Upload app.py

app.py

@@ -36,24 +36,12 @@ text_splitter = RecursiveCharacterTextSplitter(
 documents = text_splitter.split_documents(data)
 
 
-#from transformers import AutoTokenizer
-
-#bloomz_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-1b7")
-
 from langchain.embeddings.openai import OpenAIEmbeddings
 
 embeddings = OpenAIEmbeddings()
 
 
 
-#text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer, chunk_size=100, chunk_overlap=0, separator="\n")
-
-#documents = text_splitter.split_documents(data)
-
-#from langchain.embeddings import HuggingFaceEmbeddings
-
-#embeddings = HuggingFaceEmbeddings()
-
 from langchain.vectorstores import Chroma
 
 persist_directory = "vector_db"
@@ -78,7 +66,7 @@ shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retrie
 
 
 
-chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+#chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
 
 def make_inference(query):
     docs = shakespeare_qa.get_relevant_documents(query)