from climateqa.engine.embeddings import get_embeddings_function embeddings_function = get_embeddings_function() from climateqa.knowledge.openalex import OpenAlex from sentence_transformers import CrossEncoder # reranker = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1") oa = OpenAlex() import gradio as gr import pandas as pd import numpy as np import os import time import re import json from gradio import ChatMessage # from gradio_modal import Modal from io import BytesIO import base64 from datetime import datetime from azure.storage.fileshare import ShareServiceClient from utils import create_user_id # ClimateQ&A imports from climateqa.engine.llm import get_llm from climateqa.engine.vectorstore import get_pinecone_vectorstore from climateqa.knowledge.retriever import ClimateQARetriever from climateqa.engine.reranker import get_reranker from climateqa.engine.embeddings import get_embeddings_function from climateqa.engine.chains.prompts import audience_prompts from climateqa.sample_questions import QUESTIONS from climateqa.constants import POSSIBLE_REPORTS from climateqa.utils import get_image_from_azure_blob_storage from climateqa.engine.keywords import make_keywords_chain # from climateqa.engine.chains.answer_rag import make_rag_papers_chain from climateqa.engine.graph import make_graph_agent,display_graph from front.utils import make_html_source,parse_output_llm_with_sources,serialize_docs,make_toolbox # Load environment variables in local mode try: from dotenv import load_dotenv load_dotenv() except Exception as e: pass # Set up Gradio Theme theme = gr.themes.Base( primary_hue="blue", secondary_hue="red", font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"], ) init_prompt = "" system_template = { "role": "system", "content": init_prompt, } account_key = os.environ["BLOB_ACCOUNT_KEY"] if len(account_key) == 86: account_key += "==" credential = { "account_key": account_key, "account_name": os.environ["BLOB_ACCOUNT_NAME"], } account_url = os.environ["BLOB_ACCOUNT_URL"] file_share_name = "climateqa" service = ShareServiceClient(account_url=account_url, credential=credential) share_client = service.get_share_client(file_share_name) user_id = create_user_id() # Create vectorstore and retriever vectorstore = get_pinecone_vectorstore(embeddings_function) llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0) reranker = get_reranker("nano") agent = make_graph_agent(llm,vectorstore,reranker) async def chat(query,history,audience,sources,reports): """taking a query and a message history, use a pipeline (reformulation, retriever, answering) to yield a tuple of: (messages in gradio format, messages in langchain format, source documents)""" date_now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") print(f">> NEW QUESTION ({date_now}) : {query}") if audience == "Children": audience_prompt = audience_prompts["children"] elif audience == "General public": audience_prompt = audience_prompts["general"] elif audience == "Experts": audience_prompt = audience_prompts["experts"] else: audience_prompt = audience_prompts["experts"] # Prepare default values if len(sources) == 0: sources = ["IPCC"] # if len(reports) == 0: # TODO reports = [] inputs = {"user_input": query,"audience": audience_prompt,"sources":sources} result = agent.astream_events(inputs,version = "v1") # path_reformulation = "/logs/reformulation/final_output" # path_keywords = "/logs/keywords/final_output" # path_retriever = "/logs/find_documents/final_output" # path_answer = "/logs/answer/streamed_output_str/-" docs = [] docs_html = "" output_query = "" output_language = "" output_keywords = "" gallery = [] start_streaming = False steps_display = { "categorize_intent":("🔄️ Analyzing user message",True), "transform_query":("🔄️ Thinking step by step to answer the question",True), "retrieve_documents":("🔄️ Searching in the knowledge base",False), } used_documents = [] answer_message_content = "" try: async for event in result: if "langgraph_node" in event["metadata"]: node = event["metadata"]["langgraph_node"] if event["event"] == "on_chain_end" and event["name"] == "retrieve_documents" :# when documents are retrieved try: docs = event["data"]["output"]["documents"] docs_html = [] for i, d in enumerate(docs, 1): docs_html.append(make_html_source(d, i)) used_documents = used_documents + [d.metadata["name"] for d in docs] history[-1].content = "Adding sources :\n\n - " + "\n - ".join(np.unique(used_documents)) docs_html = "".join(docs_html) except Exception as e: print(f"Error getting documents: {e}") print(event) elif event["name"] in steps_display.keys() and event["event"] == "on_chain_start": #display steps event_description,display_output = steps_display[node] if not hasattr(history[-1], 'metadata') or history[-1].metadata["title"] != event_description: # if a new step begins history.append(ChatMessage(role="assistant", content = "", metadata={'title' :event_description})) elif event["name"] != "transform_query" and