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import chainlit as cl
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 an air-headed beach bro.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Example of your response should be:
```
The answer is 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 = {"RetrievalQA": "Consulting The Kens"}
return rename_dict.get(orig_author, orig_author)
@cl.on_chat_start
async def init():
msg = cl.Message(content=f"Building Index...")
await msg.send()
# build FAISS index from csv
loader = CSVLoader(file_path="./data/barbie.csv", source_column="Review_Url")
data = loader.load()
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
)
# make async docsearch
docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
chain = RetrievalQA.from_chain_type(
ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
chain_type="stuff",
return_source_documents=True,
retriever=docsearch.as_retriever(),
chain_type_kwargs = {"prompt": prompt}
)
msg.content = f"Index built!"
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=False, answer_prefix_tokens=["FINAL", "ANSWER"]
)
cb.answer_reached = True
res = await chain.acall(message, callbacks=[cb])
answer = res["result"]
source_elements = []
visited_sources = set()
# Get the documents from the user session
docs = res["source_documents"]
metadatas = [doc.metadata for doc in docs]
all_sources = [m["source"] for m in metadatas]
for source in all_sources:
if source in visited_sources:
continue
visited_sources.add(source)
# Create the text element referenced in the message
source_elements.append(
cl.Text(content="https://www.imdb.com" + source, name="Review URL")
)
if source_elements:
answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
else:
answer += "\nNo sources found"
await cl.Message(content=answer, elements=source_elements).send()
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