedia.org/wiki/Human",
@ -591,6 +590,7 @@ def test_agent_observation_embeddings(mock_voyage_client):
agent_model="gpt-4o",
inserted_at=datetime(2025, 1, 1, 12, 0, 0),
)
item.update_confidences({"observation_accuracy": 0.8})
metadata = item.as_payload()
metadata["tags"] = {"bla"}
expected = [
@ -600,7 +600,7 @@ def test_agent_observation_embeddings(mock_voyage_client):
metadata | {"embedding_type": "semantic"},
),
(
"Time: 12:00 on Wednesday (afternoon) | Subject: humans | Observation: The user thinks that all men must die. | Confidence: 0.8",
"Time: 12:00 on Wednesday (afternoon) | Subject: humans | Observation: The user thinks that all men must die.",
[],
metadata | {"embedding_type": "temporal"},
),
@ -625,7 +625,7 @@ def test_agent_observation_embeddings(mock_voyage_client):
assert mock_voyage_client.embed.call_args == call(
[
"Subject: humans | Type: belief | Observation: The user thinks that all men must die. | Quote: All humans are mortal.",
"Time: 12:00 on Wednesday (afternoon) | Subject: humans | Observation: The user thinks that all men must die. | Confidence: 0.8",
"Time: 12:00 on Wednesday (afternoon) | Subject: humans | Observation: The user thinks that all men must die.",
"The user thinks that all men must die.",
"All humans are mortal.",
],

27
tests/memory/common/db/models/test_source_items.py
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@ -499,7 +499,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -513,7 +513,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -531,7 +531,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -546,7 +546,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -565,7 +565,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -580,7 +580,7 @@ def test_blog_post_chunk_contents_with_image_long_content(tmp_path, default_chun
"size": None,
"observation_type": "preference",
"subject": "programming preferences",
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"evidence": {
"quote": "I really like Python",
"context": "discussion about languages",
@ -603,7 +603,6 @@ def test_agent_observation_data_chunks(
content="User prefers Python over JavaScript",
subject="programming preferences",
observation_type="preference",
confidence=0.9,
evidence={
"quote": "I really like Python",
"context": "discussion about languages",
@ -612,6 +611,7 @@ def test_agent_observation_data_chunks(
session_id=session_id,
tags=observation_tags,
)
observation.update_confidences({"observation_accuracy": 0.9})
# Set inserted_at using object.__setattr__ to bypass SQLAlchemy restrictions
object.__setattr__(observation, "inserted_at", datetime(2023, 1, 1, 12, 0, 0))
@ -634,7 +634,7 @@ def test_agent_observation_data_chunks(
assert cast(str, semantic_chunk.collection_name) == "semantic"
temporal_chunk = result[1]
expected_temporal_text = "Time: 12:00 on Sunday (afternoon) | Subject: programming preferences | Observation: User prefers Python over JavaScript | Confidence: 0.9"
expected_temporal_text = "Time: 12:00 on Sunday (afternoon) | Subject: programming preferences | Observation: User prefers Python over JavaScript"
assert temporal_chunk.data == [expected_temporal_text]
# Add session_id to expected metadata and remove tags if empty
@ -654,11 +654,11 @@ def test_agent_observation_data_chunks_with_none_values():
content="Content",
subject="subject",
observation_type="belief",
confidence=0.7,
evidence=None,
agent_model="gpt-4",
session_id=None,
)
observation.update_confidences({"observation_accuracy": 0.7})
object.__setattr__(observation, "inserted_at", datetime(2023, 2, 15, 9, 30, 0))
result = observation.data_chunks()
@ -671,7 +671,7 @@ def test_agent_observation_data_chunks_with_none_values():
assert [i.data for i in result] == [
["Subject: subject | Type: belief | Observation: Content"],
[
"Time: 09:30 on Wednesday (morning) | Subject: subject | Observation: Content | Confidence: 0.7"
"Time: 09:30 on Wednesday (morning) | Subject: subject | Observation: Content"
],
["Content"],
]
@ -684,11 +684,11 @@ def test_agent_observation_data_chunks_merge_metadata_behavior():
content="test",
subject="test",
observation_type="test",
confidence=0.8,
evidence={},
agent_model="test",
tags=["base_tag"], # Set base tags so they appear in both chunks
)
observation.update_confidences({"observation_accuracy": 0.9})
object.__setattr__(observation, "inserted_at", datetime.now())
# Test that metadata merging preserves original values and adds new ones
@ -723,11 +723,10 @@ def test_note_data_chunks(subject, content, expected):
content=content,
subject=subject,
note_type="quicky",
confidence=0.9,
size=123,
tags=["bla"],
)
note.update_confidences({"observation_accuracy": 0.9})
chunks = note.data_chunks()
assert [chunk.content for chunk in chunks] == expected
for chunk in chunks:
@ -736,7 +735,7 @@ def test_note_data_chunks(subject, content, expected):
if cast(str, chunk.content) == "test summary":
tags |= {"tag1", "tag2"}
assert chunk.item_metadata == {
"confidence": 0.9,
"confidence": {"observation_accuracy": 0.9},
"note_type": "quicky",
"size": 123,
"source_id": None,

