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import streamlit as st
from langchain_community.document_loaders import PyPDFLoader
## dockerized streamlit app wants to read from os.getenv(), otherwise use st.secrets
import os
api_key = os.getenv("LITELLM_KEY")
if api_key is None:
api_key = st.secrets["LITELLM_KEY"]
cirrus_key = os.getenv("CIRRUS_KEY")
if cirrus_key is None:
cirrus_key = st.secrets["CIRRUS_KEY"]
st.title("HWC LLM Testing")
'''
(Demo will take a while to load first while processing all data! Will be pre-processed in future...)
'''
# +
import bs4
from langchain import hub
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
import os
import requests
import zipfile
def download_and_unzip(url, output_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
response = requests.get(url)
zip_file_path = os.path.basename(url)
with open(zip_file_path, 'wb') as f:
f.write(response.content)
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(output_dir)
os.remove(zip_file_path)
url = "https://minio.carlboettiger.info/public-data/hwc.zip"
output_dir = "hwc"
download_and_unzip(url, "hwc")
import pathlib
@st.cache_data
def pdf_loader(path):
all_documents = []
docs_dir = pathlib.Path(path)
for file in docs_dir.iterdir():
loader = PyPDFLoader(file)
documents = loader.load()
all_documents.extend(documents)
return all_documents
docs = pdf_loader('hwc/')
# Set up the language model
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model = "llama3", api_key = api_key, base_url = "https://llm.nrp-nautilus.io", temperature=0)
## Cirrus instead:
embedding = OpenAIEmbeddings(
model = "cirrus",
api_key = cirrus_key,
base_url = "https://llm.cirrus.carlboettiger.info/v1",
)
# Build a retrival agent
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = InMemoryVectorStore.from_documents(documents=splits, embedding=embedding)
retriever = vectorstore.as_retriever()
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
system_prompt = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer "
"the question. If you don't know the answer, say that you "
"don't know. Use three sentences maximum and keep the "
"answer concise."
"\n\n"
"{context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
# Place agent inside a streamlit application:
if prompt := st.chat_input("What is the goal of CA 30x30?"):
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
results = rag_chain.invoke({"input": prompt})
st.write(results['answer'])
with st.expander("See context matched"):
st.write(results['context'][0].page_content)
st.write(results['context'][0].metadata)
# adapt for memory / multi-question interaction with:
# https://python.langchain.com/docs/tutorials/qa_chat_history/
# Also see structured outputs.
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