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import gradio as gr
from langchain_chroma import Chroma
from pathlib import Path
from utils import create_vector_db, Rag, get_chunks, get_conversation_chain, get_local_vector_db
def chat(question, history) -> str:
"""
Get the chat data need for the gradio app
:param question:
The question being asked in the chat app.
:type question: str
:param history:
A list of the conversation questions and answers.
:type history: list
:return:
The answer from the current question.
"""
result = conversation_chain.invoke({"question": question})
answer = result['answer']
# include source documents if they exist
# grab the first one as that should be related to the answer
source_doc = ""
if result.get('source_documents'):
source_doc = result['source_documents'][0]
response = f"{answer}\n\n**Source:**\n{source_doc.metadata.get('source', 'Source')}" \
if source_doc \
else answer
return response
def main():
gr.ChatInterface(chat, type="messages").launch(inbrowser=True)
if __name__ == '__main__':
create_new_db = False if Path('vector_db').exists() else True
if create_new_db:
folders = Path('knowledge_base').glob('*')
chunks = get_chunks(folders=folders)
vector_store = create_vector_db(chunks=chunks, db_name=Rag.DB_NAME.value, embeddings=Rag.EMBED_MODEL.value)
else:
client = get_local_vector_db(path='../rag_chat_example/vector_db')
vector_store = Chroma(client=client, embedding_function=Rag.EMBED_MODEL.value)
conversation_chain = get_conversation_chain(vectorstore=vector_store)
main()
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