import gradio as gr import numpy as np from abc import ABC, abstractmethod from typing import List, Dict, Any, Tuple from collections import defaultdict import pandas as pd from datetime import datetime, date from datasets import load_dataset, load_from_disk from collections import Counter import yaml, json, requests, sys, os, time import urllib.parse import concurrent.futures from langchain import hub from langchain_openai import ChatOpenAI as openai_llm from langchain_openai import OpenAIEmbeddings from langchain_core.runnables import RunnableConfig, RunnablePassthrough, RunnableParallel from langchain_core.prompts import PromptTemplate from langchain_community.callbacks import StreamlitCallbackHandler from langchain_community.utilities import DuckDuckGoSearchAPIWrapper from langchain_community.vectorstores import Chroma from langchain_community.document_loaders import TextLoader from langchain.agents import create_react_agent, Tool, AgentExecutor from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_core.output_parsers import StrOutputParser from langchain.callbacks import FileCallbackHandler from langchain.callbacks.manager import CallbackManager from langchain.schema import Document import instructor from pydantic import BaseModel, Field from typing import List, Literal from nltk.corpus import stopwords import nltk from openai import OpenAI, moderations # import anthropic import cohere import faiss import matplotlib.pyplot as plt import spacy from string import punctuation import pytextrank from prompts import * openai_key = os.environ['openai_key'] cohere_key = os.environ['cohere_key'] os.environ["OPENAI_API_KEY"] = os.environ['openai_key'] def load_nlp(): nlp = spacy.load("en_core_web_sm") nlp.add_pipe("textrank") try: stopwords.words('english') except: nltk.download('stopwords') stopwords.words('english') return nlp gen_llm = openai_llm(temperature=0, model_name='gpt-4o-mini', openai_api_key = openai_key) consensus_client = instructor.patch(OpenAI(api_key=openai_key)) embed_client = OpenAI(api_key = openai_key) embed_model = "text-embedding-3-small" embeddings = OpenAIEmbeddings(model = embed_model, api_key = openai_key) nlp = load_nlp() def check_mod(query): mod_report = moderations.create(input=query) for i in mod_report.results[0].categories: if i[1] == True: return True return False def get_keywords(text, nlp=nlp): result = [] pos_tag = ['PROPN', 'ADJ', 'NOUN'] doc = nlp(text.lower()) for token in doc: if(token.text in nlp.Defaults.stop_words or token.text in punctuation): continue if(token.pos_ in pos_tag): result.append(token.text) return result def load_arxiv_corpus(): # arxiv_corpus = load_from_disk('data/') # arxiv_corpus.load_faiss_index('embed', 'data/astrophindex.faiss') # keeping it up to date with the dataset arxiv_corpus = load_dataset('kiyer/pathfinder_arxiv_data', split='train') arxiv_corpus.add_faiss_index(column='embed') print('loading arxiv corpus from disk') return arxiv_corpus class RetrievalSystem(): def __init__(self): self.dataset = arxiv_corpus self.client = OpenAI(api_key = openai_key) self.embed_model = "text-embedding-3-small" self.generation_client = openai_llm(temperature=0,model_name='gpt-4o-mini', openai_api_key = openai_key) self.hyde_client = openai_llm(temperature=0.5,model_name='gpt-4o-mini', openai_api_key = openai_key) self.cohere_client = cohere.Client(cohere_key) def make_embedding(self, text): str_embed = self.client.embeddings.create(input = [text], model = self.embed_model).data[0].embedding return str_embed def embed_batch(self, texts: List[str]) -> List[np.ndarray]: embeddings = self.client.embeddings.create(input=texts, model=self.embed_model).data return [np.array(embedding.embedding, dtype=np.float32) for embedding in embeddings] def get_query_embedding(self, query): return self.make_embedding(query) def calc_faiss(self, query_embedding, top_k = 100): # xq = query_embedding.reshape(-1,1).T.astype('float32') # D, I = self.index.search(xq, top_k) # return I[0], D[0] tmp = self.dataset.search('embed', query_embedding, k=top_k) return [tmp.indices, tmp.scores, self.dataset[tmp.indices]] def rank_and_filter(self, query, query_embedding, top_k = 10, top_k_internal = 1000, return_scores=False): if 'Keywords' in self.toggles: self.weight_keywords = True