Chroma batching (#11203)

- **Description:** Chroma >= 0.4.10 added support for batch sizes
validation of add/upsert. This batch size is dependent on the SQLite
limits of the target system and varies. In this change, for
Chroma>=0.4.10 batch splitting was added as the aforementioned
validation is starting to surface in the Chroma community (users using
LC)
 - **Issue:** N/A
 - **Dependencies:** N/A
 - **Tag maintainer:** @eyurtsev
 - **Twitter handle:** t_azarov

libs/langchain/langchain/vectorstores/chroma.py

@@ -558,12 +558,31 @@ class Chroma(VectorStore):
             )
         embeddings = self._embedding_function.embed_documents(text)
 
-        self._collection.update(
+        if hasattr(
-            ids=ids,
+            self._collection._client, "max_batch_size"
-            embeddings=embeddings,
+        ):  # for Chroma 0.4.10 and above
-            documents=text,
+            from chromadb.utils.batch_utils import create_batches
-            metadatas=metadata,
+
-        )
+            for batch in create_batches(
+                api=self._collection._client,
+                ids=ids,
+                metadatas=metadata,
+                documents=text,
+                embeddings=embeddings,
+            ):
+                self._collection.update(
+                    ids=batch[0],
+                    embeddings=batch[1],
+                    documents=batch[3],
+                    metadatas=batch[2],
+                )
+        else:
+            self._collection.update(
+                ids=ids,
+                embeddings=embeddings,
+                documents=text,
+                metadatas=metadata,
+            )
 
     @classmethod
     def from_texts(
@@ -607,7 +626,24 @@ class Chroma(VectorStore):
             collection_metadata=collection_metadata,
             **kwargs,
         )
-        chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
+        if hasattr(
+            chroma_collection._client, "max_batch_size"
+        ):  # for Chroma 0.4.10 and above
+            from chromadb.utils.batch_utils import create_batches
+
+            for batch in create_batches(
+                api=chroma_collection._client,
+                ids=ids,
+                metadatas=metadatas,
+                documents=texts,
+            ):
+                chroma_collection.add_texts(
+                    texts=batch[3] if batch[3] else [],
+                    metadatas=batch[2] if batch[2] else None,
+                    ids=batch[0],
+                )
+        else:
+            chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
         return chroma_collection
 
     @classmethod

libs/langchain/tests/integration_tests/vectorstores/test_chroma.py

@@ -1,7 +1,11 @@
 """Test Chroma functionality."""
+import uuid
+
 import pytest
+import requests
 
 from langchain.docstore.document import Document
+from langchain.embeddings import FakeEmbeddings as Fak
 from langchain.vectorstores import Chroma
 from tests.integration_tests.vectorstores.fake_embeddings import (
     ConsistentFakeEmbeddings,
@@ -301,3 +305,112 @@ def test_chroma_add_documents_mixed_metadata() -> None:
     assert sorted(search, key=lambda d: d.page_content) == sorted(
         docs, key=lambda d: d.page_content
     )
+
+
+def is_api_accessible(url: str) -> bool:
+    try:
+        response = requests.get(url)
+        return response.status_code == 200
+    except Exception:
+        return False
+
+
+def batch_support_chroma_version() -> bool:
+    import chromadb
+
+    major, minor, patch = chromadb.__version__.split(".")
+    if int(major) == 0 and int(minor) >= 4 and int(patch) >= 10:
+        return True
+    return False
+
+
+@pytest.mark.requires("chromadb")
+@pytest.mark.skipif(
+    not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
+    reason="API not accessible",
+)
+@pytest.mark.skipif(
+    not batch_support_chroma_version(),
+    reason="ChromaDB version does not support batching",
+)
+def test_chroma_large_batch() -> None:
+    import chromadb
+
+    client = chromadb.HttpClient()
+    embedding_function = Fak(size=255)
+    col = client.get_or_create_collection(
+        "my_collection",
+        embedding_function=embedding_function.embed_documents,  # type: ignore
+    )
+    docs = ["This is a test document"] * (client.max_batch_size + 100)
+    Chroma.from_texts(
+        client=client,
+        collection_name=col.name,
+        texts=docs,
+        embedding=embedding_function,
+        ids=[str(uuid.uuid4()) for _ in range(len(docs))],
+    )
+
+
+@pytest.mark.requires("chromadb")
+@pytest.mark.skipif(
+    not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
+    reason="API not accessible",
+)
+@pytest.mark.skipif(
+    not batch_support_chroma_version(),
+    reason="ChromaDB version does not support batching",
+)
+def test_chroma_large_batch_update() -> None:
+    import chromadb
+
+    client = chromadb.HttpClient()
+    embedding_function = Fak(size=255)
+    col = client.get_or_create_collection(
+        "my_collection",
+        embedding_function=embedding_function.embed_documents,  # type: ignore
+    )
+    docs = ["This is a test document"] * (client.max_batch_size + 100)
+    ids = [str(uuid.uuid4()) for _ in range(len(docs))]
+    db = Chroma.from_texts(
+        client=client,
+        collection_name=col.name,
+        texts=docs,
+        embedding=embedding_function,
+        ids=ids,
+    )
+    new_docs = [
+        Document(
+            page_content="This is a new test document", metadata={"doc_id": f"{i}"}
+        )
+        for i in range(len(docs) - 10)
+    ]
+    new_ids = [_id for _id in ids[: len(new_docs)]]
+    db.update_documents(ids=new_ids, documents=new_docs)
+
+
+@pytest.mark.requires("chromadb")
+@pytest.mark.skipif(
+    not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
+    reason="API not accessible",
+)
+@pytest.mark.skipif(
+    batch_support_chroma_version(), reason="ChromaDB version does not support batching"
+)
+def test_chroma_legacy_batching() -> None:
+    import chromadb
+
+    client = chromadb.HttpClient()
+    embedding_function = Fak(size=255)
+    col = client.get_or_create_collection(
+        "my_collection",
+        embedding_function=embedding_function.embed_documents,  # type: ignore
+    )
+    docs = ["This is a test document"] * 100
+    Chroma.from_texts(
+        client=client,
+        collection_name=col.name,
+        texts=docs,
+        embedding=embedding_function,
+        ids=[str(uuid.uuid4()) for _ in range(len(docs))],
+    )