feat: Add vector search (#2535)

- Initializing the store with an 'embedding config' -> this contains the
'dims' (used to create the table) and the encoder object (rn langchain
embeddings object, though that is ......)
- Call setup() -> creates the vector table.

Each document has 1 or more vectors associated with it for each json
path in the embedding config.

Would welcome critique and requests! 

Leaving the params as the defaults for pgvector but open to feedback if
you think it's important to be able to more transparently configure that
in setup()

```python
from typing import TypedDict, List, Dict, Any, Optional

from langchain_openai import OpenAIEmbeddings
from langgraph.graph import StateGraph
from langgraph.store.postgres import PostgresStore

emb_config = {
    "dims": 1536,  # OpenAI embedding dimensions
    "embed": OpenAIEmbeddings(model="text-embedding-3-small"),
    "distance_type": "cosine",
}
with PostgresStore.from_conn_string(
    "postgres://postgres:postgres@localhost:5441",
    embedding=emb_config,
) as store:
    store.setup()


# Define the state type for our graph
class State(TypedDict):
    query: str
    results: Optional[List[Dict[str, Any]]]


def put_stuff(state: State) -> State:
    docs = [
        ("doc1", {"text": "red apple in kitchen"}),
        ("doc2", {"text": "blue car in garage"}),
        ("doc3", {"text": "green apple on table"}),
    ]
    for key, value in docs:
        store.put(("docs",), key, value)


def search_stuff(state: State) -> State:
    """Search for documents using vector similarity."""
    results = store.search(("docs",), query=state["query"])

    return {"results": results}


builder = StateGraph(State)
builder.add_node(put_stuff)
builder.add_node(search_stuff)
builder.add_edge("__start__", "put_stuff")
builder.add_edge("put_stuff", "search_stuff")
# Compile
with PostgresStore.from_conn_string(
    "postgres://postgres:postgres@localhost:5441",
    embedding=emb_config,
) as store:
    chain = builder.compile(store=store)

    result = chain.invoke({"query": "sour apple"})

# Print results
for doc in result["results"]:
    print(doc.key)
    print(doc.value)
    print(doc.response_metadata)

```

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

@@ -1,6 +1,7 @@
 import threading
+from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any, Iterator, Optional, Sequence
+from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
 from psycopg import Capabilities, Connection, Cursor, Pipeline

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

@@ -1,7 +1,8 @@
 """Shared async utility functions for the Postgres checkpoint & storage classes."""
 
+from collections.abc import AsyncIterator
 from contextlib import asynccontextmanager
-from typing import AsyncIterator, Union
+from typing import Union
 
 from psycopg import AsyncConnection
 from psycopg.rows import DictRow

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

@@ -1,7 +1,8 @@
 """Shared utility functions for the Postgres checkpoint & storage classes."""
 
+from collections.abc import Iterator
 from contextlib import contextmanager
-from typing import Iterator, Union
+from typing import Union
 
 from psycopg import Connection
 from psycopg.rows import DictRow

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

@@ -1,6 +1,7 @@
 import asyncio
+from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any, AsyncIterator, Iterator, Optional, Sequence
+from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
@@ -385,7 +386,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         while True:
             try:
                 yield asyncio.run_coroutine_threadsafe(
-                    anext(aiter_),
+                    anext(aiter_),  # noqa: F821
                     self.loop,
                 ).result()
             except StopAsyncIteration:

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

@@ -1,5 +1,6 @@
 import random
-from typing import Any, List, Optional, Sequence, Tuple, cast
+from collections.abc import Sequence
+from typing import Any, Optional, cast
 
 from langchain_core.runnables import RunnableConfig
 from psycopg.types.json import Jsonb
@@ -249,7 +250,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         config: Optional[RunnableConfig],
         filter: MetadataInput,
         before: Optional[RunnableConfig] = None,
-    ) -> Tuple[str, List[Any]]:
+    ) -> tuple[str, list[Any]]:
         """Return WHERE clause predicates for alist() given config, filter, before.
 
         This method returns a tuple of a string and a tuple of values. The string