feat(core): update document in vector store

packages/ragbits-core/src/ragbits/core/vector_stores/base.py

@@ -86,6 +86,15 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
             The entries.
         """
 
+    @abstractmethod
+    async def remove(self, ids: list[str]) -> None:
+        """
+        Remove entries from the vector store.
+
+        Args:
+            ids: The list of entries' IDs to remove.
+        """
+
     @abstractmethod
     async def list(
         self, where: WhereQuery | None = None, limit: int | None = None, offset: int = 0

packages/ragbits-core/src/ragbits/core/vector_stores/chroma.py

@@ -135,6 +135,16 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
             if options.max_distance is None or distance <= options.max_distance
         ]
 
+    @traceable
+    async def remove(self, ids: list[str]) -> None:
+        """
+        Remove entries from the vector store.
+
+        Args:
+            ids: The list of entries' IDs to remove.
+        """
+        self._collection.delete(ids=ids)
+
     @traceable
     async def list(
         self, where: WhereQuery | None = None, limit: int | None = None, offset: int = 0

packages/ragbits-core/src/ragbits/core/vector_stores/in_memory.py

@@ -81,6 +81,17 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
             if options.max_distance is None or distance <= options.max_distance
         ]
 
+    @traceable
+    async def remove(self, ids: list[str]) -> None:
+        """
+        Remove entries from the vector store.
+
+        Args:
+            ids: The list of entries' IDs to remove.
+        """
+        for id in ids:
+            del self._storage[id]
+
     @traceable
     async def list(
         self, where: WhereQuery | None = None, limit: int | None = None, offset: int = 0

packages/ragbits-core/src/ragbits/core/vector_stores/qdrant.py

@@ -1,7 +1,8 @@
 import json
+import typing
 
 import qdrant_client
-from qdrant_client import AsyncQdrantClient
+from qdrant_client import AsyncQdrantClient, models
 from qdrant_client.models import Distance, Filter, VectorParams
 
 from ragbits.core.audit import traceable
@@ -146,6 +147,24 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
             for id, document, vector, metadata in zip(ids, documents, vectors, metadatas, strict=True)
         ]
 
+    @traceable
+    async def remove(self, ids: list[str]) -> None:
+        """
+        Remove entries from the vector store.
+
+        Args:
+            ids: The list of entries' IDs to remove.
+
+        Raises:
+            ValueError: If collection named `self._index_name` is not present in the vector store.
+        """
+        await self._client.delete(
+            collection_name=self._index_name,
+            points_selector=models.PointIdsList(
+                points=typing.cast(list[int | str], ids),
+            ),
+        )
+
     @traceable
     async def list(  # type: ignore
         self,
@@ -168,10 +187,16 @@ async def list(  # type: ignore
         Raises:
             MetadataNotFoundError: If the metadata is not found.
         """
+        collection_exists = await self._client.collection_exists(collection_name=self._index_name)
+        if not collection_exists:
+            return []
+
+        limit = limit or (await self._client.count(collection_name=self._index_name)).count
+
         results = await self._client.query_points(
             collection_name=self._index_name,
             query_filter=where,
-            limit=limit or 10,
+            limit=limit,
             offset=offset,
             with_payload=True,
             with_vectors=True,

packages/ragbits-core/tests/integration/vector_stores/test_vector_store.py

@@ -0,0 +1,59 @@
+from unittest.mock import AsyncMock
+
+import pytest
+from chromadb import EphemeralClient
+from qdrant_client import AsyncQdrantClient
+
+from ragbits.core.vector_stores.base import VectorStore
+from ragbits.core.vector_stores.chroma import ChromaVectorStore
+from ragbits.core.vector_stores.in_memory import InMemoryVectorStore
+from ragbits.core.vector_stores.qdrant import QdrantVectorStore
+from ragbits.document_search import DocumentSearch
+from ragbits.document_search.documents.document import DocumentMeta
+from ragbits.document_search.documents.sources import LocalFileSource
+
+
+@pytest.mark.parametrize(
+    "vector_store",
+    [
+        InMemoryVectorStore(),
+        ChromaVectorStore(
+            client=EphemeralClient(),
+            index_name="test_index_name",
+        ),
+        QdrantVectorStore(
+            client=AsyncQdrantClient(":memory:"),
+            index_name="test_index_name",
+        ),
+    ],
+)
+async def test_handling_document_ingestion_with_different_content_and_verifying_replacement(
+    vector_store: VectorStore,
+) -> None:
+    document_1_content = "This is a test sentence and it should be in the vector store"
+    document_2_content = "This is another test sentence and it should be removed from the vector store"
+    document_2_new_content = "This is one more test sentence and it should be added to the vector store"
+
+    document_1 = DocumentMeta.create_text_document_from_literal(document_1_content)
+    document_2 = DocumentMeta.create_text_document_from_literal(document_2_content)
+
+    embedder = AsyncMock()
+    embedder.embed_text.return_value = [[0.0], [0.0]]
+    document_search = DocumentSearch(
+        embedder=embedder,
+        vector_store=vector_store,
+    )
+    await document_search.ingest([document_1, document_2])
+
+    if isinstance(document_2.source, LocalFileSource):
+        document_2_path = document_2.source.path
+    with open(document_2_path, "w") as file:
+        file.write(document_2_new_content)
+
+    await document_search.ingest([document_2])
+
+    document_contents = {entry.key for entry in await vector_store.list()}
+
+    assert document_1_content in document_contents
+    assert document_2_new_content in document_contents
+    assert document_2_content not in document_contents

packages/ragbits-core/tests/unit/vector_stores/test_chroma.py

@@ -101,6 +101,15 @@ async def test_retrieve(
         assert entry.key == result["content"]
 
 
+async def test_remove(mock_chromadb_store: ChromaVectorStore) -> None:
+    ids_to_remove = ["1c7d6b27-4ef1-537c-ad7c-676edb8bc8a8"]
+
+    await mock_chromadb_store.remove(ids_to_remove)
+
+    mock_chromadb_store._client.get_or_create_collection().delete.assert_called_once()  # type: ignore
+    mock_chromadb_store._client.get_or_create_collection().delete.assert_called_with(ids=ids_to_remove)  # type: ignore
+
+
 async def test_list(mock_chromadb_store: ChromaVectorStore) -> None:
     mock_chromadb_store._collection.get.return_value = {  # type: ignore
         "metadatas": [

packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py

@@ -76,6 +76,16 @@ async def test_retrieve(store: InMemoryVectorStore, k: int, max_distance: float
         assert entry.metadata["name"] == result
 
 
+async def test_remove(store: InMemoryVectorStore) -> None:
+    entries = await store.list()
+    entry_number = len(entries)
+
+    ids_to_remove = [entries[0].id]
+    await store.remove(ids_to_remove)
+
+    assert len(await store.list()) == entry_number - 1
+
+
 async def test_list_all(store: InMemoryVectorStore) -> None:
     results = await store.list()
 

packages/ragbits-core/tests/unit/vector_stores/test_qdrant.py

@@ -1,3 +1,4 @@
+import typing
 from unittest.mock import AsyncMock
 
 import pytest
@@ -96,7 +97,22 @@ async def test_retrieve(mock_qdrant_store: QdrantVectorStore) -> None:
         assert entry.vector == result["vector"]
 
 
+async def test_remove(mock_qdrant_store: QdrantVectorStore) -> None:
+    ids_to_remove = ["1c7d6b27-4ef1-537c-ad7c-676edb8bc8a8"]
+
+    await mock_qdrant_store.remove(ids_to_remove)
+
+    mock_qdrant_store._client.delete.assert_called_once()  # type: ignore
+    mock_qdrant_store._client.delete.assert_called_with(  # type: ignore
+        collection_name="test_collection",
+        points_selector=models.PointIdsList(
+            points=typing.cast(list[int | str], ids_to_remove),
+        ),
+    )
+
+
 async def test_list(mock_qdrant_store: QdrantVectorStore) -> None:
+    mock_qdrant_store._client.collection_exists.return_value = True  # type: ignore
     mock_qdrant_store._client.query_points.return_value = models.QueryResponse(  # type: ignore
         points=[
             models.ScoredPoint(

packages/ragbits-document-search/src/ragbits/document_search/_main.py

@@ -141,8 +141,27 @@ async def ingest(
         elements = await self.processing_strategy.process_documents(
             documents, self.document_processor_router, document_processor
         )
+        await self._remove_entries_with_same_sources(elements)
         await self.insert_elements(elements)
 
+    async def _remove_entries_with_same_sources(self, elements: list[Element]) -> None:
+        """
+        Remove entries from the vector store whose source id is present in the elements' metadata.
+
+        Args:
+            elements: List of elements whose source ids will be checked and removed from the vector store if present.
+        """
+        unique_source_ids = {element.document_meta.source.id for element in elements}
+
+        ids_to_delete = []
+        # TODO: Pass 'where' argument to the list method to filter results and optimize search
+        for entry in await self.vector_store.list():
+            if entry.metadata.get("document_meta", {}).get("source", {}).get("id") in unique_source_ids:
+                ids_to_delete.append(entry.id)
+
+        if ids_to_delete:
+            await self.vector_store.remove(ids_to_delete)
+
     async def insert_elements(self, elements: list[Element]) -> None:
         """
         Insert Elements into the vector store.

packages/ragbits-document-search/src/ragbits/document_search/documents/element.py

@@ -121,6 +121,7 @@ def to_vector_db_entry(self, vector: list[float], embedding_type: EmbeddingType)
         vector_store_entry_id = str(uuid.uuid5(uuid.NAMESPACE_OID, ";".join(id_components)))
         metadata = self.model_dump(exclude={"id", "key"})
         metadata["embedding_type"] = str(embedding_type)
+        metadata["document_meta"]["source"]["id"] = self.document_meta.source.id
         return VectorStoreEntry(id=vector_store_entry_id, key=str(self.key), vector=vector, metadata=metadata)