app.py

import streamlit as st
st.set_page_config(layout="wide")

import numpy as np
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Tuple
from collections import defaultdict
from tqdm import tqdm
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 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

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 spacy
from string import punctuation
import pytextrank

from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.io import output_notebook
from bokeh.palettes import Spectral5
from bokeh.transform import linear_cmap

ts = time.time()
st.session_state.ts = ts

openai_key = st.secrets["openai_key"]
# cohere_key = st.secrets['cohere_key']
cohere_key = 'Of1MjzFjGmvzBAqdvNHTQLkAjecPcOKpiIPAnFMn'

if 'nlp' not in st.session_state:
    nlp = spacy.load("en_core_web_sm")
    nlp.add_pipe("textrank")
    st.session_state.nlp = nlp

try:
    stopwords.words('english')
except:
    nltk.download('stopwords')
    stopwords.words('english')

st.session_state.gen_llm = openai_llm(temperature=0,
                     model_name='gpt-4o-mini', 
                     openai_api_key = openai_key)
st.session_state.consensus_client = instructor.patch(OpenAI(api_key=openai_key))
st.session_state.embed_client = OpenAI(api_key = openai_key)
embed_model = "text-embedding-3-small"
st.session_state.embeddings = OpenAIEmbeddings(model = embed_model, api_key = openai_key)

st.image('local_files/pathfinder_logo.png')

st.expander("What is Pathfinder / How do I use it?", expanded=False).write(
        """
        Pathfinder v2.0 is a framework for searching and visualizing astronomy papers on the [arXiv](https://arxiv.org/) and [ADS](https://ui.adsabs.harvard.edu/) using the context
        sensitivity from modern large language models (LLMs) to better parse patterns in paper contexts.

        This tool was built during the [JSALT workshop](https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/) to do awesome things.

        **👈 Use the sidebar to tweak the search parameters to get better results**.

        ### Tool summary:
        - Please wait while the initial data loads and compiles, this takes about a minute initially.

        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 (astrophysics of galaxies) papers up to last-year-ish mined from arxiv and supplemented with ADS metadata,
        if you are interested in extending it please reach out!

        Also add: feedback form, socials, literature, contact us, copyright, collaboration, etc.

        The image below shows a representation of all the astro-ph.GA papers that can be explored in more detail
        using the `Arxiv embedding` page. The papers tend to cluster together by similarity, and result in an
        atlas that shows well studied (forests) and currently uncharted areas (water).
        """
    )


st.sidebar.header("Fine-tune the search")
top_k = st.sidebar.slider("Number of papers to retrieve:", 3, 30, 10)
extra_keywords = st.sidebar.text_input("Enter extra keywords (comma-separated):")

st.sidebar.subheader("Toggles")
toggle_a = st.sidebar.toggle("Weight by keywords", value = False)
toggle_b = st.sidebar.toggle("Weight by date", value = False)
toggle_c = st.sidebar.toggle("Weight by citations", value = False)

method = st.sidebar.radio("Retrieval method:", ["Semantic search", "Semantic search + HyDE", "Semantic search + HyDE + CoHERE"], index=2)

method2 = st.sidebar.radio("Generation complexity:", ["Basic RAG","ReAct Agent"])

question_type = st.sidebar.selectbox("Select question type:", ["Multi-paper (Default)", "Single-paper", "Bibliometric", "Broad but nuanced"])
st.session_state.question_type = question_type
# store_output = st.sidebar.button("Save output")

query = st.text_input("Ask me anything:")
submit_button = st.button("Run pathfinder!")