else: self.weight_keywords = False if 'Time' in self.toggles: self.weight_date = True else: self.weight_date = False if 'Citations' in self.toggles: self.weight_citation = True else: self.weight_citation = False topk_indices, similarities, small_corpus = self.calc_faiss(np.array(query_embedding), top_k = top_k_internal) similarities = 1/similarities # converting from a distance (less is better) to a similarity (more is better) if self.weight_keywords == True: query_kws = get_keywords(query) input_kws = self.query_input_keywords query_kws = query_kws + input_kws self.query_kws = query_kws sub_kws = [small_corpus['keywords'][i] for i in range(top_k_internal)] kw_weight = np.zeros((len(topk_indices),)) + 0.1 for k in query_kws: for i in (range(len(topk_indices))): for j in range(len(sub_kws[i])): if k.lower() in sub_kws[i][j].lower(): kw_weight[i] = kw_weight[i] + 0.1 # print(i, k, sub_kws[i][j]) # kw_weight = kw_weight**0.36 / np.amax(kw_weight**0.36) kw_weight = kw_weight / np.amax(kw_weight) else: kw_weight = np.ones((len(topk_indices),)) if self.weight_date == True: sub_dates = [small_corpus['date'][i] for i in range(top_k_internal)] date = datetime.now().date() date_diff = np.array([((date - i).days / 365.) for i in sub_dates]) # age_weight = (1 + np.exp(date_diff/2.1))**(-1) + 0.5 age_weight = (1 + np.exp(date_diff/0.7))**(-1) age_weight = age_weight / np.amax(age_weight) else: age_weight = np.ones((len(topk_indices),)) if self.weight_citation == True: # st.write('weighting by citations') sub_cites = np.array([small_corpus['cites'][i] for i in range(top_k_internal)]) temp = sub_cites.copy() temp[sub_cites > 300] = 300. cite_weight = (1 + np.exp((300-temp)/42.0))**(-1.) cite_weight = cite_weight / np.amax(cite_weight) else: cite_weight = np.ones((len(topk_indices),)) similarities = similarities * (kw_weight) * (age_weight) * (cite_weight) filtered_results = [[topk_indices[i], similarities[i]] for i in range(len(similarities))] top_results = sorted(filtered_results, key=lambda x: x[1], reverse=True)[:top_k] top_scores = [doc[1] for doc in top_results] top_indices = [doc[0] for doc in top_results] small_df = self.dataset[top_indices] if return_scores: return {doc[0]: doc[1] for doc in top_results}, small_df # Only keep the document IDs top_results = [doc[0] for doc in top_results] return top_results, small_df def generate_doc(self, query: str): prompt = """You are an expert astronomer. Given a scientific query, generate the abstract of an expert-level research paper that answers the question. Stick to a maximum length of {} tokens and return just the text of the abstract and conclusion. Do not include labels for any section. Use research-specific jargon.""".format(self.max_doclen) messages = [("system",prompt,),("human", query),] return self.hyde_client.invoke(messages).content def generate_docs(self, query: str): docs = [] for i in range(self.generate_n): docs.append(self.generate_doc(query)) return docs def embed_docs(self, docs: List[str]): return self.embed_batch(docs) def retrieve(self, query, top_k, return_scores = False, embed_query=True, max_doclen=250, generate_n=1, temperature=0.5, rerank_top_k = 250): if max_doclen * generate_n > 8191: raise ValueError("Too many tokens. Please reduce max_doclen or generate_n.") query_embedding = self.get_query_embedding(query) if self.hyde == True: self.max_doclen = max_doclen self.generate_n = generate_n self.hyde_client.temperature = temperature self.embed_query = embed_query docs = self.generate_docs(query) # st.expander('Abstract generated with hyde', expanded=False).write(docs) doc_embeddings = self.embed_docs(docs) if self.embed_query: query_emb = self.embed_docs([query])[0] doc_embeddings.append(query_emb) query_embedding = np.mean(np.array(doc_embeddings), axis = 0) if self.rerank == True: top_results, small_df = self.rank_and_filter(query, query_embedding, rerank_top_k, return_scores = False) # try: docs_for_rerank = [small_df['abstract'][i] for i in range(rerank_top_k)] if len(docs_for_rerank) == 0: return [] reranked_results = self.cohere_client.rerank( query=query, documents=docs_for_rerank, model='rerank-english-v3.0', top_n=top_k ) final_results = [] for result in reranked_results.results: doc_id = top_results[result.index] doc_text = docs_for_rerank[result.index] score = float(result.relevance_score) final_results.append([doc_id, "", score]) final_indices = [doc[0] for doc in final_results] if return_scores: return {result[0]: result[2] for result in final_results}, self.dataset[final_indices] return [doc[0] for doc in final_results], self.dataset[final_indices] # except: # print('heavy load, please wait 10s and try again.') else: top_results, small_df = self.rank_and_filter(query, query_embedding, top_k, return_scores = return_scores) return top_results, small_df def return_formatted_df(self, top_results, small_df): df = pd.DataFrame(small_df) df = df.drop(columns=['umap_x','umap_y','cite_bibcodes','ref_bibcodes']) links = ['['+i+'](https://ui.adsabs.harvard.edu/abs/'+i+'/abstract)' for i in small_df['bibcode']] # st.write(top_results[0:10]) scores = [top_results[i] for i in top_results] indices = [i for i in top_results] df.insert(1,'ADS Link',links,True) df.insert(2,'Relevance',scores,True) df.insert(3,'indices',indices,True) df = df[['ADS Link','Relevance','date','cites','title','authors','abstract','keywords','ads_id','indices','embed']] df.index += 1 return df arxiv_corpus = load_arxiv_corpus() ec = RetrievalSystem() print('loaded retrieval system') def Library(papers_df): op_docs = '' for i in range(len(papers_df)): op_docs = op_docs + 'Paper %.0f:' %(i+1) + papers_df['title'][i+1] + '\n' + papers_df['abstract'][i+1] + '\n\n' return op_docs def run_rag_qa(query, papers_df, question_type): loaders = [] documents = [] for i, row in papers_df.iterrows(): content = f"Paper {i+1}: {row['title']}\n{row['abstract']}\n\n" metadata = {"source": row['ads_id']} doc = Document(page_content=content, metadata=metadata) documents.append(doc) text_splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=50, add_start_index=True) splits = text_splitter.split_documents(documents) vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings, collection_name='retdoc4') retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6}) if question_type == 'Bibliometric': template = bibliometric_prompt elif question_type == 'Single-paper': template = single_paper_prompt elif question_type == 'Broad but nuanced': template = deep_knowledge_prompt else: template = regular_prompt prompt = PromptTemplate.from_template(template) def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) rag_chain_from_docs = ( RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"]))) | prompt | gen_llm | StrOutputParser() ) rag_chain_with_source = RunnableParallel( {"context": retriever, "question": RunnablePassthrough()} ).assign(answer=rag_chain_from_docs) rag_answer = rag_chain_with_source.invoke(query, ) vectorstore.delete_collection() # except: # st.subheader('heavy load! please wait 10 seconds and try again.') return rag_answer def guess_question_type(query: str): gen_client = openai_llm(temperature=0,model_name='gpt-4o-mini', openai_api_key = openai_key) messages = [("system",question_categorization_prompt,),("human", query),] return gen_client.invoke(messages).content def log_to_gist(strings): # Adding query logs to prevent and account for possible malicious use. # Logs will be deleted periodically if not needed. github_token = os.environ['github_token'] gist_id = os.environ['gist_id'] timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") content = f"\n{timestamp}: {' '.join(strings)}\n" headers = {'Authorization': f'token {github_token}','Accept': 'application/vnd.github.v3+json'} response = requests.get(f'https://api.github.com/gists/{gist_id}', headers=headers) if response.status_code == 200: existing_content = response.json()['files']['log.txt']['content'] content = existing_content + content data = {"description": "Logged Strings","public": False,"files": {"log.txt": {"content": content}}} headers = {'Authorization': f'token {github_token}','Accept': 'application/vnd.github.v3+json'} response = requests.patch(f'https://api.github.com/gists/{gist_id}', headers=headers, data=json.dumps(data)) # Update existing gist return class OverallConsensusEvaluation(BaseModel): rewritten_statement: str = Field( ..., description="The query rewritten as a statement if it was initially a question" ) consensus: Literal[ "Strong Agreement Between Abstracts and Query", "Moderate Agreement Between