langgraph/libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

from __future__ import annotations

import threading
from collections import defaultdict
from collections.abc import Iterator, Mapping, Sequence
from contextlib import contextmanager
from typing import Any, cast

from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
    DeltaChannelHistory,
    get_checkpoint_id,
    get_serializable_checkpoint_metadata,
)
from langgraph.checkpoint.serde.base import SerializerProtocol
from langgraph.checkpoint.serde.types import _DeltaSnapshot
from psycopg import Capabilities, Connection, Cursor, Pipeline
from psycopg.rows import DictRow, dict_row
from psycopg.types.json import Jsonb
from psycopg_pool import ConnectionPool

from langgraph.checkpoint.postgres import _internal
from langgraph.checkpoint.postgres.base import (
    _DELTA_PAGE_SIZE,
    BasePostgresSaver,
    _build_delta_stage1_sql,
    _build_delta_stage2_sql,
    _DeltaStage2Row,
)
from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver

Conn = _internal.Conn  # For backward compatibility


class PostgresSaver(BasePostgresSaver):
    """Checkpointer that stores checkpoints in a Postgres database."""

    lock: threading.Lock

    def __init__(
        self,
        conn: _internal.Conn,
        pipe: Pipeline | None = None,
        serde: SerializerProtocol | None = None,
    ) -> None:
        super().__init__(serde=serde)
        if isinstance(conn, ConnectionPool) and pipe is not None:
            raise ValueError(
                "Pipeline should be used only with a single Connection, not ConnectionPool."
            )

        self.conn = conn
        self.pipe = pipe
        self.lock = threading.Lock()
        self.supports_pipeline = Capabilities().has_pipeline()

    @classmethod
    @contextmanager
    def from_conn_string(
        cls, conn_string: str, *, pipeline: bool = False
    ) -> Iterator[PostgresSaver]:
        """Create a new PostgresSaver instance from a connection string.

        Args:
            conn_string: The Postgres connection info string.
            pipeline: whether to use Pipeline

        Returns:
            PostgresSaver: A new PostgresSaver instance.
        """
        with Connection.connect(
            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
        ) as conn:
            if pipeline:
                with conn.pipeline() as pipe:
                    yield cls(conn, pipe)
            else:
                yield cls(conn)

    def setup(self) -> None:
        """Set up the checkpoint database asynchronously.

        This method creates the necessary tables in the Postgres database if they don't
        already exist and runs database migrations. It MUST be called directly by the user
        the first time checkpointer is used.
        """
        with self._cursor() as cur:
            cur.execute(self.MIGRATIONS[0])
            results = cur.execute(
                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
            )
            row = results.fetchone()
            if row is None:
                version = -1
            else:
                version = row["v"]
            for v, migration in zip(
                range(version + 1, len(self.MIGRATIONS)),
                self.MIGRATIONS[version + 1 :],
                strict=False,
            ):
                cur.execute(migration)
                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
        if self.pipe:
            self.pipe.sync()

    def list(
        self,
        config: RunnableConfig | None,
        *,
        filter: dict[str, Any] | None = None,
        before: RunnableConfig | None = None,
        limit: int | None = None,
    ) -> Iterator[CheckpointTuple]:
        """List checkpoints from the database.

        This method retrieves a list of checkpoint tuples from the Postgres database based
        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).

        Args:
            config: The config to use for listing the checkpoints.
            filter: Additional filtering criteria for metadata.
            before: If provided, only checkpoints before the specified checkpoint ID are returned.
            limit: The maximum number of checkpoints to return.

        Yields:
            An iterator of checkpoint tuples.

        Examples:
            >>> from langgraph.checkpoint.postgres import PostgresSaver
            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            ... # Run a graph, then list the checkpoints
            >>>     config = {"configurable": {"thread_id": "1"}}
            >>>     checkpoints = list(memory.list(config, limit=2))
            >>> print(checkpoints)
            [CheckpointTuple(...), CheckpointTuple(...)]