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...']

if 'arxiv_corpus' not in st.session_state:
    with st.spinner('loading data (please wait for this to finish before querying)...'):
        # try:
        arxiv_corpus = load_from_disk('data/')
        # except:
        #     st.write('downloading data')
        #     arxiv_corpus = load_dataset('kiyer/pathfinder_arxiv_data',split='train')
        #     # arxiv_corpus = load_dataset('kiyer/pathfinder_arxiv_data_galaxy',split='train')
        #     arxiv_corpus.save_to_disk('data/')
        arxiv_corpus.add_faiss_index('embed')
        st.session_state.arxiv_corpus = arxiv_corpus
        st.toast('loaded arxiv corpus')

if 'ids' not in st.session_state:
    with st.spinner('making the LLM talk to the astro papers...'):
        st.session_state.ids = st.session_state.arxiv_corpus['ads_id']
        st.session_state.titles = st.session_state.arxiv_corpus['title']
        st.session_state.abstracts = st.session_state.arxiv_corpus['abstract']
        st.session_state.authors = st.session_state.arxiv_corpus['authors']
        st.session_state.cites = st.session_state.arxiv_corpus['cites']
        st.session_state.years = st.session_state.arxiv_corpus['date']
        st.session_state.kws = st.session_state.arxiv_corpus['keywords']
        st.session_state.ads_kws = st.session_state.arxiv_corpus['ads_keywords']
        st.session_state.bibcode = st.session_state.arxiv_corpus['bibcode']
        st.session_state.umap_x = st.session_state.arxiv_corpus['umap_x']
        st.session_state.umap_y = st.session_state.arxiv_corpus['umap_y']
        st.toast('done caching. time taken: %.2f sec' %(time.time()-ts))   

def get_keywords(text):
    result = []
    pos_tag = ['PROPN', 'ADJ', 'NOUN']
    doc = st.session_state.nlp(text.lower())
    for token in doc:
        if(token.text in st.session_state.nlp.Defaults.stop_words or token.text in punctuation):
            continue
        if(token.pos_ in pos_tag):
            result.append(token.text)
    return result

def parse_doc(text, nret = 10):
    local_kws = []
    doc = st.session_state.nlp(text)
    # examine the top-ranked phrases in the document
    for phrase in doc._.phrases[:nret]:
        # print(phrase.text)
        local_kws.append(phrase.text)
    return local_kws

class EmbeddingRetrievalSystem():

    def __init__(self, weight_citation = False, weight_date = False, weight_keywords = False):

        self.ids = st.session_state.ids
        self.years = st.session_state.years
        self.abstract = st.session_state.abstracts
        self.client = OpenAI(api_key = openai_key)
        self.embed_model = "text-embedding-3-small"
        self.dataset = st.session_state.arxiv_corpus
        self.kws = st.session_state.kws
        self.cites = st.session_state.cites

        self.weight_citation = weight_citation
        self.weight_date = weight_date
        self.weight_keywords = weight_keywords
        self.id_to_index = {self.ids[i]: i for i in range(len(self.ids))}

        # self.citation_filter = CitationFilter(self.dataset)
        # self.date_filter = DateFilter(self.dataset['date'])
        # self.keyword_filter = KeywordFilter(corpus=self.dataset, remove_capitals=True)

    def parse_date(self, id):
        # indexval = np.where(self.ids == id)[0][0]
        indexval = id
        return self.years[indexval]

    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 analyze_temporal_query(self, query):
        return

    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]

    def rank_and_filter(self, query, query_embedding, query_date, top_k = 10, return_scores=False, time_result=None):

        # st.write('status')

        # st.write('toggles', self.toggles)
        # st.write('question_type', self.question_type)
        # st.write('rag method', self.rag_method)
        # st.write('gen method', self.gen_method)

        self.weight_keywords = self.toggles["Keyword weighting"]
        self.weight_date = self.toggles["Time weighting"]
        self.weight_citation = self.toggles["Citation weighting"]

        topk_indices, similarities = self.calc_faiss(np.array(query_embedding), top_k = 1000)
        similarities = 1/similarities # converting from a distance (less is better) to a similarity (more is better)

        query_kws = get_keywords(query)
        input_kws = self.query_input_keywords
        query_kws = query_kws + input_kws
        self.query_kws = query_kws

        if self.weight_keywords == True:
            sub_kws = [self.kws[i] for i in topk_indices]
            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 = [self.years[i] for i in topk_indices]
            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([self.cites[i] for i in topk_indices])
            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]

        if return_scores:
            return {doc[0]: doc[1] for doc in top_results}

        # Only keep the document IDs
        top_results = [doc[0] for doc in top_results]
        return top_results

    def retrieve(self, query, top_k, time_result=None, query_date = None, return_scores = False):

        query_embedding = self.get_query_embedding(query)

        # Judge time relevance
        if time_result is None:
            if self.weight_date:
                time_result, time_taken = self.analyze_temporal_query(query, self.anthropic_client)
            else:
                time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None}

        top_results = self.rank_and_filter(query,
                                           query_embedding,
                                           query_date,
                                           top_k,
                                           return_scores = return_scores,
                                           time_result = time_result)

        return top_results

class HydeRetrievalSystem(EmbeddingRetrievalSystem):
    def __init__(self, generation_model: str = "claude-3-haiku-20240307",
                 embedding_model: str = "text-embedding-3-small",
             temperature: float = 0.5,
                 max_doclen: int = 500,
                 generate_n: int = 1,
                 embed_query = True,
                 conclusion = False, **kwargs):

        # Handle the kwargs for the superclass init -- filters/citation weighting
        super().__init__(**kwargs)

        if max_doclen * generate_n > 8191:
            raise ValueError("Too many tokens. Please reduce max_doclen or generate_n.")

        self.embedding_model = embedding_model
        self.generation_model = generation_model

        # HYPERPARAMETERS
        self.temperature = temperature # generation temperature
        self.max_doclen = max_doclen # max tokens for generation
        self.generate_n = generate_n # how many documents
        self.embed_query = embed_query # embed the query vector?
        self.conclusion = conclusion # generate conclusion as well?

        # self.anthropic_key = anthropic_key
        # self.generation_client = anthropic.Anthropic(api_key = self.anthropic_key)
        self.generation_client = openai_llm(temperature=0,model_name='gpt-4o-mini', openai_api_key = openai_key)

    def retrieve(self, query: str, top_k: int = 10, return_scores = False, time_result = None) -> List[Tuple[str, str, float]]:
        if time_result is None:
            if self.weight_date: time_result, time_taken = analyze_temporal_query(query, self.anthropic_client)
            else: time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None}

        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)

        embedding = np.mean(np.array(doc_embeddings), axis = 0)

        top_results = self.rank_and_filter(query, embedding, query_date=None, top_k = top_k, return_scores = return_scores, time_result = time_result)

        return top_results

    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)
        # st.write('invoking hyde generation')

        # message = self.generation_client.messages.create(
        #         model = self.generation_model,
        #         max_tokens = self.max_doclen,
        #         temperature = self.temperature,
        #         system = prompt,
        #         messages=[{ "role": "user",
        #                 "content": [{"type": "text", "text": query,}] }]
        #     )
        # return message.content[0].text

        messages = [("system",prompt,),("human", query),]
        return self.generation_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)

class HydeCohereRetrievalSystem(HydeRetrievalSystem):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cohere_key = cohere_key
        self.cohere_client = cohere.Client(self.cohere_key)

    def retrieve(self, query: str,
                 top_k: int = 10,
                 rerank_top_k: int = 250,
                 return_scores = False, time_result = None,
                 reweight = False) -> List[Tuple[str, str, float]]:

        if time_result is None:
            if self.weight_date: time_result, time_taken = analyze_temporal_query(query, self.anthropic_client)
            else: time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None}

        top_results = super().retrieve(query, top_k = rerank_top_k, time_result = time_result)

        # doc_texts = self.get_document_texts(top_results)
        # docs_for_rerank = [f"Abstract: {doc['abstract']}\nConclusions: {doc['conclusions']}" for doc in doc_texts]
        docs_for_rerank = [self.abstract[i] for i in top_results]

        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])

        if reweight:
            if time_result['has_temporal_aspect']:
                final_results = self.date_filter.filter(final_results, time_score = time_result['expected_recency_weight'])

            if self.weight_citation: self.citation_filter.filter(final_results)

        if return_scores:
            return {result[0]: result[2] for result in final_results}

        return [doc[0] for doc in final_results]

    def embed_docs(self, docs: List[str]):
        return self.embed_batch(docs)

# --------- other fns ------------------

def get_topk(query, top_k):
    print('running retrieval')
    rs = st.session_state.ec.retrieve(query, top_k, return_scores=True)
    return rs

def Library(query, top_k = 7):
    rs = get_topk(query, top_k = top_k)
    op_docs = ''
    for paperno, i in enumerate(rs):
        op_docs = op_docs + 'Paper %.0f:' %(paperno+1) +' (published in '+st.session_state.bibcode[i][0:4] + ') ' + st.session_state.titles[i]  + '\n' + st.session_state.abstracts[i] + '\n\n'

    return op_docs

def Library2(query, top_k = 7):
    rs = get_topk(query, top_k = top_k)
    absts, fnames = [], []
    for paperno, i in enumerate(rs):
        absts.append(st.session_state.abstracts[i])
        fnames.append(st.session_state.bibcode[i])
    return absts, fnames, rs

def get_paper_df(ids):

    papers, scores, yrs, links, cites, kws, authors, absts = [], [], [], [], [], [], [], []
    for i in ids:
        papers.append(st.session_state.titles[i])
        scores.append(ids[i])
        links.append('https://ui.adsabs.harvard.edu/abs/'+st.session_state.bibcode[i]+'/abstract')
        yrs.append(st.session_state.bibcode[i][0:4])
        cites.append(st.session_state.cites[i])
        authors.append(st.session_state.authors[i][0])
        kws.append(st.session_state.ads_kws[i])
        absts.append(st.session_state.abstracts[i])

    return pd.DataFrame({
        'Title': papers,
        'Relevance': scores,
        'Lead author': authors,
        'Year': yrs,
        'ADS Link': links,
        'Citations': cites,
        'Keywords': kws,
        'Abstract': absts
    })

def extract_keywords(question, ec):
    # Simulated keyword extraction (replace with actual logic)
    return ['keyword1', 'keyword2', 'keyword3']

# Function to estimate consensus (replace with actual implementation)
def estimate_consensus():
    # Simulated consensus estimation (replace with actual calculation)
    return 0.75


def run_agent_qa(query, top_k):

    # define tools
    search = DuckDuckGoSearchAPIWrapper()
    tools = [
        Tool(
            name="Library",
            func=Library,
            description="A source of information pertinent to your question. Do not answer a question without consulting this!"
        ),
        Tool(
            name="Search",
            func=search.run,
            description="useful for when you need to look up knowledge about common topics or current events",
        )
    ]

    if 'tools' not in st.session_state:
        st.session_state.tools = tools

    # define prompt

    # for another question type:
    # First, find the quotes from the document that are most relevant to answering the question, and then print them in numbered order.
    # Quotes should be relatively short. If there are no relevant quotes, write “No relevant quotes” instead.


    template = """You are an expert astronomer and cosmologist.
    Answer the following question as best you can using information from the library, but speaking in a concise and factual manner.
    If you can not come up with an answer, say you do not know.
    Try to break the question down into smaller steps and solve it in a logical manner.

    You have access to the following tools:

    {tools}

    Use the following format:

    Question: the input question you must answer
    Thought: you should always think about what to do
    Action: the action to take, should be one of [{tool_names}]
    Action Input: the input to the action
    Observation: the result of the action
    ... (this Thought/Action/Action Input/Observation can repeat N times)
    Thought: I now know the final answer
    Final Answer: the final answer to the original input question. provide information about how you arrived at the answer, and any nuances or uncertainties the reader should be aware of

    Begin! Remember to speak in a pedagogical and factual manner."

    Question: {input}
    Thought:{agent_scratchpad}"""

    prompt = hub.pull("hwchase17/react")
    prompt.template=template

    # path to write intermediate trace to

    file_path = "agent_trace.txt"
    try:
        os.remove(file_path)
    except:
        pass
    file_handler = FileCallbackHandler(file_path)
    callback_manager=CallbackManager([file_handler])

    # define and execute agent

    tool_names = [tool.name for tool in st.session_state.tools]
    if 'agent' not in st.session_state:
        # agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
        agent = create_react_agent(llm=st.session_state.gen_llm, tools=tools, prompt=prompt)
        st.session_state.agent = agent

    if 'agent_executor' not in st.session_state:
        agent_executor = AgentExecutor(agent=st.session_state.agent, tools=st.session_state.tools, verbose=True, handle_parsing_errors=True, callbacks=CallbackManager([file_handler]))
        st.session_state.agent_executor = agent_executor

    answer = st.session_state.agent_executor.invoke({"input": query,})
    return answer

regular_prompt = """You are an expert astronomer and cosmologist.
Answer the following question as best you can using information from the library, but speaking in a concise and factual manner.
If you can not come up with an answer, say you do not know.
Try to break the question down into smaller steps and solve it in a logical manner.

Provide information about how you arrived at the answer, and any nuances or uncertainties the reader should be aware of.

Begin! Remember to speak in a pedagogical and factual manner."

Relevant documents:{context}

Question: {question}
Answer:"""

bibliometric_prompt = """You are an AI assistant with expertise in astronomy and astrophysics literature. Your task is to assist with relevant bibliometric information in response to a user question. The user question may consist of identifying key papers, authors, or trends in a specific area of astronomical research.

Depending on what the user wants, direct them to consult the NASA Astrophysics Data System (ADS) at https://ui.adsabs.harvard.edu/. Provide them with the recommended ADS query depending on their question.

Here's a more detailed guide on how to use NASA ADS for various types of queries:

Basic topic search: Enter keywords in the search bar, e.g., "exoplanets". Use quotation marks for exact phrases, e.g., "dark energy”
Author search: Use the syntax author:"Last Name, First Name", e.g., author:"Hawking, S". For papers by multiple authors, use AND, e.g., author:"Hawking, S" AND author:"Ellis, G"
Date range: Use year:YYYY-YYYY, e.g., year:2010-2020. For papers since a certain year, use year:YYYY-, e.g., year:2015-
4.Combining search terms: Use AND, OR, NOT operators, e.g., "black holes" AND (author:"Hawking, S" OR author:"Penrose, R")
Filtering results: Use the left sidebar to filter by publication year, article type, or astronomy database
Sorting results: Use the "Sort" dropdown menu to order by options like citation count, publication date, or relevance
Advanced searches: Click on the "Search" dropdown menu and select "Classic Form" for field-specific searchesUse bibcode:YYYY for a specific journal/year, e.g., bibcode:2020ApJ to find all Astrophysical Journal papers from 2020
Finding review articles: Wrap the query in the reviews() operator (e.g. reviews(“dark energy”)) 
Excluding preprints: Add NOT doctype:"eprint" to your search
Citation metrics: Click on the citation count of a paper to see its citation history and who has cited it

Some examples:

Example 1:
“How many papers published in 2022 used data from MAST missions?”
Your response should be: year:2022  data:"MAST"

Example 2:
“What are the most cited papers on spiral galaxy halos measured in X-rays, with publication date from 2010 to 2023?
Your response should be: "spiral galaxy halos" AND "x-ray" year:2010-2024

Example 3:
“Can you list 3 papers published by “< name>” as first author?”
Your response should be: author: “^X”

Example 4:
“Based on papers with “<name>” as an author or co-author, can you suggest the five most recent astro-ph papers that would be relevant?”
Your response should be: 

Remember to advise users that while these examples cover many common scenarios, NASA ADS has many more advanced features that can be explored through its documentation.

Relevant documents:{context}
Question: {question}

Response:"""

single_paper_prompt = """You are an astronomer with access to a vast database of astronomical facts and figures. Your task is to provide a concise, accurate answer to the following specific factual question about astronomy or astrophysics.
Provide the requested information clearly and directly. If relevant, include the source of your information or any recent updates to this fact. If there's any uncertainty or variation in the accepted value, briefly explain why.
If the question can't be answered with a single fact, provide a short, focused explanation. Always prioritize accuracy over speculation.
Relevant documents:{context}
Question: {question}
Response:"""

deep_knowledge_prompt = """You are an expert astronomer with deep knowledge across various subfields of astronomy and astrophysics. Your task is to provide a comprehensive and nuanced answer to the following question, which involves an unresolved topic or requires broad, common-sense understanding.
Consider multiple perspectives and current debates in the field. Explain any uncertainties or ongoing research. If relevant, mention how this topic connects to other areas of astronomy.
Provide your response in a clear, pedagogical manner, breaking down complex concepts for easier understanding. If appropriate, suggest areas where further research might be needed.
After formulating your initial response, take a moment to reflect on your answer. Consider:
1. Have you addressed all aspects of the question?
2. Are there any potential biases or assumptions in your explanation?
3. Is your explanation clear and accessible to someone with a general science background?
4. Have you adequately conveyed the uncertainties or debates surrounding this topic?
Based on this reflection, refine your answer as needed.
Remember, while you have extensive knowledge, it's okay to acknowledge the limits of current scientific understanding. If parts of the question cannot be answered definitively, explain why.
Relevant documents:{context}

Question: {question}

Initial Response:
[Your initial response here]

Reflection and Refinement:
[Your reflections and any refinements to your answer here]

Final Response:
[Your final, refined answer here]"""

def make_rag_qa_answer(query, top_k = 10):


    # try:
        absts, fhdrs, rs = Library2(query, top_k = top_k)
    
        temp_abst = ''
        loaders = []
        for i in range(len(absts)):
            temp_abst = absts[i]
    
            try:
                text_file = open("absts/"+fhdrs[i]+".txt", "w")
            except:
                os.mkdir('absts')
                text_file = open("absts/"+fhdrs[i]+".txt", "w")
            n = text_file.write(temp_abst)
            text_file.close()
            loader = TextLoader("absts/"+fhdrs[i]+".txt")
            loaders.append(loader)
    
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=50, add_start_index=True)
    
        splits = text_splitter.split_documents([loader.load()[0] for loader in loaders])
        vectorstore = Chroma.from_documents(documents=splits, embedding=st.session_state.embeddings, collection_name='retdoc4')
        # retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6,  "fetch_k": len(splits)})
        retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
    
        for i in range(len(absts)):
            try:
                os.remove("absts/"+fhdrs[i]+".txt")
            except:
                print("absts/"+fhdrs[i]+".txt not found")

        if st.session_state.question_type == 'Bibliometric':
            template = bibliometric_prompt
        elif st.session_state.question_type == 'Single-paper':
            template = single_paper_prompt
        elif st.session_state.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
            | st.session_state.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.write('heavy load! please wait 10 seconds and try again.')
    
        return rag_answer, rs

def guess_question_type(query: str):
    categorization_prompt = """You are an expert astrophysicist and computer scientist specializing in linguistics and semantics. Your task is to categorize a given query into one of the following categories:

    1. Summarization
    2. Single-paper factual
    3. Multi-paper factual
    4. Named entity recognition
    5. Jargon-specific questions / overloaded words
    6. Time-sensitive
    7. Consensus evaluation
    8. What-ifs and counterfactuals
    9. Compositional

    Analyze the query carefully, considering its content, structure, and implications. Then, determine which of the above categories best fits the query.

    In your analysis, consider the following:
    - Does the query ask for a well-known datapoint or mechanism?
    - Can it be answered by a single paper or does it require multiple sources?
    - Does it involve proper nouns or specific scientific terms?
    - Is it time-dependent or likely to change in the near future?
    - Does it require evaluating consensus across multiple sources?
    - Is it a hypothetical or counterfactual question?
    - Does it need to be broken down into sub-queries (i.e. compositional)?

    After your analysis, categorize the query into one of the nine categories listed above.

    Provide a brief explanation for your categorization, highlighting the key aspects of the query that led to your decision.

    Present your final answer in the following format:

    <categorization>
    Category: [Selected category]
    Explanation: [Your explanation for the categorization]
    </categorization>"""
    # st.write('invoking hyde generation')

    # message = self.generation_client.messages.create(
    #         model = self.generation_model,
    #         max_tokens = self.max_doclen,
    #         temperature = self.temperature,
    #         system = prompt,
    #         messages=[{ "role": "user",
    #                 "content": [{"type": "text", "text": query,}] }]
    #     )
    # return message.content[0].text

    messages = [("system",categorization_prompt,),("human", query),]
    return st.session_state.ec.generation_client.invoke(messages).content

class OverallConsensusEvaluation(BaseModel):
    consensus: Literal["Strong Agreement", "Moderate Agreement", "Weak Agreement", "No Clear Consensus", "Weak Disagreement", "Moderate Disagreement", "Strong Disagreement"] = Field(
        ...,
        description="The overall level of consensus between the query and the abstracts"
    )
    explanation: str = Field(
        ...,
        description="A detailed explanation of the consensus evaluation"
    )
    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:
    """
    Evaluates the overall consensus of the abstracts in relation to the query in a single LLM call.
    """
    prompt = f"""
    Query: {query}

    You will be provided with {len(abstracts)} scientific abstracts. Your task is to:
    1. Evaluate the overall consensus between the query and the abstracts.
    2. Provide a detailed explanation of your consensus evaluation.
    3. Assign an overall relevance score from 0 to 1, where 0 means completely irrelevant and 1 means highly relevant.

    For the consensus evaluation, use one of the following levels:
    Strong Agreement, Moderate Agreement, Weak Agreement, No Clear Consensus, Weak Disagreement, Moderate Disagreement, Strong Disagreement

    Here are the abstracts:

    {' '.join([f"Abstract {i+1}: {abstract}" for i, abstract in enumerate(abstracts)])}

    Provide your evaluation in a structured format.
    """

    response = st.session_state.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 create_embedding_plot(rs):
    """
    function to create embedding plot
    """

    pltsource = ColumnDataSource(data=dict(
        x=st.session_state.umap_x,
        y=st.session_state.umap_y,
        title=st.session_state.titles,
        link=st.session_state.bibcode,
    ))

    rsflag = np.zeros((len(st.session_state.ids),))
    rsflag[np.array([k for k in rs])] = 1

    # outflag = np.zeros((len(st.session_state.ids),))
    # outflag[np.array([k for k in find_outliers(rs)])] = 1
    pltsource.data['colors'] = rsflag * 0.8 + 0.1
    # pltsource.data['colors'][outflag] = 0.5
    pltsource.data['sizes'] = (rsflag + 1)**5 / 100

    TOOLTIPS = """
    <div style="width:300px;">
    ID: $index
    ($x, $y)
    @title <br>
    @link <br> <br>
    </div>
    """

    mapper = linear_cmap(field_name="colors", palette=Spectral5, low=0., high=1.)

    p = figure(width=700, height=900, tooltips=TOOLTIPS, x_range=(0, 20), y_range=(-4.2,18),
            title="UMAP projection of embeddings for the astro-ph corpus")

    p.axis.visible=False
    p.grid.visible=False
    p.outline_line_alpha = 0.

