app.py

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import CacheBackedEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.storage import LocalFileStore
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
import chainlit as cl


text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

system_template = """
Use the following pieces of context to answer the user's question.
Please respond as if you are a human female customer service representative for Daysoff, 
a Norwegian company that provides welfare services by offering access to cottages and 
apartments for employees of member companies.
By default, you respond (in Norwegian language) using a warm, direct, and professional tone. 
Your expertise covers FAQs, and privacy policies. 
If you don't know the answer, just say that you don't know, don't try to make up an answer:
politely redirect the user to customer service at [email protected] and remind them to always 
include their booking id (bestillingskode). 
You can make inferences based on the context as long as it still faithfully represents the feedback.

Example of how your response should be direct:

```
foo      
```

Begin!
----------------
{context}"""

messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate(messages=messages)
chain_type_kwargs = {"prompt": prompt}

@cl.author_rename
def rename(orig_author: str):
    rename_dict = {"Just a moment": "Thinking.."}
    return rename_dict.get(orig_author, orig_author)

@cl.on_chat_start
async def init():
    msg = cl.Message(content=f"Building vector store...")
    await msg.send()

    loader = CSVLoader(file_path="./data/total_faq.csv", source_column="Answer") 
    data = loader.load()

    for i, doc in enumerate(data):
        doc.metadata["row_index"] = i + 1  
        doc.metadata["source"] = doc.metadata.get("Info_Url", "") 

    documents = text_splitter.transform_documents(data)
    store = LocalFileStore("./cache/")
    core_embeddings_model = OpenAIEmbeddings()
    embedder = CacheBackedEmbeddings.from_bytes_store(
        core_embeddings_model, store, namespace=core_embeddings_model.model
    )
    docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)

    chain = RetrievalQA.from_chain_type(
        ChatOpenAI(model="gpt-4", temperature=0.0, streaming=True), # streaming=False
        chain_type="stuff",
        return_source_documents=True,
        retriever=docsearch.as_retriever(),
        chain_type_kwargs = {"prompt": prompt}
    )

    markdown_table = """
        ### Eksempler på spørsmål
        
        | # | Spørsmål                               |
        |---|----------------------------------------|
        | 1 | Hvordan registrerer jeg meg som bruker?|
        | 2 | Kan jeg ha med kjæledyr på hytta?      |
        | 3 | Adferdsmessig annonsering?             |
        | 4 | Hvordan blir dataene mine beskyttet?   |
    """

    msg.content = f"FAISS ready. Bare spør ivei..🤓\n\n{markdown_table}"
    await msg.send()
    
    cl.user_session.set("chain", chain)

@cl.on_message
async def main(message):
   
    chain = cl.user_session.get("chain")
    
    cb = cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True, 
        answer_prefix_tokens=["FINAL", "ANSWER"]
    )
    
    cb.answer_reached = True
    
    cb.suppress_intermediate = True
    
    res = await chain.acall(message, callbacks=[cb])
    
    answer = res["result"]
    source_elements = []
    #visited_sources = set()

    docs = res.get("source_documents", [])
    metadatas = [doc.metadata for doc in docs]
  
    for doc, metadata in zip(docs, metadatas):
        row_index = metadata.get("row_index", -1) 
        source = metadata.get("source", "") 

        if row_index in [2, 8, 14] and source and source not in visited_sources:
            visited_sources.add(source)
            source_elements.append(
                cl.Text(content="https://www.daysoff.no" + source, name="Info_Url")
            )

    if source_elements:
        answer += f"\nSources: {', '.join([e.content for e in source_elements])}"
        #await cl.Message(content="Sources: " + ", ".join([e.content for e in source_elements])).send()
    
    if answer != last_response:
        await cl.Message(content=answer, elements=source_elements if source_elements else None).send()
        last_response = answer
        return
        
    else:
        await cl.Message(content="No new information available.").send()