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Fix docstring in pg store init (#3094)

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William FH
2025-01-18 00:03:51 +00:00
committed by GitHub
parent 29db5a8672
commit a11f9b3535
2 changed files with 53 additions and 31 deletions
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libs/checkpoint-postgres/langgraph/store/postgres/aio.py
+16 -13
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@@ -39,14 +39,14 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
"""Asynchronous Postgres-backed store with optional vector search using pgvector.
!!! example "Examples"
Basic setup and key-value storage:
Basic setup and usage:
```python
from langgraph.store.postgres import AsyncPostgresStore
async with AsyncPostgresStore.from_conn_string(
"postgresql://user:pass@localhost:5432/dbname"
) as store:
await store.setup()
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"})
@@ -58,38 +58,41 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
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(
"postgresql://user:pass@localhost:5432/dbname",
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() # Do this once to run migrations
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"})
# Don't index the following
await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)
await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False) # don't index
# Search by similarity
results = await store.asearch(("docs",), query="python programming")
results = await store.asearch(("docs",), "programming guides", limit=2)
```
Using connection pooling for better performance:
```python
from langgraph.store.postgres import AsyncPostgresStore, PoolConfig
conn_string = "postgresql://user:pass@localhost:5432/dbname"
async with AsyncPostgresStore.from_conn_string(
"postgresql://user:pass@localhost:5432/dbname",
conn_string,
pool_config=PoolConfig(
min_size=5,
max_size=20
)
) as store:
await store.setup()
await store.setup() # Run migrations. Done once
# Use store with connection pooling...
```
@@ -102,7 +105,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
Note:
Semantic search is disabled by default. You can enable it by providing an `index` configuration
when creating the store. Without this configuration, all `index` arguments passed to
`put` or `aput`will have no effect.
`put` or `aput` will have no effect.
"""
__slots__ = (
libs/checkpoint-postgres/langgraph/store/postgres/base.py
+37 -18
View File
@@ -536,18 +536,35 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
"""Postgres-backed store with optional vector search using pgvector.
!!! example "Examples"
Basic setup and key-value storage:
Basic setup and usage:
```python
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:
```python
from langgraph.store.postgres import PostgresStore
store = PostgresStore(
connection_string="postgresql://user:pass@localhost:5432/dbname"
)
store.setup()
conn_string = "postgresql://user:pass@localhost:5432/dbname"
# Store and retrieve data
store.put(("users", "123"), "prefs", {"theme": "dark"})
item = store.get(("users", "123"), "prefs")
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:
@@ -555,23 +572,25 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
from langchain.embeddings import init_embeddings
from langgraph.store.postgres import PostgresStore
store = PostgresStore(
connection_string="postgresql://user:pass@localhost:5432/dbname",
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
}
)
store.setup() # Do this once to run migrations
) 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
# 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="python programming")
# Search by similarity
results = store.search(("docs",), "programming guides", limit=2)
```
Note: