Spaces:
Running
on
T4
Running
on
T4
File size: 13,883 Bytes
41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 f8bbcd6 c313b25 41ba402 c313b25 41ba402 f8bbcd6 41ba402 7f75764 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 7f75764 c313b25 41ba402 c313b25 41ba402 c313b25 7f75764 f8bbcd6 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 c313b25 41ba402 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 |
# RAG_QA_Chat_tab.py
# Description: Gradio UI for RAG QA Chat
#
# Imports
import csv
import logging
import json
import os
from datetime import datetime
#
# External Imports
import docx2txt
import gradio as gr
# Local Imports
from App_Function_Libraries.Books.Book_Ingestion_Lib import read_epub
from App_Function_Libraries.DB.DB_Manager import DatabaseError, get_paginated_files, add_media_with_keywords
from App_Function_Libraries.PDF.PDF_Ingestion_Lib import extract_text_and_format_from_pdf
from App_Function_Libraries.RAG.RAG_Libary_2 import generate_answer, enhanced_rag_pipeline
from App_Function_Libraries.RAG.RAG_QA_Chat import search_database, rag_qa_chat
# Eventually... FIXME
from App_Function_Libraries.RAG.RAG_QA_Chat import load_chat_history, save_chat_history
#
########################################################################################################################
#
# Functions:
def create_rag_qa_chat_tab():
with gr.TabItem("RAG QA Chat"):
gr.Markdown("# RAG QA Chat")
with gr.Row():
with gr.Column(scale=1):
context_source = gr.Radio(
["All Files in the Database", "Search Database", "Upload File"],
label="Context Source",
value="All Files in the Database"
)
existing_file = gr.Dropdown(label="Select Existing File", choices=[], interactive=True)
file_page = gr.State(value=1)
with gr.Row():
prev_page_btn = gr.Button("Previous Page")
next_page_btn = gr.Button("Next Page")
page_info = gr.HTML("Page 1")
search_query = gr.Textbox(label="Search Query", visible=False)
search_button = gr.Button("Search", visible=False)
search_results = gr.Dropdown(label="Search Results", choices=[], visible=False)
file_upload = gr.File(
label="Upload File",
visible=False,
file_types=["txt", "pdf", "epub", "md", "rtf", "json", "csv"]
)
convert_to_text = gr.Checkbox(label="Convert to plain text", visible=False)
keywords = gr.Textbox(label="Keywords (comma-separated)", visible=False)
api_choice = gr.Dropdown(
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek", "Mistral", "OpenRouter", "Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama", "HuggingFace"],
label="Select API for RAG",
value="OpenAI"
)
with gr.Column(scale=2):
chatbot = gr.Chatbot(height=500)
msg = gr.Textbox(label="Enter your message")
submit = gr.Button("Submit (Might take a few seconds/turns blue while processing...)")
clear_chat = gr.Button("Clear Chat History")
loading_indicator = gr.HTML(visible=False)
def update_file_list(page):
files, total_pages, current_page = get_paginated_files(page)
choices = [f"{title} (ID: {id})" for id, title in files]
return gr.update(choices=choices), gr.update(value=f"Page {current_page} of {total_pages}"), current_page
def next_page_fn(current_page):
return update_file_list(current_page + 1)
def prev_page_fn(current_page):
return update_file_list(max(1, current_page - 1))
def update_context_source(choice):
return {
existing_file: gr.update(visible=choice == "Existing File"),
prev_page_btn: gr.update(visible=choice == "Existing File"),
next_page_btn: gr.update(visible=choice == "Existing File"),
page_info: gr.update(visible=choice == "Existing File"),
search_query: gr.update(visible=choice == "Search Database"),
search_button: gr.update(visible=choice == "Search Database"),
search_results: gr.update(visible=choice == "Search Database"),
file_upload: gr.update(visible=choice == "Upload File"),
convert_to_text: gr.update(visible=choice == "Upload File"),
keywords: gr.update(visible=choice == "Upload File")
}
context_source.change(update_context_source, context_source,
[existing_file, prev_page_btn, next_page_btn, page_info, search_query, search_button,
search_results, file_upload, convert_to_text, keywords])
next_page_btn.click(next_page_fn, inputs=[file_page], outputs=[existing_file, page_info, file_page])
prev_page_btn.click(prev_page_fn, inputs=[file_page], outputs=[existing_file, page_info, file_page])
# Initialize the file list
context_source.change(lambda: update_file_list(1), outputs=[existing_file, page_info, file_page])
loading_indicator = gr.HTML(visible=False)
def rag_qa_chat_wrapper(message, history, context_source, existing_file, search_results, file_upload,
convert_to_text, keywords, api_choice):
try:
logging.info(f"Starting rag_qa_chat_wrapper with message: {message}")
logging.info(f"Context source: {context_source}")
logging.info(f"API choice: {api_choice}")
# Show loading indicator
yield history, "", gr.update(visible=True)
# Ensure api_choice is a string
api_choice = api_choice.value if isinstance(api_choice, gr.components.Dropdown) else api_choice
logging.info(f"Resolved API choice: {api_choice}")
# Only rephrase the question if it's not the first query
if len(history) > 0:
rephrased_question = rephrase_question(history, message, api_choice)
logging.info(f"Original question: {message}")
logging.info(f"Rephrased question: {rephrased_question}")
else:
rephrased_question = message
logging.info(f"First question, no rephrasing: {message}")