            >>> config = {"configurable": {"thread_id": "1"}}
            >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}}
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            ... # Run a graph, then list the checkpoints
            >>>     checkpoints = list(memory.list(config, before=before))
            >>> print(checkpoints)
            [CheckpointTuple(...), ...]
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
        params = list(args)
        if limit is not None:
            query += " LIMIT %s"
            params.append(int(limit))
        # if we change this to use .stream() we need to make sure to close the cursor
        with self._cursor() as cur:
            cur.execute(query, params)
            values = cur.fetchall()
            if not values:
                return
            # migrate pending sends if necessary
            if to_migrate := [
                v
                for v in values
                if v["checkpoint"]["v"] < 4 and v["parent_checkpoint_id"]
            ]:
                cur.execute(
                    self.SELECT_PENDING_SENDS_SQL,
                    (
                        values[0]["thread_id"],
                        [v["parent_checkpoint_id"] for v in to_migrate],
                    ),
                )
                grouped_by_parent = defaultdict(list)
                for value in to_migrate:
                    grouped_by_parent[value["parent_checkpoint_id"]].append(value)
                for sends in cur:
                    for value in grouped_by_parent[sends["checkpoint_id"]]:
                        if value["channel_values"] is None:
                            value["channel_values"] = []
                        self._migrate_pending_sends(
                            sends["sends"],
                            value["checkpoint"],
                            value["channel_values"],
                        )
            for value in values:
                yield self._load_checkpoint_tuple(value)

    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
        """Get a checkpoint tuple from the database.

        This method retrieves a checkpoint tuple from the Postgres database based on the
        provided config. If the config contains a `checkpoint_id` key, the checkpoint with
        the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
        for the given thread ID is retrieved.

        Args:
            config: The config to use for retrieving the checkpoint.

        Returns:
            The retrieved checkpoint tuple, or None if no matching checkpoint was found.

        Examples:

            Basic:
            >>> config = {"configurable": {"thread_id": "1"}}
            >>> checkpoint_tuple = memory.get_tuple(config)
            >>> print(checkpoint_tuple)
            CheckpointTuple(...)

            With timestamp:

            >>> config = {
            ...    "configurable": {
            ...        "thread_id": "1",
            ...        "checkpoint_ns": "",
            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
            ...    }
            ... }
            >>> checkpoint_tuple = memory.get_tuple(config)
            >>> print(checkpoint_tuple)
            CheckpointTuple(...)
        """
        thread_id = config["configurable"]["thread_id"]
        checkpoint_id = get_checkpoint_id(config)
        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
        if checkpoint_id:
            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
        else:
            args = (thread_id, checkpoint_ns)
            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"

        with self._cursor() as cur:
            cur.execute(
                self.SELECT_SQL + where,
                args,
            )
            value = cur.fetchone()
            if value is None:
                return None

            # migrate pending sends if necessary
            if value["checkpoint"]["v"] < 4 and value["parent_checkpoint_id"]:
                cur.execute(
                    self.SELECT_PENDING_SENDS_SQL,
                    (thread_id, [value["parent_checkpoint_id"]]),
                )
                if sends := cur.fetchone():
                    if value["channel_values"] is None:
                        value["channel_values"] = []
                    self._migrate_pending_sends(
                        sends["sends"],
                        value["checkpoint"],
                        value["channel_values"],
                    )

            return self._load_checkpoint_tuple(value)

    def put(
        self,
        config: RunnableConfig,
        checkpoint: Checkpoint,
        metadata: CheckpointMetadata,
        new_versions: ChannelVersions,
    ) -> RunnableConfig:
        """Save a checkpoint to the database.

        This method saves a checkpoint to the Postgres database. The checkpoint is associated
        with the provided config and its parent config (if any).

        Args:
            config: The config to associate with the checkpoint.
            checkpoint: The checkpoint to save.
            metadata: Additional metadata to save with the checkpoint.
            new_versions: New channel versions as of this write.

        Returns:
            RunnableConfig: Updated configuration after storing the checkpoint.

        Examples:

            >>> from langgraph.checkpoint.postgres import PostgresSaver
            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
            >>> print(saved_config)
            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
        """
        configurable = config["configurable"].copy()
        thread_id = configurable.pop("thread_id")
        checkpoint_ns = configurable.pop("checkpoint_ns")
        checkpoint_id = configurable.pop("checkpoint_id", None)
        copy = checkpoint.copy()
        copy["channel_values"] = copy["channel_values"].copy()
        next_config = {
            "configurable": {
                "thread_id": thread_id,
                "checkpoint_ns": checkpoint_ns,
                "checkpoint_id": checkpoint["id"],
            }
        }

