core: implement a batch_size parameter for CacheBackedEmbeddings (#18070)

**Description:**

Currently, `CacheBackedEmbeddings` computes vectors for *all* uncached
documents before updating the store. This pull request updates the
embedding computation loop to compute embeddings in batches, updating
the store after each batch.

I noticed this when I tried `CacheBackedEmbeddings` on our 30k document
set and the cache directory hadn't appeared on disk after 30 minutes.

The motivation is to minimize compute/data loss when problems occur:

* If there is a transient embedding failure (e.g. a network outage at
the embedding endpoint triggers an exception), at least the completed
vectors are written to the store instead of being discarded.
* If there is an issue with the store (e.g. no write permissions), the
condition is detected early without computing (and discarding!) all the
vectors.

**Issue:**
Implements enhancement #18026.

**Testing:**
I was unable to run unit tests; details in [this
post](https://github.com/langchain-ai/langchain/discussions/15019#discussioncomment-8576684).

---------

Signed-off-by: chrispy <chrispy@synopsys.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

libs/langchain/langchain/embeddings/cache.py

@@ -12,10 +12,11 @@ import hashlib
 import json
 import uuid
 from functools import partial
-from typing import Callable, List, Sequence, Union, cast
+from typing import Callable, List, Optional, Sequence, Union, cast
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.stores import BaseStore, ByteStore
+from langchain_core.utils.iter import batch_iterate
 
 from langchain.storage.encoder_backed import EncoderBackedStore
 
@@ -84,16 +85,20 @@ class CacheBackedEmbeddings(Embeddings):
         self,
         underlying_embeddings: Embeddings,
         document_embedding_store: BaseStore[str, List[float]],
+        *,
+        batch_size: Optional[int] = None,
     ) -> None:
         """Initialize the embedder.
 
         Args:
             underlying_embeddings: the embedder to use for computing embeddings.
             document_embedding_store: The store to use for caching document embeddings.
+            batch_size: The number of documents to embed between store updates.
         """
         super().__init__()
         self.document_embedding_store = document_embedding_store
         self.underlying_embeddings = underlying_embeddings
+        self.batch_size = batch_size
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Embed a list of texts.
@@ -111,12 +116,12 @@ class CacheBackedEmbeddings(Embeddings):
         vectors: List[Union[List[float], None]] = self.document_embedding_store.mget(
             texts
         )
-        missing_indices: List[int] = [
+        all_missing_indices: List[int] = [
             i for i, vector in enumerate(vectors) if vector is None
         ]
-        missing_texts = [texts[i] for i in missing_indices]
 
-        if missing_texts:
+        for missing_indices in batch_iterate(self.batch_size, all_missing_indices):
+            missing_texts = [texts[i] for i in missing_indices]
             missing_vectors = self.underlying_embeddings.embed_documents(missing_texts)
             self.document_embedding_store.mset(
                 list(zip(missing_texts, missing_vectors))
@@ -144,12 +149,14 @@ class CacheBackedEmbeddings(Embeddings):
         vectors: List[
             Union[List[float], None]
         ] = await self.document_embedding_store.amget(texts)
-        missing_indices: List[int] = [
+        all_missing_indices: List[int] = [
             i for i, vector in enumerate(vectors) if vector is None
         ]
-        missing_texts = [texts[i] for i in missing_indices]
 
-        if missing_texts:
+        # batch_iterate supports None batch_size which returns all elements at once
+        # as a single batch.
+        for missing_indices in batch_iterate(self.batch_size, all_missing_indices):
+            missing_texts = [texts[i] for i in missing_indices]
             missing_vectors = await self.underlying_embeddings.aembed_documents(
                 missing_texts
             )
@@ -210,6 +217,7 @@ class CacheBackedEmbeddings(Embeddings):
         document_embedding_cache: ByteStore,
         *,
         namespace: str = "",
+        batch_size: Optional[int] = None,
     ) -> CacheBackedEmbeddings:
         """On-ramp that adds the necessary serialization and encoding to the store.
 
@@ -220,6 +228,7 @@ class CacheBackedEmbeddings(Embeddings):
             namespace: The namespace to use for document cache.
                        This namespace is used to avoid collisions with other caches.
                        For example, set it to the name of the embedding model used.
+            batch_size: The number of documents to embed between store updates.
         """
         namespace = namespace
         key_encoder = _create_key_encoder(namespace)
@@ -229,4 +238,4 @@ class CacheBackedEmbeddings(Embeddings):
             _value_serializer,
             _value_deserializer,
         )
-        return cls(underlying_embeddings, encoder_backed_store)
+        return cls(underlying_embeddings, encoder_backed_store, batch_size=batch_size)

libs/langchain/tests/unit_tests/embeddings/test_caching.py

@@ -13,6 +13,8 @@ class MockEmbeddings(Embeddings):
         # Simulate embedding documents
         embeddings: List[List[float]] = []
         for text in texts:
+            if text == "RAISE_EXCEPTION":
+                raise ValueError("Simulated embedding failure")
             embeddings.append([len(text), len(text) + 1])
         return embeddings
 
@@ -31,6 +33,16 @@ def cache_embeddings() -> CacheBackedEmbeddings:
     )
 
 
+@pytest.fixture
+def cache_embeddings_batch() -> CacheBackedEmbeddings:
+    """Create a cache backed embeddings with a batch_size of 3."""
+    store = InMemoryStore()
+    embeddings = MockEmbeddings()
+    return CacheBackedEmbeddings.from_bytes_store(
+        embeddings, store, namespace="test_namespace", batch_size=3
+    )
+
+
 def test_embed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
     texts = ["1", "22", "a", "333"]
     vectors = cache_embeddings.embed_documents(texts)
@@ -42,6 +54,20 @@ def test_embed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
     assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
 
 
+def test_embed_documents_batch(cache_embeddings_batch: CacheBackedEmbeddings) -> None:
+    # "RAISE_EXCEPTION" forces a failure in batch 2
+    texts = ["1", "22", "a", "333", "RAISE_EXCEPTION"]
+    try:
+        cache_embeddings_batch.embed_documents(texts)
+    except ValueError:
+        pass
+    keys = list(cache_embeddings_batch.document_embedding_store.yield_keys())
+    # only the first batch of three embeddings should exist
+    assert len(keys) == 3
+    # UUID is expected to be the same for the same text
+    assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
+
+
 def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
     text = "query_text"
     vector = cache_embeddings.embed_query(text)
@@ -62,6 +88,25 @@ async def test_aembed_documents(cache_embeddings: CacheBackedEmbeddings) -> None
     assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
 
 
+async def test_aembed_documents_batch(
+    cache_embeddings_batch: CacheBackedEmbeddings,
+) -> None:
+    # "RAISE_EXCEPTION" forces a failure in batch 2
+    texts = ["1", "22", "a", "333", "RAISE_EXCEPTION"]
+    try:
+        await cache_embeddings_batch.aembed_documents(texts)
+    except ValueError:
+        pass
+    keys = [
+        key
+        async for key in cache_embeddings_batch.document_embedding_store.ayield_keys()
+    ]
+    # only the first batch of three embeddings should exist
+    assert len(keys) == 3
+    # UUID is expected to be the same for the same text
+    assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
+
+
 async def test_aembed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
     text = "query_text"
     vector = await cache_embeddings.aembed_query(text)