diff --git a/duas_query.py b/duas_query.py
index 9c786e4..5956e3a 100644
--- a/duas_query.py
+++ b/duas_query.py
@@ -1,4 +1,4 @@
-from langchain_openai import OpenAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from dotenv import load_dotenv
 from langchain_postgres import PGVector
 import os
@@ -9,7 +9,7 @@ COLLECTION_NAME = os.getenv('COLLECTION_NAME')
 
 
 # Initialize embeddings (needed for query encoding only)
-embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
 # Load existing vector store
 db = PGVector(
diff --git a/generate_dua_tags_embedding.py b/generate_dua_tags_embedding.py
index 103f373..d95d37f 100644
--- a/generate_dua_tags_embedding.py
+++ b/generate_dua_tags_embedding.py
@@ -1,5 +1,5 @@
 import json
-from langchain_openai import OpenAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 # from langchain.vectorstores.pgvector import PGVector
 from langchain_postgres import PGVector
 # from langchain.schema import Document
@@ -56,7 +56,7 @@ for dua in duas_data:
 print(f"Created {len(documents)} documents from tags")
 
 # Initialize embeddings
-embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
 # Create vector store - embeddings will be created only from tags (page_content)
 print("Creating embeddings and storing in pgvector...")
diff --git a/main.py b/main.py
index b8a4549..c44d89f 100644
--- a/main.py
+++ b/main.py
@@ -2,7 +2,7 @@ from fastapi import FastAPI, HTTPException, Query
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 from typing import List, Optional
-from langchain_openai import OpenAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_postgres import PGVector
 from dotenv import load_dotenv
 import uvicorn
@@ -32,7 +32,7 @@ COLLECTION_NAME = os.getenv('COLLECTION_NAME')
 
 
 # Initialize embeddings and vector store
-embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 db = PGVector(
     collection_name=COLLECTION_NAME,
     connection=CONNECTION_STRING,