switching to gradio for better memory handling.

streamlit doesn't handle heavy traffic as well, so keeping gradio's queue for now. access the original streamlit version at https://huggingface.co/spaces/kiyer/pathfinder_v3

README.md

@@ -3,9 +3,9 @@ title: Pathfinder
 emoji: 🔎
 colorFrom: yellow
 colorTo: blue
-sdk: streamlit
-sdk_version: 1.37.0
-app_file: app.py
+sdk: gradio
+sdk_version: 4.40.0
+app_file: app_gradio.py
 pinned: true
 license: mit
 ---

app_gradio.py

@@ -0,0 +1,550 @@
+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
+# 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']
+
+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 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')
+    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
+
+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...']
+
+    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 + 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 '<categorization>' in question_type_gen:
+        question_type_gen = question_type_gen.split('<categorization>')[1]
+    if '</categorization>' in question_type_gen:
+        question_type_gen = question_type_gen.split('</categorization>')[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 + 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()

prompts.py

@@ -142,3 +142,66 @@ Present your final answer in the following format:
 Category: [Selected category]
 Explanation: [Your explanation for the categorization]
 </categorization>"""
+
+
+pathfinder_text = """# Welcome to Pathfinder
+
+## Discover the Universe Through AI-Powered Astronomy ReSearch
+
+### What is Pathfinder?
+
+Pathfinder (https://pfdr.app) harnesses the power of modern large language models (LLMs) in combination with papers on the [arXiv](https://arxiv.org/) and [ADS](https://ui.adsabs.harvard.edu/) to navigate the vast expanse of astronomy literature.
+Our tool empowers researchers, students, and astronomy enthusiasts to get started on their journeys to find answers to complex research questions quickly and efficiently.
+
+To use the old streamlit pathfinder (with the ReAct agent), you can use the [pfdr streamlit mirror](https://huggingface.co/spaces/kiyer/pathfinder_v3/). 
+
+This is not meant to be a replacement to existing tools like the [ADS](https://ui.adsabs.harvard.edu/), [arxivsorter](https://www.arxivsorter.org/), semantic search or google scholar, but rather a supplement to find papers that otherwise might be missed during a literature survey. It is trained on astro-ph papers up to July 2024.
+
+### How to Use Pathfinder
+
+You can use pathfinder to find papers of interest with natural-language questions, and generate basic answers to questions using the retrieved papers. Try asking it questions like
+
+- What is the value of the Hubble Constant?
+- Are there open source radiative transfer codes for planetary atmospheres?
+- Can I predict a galaxy spectrum from an image cutout? Please reply in Hindi.
+- How would galaxy evolution differ in a universe with no dark matter?
+
+**👈 Use the sidebar to tweak the search parameters to get better results**. Changing the number of retrieved papers (**top-k**), weighting by keywords, time, or citations, or changing the prompt type might help better refine the paper search and synthesized answers for your specific question.
+
+1. **Enter Your Query**: Type your astronomy question in the search bar & hit `run pathfinder`.
+2. **Review Results**: Pathfinder will analyze relevant literature and present you with a concise answer.
+3. **Explore Further**: Click on provided links to delve deeper into the source material on ADS.
+4. **Refine Your Search**: Use our advanced filters to narrow down results by date, author, or topic.
+5. **Download results:** You can download the results of your query as a json file.
+
+### Why Use Pathfinder?
+
+- **Time-Saving**: Get started finding answers that would take hours of manual research.
+- **Comprehensive**: Access information from papers across a large database of astronomy literature.
+- **User-Friendly**: Intuitive interface designed for researchers at all levels.
+- **Constantly Updated**: Our database is regularly refreshed with the latest publications.
+
+### Learn More
+
+- Read our paper on [arXiv](https://arxiv.org/abs/2408.01556) to understand the technology behind Pathfinder.
+- Discover how Pathfinder was developed in collaboration with [UniverseTBD](https://www.universetbd.org) on its mission is to democratise science for everyone, and [JSALT](https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/).
+
+---
+
+### Copyright and Terms of Use
+
+© 2024 Pathfinder. All rights reserved.
+
+Pathfinder is provided "as is" without warranty of any kind. By using this service, you agree to our [Terms of Service] and [Privacy Policy].
+
+### Contact Us
+
+Have questions or feedback? We'd love to hear from you!
+- Email: pfdr@universetbd.org
+- Twitter: [@universe_tbd](https://twitter.com/universe_tbd)
+- Huggingface: [https://huggingface.co/spaces/kiyer/pathfinder/](https://huggingface.co/spaces/kiyer/pathfinder/)
+
+---
+
+*Empowering astronomical discoveries, one query at a time.*
+"""