        # inline primitive values in checkpoint table
        # others are stored in blobs table
        blob_values = {}
        for k, v in checkpoint["channel_values"].items():
            if isinstance(v, _DeltaSnapshot):
                blob_values[k] = copy["channel_values"].pop(k)
                copy["channel_values"][k] = True
            elif v is None or isinstance(v, (str, int, float, bool)):
                pass
            else:
                blob_values[k] = copy["channel_values"].pop(k)

        with self._cursor(pipeline=True) as cur:
            if blob_versions := {
                k: v for k, v in new_versions.items() if k in blob_values
            }:
                cur.executemany(
                    self.UPSERT_CHECKPOINT_BLOBS_SQL,
                    self._dump_blobs(
                        thread_id,
                        checkpoint_ns,
                        blob_values,
                        blob_versions,
                    ),
                )
            cur.execute(
                self.UPSERT_CHECKPOINTS_SQL,
                (
                    thread_id,
                    checkpoint_ns,
                    checkpoint["id"],
                    checkpoint_id,
                    Jsonb(copy),
                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
                ),
            )
        return next_config

    def put_writes(
        self,
        config: RunnableConfig,
        writes: Sequence[tuple[str, Any]],
        task_id: str,
        task_path: str = "",
    ) -> None:
        """Store intermediate writes linked to a checkpoint.

        This method saves intermediate writes associated with a checkpoint to the Postgres database.

        Args:
            config: Configuration of the related checkpoint.
            writes: List of writes to store.
            task_id: Identifier for the task creating the writes.
        """
        query = (
            self.UPSERT_CHECKPOINT_WRITES_SQL
            if all(w[0] in WRITES_IDX_MAP for w in writes)
            else self.INSERT_CHECKPOINT_WRITES_SQL
        )
        with self._cursor(pipeline=True) as cur:
            cur.executemany(
                query,
                self._dump_writes(
                    config["configurable"]["thread_id"],
                    config["configurable"]["checkpoint_ns"],
                    config["configurable"]["checkpoint_id"],
                    task_id,
                    task_path,
                    writes,
                ),
            )

    def delete_thread(self, thread_id: str) -> None:
        """Delete all checkpoints and writes associated with a thread ID.

        Args:
            thread_id: The thread ID to delete.

        Returns:
            None
        """
        with self._cursor(pipeline=True) as cur:
            cur.execute(
                "DELETE FROM checkpoints WHERE thread_id = %s",
                (str(thread_id),),
            )
            cur.execute(
                "DELETE FROM checkpoint_blobs WHERE thread_id = %s",
                (str(thread_id),),
            )
            cur.execute(
                "DELETE FROM checkpoint_writes WHERE thread_id = %s",
                (str(thread_id),),
            )

    @contextmanager
    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
        """Create a database cursor as a context manager.

        Args:
            pipeline: whether to use pipeline for the DB operations inside the context manager.
                Will be applied regardless of whether the PostgresSaver instance was initialized with a pipeline.
                If pipeline mode is not supported, will fall back to using transaction context manager.
        """
        with self.lock, _internal.get_connection(self.conn) as conn:
            if self.pipe:
                # a connection in pipeline mode can be used concurrently
                # in multiple threads/coroutines, but only one cursor can be
                # used at a time
                try:
                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
                        yield cur
                finally:
                    if pipeline:
                        self.pipe.sync()
            elif pipeline:
                # 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 (
                        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 (
                        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:
                    yield cur

    def get_delta_channel_history(
        self, *, config: RunnableConfig, channels: Sequence[str]
    ) -> Mapping[str, DeltaChannelHistory]:
        """Fast-path override of `BaseCheckpointSaver.get_delta_channel_history`.

        Two-stage query, both stages cover ALL requested channels:

        * Stage 1 (paged): dynamic SELECT over `checkpoints` with K parallel
          JSONB key lookups (one column pair per channel) — no subquery, no
          aggregation. Pages newest-first by `checkpoint_id` with a cursor;
          page size is `_DELTA_PAGE_SIZE`. Stops paging when every channel
          has found its seed or the chain is exhausted.

        * Stage 2 (per-channel UNION ALL): one branch per channel reading
          `checkpoint_writes` filtered to that channel's specific
          `chain_cids`, plus one branch per channel that has a seed reading
          `checkpoint_blobs` for that channel + version. Avoids the
          over-fetch of a single `channel = ANY(channels)` filter when
          channels have different chain depths.
        """
        if not channels:
            return {}
        channels = list(channels)
        thread_id = config["configurable"]["thread_id"]
        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
        checkpoint_id = get_checkpoint_id(config)
        if checkpoint_id is None:
            target = self.get_tuple(config)
            if target is None:
                return {ch: {"writes": []} for ch in channels}
            checkpoint_id = target.config["configurable"]["checkpoint_id"]

