embed

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")

Using LangChain's initialization with InMemoryStore:

from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore
    
store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": init_embeddings("openai:text-embedding-3-small")
    }
)

Using a custom embedding function with InMemoryStore:

from openai import OpenAI
from langgraph.store.memory import InMemoryStore
    
client = OpenAI()
    
def embed_texts(texts: list[str]) -> list[list[float]]:
    response = client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]
        
store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": embed_texts
    }
)

Using an asynchronous embedding function with InMemoryStore:

from openai import AsyncOpenAI
from langgraph.store.memory import InMemoryStore
    
client = AsyncOpenAI()
    
async def aembed_texts(texts: list[str]) -> list[list[float]]:
    response = await client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]
        
store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": aembed_texts
    }
)