community: Cassandra Vector Store: extend metadata-related methods (#27078)

**Description:** this PR adds a set of methods to deal with metadata
associated to the vector store entries. These, while essential to the
Graph-related extension of the `Cassandra` vector store, are also useful
in themselves. These are (all come in their sync+async versions):

- `[a]delete_by_metadata_filter`
- `[a]replace_metadata`
- `[a]get_by_document_id`
- `[a]metadata_search`

Additionally, a `[a]similarity_search_with_embedding_id_by_vector`
method is introduced to better serve the store's internal working (esp.
related to reranking logic).

**Issue:** no issue number, but now all Document's returned bear their
`.id` consistently (as a consequence of a slight refactoring in how the
raw entries read from DB are made back into `Document` instances).

**Dependencies:** (no new deps: packaging comes through langchain-core
already; `cassio` is now required to be version 0.1.10+)


**Add tests and docs**
Added integration tests for the relevant newly-introduced methods.
(Docs will be updated in a separate PR).

**Lint and test** Lint and (updated) test all pass.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/tests/integration_tests/vectorstores/test_cassandra.py

@@ -17,6 +17,17 @@ from tests.integration_tests.vectorstores.fake_embeddings import (
 )
 
 
+def _strip_docs(documents: List[Document]) -> List[Document]:
+    return [_strip_doc(doc) for doc in documents]
+
+
+def _strip_doc(document: Document) -> Document:
+    return Document(
+        page_content=document.page_content,
+        metadata=document.metadata,
+    )
+
+
 def _vectorstore_from_texts(
     texts: List[str],
     metadatas: Optional[List[dict]] = None,
@@ -110,9 +121,9 @@ async def test_cassandra() -> None:
     texts = ["foo", "bar", "baz"]
     docsearch = _vectorstore_from_texts(texts)
     output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
     output = await docsearch.asimilarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
 
 
 async def test_cassandra_with_score() -> None:
@@ -130,13 +141,13 @@ async def test_cassandra_with_score() -> None:
     output = docsearch.similarity_search_with_score("foo", k=3)
     docs = [o[0] for o in output]
     scores = [o[1] for o in output]
-    assert docs == expected_docs
+    assert _strip_docs(docs) == _strip_docs(expected_docs)
     assert scores[0] > scores[1] > scores[2]
 
     output = await docsearch.asimilarity_search_with_score("foo", k=3)
     docs = [o[0] for o in output]
     scores = [o[1] for o in output]
-    assert docs == expected_docs
+    assert _strip_docs(docs) == _strip_docs(expected_docs)
     assert scores[0] > scores[1] > scores[2]
 
 
@@ -239,7 +250,7 @@ async def test_cassandra_no_drop_async() -> None:
 def test_cassandra_delete() -> None:
     """Test delete methods from vector store."""
     texts = ["foo", "bar", "baz", "gni"]
-    metadatas = [{"page": i} for i in range(len(texts))]
+    metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
     docsearch = _vectorstore_from_texts([], metadatas=metadatas)
 
     ids = docsearch.add_texts(texts, metadatas)
@@ -263,11 +274,21 @@ def test_cassandra_delete() -> None:
     output = docsearch.similarity_search("foo", k=10)
     assert len(output) == 0
 
+    docsearch.add_texts(texts, metadatas)
+    num_deleted = docsearch.delete_by_metadata_filter({"mod2": 0}, batch_size=1)
+    assert num_deleted == 2
+    output = docsearch.similarity_search("foo", k=10)
+    assert len(output) == 2
+    docsearch.clear()
+
+    with pytest.raises(ValueError):
+        docsearch.delete_by_metadata_filter({})
+
 
 async def test_cassandra_adelete() -> None:
     """Test delete methods from vector store."""
     texts = ["foo", "bar", "baz", "gni"]
-    metadatas = [{"page": i} for i in range(len(texts))]
+    metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
     docsearch = await _vectorstore_from_texts_async([], metadatas=metadatas)
 
     ids = await docsearch.aadd_texts(texts, metadatas)
@@ -291,6 +312,16 @@ async def test_cassandra_adelete() -> None:
     output = docsearch.similarity_search("foo", k=10)
     assert len(output) == 0
 