event["event"] == "on_chat_model_stream" and node in ["answer_rag", "answer_search"]:# if streaming answer if start_streaming == False: start_streaming = True history.append(ChatMessage(role="assistant", content = "")) answer_message_content += event["data"]["chunk"].content answer_message_content = parse_output_llm_with_sources(answer_message_content) history[-1] = ChatMessage(role="assistant", content = answer_message_content) # history.append(ChatMessage(role="assistant", content = new_message_content)) if event["name"] == "transform_query" and event["event"] =="on_chain_end": if hasattr(history[-1],"content"): history[-1].content += "Decompose question into sub-questions: \n\n - " + "\n - ".join([q["question"] for q in event["data"]["output"]["remaining_questions"]]) if event["name"] == "categorize_intent" and event["event"] == "on_chain_start": print("X") yield history,docs_html,output_query,output_language,gallery #,output_query,output_keywords except Exception as e: print(event, "has failed") raise gr.Error(f"{e}") try: # Log answer on Azure Blob Storage if os.getenv("GRADIO_ENV") != "local": timestamp = str(datetime.now().timestamp()) file = timestamp + ".json" prompt = history[-1][0] logs = { "user_id": str(user_id), "prompt": prompt, "query": prompt, "question":output_query, "sources":sources, "docs":serialize_docs(docs), "answer": history[-1][1], "time": timestamp, } log_on_azure(file, logs, share_client) except Exception as e: print(f"Error logging on Azure Blob Storage: {e}") raise gr.Error(f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)") image_dict = {} for i,doc in enumerate(docs): if doc.metadata["chunk_type"] == "image": try: key = f"Image {i+1}" image_path = doc.metadata["image_path"].split("documents/")[1] img = get_image_from_azure_blob_storage(image_path) # Convert the image to a byte buffer buffered = BytesIO() img.save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()).decode() # Embedding the base64 string in Markdown markdown_image = f"![Alt text](data:image/png;base64,{img_str})" image_dict[key] = {"img":img,"md":markdown_image,"caption":doc.page_content,"key":key,"figure_code":doc.metadata["figure_code"]} except Exception as e: print(f"Skipped adding image {i} because of {e}") ## temp removing # if len(image_dict) > 0: # gallery = [x["img"] for x in list(image_dict.values())] # img = list(image_dict.values())[0] # img_md = img["md"] # img_caption = img["caption"] # img_code = img["figure_code"] # if img_code != "N/A": # img_name = f"{img['key']} - {img['figure_code']}" # else: # img_name = f"{img['key']}" # answer_yet = history[-1][1] + f"\n\n{img_md}\n

{img_name} - {img_caption}

" # history[-1] = (history[-1][0],answer_yet) # history = [tuple(x) for x in history] # yield history,docs_html,output_query,output_language,gallery#,output_query,output_keywords def save_feedback(feed: str, user_id): if len(feed) > 1: timestamp = str(datetime.now().timestamp()) file = user_id + timestamp + ".json" logs = { "user_id": user_id, "feedback": feed, "time": timestamp, } log_on_azure(file, logs, share_client) return "Feedback submitted, thank you!" def log_on_azure(file, logs, share_client): logs = json.dumps(logs) file_client = share_client.get_file_client(file) file_client.upload_file(logs) def generate_keywords(query): chain = make_keywords_chain(llm) keywords = chain.invoke(query) keywords = " AND ".join(keywords["keywords"]) return keywords papers_cols_widths = { "doc":50, "id":100, "title":300, "doi":100, "publication_year":100, "abstract":500, "rerank_score":100, "is_oa":50, } papers_cols = list(papers_cols_widths.keys()) papers_cols_widths = list(papers_cols_widths.values()) # -------------------------------------------------------------------- # Gradio # -------------------------------------------------------------------- init_prompt = """ Hello, I am ClimateQ&A, a conversational assistant designed to help you understand climate change and biodiversity loss. I will answer your questions by **sifting through the IPCC and IPBES scientific reports**. ❓ How to use - **Language**: You can ask me your questions in any language. - **Audience**: You can specify your audience (children, general public, experts) to get a more adapted answer. - **Sources**: You can choose to search in the IPCC or IPBES reports, or both. ⚠️ Limitations *Please note that the AI is not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.