diff --git a/app/core/config.py b/app/core/config.py
index ec1b6e6..c7425f1 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -76,7 +76,7 @@ class Settings(BaseSettings):
     # Caching
     CACHE_TTL: int = 300  # 5 minutes
 
-    GEMINI_API_KEY: str
+    GEMINI_API_KEY: str = "secret"
 
     model_config = SettingsConfigDict(
         env_file=ENV_FILE,
diff --git a/app/services/rag_service.py b/app/services/rag_service.py
index a619515..c8c23c2 100644
--- a/app/services/rag_service.py
+++ b/app/services/rag_service.py
@@ -6,6 +6,8 @@ import litellm
 import numpy as np
 import psycopg2
 from dotenv import load_dotenv
+from psycopg2 import extras
+from psycopg2.extensions import AsIs, register_adapter
 from PyPDF2 import PdfReader
 from sentence_transformers import SentenceTransformer
 from structlog import get_logger
@@ -14,6 +16,7 @@ from app.core.config import settings
 from app.core.exception import DocumentExtractionError, DocumentInsertionError
 from app.core.utils import RecursiveCharacterTextSplitter
 
+register_adapter(np.ndarray, AsIs)  # for psycopg2 adapt
 logger = get_logger()
 
 # pyright: reportArgumentType=false
@@ -79,47 +82,64 @@ Answer:"""
         )
         return text_splitter.split_text(text)
 
-    def _get_embedding(self, text: str) -> np.ndarray:
+    def _get_embedding(self, text: str, show_progress_bar: bool = False) -> np.ndarray:
         """
         Generate embedding for a text chunk.
 
         Args:
             text: Input text to embed
+            show_progress_bar: Whether to show a progress bar
 
         Returns:
             Numpy array containing the embedding vector
 
         """
-        return EMBEDDING_MODEL.encode(text, convert_to_numpy=True)
+        return EMBEDDING_MODEL.encode(
+            text, convert_to_numpy=True, show_progress_bar=show_progress_bar
+        )
 
-    def _store_document(self, content: str, embedding: np.ndarray, source: str) -> int:
+    def _store_document(
+        self, contents: list[str], embeddings: list[np.ndarray], source: str
+    ) -> int:
         """
         Store a document chunk in the database.
 
         Args:
-            content: Text content of the chunk
-            embedding: Embedding vector of the chunk
+            contents: List of text content of the chunk
+            embeddings: List of embedding vectors of the chunk
             source: Source file path
 
         Returns:
             ID of the inserted document
 
         """
+        data_to_insert = [
+            (chunk, f"[{', '.join(map(str, embedding))}]", source)
+            for chunk, embedding in zip(contents, embeddings, strict=True)
+        ]
+
+        query = """
+            INSERT INTO documents (content, embedding, source)
+            VALUES %s
+            RETURNING id
+        """
         with self.db_conn.cursor() as cursor:
-            cursor.execute(
-                """
-                INSERT INTO documents (content, embedding, source)
-                VALUES (%s, %s, %s)
-                RETURNING id
-            """,
-                (content, embedding.tolist(), source),
+            extras.execute_values(
+                cursor,
+                query,
+                data_to_insert,
+                template="(%s, %s::vector, %s)",
+                page_size=100,
             )
-            doc_id = cursor.fetchone()
-            if doc_id is None:
-                err = "Failed to insert document into database"
-                raise DocumentInsertionError(err)
+            inserted_ids = [row[0] for row in cursor.fetchall()]
             self.db_conn.commit()
-            return doc_id[0]
+
+            if not inserted_ids:
+                raise DocumentInsertionError("No documents were inserted.")
+
+            logger.info("Successfully bulk-ingested %d documents", len(inserted_ids))
+            logger.info("Inserted document IDs: %s", inserted_ids)
+            return inserted_ids[0]
 
     def _extract_text_from_pdf(self, pdf_path: str) -> str:
         """
@@ -182,10 +202,8 @@ Answer:"""
         chunks = self._split_text(text)
         logger.info("Split PDF into %d chunks", len(chunks))
 
-        for i, chunk in enumerate(chunks, 1):
-            logger.info("Processing chunk %d/%d", i, len(chunks))
-            embedding = self._get_embedding(chunk)
-            self._store_document(chunk, embedding, filename)
+        embeddings = self._get_embedding(chunks, show_progress_bar=True)
+        self._store_document(chunks, embeddings, filename)
 
         logger.info("Successfully processed %d chunks from %s", len(chunks), filename)