    p.circle('x', 'y', radius='sizes', source=pltsource, alpha=0.3, fill_color=mapper, fill_alpha='colors', line_color="lightgrey",line_alpha=0.1)

    return p

if submit_button:

    keywords = [kw.strip() for kw in extra_keywords.split(',')] if extra_keywords else []
    toggles = {'Keyword weighting': toggle_a, 'Time weighting': toggle_b, 'Citation weighting': toggle_c}
    
    if (method == "Semantic search"):
        with st.spinner('set retrieval method to'+ method):
            st.session_state.ec = EmbeddingRetrievalSystem()
    elif (method == "Semantic search + HyDE"):
        with st.spinner('set retrieval method to'+ method):
            st.session_state.ec = HydeRetrievalSystem()
    elif (method == "Semantic search + HyDE + CoHERE"):
        with st.spinner('set retrieval method to'+ method):
            st.session_state.ec = HydeCohereRetrievalSystem()
    st.toast('loaded retrieval system')
    
    with st.spinner(search_text_list[np.random.choice(len(search_text_list))]):

        st.session_state.ec.query_input_keywords = keywords
        st.session_state.ec.toggles = toggles
        st.session_state.ec.question_type = question_type
        st.session_state.ec.rag_method = method
        st.session_state.ec.gen_method = method2
        
        if method2 == "Basic RAG":
            st.session_state.gen_method = 'rag'
        elif method2 == "ReAct Agent":
            st.session_state.gen_method = 'agent'

        if st.session_state.gen_method == 'agent':
            answer = run_agent_qa(query, top_k)
            rs = get_topk(query, top_k)

            answer_text = answer['output']
            st.write(answer_text)

            file_path = "agent_trace.txt"
            with open(file_path, 'r') as file:
                intermediate_steps = file.read()
            st.expander('Intermediate steps', expanded=False).write(intermediate_steps)

        elif st.session_state.gen_method == 'rag':
            answer, rs = make_rag_qa_answer(query, top_k)
            answer_text = answer['answer']
            st.write(answer_text)

        triggered_keywords = st.session_state.ec.query_kws

        with st.spinner('compiling top-k papers'+ method):
            papers_df = get_paper_df(rs)
            
            with st.expander("Relevant papers", expanded=True):
                # st.dataframe(papers_df, hide_index=True)
                st.data_editor(papers_df, column_config = {'ADS Link':st.column_config.LinkColumn(display_text= 'https://ui.adsabs.harvard.edu/abs/(.*?)/abstract')})

        st.write('**Triggered keywords:** `'+ "`, `".join(triggered_keywords)+'`')
    
    col1, col2 = st.columns(2)

    with col1:
        with st.expander("Evaluating question type", expanded=True):
            st.subheader("Question type suggestion")
            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')
            st.markdown(question_type_gen)

    with col2:
        with st.expander("Evaluating abstract consensus", expanded=True):
            consensus_answer = evaluate_overall_consensus(query, [st.session_state.abstracts[i] for i in rs])
            st.subheader("Consensus: "+consensus_answer.consensus)
            st.markdown(consensus_answer.explanation)
            st.markdown('Relevance of retrieved papers to answer: %.1f' %consensus_answer.relevance_score)
                
    session_vars = {
        "runtime": "pathfinder_v1_online",
        "query": query,
        "question_type": question_type,
        'Keyword weighting': toggle_a, 
        'Time weighting': toggle_b, 
        'Citation weighting': toggle_c,
        "rag_method" : method,
        "gen_method" : method2,
        "answer" : answer_text,
        "topk" : ['%.0f' %i for i in rs],
        "topk_scores" : ['%.6f' %rs[i] for i in rs], 
        "topk_papers": list(papers_df['ADS Link']),
    }
    
    @st.fragment()
    def download_op(data):
        json_string = json.dumps(data)
        st.download_button(
            label='Download output',
            file_name="pathfinder_data.json",
            mime="application/json",
            data=json_string,)
        
    with st.sidebar:
        download_op(session_vars)
        
    embedding_plot = create_embedding_plot(rs)
    st.bokeh_chart(embedding_plot)
    
else:
    st.info("Use the sidebar to tweak the search parameters to get better results.")