updating to do batch inserts

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/providers/inline/vector_io/sqlite_vec/config.py

@@ -18,6 +18,7 @@ from llama_stack.providers.utils.kvstore.config import (
 class SQLiteVectorIOConfig(BaseModel):
     db_path: str
     kvstore: KVStoreConfig
+    batch_size: bool = 500
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -52,7 +52,7 @@ class SQLiteVecIndex(EmbeddingIndex):
         # Create the table to store chunk metadata.
         cur.execute(f"""
             CREATE TABLE IF NOT EXISTS {self.metadata_table} (
-                id INTEGER PRIMARY KEY,
+                id TEXT PRIMARY KEY,
                 chunk TEXT
             );
         """)
@@ -74,33 +74,44 @@ class SQLiteVecIndex(EmbeddingIndex):
         Add new chunks along with their embeddings using batch inserts.
         For each chunk, we insert its JSON into the metadata table and then insert its
         embedding (serialized to raw bytes) into the virtual table using the assigned rowid.
-        If any insert fails, the transaction is rolled back.
+        If any insert fails, the transaction is rolled back to maintain consistency.
         """
         cur = self.connection.cursor()
         try:
             # Start transaction
             cur.execute("BEGIN TRANSACTION")
-            # Serialize and insert the chunk metadata.
+            for i in range(0, len(chunks), self.config.batch_size):
-            chunk_data = [(chunk.model_dump_json(),) for chunk in chunks]
+                batch_chunks = chunks[i : i + self.config.batch_size]
-            cur.executemany(f"INSERT INTO {self.metadata_table} (chunk) VALUES (?)", chunk_data)
+                batch_embeddings = embeddings[i : i + self.config.batch_size]
-            # Fetch the last *n* inserted row_ids -- note: this is more reliable than `row_id = cur.lastrowid`
+                # Prepare metadata inserts
-            cur.execute(f"SELECT rowid FROM {self.metadata_table} ORDER BY rowid DESC LIMIT {len(chunks)}")
+                metadata_data = [
-            row_ids = [row[0] for row in cur.fetchall()]
+                    (f"{chunk.metadata['document_id']}:chunk-{j}", chunk.model_dump_json())
-            row_ids.reverse()  # Reverse to maintain the correct order of insertion
+                    for j, chunk in enumerate(batch_chunks)
-            # Insert embeddings using the retrieved row IDs
+                ]
-            embedding_data = [
+                # Insert metadata (ON CONFLICT to avoid duplicates)
-                (row_id, serialize_vector(emb.tolist() if isinstance(emb, np.ndarray) else list(emb)))
+                cur.executemany(
-                for row_id, emb in zip(row_ids, embeddings, strict=True)
+                    f"""
-            ]
+                    INSERT INTO {self.metadata_table} (id, document)
-            cur.executemany(f"INSERT INTO {self.vector_table} (rowid, embedding) VALUES (?, ?)", embedding_data)
+                    VALUES (?, ?)
-            # Commit transaction if all inserts succeed
+                    ON CONFLICT(id) DO UPDATE SET document = excluded.document;
+                    """,
+                    metadata_data,
+                )
+                # Prepare embeddings inserts
+                embedding_data = [
+                    (f"{chunk.metadata['document_id']}:chunk-{j}", serialize_vector(emb.tolist()))
+                    for j, (chunk, emb) in enumerate(zip(batch_chunks, batch_embeddings, strict=True))
+                ]
+                # Insert embeddings in batch
+                cur.executemany(f"INSERT INTO {self.vector_table} (rowid, embedding) VALUES (?, ?);", embedding_data)
             self.connection.commit()
+
         except sqlite3.Error as e:
             self.connection.rollback()  # Rollback on failure
             logger.error(f"Error inserting into {self.vector_table}: {e}")
 
         finally:
-            cur.close()
+            cur.close()  # Ensure cursor is closed
 
     async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         """