Add more configurations for hnswlib

Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>

langchain/vectorstores/hnsw_lib.py

@@ -24,6 +24,13 @@ def __init__(
         work_dir: str,
         n_dim: int,
         dist_metric: str = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
     ) -> None:
         """Initialize HnswLib store.
 
@@ -33,6 +40,19 @@ def __init__(
             n_dim (int): dimension of an embedding.
             dist_metric (str): Distance metric for HnswLib can be one of: "cosine",
                 "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
         """
         _check_docarray_import()
         from docarray.index import HnswDocumentIndex
@@ -45,7 +65,19 @@ def __init__(
                 "Please install it with `pip install \"langchain[hnswlib]\"`."
             )
 
-        doc_cls = self._get_doc_cls({"dim": n_dim, "space": dist_metric})
+        doc_cls = self._get_doc_cls(
+            {
+                "dim": n_dim,
+                "space": dist_metric,
+                "max_elements": max_elements,
+                "index": index,
+                "ef_construction": ef_construction,
+                "ef": ef,
+                "M": M,
+                "allow_replace_deleted": allow_replace_deleted,
+                "num_threads": num_threads,
+            }
+        )
         doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)
         super().__init__(doc_index, embedding)
 
@@ -58,6 +90,13 @@ def from_texts(
         work_dir: str = None,
         n_dim: int = None,
         dist_metric: str = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
     ) -> HnswLib:
         """Create an HnswLib store and insert data.
 
@@ -70,6 +109,19 @@ def from_texts(
             n_dim (int): dimension of an embedding.
             dist_metric (str): Distance metric for HnswLib can be one of: "cosine",
                 "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
 
         Returns:
             HnswLib Vector Store

tests/integration_tests/vectorstores/test_hnsw_lib.py

@@ -36,7 +36,7 @@ def test_hnswlib_vec_store_add_texts(tmp_path) -> None:
     assert docsearch.doc_index.num_docs() == 3
 
 
-@pytest.mark.parametrize('metric', ['cosine', 'ip', 'l2'])
+@pytest.mark.parametrize('metric', ['cosine', 'l2'])
 def test_sim_search(metric, tmp_path) -> None:
     """Test end to end construction and simple similarity search."""
     texts = ["foo", "bar", "baz"]
@@ -45,12 +45,35 @@ def test_sim_search(metric, tmp_path) -> None:
         FakeEmbeddings(),
         work_dir=str(tmp_path),
         n_dim=10,
+        dist_metric=metric,
+    )
+    output = hnswlib_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize('metric', ['cosine', 'l2'])
+def test_sim_search_all_configurations(metric, tmp_path) -> None:
+    """Test end to end construction and simple similarity search."""
+    texts = ["foo", "bar", "baz"]
+    hnswlib_vec_store = HnswLib.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        dist_metric=metric,
+        n_dim=10,
+        max_elements=8,
+        index=False,
+        ef_construction=300,
+        ef=20,
+        M=8,
+        allow_replace_deleted=False,
+        num_threads=2,
     )
     output = hnswlib_vec_store.similarity_search("foo", k=1)
     assert output == [Document(page_content="foo")]
 
 
-@pytest.mark.parametrize('metric', ['cosine', 'ip', 'l2'])
+@pytest.mark.parametrize('metric', ['cosine', 'l2'])
 def test_sim_search_by_vector(metric, tmp_path) -> None:
     """Test end to end construction and similarity search by vector."""
     texts = ["foo", "bar", "baz"]
@@ -59,14 +82,15 @@ def test_sim_search_by_vector(metric, tmp_path) -> None:
         FakeEmbeddings(),
         work_dir=str(tmp_path),
         n_dim=10,
+        dist_metric=metric,
     )
     embedding = [1.0] * 10
     output = hnswlib_vec_store.similarity_search_by_vector(embedding, k=1)
 
     assert output == [Document(page_content="bar")]
 
 
-@pytest.mark.parametrize('metric', ['cosine', 'ip', 'l2'])
+@pytest.mark.parametrize('metric', ['cosine', 'l2'])
 def test_sim_search_with_score(metric, tmp_path) -> None:
     """Test end to end construction and similarity search with score."""
     texts = ["foo", "bar", "baz"]
@@ -75,6 +99,7 @@ def test_sim_search_with_score(metric, tmp_path) -> None:
         FakeEmbeddings(),
         work_dir=str(tmp_path),
         n_dim=10,
+        dist_metric=metric,
     )
     output = hnswlib_vec_store.similarity_search_with_score("foo", k=1)
     assert len(output) == 1
@@ -84,6 +109,26 @@ def test_sim_search_with_score(metric, tmp_path) -> None:
     assert np.isclose(out_score, 0.0, atol=1.e-6)
 
 
+def test_sim_search_with_score_for_ip_metric(tmp_path) -> None:
+    """
+    Test end to end construction and similarity search with score for ip
+    (inner-product) metric.
+    """
+    texts = ["foo", "bar", "baz"]
+    hnswlib_vec_store = HnswLib.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric='ip',
+    )
+    output = hnswlib_vec_store.similarity_search_with_score("foo", k=3)
+    assert len(output) == 3
+
+    for result in output:
+        assert result[1] == -8.0
+
+
 @pytest.mark.parametrize('metric', ['cosine', 'l2'])
 def test_max_marginal_relevance_search(metric, tmp_path) -> None:
     """Test MRR search."""