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| import torch | |
| import transformers | |
| import gradio as gr | |
| from ragatouille import RAGPretrainedModel | |
| from huggingface_hub import InferenceClient | |
| import re | |
| from datetime import datetime | |
| import json | |
| import arxiv | |
| from utils import get_md_text_abstract, search_cleaner, get_arxiv_live_search | |
| retrieve_results = 20 | |
| show_examples = True | |
| llm_models_to_choose = ['mistralai/Mixtral-8x7B-Instruct-v0.1','mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] | |
| generate_kwargs = dict( | |
| temperature = None, | |
| max_new_tokens = 512, | |
| top_p = None, | |
| do_sample = False, | |
| ) | |
| ## RAG Model | |
| RAG = RAGPretrainedModel.from_index("colbert/indexes/arxiv_colbert") | |
| try: | |
| gr.Info("Setting up retriever, please wait...") | |
| rag_initial_output = RAG.search("What is Generative AI in Healthcare?", k = 1) | |
| gr.Info("Retriever working successfully!") | |
| except: | |
| gr.Warning("Retriever not working!") | |
| ## Header | |
| mark_text = '# 🩺🔍 Search Results\n' | |
| header_text = "## Arxiv Paper Summary With QA Retrieval Augmented Generation \n" | |
| try: | |
| with open("README.md", "r") as f: | |
| mdfile = f.read() | |
| date_pattern = r'Index Last Updated : \d{4}-\d{2}-\d{2}' | |
| match = re.search(date_pattern, mdfile) | |
| date = match.group().split(': ')[1] | |
| formatted_date = datetime.strptime(date, '%Y-%m-%d').strftime('%d %b %Y') | |
| header_text += f'Index Last Updated: {formatted_date}\n' | |
| index_info = f"Semantic Search - up to {formatted_date}" | |
| except: | |
| index_info = "Semantic Search" | |
| database_choices = [index_info,'Arxiv Search - Latest - (EXPERIMENTAL)'] | |
| ## Arxiv API | |
| arx_client = arxiv.Client() | |
| is_arxiv_available = True | |
| check_arxiv_result = get_arxiv_live_search("What is Self Rewarding AI and how can it be used in Multi-Agent Systems?", arx_client, retrieve_results) | |
| if len(check_arxiv_result) == 0: | |
| is_arxiv_available = False | |
| print("Arxiv search not working, switching to default search ...") | |
| database_choices = [index_info] | |
| ## Show examples | |
| sample_outputs = { | |
| 'output_placeholder': 'The LLM will provide an answer to your question here...', | |
| 'search_placeholder': ''' | |
| 1. What is MoE? | |
| 2. What are Multi Agent Systems? | |
| 3. What is Self Rewarding AI? | |
| 4. What is Semantic and Episodic memory? | |
| 5. What is AutoGen? | |
| 6. What is ChatDev? | |
| 7. What is Omniverse? | |
| 8. What is Lumiere? | |
| 9. What is SORA? | |
| ''' | |
| } | |
| output_placeholder = sample_outputs['output_placeholder'] | |
| md_text_initial = sample_outputs['search_placeholder'] | |
| def rag_cleaner(inp): | |
| rank = inp['rank'] | |
| title = inp['document_metadata']['title'] | |
| content = inp['content'] | |
| date = inp['document_metadata']['_time'] | |
| return f"{rank}. <b> {title} </b> \n Date : {date} \n Abstract: {content}" | |
| def get_prompt_text(question, context, formatted = True, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2'): | |
| if formatted: | |
| sys_instruction = f"Context:\n {context} \n Given the following scientific paper abstracts, take a deep breath and lets think step by step to answer the question. Cite the titles of your sources when answering, do not cite links or dates." | |
| message = f"Question: {question}" | |
| if 'mistralai' in llm_model_picked: | |
| return f"<s>" + f"[INST] {sys_instruction}" + f" {message}[/INST]" | |
| elif 'gemma' in llm_model_picked: | |
| return f"<bos><start_of_turn>user\n{sys_instruction}" + f" {message}<end_of_turn>\n" | |
| return f"Context:\n {context} \n Given the following info, take a deep breath and lets think step by step to answer the question: {question}. Cite the titles of your sources when answering.\n\n" | |
| def get_references(question, retriever, k = retrieve_results): | |
| rag_out = retriever.search(query=question, k=k) | |
| return rag_out | |
| def get_rag(message): | |
