Update app.py

app.py

@@ -16,7 +16,7 @@ token=""
 repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 emb = "sentence-transformers/all-mpnet-base-v2"
 hf = HuggingFaceEmbeddings(model_name=emb)
-db = Chroma(persist_directory="./chroma_langchain_db",embedding_function=HuggingFaceEmbeddings(model_name=emb))
+db = Chroma()
 #db.persist()
 # Load the document, split it into chunks, embed each chunk and load it into the vector store.
 #raw_documents = TextLoader('state_of_the_union.txt').load()
@@ -25,7 +25,7 @@ def embed_fn(inp):
     documents = text_splitter.split_text(inp)
     out_emb= hf.embed_documents(documents)
     string_representation = dumps(out_emb, pretty=True)
-    db.from_texts(documents)
+    db.from_texts(documents,persist_directory="./chroma_langchain_db",embedding_function=HuggingFaceEmbeddings(model_name=emb))
     
 def proc_doc(doc_in):
     for doc in doc_in:
@@ -60,7 +60,7 @@ def run_llm(input_text,history):
     MAX_TOKENS=20000
     try:
         qur= hf.embed_query(input_text)
-        docs = db.similarity_search_by_vector(qur, k=3)
+        docs = db.similarity_search_by_vector(qur, k=3,persist_directory="./chroma_langchain_db",embedding_function=HuggingFaceEmbeddings(model_name=emb))
         
         print(docs)
     except Exception as e: