AsyncPostgresStore

Basic setup and usage:

from langgraph.store.postgres import AsyncPostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(conn_string) as store:
    await store.setup()  # Run migrations. Done once

    # Store and retrieve data
    await store.aput(("users", "123"), "prefs", {"theme": "dark"})
    item = await store.aget(("users", "123"), "prefs")

Vector search using LangChain embeddings:

from langchain.embeddings import init_embeddings
from langgraph.store.postgres import AsyncPostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(
    conn_string,
    index={
        "dims": 1536,
        "embed": init_embeddings("openai:text-embedding-3-small"),
        "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
    }
) as store:
    await store.setup()  # Run migrations. Done once

    # Store documents
    await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
    await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
    await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index

    # Search by similarity
    results = await store.asearch(("docs",), query="programming guides", limit=2)

Using connection pooling for better performance:

from langgraph.store.postgres import AsyncPostgresStore, PoolConfig

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(
    conn_string,
    pool_config=PoolConfig(
        min_size=5,
        max_size=20
    )
) as store:
    await store.setup()  # Run migrations. Done once
    # Use store with connection pooling...