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memory evolution phase 1

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This commit is contained in:
Roberto Musso
2026-04-16 15:46:12 +02:00
parent e668e3fd20
commit 2d8abb6311
8 changed files with 240 additions and 12 deletions
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4
app/billing/tier_manager.py
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@@ -25,6 +25,7 @@ FEATURES: dict[str, dict[str, Any]] = {
"providers": 1,
"batch_builder": False,
"sso": False,
"real_embeddings": False, # keyword fallback only
},
"pro": {
"agents": -1, # unlimited
@@ -33,6 +34,7 @@ FEATURES: dict[str, dict[str, Any]] = {
"providers": -1,
"batch_builder": False,
"sso": False,
"real_embeddings": True, # pgvector cosine search
},
"power": {
"agents": -1,
@@ -41,6 +43,7 @@ FEATURES: dict[str, dict[str, Any]] = {
"providers": -1,
"batch_builder": True,
"sso": False,
"real_embeddings": True,
},
"team": {
"agents": -1,
@@ -49,6 +52,7 @@ FEATURES: dict[str, dict[str, Any]] = {
"providers": -1,
"batch_builder": True,
"sso": True,
"real_embeddings": True,
},
}

2
app/config/settings.py
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@@ -71,7 +71,7 @@ class Settings(BaseSettings):
ENV: Literal["dev", "prod"] = "dev"
model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
settings = Settings()

34
app/core/embeddings.py Normal file
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@@ -0,0 +1,34 @@
"""OpenAI embedding helper for associative memory tier.
Single public function: ``embed_text(text) -> list[float] | None``.
Returns None on any failure — callers must implement a keyword fallback.
Never raises; all exceptions are logged as warnings.
"""
from __future__ import annotations
import logging
from openai import AsyncOpenAI
logger = logging.getLogger(__name__)
_MAX_INPUT_CHARS = 8000
_EMBEDDING_MODEL = "text-embedding-3-small"
async def embed_text(text: str) -> list[float] | None:
"""Call OpenAI text-embedding-3-small. Return None on failure (caller falls back to keyword)."""
try:
client = AsyncOpenAI()
truncated = text[:_MAX_INPUT_CHARS]
response = await client.embeddings.create(
input=truncated,
model=_EMBEDDING_MODEL,
)
result: list[float] = response.data[0].embedding
logger.debug("embeddings: embed_text dims=%d", len(result))
return result
except Exception as exc:
logger.warning("embeddings: embed_text failed: %s", exc)
return None