        # Stage 1: paged K-JSONB-lookup scan, walking the parent chain in
        # Python after each page. Stops as soon as every channel has its seed.
        stage1_sql = _build_delta_stage1_sql(channels, paged=True)
        parent_of: dict[str, str | None] = {}
        ver_by_i_by_cid: list[dict[str, str | None]] = [{} for _ in channels]
        hb_by_i_by_cid: list[dict[str, bool]] = [{} for _ in channels]
        inline_by_i_by_cid: list[dict[str, Any]] = [{} for _ in channels]
        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
        seed_ver_by_ch: dict[str, str | None] = {ch: None for ch in channels}
        seed_inline_by_ch: dict[str, Any] = {}
        walk_cursor_by_ch: dict[str, str | None] = {}
        seeded: set[str] = set()
        cursor: str | None = None

        with self._cursor() as cur:
            while True:
                stage1_params: list[Any] = []
                for ch in channels:
                    # ver_i, blob channel, blob version, inline_i
                    stage1_params.extend([ch, ch, ch, ch])
                stage1_params.extend(
                    [thread_id, checkpoint_ns, cursor, cursor, _DELTA_PAGE_SIZE]
                )
                cur.execute(stage1_sql, stage1_params)
                page = cur.fetchall()
                if not page:
                    break
                oldest = self._ingest_stage1_page(
                    cast("list[Mapping[str, Any]]", page),
                    channels,
                    parent_of,
                    ver_by_i_by_cid,
                    hb_by_i_by_cid,
                    inline_by_i_by_cid,
                )
                self._try_advance_walks(
                    checkpoint_id,
                    channels,
                    parent_of,
                    ver_by_i_by_cid,
                    hb_by_i_by_cid,
                    inline_by_i_by_cid,
                    chain_by_ch,
                    seed_ver_by_ch,
                    seed_inline_by_ch,
                    walk_cursor_by_ch,
                    seeded,
                )
                # Stop if every channel is seeded, or the page was short
                # (chain exhausted — no more rows to fetch).
                if len(seeded) == len(channels) or len(page) < _DELTA_PAGE_SIZE:
                    break
                cursor = oldest

        # Stage 2: per-channel UNION ALL — one writes branch per channel
        # with non-empty chain, plus one blob branch per seeded channel.
        channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
        channels_with_seed = [ch for ch in channels if seed_ver_by_ch[ch] is not None]
        stage2_sql = _build_delta_stage2_sql(
            channels_with_chain=channels_with_chain,
            channels_with_seed=channels_with_seed,
        )

        if stage2_sql:
            stage2_params: list[Any] = []
            for ch in channels_with_chain:
                stage2_params.extend([thread_id, checkpoint_ns, ch, chain_by_ch[ch]])
            for ch in channels_with_seed:
                stage2_params.extend([thread_id, checkpoint_ns, ch, seed_ver_by_ch[ch]])
            with self._cursor() as cur:
                cur.execute(stage2_sql, stage2_params)
                stage2_rows = cur.fetchall()
        else:
            stage2_rows = []

        return self._build_delta_channels_writes_history(
            channels=channels,
            chain_by_ch=chain_by_ch,
            seed_ver_by_ch=seed_ver_by_ch,
            seed_inline_by_ch=seed_inline_by_ch,
            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
        )

    def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
        """
        Convert a database row into a CheckpointTuple object.

        Args:
            value: A row from the database containing checkpoint data.

        Returns:
            CheckpointTuple: A structured representation of the checkpoint,
            including its configuration, metadata, parent checkpoint (if any),
            and pending writes.
        """
        return CheckpointTuple(
            {
                "configurable": {
                    "thread_id": value["thread_id"],
                    "checkpoint_ns": value["checkpoint_ns"],
                    "checkpoint_id": value["checkpoint_id"],
                }
            },
            {
                **value["checkpoint"],
                "channel_values": {
                    **(value["checkpoint"].get("channel_values") or {}),
                    **self._load_blobs(value["channel_values"]),
                },
            },
            value["metadata"],
            (
                {
                    "configurable": {
                        "thread_id": value["thread_id"],
                        "checkpoint_ns": value["checkpoint_ns"],
                        "checkpoint_id": value["parent_checkpoint_id"],
                    }
                }
                if value["parent_checkpoint_id"]
                else None
            ),
            self._load_writes(value["pending_writes"]),
        )


__all__ = ["PostgresSaver", "BasePostgresSaver", "ShallowPostgresSaver", "Conn"]