app.py

import streamlit as st

st.title('🦜🔗 NHS AI assisted search')
st.write('A demo of a search empowered by OpenAI LLM')
query = st.text_input('Your query:') 

# get relevant docs from vector store
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
vectorstore = Chroma(
    embedding_function = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2'),
    persist_directory='vectorstore',
)

# arrange search template
template = """As a doctor's assistant, when presented with a user's medical query and specific website context, generate consolidated guidance directing them to pertinent website links.
If multiple context chunks are similar, combine them into a single suggestion, citing all relevant sources using hyperlinked text in the format <a href="SOURCE_HERE">description</a>.
If the context doesn't directly address the query, state the absence of a direct match and provide the closest matching guidance.
Always adhere strictly to the provided context and maintain terminology consistency.
Don't mention the word "context" in your answer. Pretend that you're answering the user directly.

Query: {question}
Context:
{context}"""

# run script if query is set
if query:

    # search vectorstores
    docs = vectorstore.similarity_search(query, 6)

    # arrange context string
    context = '';
    for doc in docs:
        context += doc.page_content + "\n"
        context += 'Source: ' + doc.metadata['source'] + "\n\n"

    from langchain.prompts import PromptTemplate
    prompt = PromptTemplate.from_template(template)

    # create an LLM
    from langchain.chat_models import ChatOpenAI
    llm = ChatOpenAI(
        model_name='gpt-3.5-turbo',
        temperature=0.8,
    )

    # arrange a LLM chain
    from langchain.chains import LLMChain
    chain = LLMChain(
        llm = llm,
        prompt = prompt,
        verbose = True,
    )

    result = chain({
        'question': query,
        'context': context,
    })
    st.write(result['text'])