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import time
import gradio as gr
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter, Language
import vector_db as vdb
from llm_model import LLMModel
chunk_size = 2000
chunk_overlap = 200
uploaded_docs = []
uploaded_df = gr.Dataframe(headers=["file_name", "content_length"])
upload_files_section = gr.Files(
file_types=[".md", ".mdx", ".rst", ".txt"],
)
chatbot_stream = gr.Chatbot(bubble_full_width=False, show_copy_button=True)
def load_docs(files):
all_docs = []
all_qa = []
for file in files:
if file.name is not None:
with open(file.name, "r") as f:
file_content = f.read()
file_name = file.name.split("/")[-1]
# Create document with metadata
doc = Document(page_content=file_content, metadata={"source": file_name})
# Create an instance of the RecursiveCharacterTextSplitter class with specific parameters.
# It splits text into chunks of 1000 characters each with a 150-character overlap.
language = get_language(file_name)
text_splitter = RecursiveCharacterTextSplitter.from_language(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
language=language
)
# Split the text into chunks using the text splitter.
doc_chunks = text_splitter.split_documents([doc])
print(f"Number of chunks: {len(doc_chunks)}")
# Foreach chunk, send to LLM to get potential questions and answers
for doc_chunk in doc_chunks:
gr.Info("Analysing document...")
potential_qa_from_doc = llm_model.get_potential_question_answer(doc_chunk.page_content)
all_qa += [Document(page_content=potential_qa_from_doc, metadata=doc_chunk.metadata)]
all_docs += doc_chunks
uploaded_docs.append(file.name)
vector_db.load_docs_into_vector_db(all_qa)
gr.Info("Loaded document(s) into vector db.")
return uploaded_docs
def get_language(file_name: str):
if file_name.endswith(".md") or file_name.endswith(".mdx"):
return Language.MARKDOWN
elif file_name.endswith(".rst"):
return Language.RST
else:
return Language.MARKDOWN
def get_vector_db():
return vdb.VectorDB()
def get_llm_model(_db: vdb.VectorDB):
retriever = _db.docs_db.as_retriever(search_kwargs={"k": 2})
# return LLMModel(retriever=retriever).create_qa_chain()
return LLMModel(retriever=retriever)
def predict(message, history):
# resp = llm_model.answer_question_inference(message)
# return resp.get("answer")
resp = llm_model.answer_question_inference_text_gen(message)
for i in range(len(resp)):
time.sleep(0.005)
yield resp[:i + 1]
# final_resp = ""
# for c in resp:
# final_resp += str(c)
# # + "▌"
# yield final_resp
# start_time = time.time()
# res = llm_model({"query": message})
# sources = []
# for source_docs in res['source_documents']:
# if 'source' in source_docs.metadata:
# sources.append(source_docs.metadata['source'])
# # Display assistant response in chat message container
# end_time = time.time()
# time_taken = "{:.2f}".format(end_time - start_time)
# format_answer = f"## Result\n\n{res['result']}\n\n### Sources\n\n{sources}\n\nTime taken: {time_taken}s"
# format_source = None
# for source_docs in res['source_documents']:
# if 'source' in source_docs.metadata:
# format_source = f"## File: {source_docs.metadata['source']}\n\n{source_docs.page_content}"
#
# return format_answer
def vote(data: gr.LikeData):
if data.liked:
gr.Info("You upvoted this response 😊", )
else:
gr.Warning("You downvoted this response 👀")
vector_db = get_vector_db()
llm_model = get_llm_model(vector_db)
chat_interface_stream = gr.ChatInterface(
predict,
title="👀 Document answering bot",
description="📚🔦 Upload some documents on the side and ask questions!",
textbox=gr.Textbox(container=False, scale=7),
chatbot=chatbot_stream,
examples=["What is Data Caterer?"],
).queue(default_concurrency_limit=1)
with gr.Blocks() as blocks:
with gr.Row():
with gr.Column(scale=1, min_width=100) as upload_col:
gr.Interface(
load_docs,
title="📖 Upload documents",
inputs=upload_files_section,
outputs=gr.Files(),
allow_flagging="never"
)
# upload_files_section.upload(load_docs, inputs=upload_files_section)
with gr.Column(scale=4, min_width=600) as chat_col:
chatbot_stream.like(vote, None, None)
chat_interface_stream.render()
blocks.queue().launch()
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