+    await docsearch.aadd_texts(texts, metadatas)
+    num_deleted = await docsearch.adelete_by_metadata_filter({"mod2": 0}, batch_size=1)
+    assert num_deleted == 2
+    output = await docsearch.asimilarity_search("foo", k=10)
+    assert len(output) == 2
+    await docsearch.aclear()
+
+    with pytest.raises(ValueError):
+        await docsearch.adelete_by_metadata_filter({})
+
 
 def test_cassandra_metadata_indexing() -> None:
     """Test comparing metadata indexing policies."""
@@ -316,3 +347,107 @@ def test_cassandra_metadata_indexing() -> None:
     with pytest.raises(ValueError):
         # "Non-indexed metadata fields cannot be used in queries."
         vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2)
+
+
+def test_cassandra_replace_metadata() -> None:
+    """Test of replacing metadata."""
+    N_DOCS = 100
+    REPLACE_RATIO = 2  # one in ... will have replaced metadata
+    BATCH_SIZE = 3
+
+    vstore_f1 = _vectorstore_from_texts(
+        texts=[],
+        metadata_indexing=("allowlist", ["field1", "field2"]),
+        table_name="vector_test_table_indexing",
+    )
+    orig_documents = [
+        Document(
+            page_content=f"doc_{doc_i}",
+            id=f"doc_id_{doc_i}",
+            metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+        )
+        for doc_i in range(N_DOCS)
+    ]
+    vstore_f1.add_documents(orig_documents)
+
+    ids_to_replace = [
+        f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
+    ]
+
+    # various kinds of replacement at play here:
+    def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
+        if mode == 0:
+            return {}
+        elif mode == 1:
+            return {"field2": f"NEW_{doc_id}"}
+        elif mode == 2:
+            return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
+        else:
+            return {"ofherf2": "post"}
+
+    ids_to_new_md = {
+        doc_id: _make_new_md(rep_i % 4, doc_id)
+        for rep_i, doc_id in enumerate(ids_to_replace)
+    }
+
+    vstore_f1.replace_metadata(ids_to_new_md, batch_size=BATCH_SIZE)
+    # thorough check
+    expected_id_to_metadata: dict[str, dict] = {
+        **{(document.id or ""): document.metadata for document in orig_documents},
+        **ids_to_new_md,
+    }
+    for hit in vstore_f1.similarity_search("doc", k=N_DOCS + 1):
+        assert hit.id is not None
+        assert hit.metadata == expected_id_to_metadata[hit.id]
+
+
+async def test_cassandra_areplace_metadata() -> None:
+    """Test of replacing metadata."""
+    N_DOCS = 100
+    REPLACE_RATIO = 2  # one in ... will have replaced metadata
+    BATCH_SIZE = 3
+
+    vstore_f1 = _vectorstore_from_texts(
+        texts=[],
+        metadata_indexing=("allowlist", ["field1", "field2"]),
+        table_name="vector_test_table_indexing",
+    )
+    orig_documents = [
+        Document(
+            page_content=f"doc_{doc_i}",
+            id=f"doc_id_{doc_i}",
+            metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+        )
+        for doc_i in range(N_DOCS)
+    ]
+    await vstore_f1.aadd_documents(orig_documents)
+
+    ids_to_replace = [
+        f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
+    ]
+
+    # various kinds of replacement at play here:
+    def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
+        if mode == 0:
+            return {}
+        elif mode == 1:
+            return {"field2": f"NEW_{doc_id}"}
+        elif mode == 2:
+            return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
+        else:
+            return {"ofherf2": "post"}
+
+    ids_to_new_md = {
+        doc_id: _make_new_md(rep_i % 4, doc_id)
+        for rep_i, doc_id in enumerate(ids_to_replace)
+    }
+
+    await vstore_f1.areplace_metadata(ids_to_new_md, concurrency=BATCH_SIZE)
+    # thorough check
+    expected_id_to_metadata: dict[str, dict] = {
+        **{(document.id or ""): document.metadata for document in orig_documents},
+        **ids_to_new_md,
+    }
+    for hit in await vstore_f1.asimilarity_search("doc", k=N_DOCS + 1):
+        assert hit.id is not None
+        assert hit.metadata == expected_id_to_metadata[hit.id]