update embed model

utils.py

@@ -24,12 +24,12 @@ def retriever(n_docs=5):
     vector_database_path = "chromadb3"
 
     #embeddings_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
-    embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
 
     vectorstore = Chroma(collection_name="chroma_db",
                         persist_directory=vector_database_path,
-                    embedding_function=embeddings_model)
+                    embedding_function=embedding_model)
 
     vs_retriever = vectorstore.as_retriever(k=n_docs)
 
@@ -96,7 +96,9 @@ def get_expression_chain(retriever: BaseRetriever, model_name="llama-3.1-70b-ver
     chain = ingress | prompt | llm
     return chain
 
-embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+#embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+
 #Generate embeddings for a given text
 def get_embeddings(text):
     return embedding_model.embed([text], task_type='search_document')[0]