File size: 1,123 Bytes
931948b 98cef23 e8df325 98cef23 533e529 98cef23 cec0fe3 98cef23 f9d56ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
import streamlit as st
from langchain.document_loaders import TextLoader
import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain import HuggingFaceHub
from langchain.chains import RetrievalQA
# load huggingface api key
hub_token = os.environ["hub_key"]
# Load text
loader = TextLoader("testing.txt")
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
docs = splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
doc_search = Chroma.from_documents(docs, embeddings)
repo_id = "mistralai/Mistral-7B-v0.1"
llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hub_token, model_kwargs={'temperature': 0.2,'min_length': 4000})
from langchain.schema import retriever
retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
if query := st.chat_input("Enter your query "):
with st.chat_message("Assistant"):
st.write(retireval_chain.run(query))
|