diff --git a/example.env b/example.env
index 149eca2..8290784 100644
--- a/example.env
+++ b/example.env
@@ -1,5 +1,5 @@
 PERSIST_DIRECTORY=db
-LLAMA_EMBEDDINGS_MODEL=models/ggml-model-q4_0.bin
 MODEL_TYPE=GPT4All
 MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin
+EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2
 MODEL_N_CTX=1000
\ No newline at end of file
diff --git a/ingest.py b/ingest.py
index 4c95586..2c70362 100644
--- a/ingest.py
+++ b/ingest.py
@@ -6,7 +6,7 @@ from dotenv import load_dotenv
 from langchain.document_loaders import TextLoader, PDFMinerLoader, CSVLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
-from langchain.embeddings import LlamaCppEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.docstore.document import Document
 from constants import CHROMA_SETTINGS
 
@@ -38,22 +38,23 @@ def main():
     # Load environment variables
     persist_directory = os.environ.get('PERSIST_DIRECTORY')
     source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
-    llama_embeddings_model = os.environ.get('LLAMA_EMBEDDINGS_MODEL')
-    model_n_ctx = os.environ.get('MODEL_N_CTX')
+    embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
 
     # Load documents and split in chunks
     print(f"Loading documents from {source_directory}")
+    chunk_size = 500
+    chunk_overlap = 50
     documents = load_documents(source_directory)
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
     texts = text_splitter.split_documents(documents)
     print(f"Loaded {len(documents)} documents from {source_directory}")
-    print(f"Split into {len(texts)} chunks of text (max. 500 tokens each)")
+    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} characters each)")
 
     # Create embeddings
-    llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
     
     # Create and store locally vectorstore
-    db = Chroma.from_documents(texts, llama, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
     db.persist()
     db = None
 
diff --git a/privateGPT.py b/privateGPT.py
index 4c603a2..ae08bb9 100644
--- a/privateGPT.py
+++ b/privateGPT.py
@@ -1,6 +1,6 @@
 from dotenv import load_dotenv
 from langchain.chains import RetrievalQA
-from langchain.embeddings import LlamaCppEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import Chroma
 from langchain.llms import GPT4All, LlamaCpp
@@ -8,7 +8,7 @@ import os
 
 load_dotenv()
 
-llama_embeddings_model = os.environ.get("LLAMA_EMBEDDINGS_MODEL")
+embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
 persist_directory = os.environ.get('PERSIST_DIRECTORY')
 
 model_type = os.environ.get('MODEL_TYPE')
@@ -18,8 +18,8 @@ model_n_ctx = os.environ.get('MODEL_N_CTX')
 from constants import CHROMA_SETTINGS
 
 def main():
-    llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
-    db = Chroma(persist_directory=persist_directory, embedding_function=llama, client_settings=CHROMA_SETTINGS)
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
     retriever = db.as_retriever()
     # Prepare the LLM
     callbacks = [StreamingStdOutCallbackHandler()]