diff --git a/chroma_preference.py b/chroma_preference.py
new file mode 100644
index 0000000..d5e4383
--- /dev/null
+++ b/chroma_preference.py
@@ -0,0 +1,11 @@
+from chromadb.config import Settings
+
+# Define the folder for storing database
+PERSIST_DIRECTORY = 'db'
+
+# Define the Chroma settings
+CHROMA_SETTINGS = Settings(
+        chroma_db_impl='duckdb+parquet',
+        persist_directory=PERSIST_DIRECTORY,
+        anonymized_telemetry=False
+)
\ No newline at end of file
diff --git a/ingest.py b/ingest.py
index e8b08e6..ee900c1 100644
--- a/ingest.py
+++ b/ingest.py
@@ -3,6 +3,8 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from langchain.embeddings import LlamaCppEmbeddings
 from sys import argv
+from chroma_preference import PERSIST_DIRECTORY
+from chroma_preference import CHROMA_SETTINGS
 
 def main():
     # Load document and split in chunks
@@ -13,8 +15,7 @@ def main():
     # Create embeddings
     llama = LlamaCppEmbeddings(model_path="./models/ggml-model-q4_0.bin")
     # Create and store locally vectorstore
-    persist_directory = 'db'
-    db = Chroma.from_documents(texts, llama, persist_directory=persist_directory)
+    db = Chroma.from_documents(texts, llama, persist_directory=PERSIST_DIRECTORY, client_settings=CHROMA_SETTINGS)
     db.persist()
     db = None
 
diff --git a/privateGPT.py b/privateGPT.py
index 817a5e3..7ebaec1 100644
--- a/privateGPT.py
+++ b/privateGPT.py
@@ -3,12 +3,13 @@ from langchain.embeddings import LlamaCppEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import Chroma
 from langchain.llms import GPT4All
+from chroma_preference import PERSIST_DIRECTORY
+from chroma_preference import CHROMA_SETTINGS
 
 def main():        
     # Load stored vectorstore
     llama = LlamaCppEmbeddings(model_path="./models/ggml-model-q4_0.bin")
-    persist_directory = 'db'
-    db = Chroma(persist_directory=persist_directory, embedding_function=llama)
+    db = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=llama, client_settings=CHROMA_SETTINGS)
     retriever = db.as_retriever()
     # Prepare the LLM
     callbacks = [StreamingStdOutCallbackHandler()]