* What do you want to learn ? """ def vote(data: gr.LikeData): if data.liked: print(data.value) else: print(data) with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component") as demo: with gr.Tab("ClimateQ&A"): with gr.Row(elem_id="chatbot-row"): with gr.Column(scale=2): chatbot = gr.Chatbot( value = [ChatMessage(role="assistant", content=init_prompt)], type = "messages", show_copy_button=True, show_label = False, elem_id="chatbot", layout = "panel", avatar_images = (None,"https://i.ibb.co/YNyd5W2/logo4.png"), ) # bot.like(vote,None,None) with gr.Row(elem_id = "input-message"): textbox=gr.Textbox(placeholder="Ask me anything here!",show_label=False,scale=7,lines = 1,interactive = True,elem_id="input-textbox") with gr.Column(scale=1, variant="panel",elem_id = "right-panel"): with gr.Tabs() as tabs: with gr.TabItem("Examples",elem_id = "tab-examples",id = 0): examples_hidden = gr.Textbox(visible = False) first_key = list(QUESTIONS.keys())[0] dropdown_samples = gr.Dropdown(QUESTIONS.keys(),value = first_key,interactive = True,show_label = True,label = "Select a category of sample questions",elem_id = "dropdown-samples") samples = [] for i,key in enumerate(QUESTIONS.keys()): examples_visible = True if i == 0 else False with gr.Row(visible = examples_visible) as group_examples: examples_questions = gr.Examples( QUESTIONS[key], [examples_hidden], examples_per_page=8, run_on_click=False, elem_id=f"examples{i}", api_name=f"examples{i}", # label = "Click on the example question or enter your own", # cache_examples=True, ) samples.append(group_examples) with gr.Tab("Sources",elem_id = "tab-citations",id = 1): sources_textbox = gr.HTML(show_label=False, elem_id="sources-textbox") docs_textbox = gr.State("") # with Modal(visible = False) as config_modal: with gr.Tab("Configuration",elem_id = "tab-config",id = 2): gr.Markdown("Reminder: You can talk in any language, ClimateQ&A is multi-lingual!") dropdown_sources = gr.CheckboxGroup( ["IPCC", "IPBES","IPOS"], label="Select source", value=["IPCC"], interactive=True, ) dropdown_reports = gr.Dropdown( POSSIBLE_REPORTS, label="Or select specific reports", multiselect=True, value=None, interactive=True, ) dropdown_audience = gr.Dropdown( ["Children","General public","Experts"], label="Select audience", value="Experts", interactive=True, ) output_query = gr.Textbox(label="Query used for retrieval",show_label = True,elem_id = "reformulated-query",lines = 2,interactive = False) output_language = gr.Textbox(label="Language",show_label = True,elem_id = "language",lines = 1,interactive = False) #--------------------------------------------------------------------------------------- # OTHER TABS #--------------------------------------------------------------------------------------- with gr.Tab("Figures",elem_id = "tab-images",elem_classes = "max-height other-tabs"): gallery_component = gr.Gallery() # with gr.Tab("Papers (beta)",elem_id = "tab-papers",elem_classes = "max-height other-tabs"): # with gr.Row(): # with gr.Column(scale=1): # query_papers = gr.Textbox(placeholder="Question",show_label=False,lines = 1,interactive = True,elem_id="query-papers") # keywords_papers = gr.Textbox(placeholder="Keywords",show_label=False,lines = 1,interactive = True,elem_id="keywords-papers") # after = gr.Slider(minimum=1950,maximum=2023,step=1,value=1960,label="Publication date",show_label=True,interactive=True,elem_id="date-papers") # search_papers = gr.Button("Search",elem_id="search-papers",interactive=True) # with gr.Column(scale=7): # with gr.Tab("Summary",elem_id="papers-summary-tab"): # papers_summary = gr.Markdown(visible=True,elem_id="papers-summary") # with gr.Tab("Relevant papers",elem_id="papers-results-tab"): # papers_dataframe = gr.Dataframe(visible=True,elem_id="papers-table",headers = papers_cols) # with gr.Tab("Citations network",elem_id="papers-network-tab"): # citations_network = gr.HTML(visible=True,elem_id="papers-citations-network") with gr.Tab("About",elem_classes = "max-height other-tabs"): with gr.Row(): with gr.Column(scale=1): gr.Markdown("See more info at [https://climateqa.com](https://climateqa.com/docs/intro/)") def start_chat(query,history): # history = history + [(query,None)] # history = [tuple(x) for x in history] history = history + [ChatMessage(role="user", content=query)] return (gr.update(interactive = False),gr.update(selected=1),history) def finish_chat(): return (gr.update(interactive = True,value = "")) (textbox .submit(start_chat, [textbox,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_textbox") .then(chat, [textbox,chatbot,dropdown_audience, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query,output_language,gallery_component],concurrency_limit = 8,api_name = "chat_textbox") .then(finish_chat, None, [textbox],api_name = "finish_chat_textbox") ) (examples_hidden .change(start_chat, [examples_hidden,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_examples") .then(chat, [examples_hidden,chatbot,dropdown_audience, dropdown_sources,dropdown_reports], [chatbot,sources_textbox,output_query,output_language,gallery_component],concurrency_limit = 8,api_name = "chat_examples") .then(finish_chat, None, [textbox],api_name = "finish_chat_examples") ) def change_sample_questions(key): index = list(QUESTIONS.keys()).index(key) visible_bools = [False] * len(samples) visible_bools[index] = True return [gr.update(visible=visible_bools[i]) for i in range(len(samples))] dropdown_samples.change(change_sample_questions,dropdown_samples,samples) demo.queue() demo.launch(ssr_mode=False)