Abstracts and Query", "Weak Agreement Between Abstracts and Query", "No Clear Agreement/Disagreement Between Abstracts and Query", "Weak Disagreement Between Abstracts and Query", "Moderate Disagreement Between Abstracts and Query", "Strong Disagreement Between Abstracts and Query" ] = Field( ..., description="The overall level of consensus between the rewritten statement and the abstracts" ) explanation: str = Field( ..., description="A detailed explanation of the consensus evaluation (maximum six sentences)" ) relevance_score: float = Field( ..., description="A score from 0 to 1 indicating how relevant the abstracts are to the query overall", ge=0, le=1 ) def evaluate_overall_consensus(query: str, abstracts: List[str]) -> OverallConsensusEvaluation: prompt = f""" Query: {query} You will be provided with {len(abstracts)} scientific abstracts. Your task is to do the following: 1. If the provided query is a question, rewrite it as a statement. This statement does not have to be true. Output this as 'Rewritten Statement:'. 2. Evaluate the overall consensus between the rewritten statement and the abstracts using one of the following levels: - Strong Agreement Between Abstracts and Query - Moderate Agreement Between Abstracts and Query - Weak Agreement Between Abstracts and Query - No Clear Agreement/Disagreement Between Abstracts and Query - Weak Disagreement Between Abstracts and Query - Moderate Disagreement Between Abstracts and Query - Strong Disagreement Between Abstracts and Query Output this as 'Consensus:' 3. Provide a detailed explanation of your consensus evaluation in maximum six sentences. Output this as 'Explanation:' 4. Assign a relevance score as a float between 0 to 1, where: - 1.0: Perfect match in content and quality - 0.8-0.9: Excellent, with minor differences - 0.6-0.7: Good, captures main points but misses some details - 0.4-0.5: Fair, partially relevant but significant gaps - 0.2-0.3: Poor, major inaccuracies or omissions - 0.0-0.1: Completely irrelevant or incorrect Output this as 'Relevance Score:' Here are the abstracts: {' '.join([f"Abstract {i+1}: {abstract}" for i, abstract in enumerate(abstracts)])} Provide your evaluation in the structured format described above. """ response = consensus_client.chat.completions.create( model="gpt-4o-mini", # used to be "gpt-4", response_model=OverallConsensusEvaluation, messages=[ {"role": "system", "content": """You are an assistant with expertise in astrophysics for question-answering tasks. Evaluate the overall consensus of the retrieved scientific abstracts in relation to a given query. If you don't know the answer, just say that you don't know. Use six sentences maximum and keep the answer concise."""}, {"role": "user", "content": prompt} ], temperature=0 ) return response def calc_outlier_flag(papers_df, top_k, cutoff_adjust = 0.1): cut_dist = np.load('pfdr_arxiv_cutoff_distances.npy') - cutoff_adjust pts = np.array(papers_df['embed'].tolist()) centroid = np.mean(pts,0) dists = np.sqrt(np.sum((pts-centroid)**2,1)) outlier_flag = (dists > cut_dist[top_k-1]) return outlier_flag def make_embedding_plot(papers_df, top_k, consensus_answer, arxiv_corpus=arxiv_corpus): plt_indices = np.array(papers_df['indices'].tolist()) xax = np.array(arxiv_corpus['umap_x']) yax = np.array(arxiv_corpus['umap_y']) outlier_flag = calc_outlier_flag(papers_df, top_k, cutoff_adjust=0.25) alphas = np.ones((len(plt_indices),)) * 0.9 alphas[outlier_flag] = 0.5 fig = plt.figure(figsize=(9*1.8,12*1.8)) plt.scatter(xax,yax, s=1, alpha=0.01, c='k') clkws = np.load('kw_tags.npz') all_x, all_y, all_topics, repeat_flag = clkws['all_x'], clkws['all_y'], clkws['all_topics'], clkws['repeat_flag'] for i in range(len(all_topics)): if repeat_flag[i] == False: plt.text(all_x[i], all_y[i], all_topics[i],fontsize=9,ha="center", va="center", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.3',alpha=0.81)) plt.scatter(xax[plt_indices], yax[plt_indices], s=300*alphas**2, alpha=alphas, c='w',zorder=1000) plt.scatter(xax[plt_indices], yax[plt_indices], s=100*alphas**2, alpha=alphas, c='dodgerblue',zorder=1001) # plt.scatter(xax[plt_indices][outlier_flag], yax[plt_indices][outlier_flag], s=100, alpha=1., c='firebrick') plt.axis([0,20,-4.2,18]) plt.axis('off') return fig def