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import os
from typing import List
from langchain.embeddings import CohereEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.chroma import Chroma
from langchain.chains import (
ConversationalRetrievalChain,
)
from langchain.llms.fireworks import Fireworks
from langchain.chat_models.fireworks import ChatFireworks
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.docstore.document import Document
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langsmith_config import setup_langsmith_config
import openai
import fireworks.client
import chainlit as cl
FIREWORKS_API_KEY = os.getenv("FIREWORKS_API_KEY")
setup_langsmith_config()
os.environ["FIREWORKS_API_KEY"] = FIREWORKS_API_KEY
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.
And if the user greets with greetings like Hi, hello, How are you, etc reply accordingly as well.
Example of your response should be:
The answer is foo
SOURCES: xyz
Begin!
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
@cl.on_chat_start
async def on_chat_start():
files = None
# Wait for the user to upload a file
while files == None:
files = await cl.AskFileMessage(
content="Please upload a text file to begin!",
accept=["text/plain"],
max_size_mb=20,
timeout=180,
).send()
file = files[0]
msg = cl.Message(
content=f"Processing `{file.name}`...", disable_human_feedback=True
)
await msg.send()
# Decode the file
text = file.content.decode("utf-8")
# Split the text into chunks
texts = text_splitter.split_text(text)
# Create a metadata for each chunk
metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
# Create a Chroma vector store
embeddings = CohereEmbeddings(cohere_api_key="COHERE_API_KEY")
docsearch = await cl.make_async(Chroma.from_texts)(
texts, embeddings, metadatas=metadatas
)
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="answer",
chat_memory=message_history,
return_messages=True,
)
# Create a chain that uses the Chroma vector store
chain = ConversationalRetrievalChain.from_llm(
ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
chain_type="stuff",
retriever=docsearch.as_retriever(),
memory=memory,
return_source_documents=True,
)
# Let the user know that the system is ready
msg.content = f"Processing `{file.name}` done. You can now ask questions!"
await msg.update()
cl.user_session.set("chain", chain)
@cl.on_message
async def main(message: cl.Message):
chain = cl.user_session.get("chain") # type: ConversationalRetrievalChain
cb = cl.AsyncLangchainCallbackHandler()
res = await chain.acall(message.content, callbacks=[cb])
answer = res["answer"]
source_documents = res["source_documents"] # type: List[Document]
text_elements = [] # type: List[cl.Text]
if source_documents:
for source_idx, source_doc in enumerate(source_documents):
source_name = f"source_{source_idx}"
# Create the text element referenced in the message
text_elements.append(
cl.Text(content=source_doc.page_content, name=source_name)
)
source_names = [text_el.name for text_el in text_elements]
if source_names:
answer += f"\nSources: {', '.join(source_names)}"
else:
answer += "\nNo sources found"
await cl.Message(content=answer, elements=text_elements).send()
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