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| import openai | |
| import streamlit as st | |
| from langchain.llms import OpenAI | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.chains import RetrievalQA | |
| from langchain.prompts.prompt import PromptTemplate | |
| from langchain.vectorstores import FAISS | |
| import re | |
| import time | |
| # import e5-large-v2 embedding model | |
| model_name = "intfloat/e5-large-v2" | |
| model_kwargs = {'device': 'cuda'} | |
| encode_kwargs = {'normalize_embeddings': False} | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name=model_name, | |
| model_kwargs=model_kwargs, | |
| encode_kwargs=encode_kwargs | |
| ) | |
| # load IPCC database | |
| db = FAISS.load_local("IPCC_index_e5_1000_pdf", embeddings) | |
| def generate_response(input_text): | |
| docs = db.similarity_search(input_text,k=5) | |
| json1 = docs[0].metadata | |
| json2 = docs[1].metadata | |
| json3 = docs[2].metadata | |
| json4 = docs[3].metadata | |
| json5 = docs[4].metadata | |
| #st.write({"source1":json1["source"], "source2":json2["source"],"source3":json3["source"]}) | |
| climate_TEMPLATE = """ You are ChatClimate, take a deep breath and provide an answer to educated general audience based on the context, and Format your answer in Markdown. :" | |
| Context: {context} | |
| Question: {question} | |
| Answer: | |
| check if you use the info below, if you used please add used source for in-text reference, if not used, do not add them . | |
| [{source1} page {page1}] | |
| [{source2} page {page2}] | |
| [{source3} page {page3}] | |
| [{source4} page {page4}] | |
| [{source5} page {page5}] | |
| Check if you use the source in your ansewer, make sure list used sources you refer to and their hyperlinks as below in a section named "sources": | |
| [{source1} page {page1}](https://www.ipcc.ch/report/ar6/wg3/downloads/report/{source1}.pdf#page={page1}) | |
| [{source2} page {page2}](https://www.ipcc.ch/report/ar6/wg3/downloads/report/{source2}.pdf#page={page2}) | |
| [{source3} page {page3}](https://www.ipcc.ch/report/ar6/wg3/downloads/report/{source3}.pdf#page={page3}) | |
| [{source4} page {page4}](https://www.ipcc.ch/report/ar6/wg3/downloads/report/{source4}.pdf#page={page4}) | |
| [{source5} page {page5}](https://www.ipcc.ch/report/ar6/wg3/downloads/report/{source5}.pdf#page={page5}) | |
| at the end of your answer, make sure to add a short highlight of your answer in humor and make sure no more than 5 words. | |
| Highlight: | |
| """ | |
| climate_PROMPT = PromptTemplate(input_variables=["question", "context"], | |
| partial_variables={"source1":json1["source"], "source2":json2["source"], | |
| "source3":json3["source"],"source4":json4["source"],"source5":json5["source"],"page1":json1["page"], | |
| "page2":json2["page"],"page3":json3["page"],"page4":json4["page"],"page5":json5["page"]}, | |
| template=climate_TEMPLATE, ) | |
| #climate_PROMPT.partial(source = docs[0].metadata) | |
| llm = ChatOpenAI( | |
| model_name="gpt-3.5-turbo", | |
| temperature=0.1, | |
| max_tokens=2000, | |
| openai_api_key=openai_api_key | |
| ) | |
| # Define retriever | |
| retriever = db.as_retriever(search_kwargs={"k": 5}) | |
| qa_chain = RetrievalQA.from_chain_type(llm, | |
| retriever=retriever, | |
| chain_type="stuff", #"stuff", "map_reduce","refine", "map_rerank" | |
| return_source_documents=True, | |
| verbose=True, | |
| chain_type_kwargs={"prompt": climate_PROMPT} | |
| ) | |
| return qa_chain({'query': input_text}) | |
| with st.sidebar: | |
| openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password") | |
| "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" | |
| st.markdown("## 🌍 Welcome to ClimateChat! 🌍") | |
| st.markdown("ClimateChat Harnesses the latest [IPCC reports](https://www.ipcc.ch/report/ar6/wg3/) and the power of Large Language Models to answer your questions about climate change. When you interact with ClimateChat not only will you receive clear, concise, and accurate answers, but each response is coupled with sources and hyperlinks for further exploration and verification.\ | |
| Our objective is to make climate change information accessible, understandable, and actionable for everyone, everywhere.") | |
| st.title("💬🌍🌡️ClimateChat") | |
| st.caption("💬 A Climate Change chatbot powered by OpenAI LLM and IPCC documents") | |
| #col1, col2, = st.columns(2) | |
| if "messages" not in st.session_state: | |
| st.session_state["messages"] = [{"role": "assistant", "content": "Any question about the climate change?"}] | |
| for msg in st.session_state.messages: | |
| st.chat_message(msg["role"]).write(msg["content"]) | |
| if prompt := st.chat_input(): | |
| if not openai_api_key: | |
| st.info("Please add your OpenAI API key to continue.") | |
| st.stop() | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| st.chat_message("user").write(prompt) | |
| result = generate_response(prompt) | |
| result_r = result["result"] | |
| index = result_r.find("Highlight:") | |
| # Extract everything after "Highlight:" | |
| match = re.search(r"Highlight: (.+)", result_r) | |
| if match: | |
| highlighted_text = match.group(1) | |
| else: | |
| highlighted_text="hello world" | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| full_response = "" | |
| assistant_response = result_r | |
| # Simulate stream of response with milliseconds delay | |
| for chunk in assistant_response.split(): | |
| full_response += chunk + " " | |
| time.sleep(0.05) | |
| # Add a blinking cursor to simulate typing | |
| message_placeholder.write(full_response + "▌") | |
| message_placeholder.write(result_r) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": result_r}) | |
| #st.session_state.messages.append({"role": "assistant", "content": result["result"]}) | |
| #st.chat_message("assistant").write(result_r) | |
| #display_typing_effect(st.chat_message("assistant"), result_r) | |
| #st.markdown(result['source_documents'][0]) | |
| #st.markdown(result['source_documents'][1]) | |
| #st.markdown(result['source_documents'][2]) | |
| #st.markdown(result['source_documents'][3]) | |
| #st.markdown(result['source_documents'][4]) | |
| #st.image("https://cataas.com/cat/says/"+highlighted_text) |