Adjust for mypy and tests

libs/langchain/langchain/vectorstores/sqlitevss.py

@@ -5,7 +5,6 @@
 import sqlite3
 import warnings
 from typing import (
-    TYPE_CHECKING,
     Any,
     Iterable,
     List,
@@ -18,9 +17,6 @@
 from langchain.embeddings.base import Embeddings
 from langchain.vectorstores.base import VectorStore
 
-if TYPE_CHECKING:
-    import sqlite_vss  # noqa  # pylint: disable=unused-import
-
 logger = logging.getLogger(__name__)
 
 
@@ -67,10 +63,10 @@ def create_table_if_not_exists(self) -> None:
             f"""
             CREATE TABLE IF NOT EXISTS {self._table}
             (
-              text_id INT PRIMARY KEY AUTOINCREMENT,
-              text text,
-              metadata blob,
-              text_embedding blob
+              rowid INTEGER PRIMARY KEY AUTOINCREMENT,
+              text TEXT,
+              metadata BLOB,
+              text_embedding BLOB
             )
             ;
             """
@@ -108,8 +104,11 @@ def add_texts(
             kwargs: vectorstore specific parameters
         """
         max_id = self._connection.execute(
-            f"SELECT max(text_id) as text_id FROM {self._table}"
-        ).fetchone()["text_id"]
+            f"SELECT max(rowid) as rowid FROM {self._table}"
+        ).fetchone()["rowid"]
+        if max_id is None:  # no text added yet
+            max_id = 0
+
         embeds = self._embedding.embed_documents(list(texts))
         if not metadatas:
             metadatas = [{} for _ in texts]
@@ -123,12 +122,11 @@ def add_texts(
             data_input,
         )
         self._connection.commit()
-
         # pulling every ids we just inserted
         results = self._connection.execute(
-            f"SELECT text_id FROM {self._table} WHERE text_id > {max_id}"
+            f"SELECT rowid FROM {self._table} WHERE rowid > {max_id}"
         )
-        return [row["text_id"] for row in results]
+        return [row["rowid"] for row in results]
 
     def similarity_search_with_score_by_vector(
         self, embedding: List[float], k: int = 4, **kwargs: Any
@@ -139,7 +137,7 @@ def similarity_search_with_score_by_vector(
                 metadata,
                 distance
             FROM {self._table} e
-            INNER JOIN vss_{self._table} v on v.text_id = e.text_id  
+            INNER JOIN vss_{self._table} v on v.rowid = e.rowid  
             WHERE vss_search(
               v.text_embedding,
               vss_search_params('{json.dumps(embedding)}', {k})
@@ -151,9 +149,8 @@ def similarity_search_with_score_by_vector(
 
         documents = []
         for row in results:
-            doc = Document(
-                page_content=row["text"], metadata=json.loads(row["metadata"])
-            )
+            metadata = json.loads(row["metadata"]) or {}
+            doc = Document(page_content=row["text"], metadata=metadata)
             score = self._euclidean_relevance_score_fn(row["distance"])
             documents.append((doc, score))
 
@@ -207,6 +204,8 @@ def from_texts(
 
     @staticmethod
     def create_connection(db_file: str) -> sqlite3.Connection:
+        import sqlite_vss
+
         connection = sqlite3.connect(db_file)
         connection.row_factory = sqlite3.Row
         connection.enable_load_extension(True)

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

@@ -27,7 +27,7 @@ def test_sqlitevss() -> None:
     """Test end to end construction and search."""
     docsearch = _sqlite_vss_from_texts()
     output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo", metadata=None)]
+    assert output == [Document(page_content="foo", metadata={})]
 
 
 @pytest.mark.requires("sqlite-vss")
@@ -44,7 +44,7 @@ def test_sqlitevss_with_score() -> None:
         Document(page_content="bar", metadata={"page": 1}),
         Document(page_content="baz", metadata={"page": 2}),
     ]
-    assert scores[0] < scores[1] < scores[2]
+    assert scores[0] > scores[1] > scores[2]
 
 
 @pytest.mark.requires("sqlite-vss")
@@ -53,8 +53,6 @@ def test_sqlitevss_add_extra() -> None:
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = _sqlite_vss_from_texts(metadatas=metadatas)
-
     docsearch.add_texts(texts, metadatas)
-
     output = docsearch.similarity_search("foo", k=10)
     assert len(output) == 6