checkpoint-postgres: vbump & add a note to readme (#1295)

* review tool calls

* spelling

* link

* link

---------

Co-authored-by: isaac hershenson <ihershenson@hmc.edu>

docs/_scripts/copy_notebooks.py

@@ -59,6 +59,7 @@ _MANUAL = {
         "human_in_the_loop/time-travel.ipynb",
         "human_in_the_loop/edit-graph-state.ipynb",
         "human_in_the_loop/wait-user-input.ipynb",
+        "human_in_the_loop/review-tool-calls.ipynb",
         "node-retries.ipynb",
     ],
     "tutorials": [

docs/docs/how-tos/index.md

@@ -38,6 +38,7 @@ These guides cover common examples of that.
 - [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
 - [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
 - [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
+- [Review tool calls](human_in_the_loop/review-tool-calls.ipynb)
 
 ## Streaming
 

docs/mkdocs.yml

@@ -142,6 +142,7 @@ nav:
           - Wait for user input: how-tos/human_in_the_loop/wait-user-input.ipynb
           - View and update past graph state: how-tos/human_in_the_loop/time-travel.ipynb
           - Edit graph state: how-tos/human_in_the_loop/edit-graph-state.ipynb
+          - Review tool calls: how-tos/human_in_the_loop/review-tool-calls.ipynb
       - Streaming:
           - Stream full state: how-tos/stream-values.ipynb
           - Stream state updates: how-tos/stream-updates.ipynb

libs/checkpoint-postgres/README.md

@@ -7,6 +7,9 @@ Implementation of LangGraph CheckpointSaver that uses Postgres.
 > [!IMPORTANT]
 > When using Postgres checkpointers for the first time, make sure to call `.setup()` method on them to create required tables. See example below.
 
+> [!IMPORTANT]
+> When manually creating Postgres connections and passing them to `PostgresSaver` or `AsyncPostgresSaver`, make sure to include `autocommit=True` and `row_factory=dict_row` (`from psycopg.rows import dict_row`). See a full example in this [how-to guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/).
+
 ```python
 from langgraph.checkpoint.postgres import PostgresSaver
 
@@ -95,4 +98,4 @@ async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
 
     # list checkpoints
     [c async for c in checkpointer.alist(read_config)]
-```
+```

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

@@ -66,11 +66,10 @@ class AsyncPostgresSaver(BasePostgresSaver):
         async with self.lock:
             async with self.conn.cursor(binary=True) as cur:
                 try:
-                    version = (
-                        await cur.execute(
-                            "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-                        )
-                    ).fetchone()["v"]
+                    results = await cur.execute(
+                        "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+                    )
+                    version = (await results.fetchone())["v"]
                 except UndefinedTable:
                     version = -1
                 for v, migration in zip(

libs/checkpoint-postgres/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "1.0.2"
+version = "1.0.3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"

libs/langgraph/langgraph/constants.py

@@ -27,11 +27,15 @@ CHECKPOINT_NAMESPACE_SEPARATOR = "|"
 class Send:
     """A message or packet to send to a specific node in the graph.
 
-    The `Send` class is used within a `StateGraph`'s conditional edges to dynamically
-    route states to different nodes based on certain conditions. This enables
-    creating "map-reduce" like workflows, where a node can be invoked multiple times
-    in parallel on different states, and the results can be aggregated back into the
-    main graph's state.
+    The `Send` class is used within a `StateGraph`'s conditional edges to
+    dynamically invoke a node with a custom state at the next step.
+
+    Importantly, the sent state can differ from the core graph's state,
+    allowing for flexible and dynamic workflow management.
+
+    One such example is a "map-reduce" workflow where your graph invokes
+    the same node multiple times in parallel with different states,
+    before aggregating the results back into the main graph's state.
 
     Attributes:
         node (str): The name of the target node to send the message to.
@@ -55,6 +59,8 @@ class Send:
         >>> builder.add_conditional_edges(START, continue_to_jokes)
         >>> builder.add_edge("generate_joke", END)
         >>> graph = builder.compile()
+        >>>
+        >>> # Invoking with two subjects results in a generated joke for each
         >>> graph.invoke({"subjects": ["cats", "dogs"]})
         {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']}
     """