run_pathfinder(query, top_k, extra_keywords, toggles, prompt_type, rag_type, ec=ec, progress=gr.Progress()): yield None, None, None, None, None search_text_list = ['rooting around in the paper pile...','looking for clarity...','scanning the event horizon...','peering into the abyss...','potatoes power this ongoing search...'] gen_text_list = ['making the LLM talk to the papers...','invoking arcane rituals...','gone to library, please wait...','is there really an answer to this...'] log_to_gist(['[mod flag: '+str(check_mod(query))+']', query]) if check_mod(query) == False: input_keywords = [kw.strip() for kw in extra_keywords.split(',')] if extra_keywords else [] query_keywords = get_keywords(query) ec.query_input_keywords = input_keywords+query_keywords ec.toggles = toggles if rag_type == "Semantic Search": ec.hyde = False ec.rerank = False elif rag_type == "Semantic + HyDE": ec.hyde = True ec.rerank = False elif rag_type == "Semantic + CoHERE": ec.hyde = False ec.rerank = True elif rag_type == "Semantic + HyDE + CoHERE": ec.hyde = True ec.rerank = True progress(0.2, desc=search_text_list[np.random.choice(len(search_text_list))]) rs, small_df = ec.retrieve(query, top_k = top_k, return_scores=True) formatted_df = ec.return_formatted_df(rs, small_df) yield formatted_df, None, None, None, None progress(0.4, desc=gen_text_list[np.random.choice(len(gen_text_list))]) rag_answer = run_rag_qa(query, formatted_df, prompt_type) yield formatted_df, rag_answer['answer'], None, None, None progress(0.6, desc="Generating consensus") consensus_answer = evaluate_overall_consensus(query, [formatted_df['abstract'][i+1] for i in range(len(formatted_df))]) consensus = '## Consensus \n'+consensus_answer.consensus + '\n\n'+consensus_answer.explanation + '\n\n > Relevance of retrieved papers to answer: %.1f' %consensus_answer.relevance_score yield formatted_df, rag_answer['answer'], consensus, None, None progress(0.8, desc="Analyzing question type") question_type_gen = guess_question_type(query) if '' in question_type_gen: question_type_gen = question_type_gen.split('')[1] if '' in question_type_gen: question_type_gen = question_type_gen.split('')[0] question_type_gen = question_type_gen.replace('\n',' \n') qn_type = question_type_gen yield formatted_df, rag_answer['answer'], consensus, qn_type, None progress(1.0, desc="Visualizing embeddings") fig = make_embedding_plot(formatted_df, top_k, consensus_answer) yield formatted_df, rag_answer['answer'], consensus, qn_type, fig def create_interface(): custom_css = """ #custom-slider-* { background-color: #ffffff; } """ with gr.Blocks(css=custom_css) as demo: with gr.Tabs(): # with gr.Tab("What is Pathfinder?"): # gr.Markdown(pathfinder_text) with gr.Tab("pathfinder"): with gr.Accordion("What is Pathfinder? / How do I use it?", open=False): gr.Markdown(pathfinder_text) with gr.Row(): query = gr.Textbox(label="Ask me anything") with gr.Row(): with gr.Column(scale=1, min_width=300): top_k = gr.Slider(1, 30, step=1, value=10, label="top-k", info="Number of papers to retrieve") keywords = gr.Textbox(label="Optional Keywords (comma-separated)",value="") toggles = gr.CheckboxGroup(["Keywords", "Time", "Citations"], label="Weight by", info="weighting retrieved papers",value=['Keywords']) prompt_type = gr.Radio(choices=["Single-paper", "Multi-paper", "Bibliometric", "Broad but nuanced"], label="Prompt Specialization", value='Multi-paper') rag_type = gr.Radio(choices=["Semantic Search", "Semantic + HyDE", "Semantic + CoHERE", "Semantic + HyDE + CoHERE"], label="RAG Method",value='Semantic + HyDE + CoHERE') with gr.Column(scale=2, min_width=300): img1 = gr.Image("local_files/pathfinder_logo.png") btn = gr.Button("Run pfdr!") # search_results_state = gr.State([]) ret_papers = gr.Dataframe(label='top-k retrieved papers', datatype='markdown') search_results_state = gr.Markdown(label='Generated Answer') qntype = gr.Markdown(label='Question type suggestion') conc = gr.Markdown(label='Consensus') plot = gr.Plot(label='top-k in embedding space') inputs = [query, top_k, keywords, toggles, prompt_type, rag_type] outputs = [ret_papers, search_results_state, qntype, conc, plot] btn.click(fn=run_pathfinder, inputs=inputs, outputs=outputs) return demo if __name__ == "__main__": pathfinder = create_interface() pathfinder.launch()