24
tests/memory/common/formatters/test_observation.py
View File

@ -123,11 +123,10 @@ def test_generate_temporal_text_time_periods(hour: int, expected_period: str):
result = generate_temporal_text(
subject="test_subject",
content="test_content",
confidence=0.8,
created_at=test_date,
)
time_str = test_date.strftime("%H:%M")
expected = f"Time: {time_str} on Monday ({expected_period}) | Subject: test_subject | Observation: test_content | Confidence: 0.8"
expected = f"Time: {time_str} on Monday ({expected_period}) | Subject: test_subject | Observation: test_content"
assert result == expected
@ -146,7 +145,7 @@ def test_generate_temporal_text_time_periods(hour: int, expected_period: str):
def test_generate_temporal_text_days_of_week(weekday: int, day_name: str):
test_date = datetime(2024, 1, 15 + weekday, 10, 30)
result = generate_temporal_text(
subject="subject", content="content", confidence=0.5, created_at=test_date
subject="subject", content="content", created_at=test_date
)
assert f"on {day_name}" in result
@ -157,10 +156,8 @@ def test_generate_temporal_text_confidence_values(confidence: float):
result = generate_temporal_text(
subject="subject",
content="content",
confidence=confidence,
created_at=test_date,
)
assert f"Confidence: {confidence}" in result
@pytest.mark.parametrize(
@ -180,7 +177,7 @@ def test_generate_temporal_text_boundary_cases(
test_date: datetime, expected_period: str
):
result = generate_temporal_text(
subject="subject", content="content", confidence=0.8, created_at=test_date
subject="subject", content="content", created_at=test_date
)
assert f"({expected_period})" in result
@ -190,22 +187,16 @@ def test_generate_temporal_text_complete_format():
result = generate_temporal_text(
subject="Important observation",
content="User showed strong preference for X",
confidence=0.95,
created_at=test_date,
)
expected = "Time: 14:45 on Friday (afternoon) | Subject: Important observation | Observation: User showed strong preference for X | Confidence: 0.95"
expected = "Time: 14:45 on Friday (afternoon) | Subject: Important observation | Observation: User showed strong preference for X"
assert result == expected
def test_generate_temporal_text_empty_strings():
test_date = datetime(2024, 1, 15, 10, 30)
result = generate_temporal_text(
subject="", content="", confidence=0.0, created_at=test_date
)
assert (
result
== "Time: 10:30 on Monday (morning) | Subject: | Observation: | Confidence: 0.0"
)
result = generate_temporal_text(subject="", content="", created_at=test_date)
assert result == "Time: 10:30 on Monday (morning) | Subject: | Observation:"
def test_generate_temporal_text_special_characters():
@ -213,8 +204,7 @@ def test_generate_temporal_text_special_characters():
result = generate_temporal_text(
subject="Subject with | pipe",
content="Content with | pipe and @#$ symbols",
confidence=0.75,
created_at=test_date,
)
expected = "Time: 15:20 on Monday (afternoon) | Subject: Subject with | pipe | Observation: Content with | pipe and @#$ symbols | Confidence: 0.75"
expected = "Time: 15:20 on Monday (afternoon) | Subject: Subject with | pipe | Observation: Content with | pipe and @#$ symbols"
assert result == expected

17
tests/memory/workers/tasks/test_notes_tasks.py
View File

@ -16,7 +16,7 @@ def mock_note_data():
"content": "This is test note content with enough text to be processed and embedded.",
"filename": "test_note.md",
"note_type": "observation",
"confidence": 0.8,
"confidences": {"observation_accuracy": 0.8},
"tags": ["test", "note"],
}
@ -90,7 +90,7 @@ def test_sync_note_success(mock_note_data, db_session, qdrant):
assert note.modality == "note"
assert note.mime_type == "text/markdown"
assert note.note_type == "observation"
assert float(note.confidence) == 0.8 # Convert Decimal to float for comparison
assert note.confidence_dict == {"observation_accuracy": 0.8}
assert note.filename is not None
assert note.tags == ["test", "note"]
@ -114,7 +114,7 @@ def test_sync_note_minimal_data(mock_minimal_note, db_session, qdrant):
assert note.subject == "Minimal Note"
assert note.content == "Minimal content"
assert note.note_type is None
assert float(note.confidence) == 0.5 # Default value, convert Decimal to float
assert note.confidence_dict == {}
assert note.tags == [] # Default empty list
assert note.filename is not None and "Minimal Note.md" in note.filename
@ -205,6 +205,9 @@ def test_sync_note_edit(mock_note_data, db_session):
embed_status="RAW",
filename="test_note.md",
)
existing_note.update_confidences(
{"observation_accuracy": 0.2, "predictive_value": 0.3}
)
db_session.add(existing_note)
db_session.commit()
@ -225,6 +228,10 @@ def test_sync_note_edit(mock_note_data, db_session):
assert len(db_session.query(Note).all()) == 1
db_session.refresh(existing_note)
assert existing_note.content == "bla bla bla" # type: ignore
assert existing_note.confidence_dict == {
"observation_accuracy": 0.8,
"predictive_value": 0.3,
}
@pytest.mark.parametrize(
@ -242,14 +249,14 @@ def test_sync_note_parameters(note_type, confidence, tags, db_session, qdrant):
subject=f"Test Note {note_type}",
content="Test content for parameter testing",
note_type=note_type,
confidence=confidence,
confidences={"observation_accuracy": confidence},
tags=tags,
)
note = db_session.query(Note).filter_by(subject=f"Test Note {note_type}").first()
assert note is not None
assert note.note_type == note_type
assert float(note.confidence) == confidence # Convert Decimal to float
assert note.confidence_dict == {"observation_accuracy": confidence}
assert note.tags == tags
# Updated to match actual return format