Add in-mem vector search (#2547)

This commit is contained in:
William FH
2024-11-27 14:53:24 -08:00
committed by GitHub
parent dfaff2511b
commit 62a36befd5
13 changed files with 2047 additions and 191 deletions
@@ -378,15 +378,19 @@ class PostgresSaver(BasePostgresSaver):
# a connection not in pipeline mode can only be used by one
# thread/coroutine at a time, so we acquire a lock
if self.supports_pipeline:
with self.lock, conn.pipeline(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
with (
self.lock,
conn.pipeline(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
# Use connection's transaction context manager when pipeline mode not supported
with self.lock, conn.transaction(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
with (
self.lock,
conn.transaction(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
@@ -338,20 +338,25 @@ class AsyncPostgresSaver(BasePostgresSaver):
# a connection not in pipeline mode can only be used by one
# thread/coroutine at a time, so we acquire a lock
if self.supports_pipeline:
async with self.lock, conn.pipeline(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
async with (
self.lock,
conn.pipeline(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
# Use connection's transaction context manager when pipeline mode not supported
async with self.lock, conn.transaction(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
async with (
self.lock,
conn.transaction(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
async with self.lock, conn.cursor(
binary=True, row_factory=dict_row
) as cur:
async with (
self.lock,
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
def list(
@@ -28,6 +28,7 @@ from langgraph.store.postgres.base import (
_decode_ns_bytes,
_group_ops,
_row_to_item,
_row_to_search_item,
)
logger = logging.getLogger(__name__)
@@ -146,7 +147,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
await cur.execute(query, params)
rows = cast(list[Row], await cur.fetchall())
items = [
_row_to_item(
_row_to_search_item(
_decode_ns_bytes(row["prefix"]), row, loader=self._deserializer
)
for row in rows
@@ -195,9 +196,11 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
async with self.lock, conn.pipeline(), conn.cursor(binary=True) as cur:
yield cur
else:
async with self.lock, conn.transaction(), conn.cursor(
binary=True
) as cur:
async with (
self.lock,
conn.transaction(),
conn.cursor(binary=True) as cur,
):
yield cur
else:
async with conn.cursor(binary=True) as cur:
@@ -36,6 +36,7 @@ from langgraph.store.base import (
Op,
PutOp,
Result,
SearchItem,
SearchOp,
)
@@ -344,14 +345,18 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
# a connection not in pipeline mode can only be used by one
# thread/coroutine at a time, so we acquire a lock
if self.supports_pipeline:
with self.lock, conn.pipeline(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
with (
self.lock,
conn.pipeline(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
with self.lock, conn.transaction(), conn.cursor(
binary=True, row_factory=dict_row
) as cur:
with (
self.lock,
conn.transaction(),
conn.cursor(binary=True, row_factory=dict_row) as cur,
):
yield cur
else:
with conn.cursor(binary=True, row_factory=dict_row) as cur:
@@ -430,7 +435,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
cur.execute(query, params)
rows = cast(list[Row], cur.fetchall())
results[idx] = [
_row_to_item(
_row_to_search_item(
_decode_ns_bytes(row["prefix"]), row, loader=self._deserializer
)
for row in rows
@@ -517,6 +522,32 @@ def _row_to_item(
)
def _row_to_search_item(
namespace: tuple[str, ...],
row: Row,
*,
loader: Optional[Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]] = None,
) -> SearchItem:
"""Convert a row from the database into an Item."""
loader = loader or _json_loads
val = row["value"]
score = row.get("score")
if score is not None:
try:
score = float(score) # type: ignore[arg-type]
except ValueError:
logger.warning("Invalid score: %s", score)
score = None
return SearchItem(
value=val if isinstance(val, dict) else loader(val),
key=row["key"],
namespace=namespace,
created_at=row["created_at"],
updated_at=row["updated_at"],
score=score,
)
def _group_ops(ops: Iterable[Op]) -> tuple[dict[type, list[tuple[int, Op]]], int]:
grouped_ops: dict[type, list[tuple[int, Op]]] = defaultdict(list)
tot = 0