Support index embed specification via string

libs/checkpoint/langgraph/store/base/__init__.py

@@ -1,4 +1,4 @@
 """Base classes and types for persistent key-value stores.
 
 Stores provide long-term memory that persists across threads and conversations.
 Supports hierarchical namespaces, key-value storage, and optional vector search.
@@ -493,13 +493,14 @@
         - cohere:embed-multilingual-light-v3.0: 384
     """
 
-    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc]
+    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str]
     """Optional function to generate embeddings from text.
 
     Can be specified in three ways:
         1. A LangChain Embeddings instance
         2. A synchronous embedding function (EmbeddingsFunc)
         3. An asynchronous embedding function (AEmbeddingsFunc)
+        4. A provider string (e.g., "openai:text-embedding-3-small")
 
     ???+ example "Examples"
         Using LangChain's initialization with InMemoryStore:

libs/checkpoint/langgraph/store/base/embed.py

@@ -7,6 +7,7 @@
 """
 
 import asyncio
+import functools
 import json
 from typing import Any, Awaitable, Callable, Optional, Sequence, Union
 
@@ -28,7 +29,7 @@
 
 
 def ensure_embeddings(
-    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, None],
+    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str, None],
 ) -> Embeddings:
     """Ensure that an embedding function conforms to LangChain's Embeddings interface.
 
@@ -62,9 +63,37 @@ async def my_async_fn(texts):
         embeddings = ensure_embeddings(my_async_fn)
         result = await embeddings.aembed_query("hello")  # Returns [0.1, 0.2]
         ```
+
+        Initialize embeddings using a provider string:
+        ```python
+        # Requires langchain>=0.3.9 and langgraph-checkpoint>=2.0.11
+        embeddings = ensure_embeddings("openai:text-embedding-3-small")
+        result = embeddings.embed_query("hello")
+        ```
     """
     if embed is None:
         raise ValueError("embed must be provided")
+    if isinstance(embed, str):
+        init_embeddings = _get_init_embeddings()
+        if init_embeddings is None:
+            from importlib.metadata import PackageNotFoundError, version
+
+            try:
+                lc_version = version("langchain")
+                version_info = f"Found langchain version {lc_version}, but"
+            except PackageNotFoundError:
+                version_info = "langchain is not installed;"
+
+            raise ValueError(
+                f"Could not load embeddings from string '{embed}'. {version_info} "
+                "loading embeddings by provider:identifier string requires langchain>=0.3.9 "
+                "as well as the provider-specific package. "
+                "Install LangChain with: pip install 'langchain>=0.3.9' "
+                "and the provider-specific package (e.g., 'langchain-openai>=0.3.0'). "
+                "Alternatively, specify 'embed' as a compatible Embeddings object or python function."
+            )
+        return init_embeddings(embed)
+
     if isinstance(embed, Embeddings):
         return embed
     return EmbeddingsLambda(embed)
@@ -373,6 +402,16 @@ def _is_async_callable(
     )
 
 
+@functools.lru_cache
+def _get_init_embeddings() -> Optional[Callable[[str], Embeddings]]:
+    try:
+        from langchain.embeddings import init_embeddings  # type: ignore
+
+        return init_embeddings
+    except ImportError:
+        return None
+
+
 __all__ = [
     "ensure_embeddings",
     "EmbeddingsFunc",

libs/checkpoint/langgraph/store/memory/__init__.py

@@ -493,7 +493,7 @@ def _cosine_similarity(X: list[float], Y: list[list[float]]) -> list[float]:
     if not Y:
         return []
     if _check_numpy():
-        import numpy as np  # type: ignore
+        import numpy as np  # type: ignore[import-not-found]
 
         X_arr = np.array(X) if not isinstance(X, np.ndarray) else X
         Y_arr = np.array(Y) if not isinstance(Y, np.ndarray) else Y

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.11"
+version = "2.0.12"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"