base

Basic setup and usage:

from langgraph.store.postgres import PostgresStore
from psycopg import Connection

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

# Using direct connection
with Connection.connect(conn_string) as conn:
    store = PostgresStore(conn)
    store.setup() # Run migrations. Done once

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

Or using the convenient from_conn_string helper:

from langgraph.store.postgres import PostgresStore

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

with PostgresStore.from_conn_string(conn_string) as store:
    store.setup()

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

Vector search using LangChain embeddings:

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

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

with PostgresStore.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:
    store.setup() # Do this once to run migrations

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

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