if context_source == "All Files in the Database":
# Use the enhanced_rag_pipeline to search the entire database
context = enhanced_rag_pipeline(rephrased_question, api_choice)
logging.info(f"Using enhanced_rag_pipeline for database search")
elif context_source == "Search Database":
context = f"media_id:{search_results.split('(ID: ')[1][:-1]}"
logging.info(f"Using search result with context: {context}")
else: # Upload File
logging.info("Processing uploaded file")
if file_upload is None:
raise ValueError("No file uploaded")
# Process the uploaded file
file_path = file_upload.name
file_name = os.path.basename(file_path)
logging.info(f"Uploaded file: {file_name}")
if convert_to_text:
logging.info("Converting file to plain text")
content = convert_file_to_text(file_path)
else:
logging.info("Reading file content")
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
logging.info(f"File content length: {len(content)} characters")
# Process keywords
if not keywords:
keywords = "default,rag-file-upload"
logging.info(f"Keywords: {keywords}")
# Add the content to the database and get the media_id
logging.info("Adding content to database")
result = add_media_with_keywords(
url=file_name,
title=file_name,
media_type='document',
content=content,
keywords=keywords,
prompt='No prompt for uploaded files',
summary='No summary for uploaded files',
transcription_model='None',
author='Unknown',
ingestion_date=datetime.now().strftime('%Y-%m-%d')
)
logging.info(f"Result from add_media_with_keywords: {result}")
if isinstance(result, tuple):
media_id, _ = result
else:
media_id = result
context = f"media_id:{media_id}"
logging.info(f"Context for uploaded file: {context}")
logging.info("Calling rag_qa_chat function")
new_history, response = rag_qa_chat(rephrased_question, history, context, api_choice)
# Log first 100 chars of response
logging.info(
f"Response received from rag_qa_chat: {response[:100]}...")
# Add the original question to the history
new_history[-1] = (message, new_history[-1][1])
gr.Info("Response generated successfully")
logging.info("rag_qa_chat_wrapper completed successfully")
yield new_history, "", gr.update(visible=False)
except ValueError as e:
logging.error(f"Input error in rag_qa_chat_wrapper: {str(e)}")
gr.Error(f"Input error: {str(e)}")
yield history, "", gr.update(visible=False)
except DatabaseError as e:
logging.error(f"Database error in rag_qa_chat_wrapper: {str(e)}")
gr.Error(f"Database error: {str(e)}")
yield history, "", gr.update(visible=False)
except Exception as e:
logging.error(f"Unexpected error in rag_qa_chat_wrapper: {e}", exc_info=True)
gr.Error("An unexpected error occurred. Please try again later.")
yield history, "", gr.update(visible=False)
def rephrase_question(history, latest_question, api_choice):
# Thank you https://www.reddit.com/r/LocalLLaMA/comments/1fi1kex/multi_turn_conversation_and_rag/
conversation_history = "\n".join([f"User: {h[0]}\nAssistant: {h[1]}" for h in history[:-1]])
prompt = f"""You are a helpful assistant. Given the conversation history and the latest question, resolve any ambiguous references in the latest question.
Conversation History:
{conversation_history}
Latest Question:
{latest_question}
Rewritten Question:"""
# Use the selected API to generate the rephrased question
rephrased_question = generate_answer(api_choice, prompt, "")
return rephrased_question.strip()
def perform_search(query):
try:
results = search_database(query)
return gr.update(choices=results)
except Exception as e:
gr.Error(f"Error performing search: {str(e)}")
return gr.update(choices=[])
def clear_chat_history():
return [], ""
search_button.click(perform_search, inputs=[search_query], outputs=[search_results])
submit.click(
rag_qa_chat_wrapper,
inputs=[msg, chatbot, context_source, existing_file, search_results, file_upload,
convert_to_text, keywords, api_choice],
outputs=[chatbot, msg, loading_indicator]
)
clear_chat.click(clear_chat_history, outputs=[chatbot, msg])
return (context_source, existing_file, search_query, search_button, search_results, file_upload,
convert_to_text, keywords, api_choice, chatbot, msg, submit, clear_chat)
def convert_file_to_text(file_path):
"""Convert various file types to plain text."""
file_extension = os.path.splitext(file_path)[1].lower()
if file_extension == '.pdf':
return extract_text_and_format_from_pdf(file_path)
elif file_extension == '.epub':
return read_epub(file_path)
elif file_extension in ['.json', '.csv']:
return read_structured_file(file_path)
elif file_extension == '.docx':
return docx2txt.process(file_path)
elif file_extension in ['.txt', '.md', '.rtf']:
with open(file_path, 'r', encoding='utf-8') as f:
return f.read()
else:
raise ValueError(f"Unsupported file type: {file_extension}")
def read_structured_file(file_path):
"""Read and convert JSON or CSV files to text."""
file_extension = os.path.splitext(file_path)[1].lower()
if file_extension == '.json':
with open(file_path, 'r') as file:
data = json.load(file)
return json.dumps(data, indent=2)
elif file_extension == '.csv':
with open(file_path, 'r', newline='') as file:
csv_reader = csv.reader(file)
return '\n'.join([','.join(row) for row in csv_reader])
else:
raise ValueError(f"Unsupported file type: {file_extension}")
#
# End of RAG_QA_Chat_tab.py
########################################################################################################################
# |