| return get_references(message, RAG) | |
| def SaveResponseAndRead(result): | |
| documentHTML5=''' | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Read It Aloud</title> | |
| <script type="text/javascript"> | |
| function readAloud() { | |
| const text = document.getElementById("textArea").value; | |
| const speech = new SpeechSynthesisUtterance(text); | |
| window.speechSynthesis.speak(speech); | |
| } | |
| </script> | |
| </head> | |
| <body> | |
| <h1>🔊 Read It Aloud</h1> | |
| <textarea id="textArea" rows="10" cols="80"> | |
| ''' | |
| documentHTML5 = documentHTML5 + result | |
| documentHTML5 = documentHTML5 + ''' | |
| </textarea> | |
| <br> | |
| <button onclick="readAloud()">🔊 Read Aloud</button> | |
| </body> | |
| </html> | |
| ''' | |
| gr.HTML(documentHTML5) | |
| with gr.Blocks(theme = gr.themes.Soft()) as demo: | |
| header = gr.Markdown(header_text) | |
| with gr.Group(): | |
| msg = gr.Textbox(label = 'Search', placeholder = 'What is Mistral?') | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(equal_height = True): | |
| llm_model = gr.Dropdown(choices = llm_models_to_choose, value = 'mistralai/Mistral-7B-Instruct-v0.2', label = 'LLM Model') | |
| llm_results = gr.Slider(minimum=4, maximum=10, value=5, step=1, interactive=True, label="Top n results as context") | |
| database_src = gr.Dropdown(choices = database_choices, value = index_info, label = 'Search Source') | |
| stream_results = gr.Checkbox(value = True, label = "Stream output", visible = False) | |
| output_text = gr.Textbox(show_label = True, container = True, label = 'LLM Answer', visible = True, placeholder = output_placeholder) | |
| input = gr.Textbox(show_label = False, visible = False) | |
| gr_md = gr.Markdown(mark_text + md_text_initial) | |
| def update_with_rag_md(message, llm_results_use = 5, database_choice = index_info, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2'): | |
| prompt_text_from_data = "" | |
| database_to_use = database_choice | |
| if database_choice == index_info: | |
| rag_out = get_rag(message) | |
| else: | |
| arxiv_search_success = True | |
| try: | |
| rag_out = get_arxiv_live_search(message, arx_client, retrieve_results) | |
| if len(rag_out) == 0: | |
| arxiv_search_success = False | |
| except: | |
| arxiv_search_success = False | |
| if not arxiv_search_success: | |
| gr.Warning("Arxiv Search not working, switching to semantic search ...") | |
| rag_out = get_rag(message) | |
| database_to_use = index_info | |
| md_text_updated = mark_text | |
| for i in range(retrieve_results): | |
| rag_answer = rag_out[i] | |
| if i < llm_results_use: | |
| md_text_paper, prompt_text = get_md_text_abstract(rag_answer, source = database_to_use, return_prompt_formatting = True) | |
| prompt_text_from_data += f"{i+1}. {prompt_text}" | |
| else: | |
| md_text_paper = get_md_text_abstract(rag_answer, source = database_to_use) | |
| md_text_updated += md_text_paper | |
| prompt = get_prompt_text(message, prompt_text_from_data, llm_model_picked = llm_model_picked) | |
| return md_text_updated, prompt | |
| def ask_llm(prompt, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2', stream_outputs = False): | |
| model_disabled_text = "LLM Model is disabled" | |
| output = "" | |
| if llm_model_picked == 'None': | |
| if stream_outputs: | |
| for out in model_disabled_text: | |
| output += out | |
| yield output | |
| return output | |
| else: | |
| return model_disabled_text | |
| client = InferenceClient(llm_model_picked) | |
| try: | |
| stream = client.text_generation(prompt, **generate_kwargs, stream=stream_outputs, details=False, return_full_text=False) | |
| except: | |
| gr.Warning("LLM Inference rate limit reached, try again later!") | |
| return "" | |
| if stream_outputs: | |
| for response in stream: | |
| output += response | |
| SaveResponseAndRead(response) | |
| yield output | |
| return output | |
| else: | |
| return stream | |
| msg.submit(update_with_rag_md, [msg, llm_results, database_src, llm_model], [gr_md, input]).success(ask_llm, [input, llm_model, stream_results], output_text) | |
| demo.queue().launch() |