cleaned comments

app.py

@@ -1,4 +1,3 @@
-from ast import Delete
 import streamlit as st
 from streamlit_chat import message
 from ingest_data import embed_doc

ingest_data.py

@@ -25,13 +25,8 @@ def embed_doc(filename):
         st.text("Load and split text: "+str(round(end - start,1)))
 
 
-        # Load Data to vectorstore
         start = time.time()
-#        embeddings = LlamaCppEmbeddings(model_path="ggml-model.bin")
-#        embeddings = HuggingFaceEmbeddings(model_name="diptanuc/all-mpnet-base-v2", model_kwargs={'device': 'cpu'})
-#        embeddings = TensorflowHubEmbeddings(model_url="https://tfhub.dev/google/universal-sentence-encoder/4")
         embeddings = TensorflowHubEmbeddings(model_url="https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3")
-#        embeddings = HuggingFaceEmbeddings(model_name="obrizum/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
         end = time.time()
         st.text("Embedding time: "+str(round(end - start,1)))
         start = time.time()

query_data.py

@@ -1,40 +1,17 @@
-from langchain.prompts.prompt import PromptTemplate
 from langchain.llms import LlamaCpp
 from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
 from huggingface_hub import hf_hub_download
 
 import psutil
 import os
 
-#_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-#You can assume the question about the uploaded document.
 
-#Chat History:
-#{chat_history}
-#Follow Up Input: {question}
-#Standalone question:"""
-#CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-#template = """You are an AI assistant for answering questions about the uploaded document.
-#You are given the following extracted parts of a long document and a question. Provide a conversational answer.
-#If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
-#If the question is not about the uploaded document, politely inform them that you are tuned to only answer questions about the uploaded document.
-#Question: {question}
-
-#Answer in Markdown:"""
-##QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
-#QA_PROMPT = PromptTemplate(template=template, input_variables=["question"])
-
-#=========
-#{context}
-#=========
 
 
 def get_chain(vectorstore):
     if not os.path.exists("ggml-vic7b-q5_1.bin"):
         hf_hub_download(repo_id="eachadea/ggml-vicuna-7b-1.1", filename="ggml-vic7b-q5_1.bin", local_dir=".")
-    llm = LlamaCpp(model_path="ggml-vic7b-q5_1.bin", n_ctx=2048, n_threads=psutil.cpu_count(logical=False)/2)
+    llm = LlamaCpp(model_path="ggml-vic7b-q5_1.bin", n_ctx=2048, n_threads=psutil.cpu_count(logical=False))
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         vectorstore.as_retriever(),