97
app/core/memory_middleware.py
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@@ -69,17 +69,19 @@ class MemoryMiddleware:
if fernet is None:
return {}
user_dbg = await self._get_user_debug(user_id)
user_tier: str = user_dbg.get("tier") or "free"
core = await self._load_core(user_id, fernet)
associative = await self._load_associative(user_id, message, fernet)
associative = await self._load_associative(user_id, message, fernet, user_tier=user_tier)
episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
proactive = await self._load_proactive(user_id, fernet)
user_dbg = await self._get_user_debug(user_id)
logger.info(
"memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d",
trace_id or "-",
user_id,
user_dbg.get("tier") or "-",
user_tier,
len(core),
len(associative),
len(episodic),
@@ -255,6 +257,50 @@ class MemoryMiddleware:
logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
return True
async def store_associative(
self,
user_id: str,
content: str,
entity_type: str | None = None,
entity_id: str | None = None,
) -> None:
"""Store associative memory; embed if user tier has real_embeddings."""
from app.billing.tier_manager import tier_manager # noqa: PLC0415
from app.core.embeddings import embed_text # noqa: PLC0415
fernet = await self._get_fernet(user_id)
if fernet is None:
return
encrypted = _encrypt(fernet, content)
user_dbg = await self._get_user_debug(user_id)
user_tier = user_dbg.get("tier") or "free"
embedding: list[float] | None = None
if tier_manager.check_feature(user_tier, "real_embeddings"):
embedding = await embed_text(content)
row = MemoryAssociative(
id=str(uuid.uuid4()),
user_id=user_id,
content_encrypted=encrypted,
embedding=embedding,
entity_type=entity_type,
entity_id=entity_id,
)
self._db.add(row)
try:
await self._db.commit()
logger.info(
"memory: store_associative user=%s embedded=%s",
user_id,
embedding is not None,
)
except Exception as exc:
logger.error("memory: store_associative failed user=%s: %s", user_id, exc)
await self._db.rollback()
async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
"""Insert a long-term archival memory entry."""
fernet = await self._get_fernet(user_id)
@@ -364,14 +410,49 @@ class MemoryMiddleware:
return out
async def _load_associative(
self, user_id: str, message: str, fernet: Fernet
self, user_id: str, message: str, fernet: Fernet, *, user_tier: str = "free"
) -> list[str]:
"""Load top-k associative memories.
Production: uses pgvector cosine similarity on the message embedding.
Current implementation: keyword-based fallback (no external embedding call)
so tests pass without a live OpenAI key.
Pro+: pgvector cosine similarity on the message embedding (real_embeddings feature).
Free / embedding failure: keyword-ordered fallback (most recent rows).
"""
from app.billing.tier_manager import tier_manager # noqa: PLC0415
from app.core.embeddings import embed_text # noqa: PLC0415
if tier_manager.check_feature(user_tier, "real_embeddings"):
vec = await embed_text(message)
if vec is not None:
try:
result = await self._db.execute(
select(MemoryAssociative)
.where(
MemoryAssociative.user_id == user_id,
MemoryAssociative.embedding.isnot(None),
)
.order_by(MemoryAssociative.embedding.cosine_distance(vec))
.limit(_ASSOCIATIVE_TOP_K)
)
rows = result.scalars().all()
out: list[str] = []
for row in rows:
plaintext = _safe_decrypt(fernet, row.content_encrypted)
if plaintext is not None:
out.append(plaintext)
logger.info(
"memory: _load_associative user=%s mode=vector hits=%d",
user_id,
len(out),
)
return out
except Exception as exc:
logger.warning(
"memory: vector search failed user=%s, falling back to keyword: %s",
user_id,
exc,
)
# Keyword fallback: most recent rows
result = await self._db.execute(
select(MemoryAssociative)
.where(MemoryAssociative.user_id == user_id)
@@ -379,7 +460,7 @@ class MemoryMiddleware:
.limit(_ASSOCIATIVE_TOP_K)
)
rows = result.scalars().all()
out: list[str] = []
out = []
for row in rows:
plaintext = _safe_decrypt(fernet, row.content_encrypted)
if plaintext is not None:

27
app/models.py
View File

@@ -21,6 +21,7 @@ from __future__ import annotations
import uuid
from datetime import datetime, timezone
from pgvector.sqlalchemy import Vector
from sqlalchemy import (
Boolean,
DateTime,
@@ -299,8 +300,8 @@ class MemoryAssociative(Base):
nullable=False, index=True,
)
content_encrypted: Mapped[str] = mapped_column(Text, nullable=False)
# JSON-encoded float list in SQLite tests; vector(1536) in Postgres via migration.
embedding: Mapped[list | None] = mapped_column(JSON, nullable=True)
# vector(1536) via pgvector; SQLite tests use NULL embeddings so no dialect issue.
embedding: Mapped[list | None] = mapped_column(Vector(1536), nullable=True)
entity_type: Mapped[str | None] = mapped_column(String(100), nullable=True)
entity_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
updated_at: Mapped[datetime] = mapped_column(
@@ -348,3 +349,25 @@ class MemoryProactive(Base):
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), nullable=False, server_default=func.now()
)
class Plugin(Base):
"""Plugin marketplace catalog entry."""
__tablename__ = "plugins"
id: Mapped[str] = mapped_column(String(255), primary_key=True)
name: Mapped[str] = mapped_column(String(255), nullable=False)
description: Mapped[str] = mapped_column(Text, nullable=False)
version: Mapped[str] = mapped_column(String(50), nullable=False)
author_name: Mapped[str] = mapped_column(String(255), nullable=False)
category: Mapped[str] = mapped_column(String(100), nullable=False)
price_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
permissions: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
status: Mapped[str] = mapped_column(String(50), nullable=False, default="pending")
s3_package_key: Mapped[str | None] = mapped_column(String(500), nullable=True)
install_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
avg_rating: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), nullable=False, server_default=func.now()
)