fix: chromadb max batch size (#1087)

private_gpt/components/vector_store/batched_chroma.py

@@ -0,0 +1,87 @@
+from typing import Any
+
+from llama_index.schema import BaseNode, MetadataMode
+from llama_index.vector_stores import ChromaVectorStore
+from llama_index.vector_stores.chroma import chunk_list
+from llama_index.vector_stores.utils import node_to_metadata_dict
+
+
+class BatchedChromaVectorStore(ChromaVectorStore):
+    """Chroma vector store, batching additions to avoid reaching the max batch limit.
+
+    In this vector store, embeddings are stored within a ChromaDB collection.
+
+    During query time, the index uses ChromaDB to query for the top
+    k most similar nodes.
+
+    Args:
+        chroma_client (from chromadb.api.API):
+            API instance
+        chroma_collection (chromadb.api.models.Collection.Collection):
+            ChromaDB collection instance
+
+    """
+
+    chroma_client: Any | None
+
+    def __init__(
+        self,
+        chroma_client: Any,
+        chroma_collection: Any,
+        host: str | None = None,
+        port: str | None = None,
+        ssl: bool = False,
+        headers: dict[str, str] | None = None,
+        collection_kwargs: dict[Any, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            chroma_collection=chroma_collection,
+            host=host,
+            port=port,
+            ssl=ssl,
+            headers=headers,
+            collection_kwargs=collection_kwargs or {},
+        )
+        self.chroma_client = chroma_client
+
+    def add(self, nodes: list[BaseNode]) -> list[str]:
+        """Add nodes to index, batching the insertion to avoid issues.
+
+        Args:
+            nodes: List[BaseNode]: list of nodes with embeddings
+
+        """
+        if not self.chroma_client:
+            raise ValueError("Client not initialized")
+
+        if not self._collection:
+            raise ValueError("Collection not initialized")
+
+        max_chunk_size = self.chroma_client.max_batch_size
+        node_chunks = chunk_list(nodes, max_chunk_size)
+
+        all_ids = []
+        for node_chunk in node_chunks:
+            embeddings = []
+            metadatas = []
+            ids = []
+            documents = []
+            for node in node_chunk:
+                embeddings.append(node.get_embedding())
+                metadatas.append(
+                    node_to_metadata_dict(
+                        node, remove_text=True, flat_metadata=self.flat_metadata
+                    )
+                )
+                ids.append(node.node_id)
+                documents.append(node.get_content(metadata_mode=MetadataMode.NONE))
+
+            self._collection.add(
+                embeddings=embeddings,
+                ids=ids,
+                metadatas=metadatas,
+                documents=documents,
+            )
+            all_ids.extend(ids)
+
+        return all_ids

private_gpt/components/vector_store/vector_store_component.py

@@ -4,9 +4,9 @@
 from injector import inject, singleton
 from llama_index import VectorStoreIndex
 from llama_index.indices.vector_store import VectorIndexRetriever
-from llama_index.vector_stores import ChromaVectorStore
 from llama_index.vector_stores.types import VectorStore
 
+from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore
 from private_gpt.open_ai.extensions.context_filter import ContextFilter
 from private_gpt.paths import local_data_path
 
@@ -36,14 +36,16 @@ class VectorStoreComponent:
 
     @inject
     def __init__(self) -> None:
-        db = chromadb.PersistentClient(
+        chroma_client = chromadb.PersistentClient(
             path=str((local_data_path / "chroma_db").absolute())
         )
-        chroma_collection = db.get_or_create_collection(
+        chroma_collection = chroma_client.get_or_create_collection(
             "make_this_parameterizable_per_api_call"
         )  # TODO
 
-        self.vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+        self.vector_store = BatchedChromaVectorStore(
+            chroma_client=chroma_client, chroma_collection=chroma_collection
+        )
 
     @staticmethod
     def get_retriever(

pyproject.toml

@@ -5,15 +5,15 @@ description = "Private GPT"
 authors = ["Zylon <hi@zylon.ai>"]
 
 [tool.poetry.dependencies]
-python = ">=3.11,<3.13"
+python = ">=3.11,<3.12"
 fastapi = { extras = ["all"], version = "^0.103.1" }
 loguru = "^0.7.2"
 boto3 = "^1.28.56"
 injector = "^0.21.0"
 pyyaml = "^6.0.1"
 python-multipart = "^0.0.6"
 pypdf = "^3.16.2"
-llama-index = "v0.8.35"
+llama-index = "0.8.47"
 chromadb = "^0.4.13"
 watchdog = "^3.0.0"
 transformers = "^4.34.0"

tests/server/ingest/test_ingest_service.py

@@ -0,0 +1,27 @@
+from unittest.mock import PropertyMock, patch
+
+from llama_index import Document
+
+from private_gpt.server.ingest.ingest_service import IngestService
+from tests.fixtures.mock_injector import MockInjector
+
+
+def test_save_many_nodes(injector: MockInjector) -> None:
+    """This is a specific test for a local Chromadb Vector Database setup.
+
+    Extend it when we add support for other vector databases in VectorStoreComponent.
+    """
+    with patch(
+        "chromadb.api.segment.SegmentAPI.max_batch_size", new_callable=PropertyMock
+    ) as max_batch_size:
+        # Make max batch size of Chromadb very small
+        max_batch_size.return_value = 10
+
+        ingest_service = injector.get(IngestService)
+
+        documents = []
+        for _i in range(100):
+            documents.append(Document(text="This is a sentence."))
+
+        ingested_docs = ingest_service._save_docs(documents)
+        assert len(ingested_docs) == len(documents)