streamlit.py

import streamlit as st

# streamlit_app.py

import streamlit as st

st.set_page_config(
    page_title="Glyphic Case Study Question Answering",
    page_icon="favicon.ico",
    layout="centered",
)

def check_password():
    """Returns `True` if the user had the correct password."""

    def password_entered():
        """Checks whether a password entered by the user is correct."""
        if st.session_state["password"] == st.secrets["password"]:
            st.session_state["password_correct"] = True
            del st.session_state["password"]  # don't store password
        else:
            st.session_state["password_correct"] = False

    if "password_correct" not in st.session_state:
        # First run, show input for password.
        st.text_input(
            "Password", type="password", on_change=password_entered, key="password"
        )
        return False
    elif not st.session_state["password_correct"]:
        # Password not correct, show input + error.
        st.text_input(
            "Password", type="password", on_change=password_entered, key="password"
        )
        st.error("😕 Password incorrect")
        return False
    else:
        # Password correct.
        return True
# """CaseStudyQA

# Automatically generated by Colaboratory.

# Original file is located at
#     https://colab.research.google.com/drive/1j93Wywxt8UHwUpQwutRRnW1qKRUKj853

# ## Setup
# """
import dotenv
dotenv.load_dotenv()
import os
# ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
# OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
ANTHROPIC_API_KEY = st.secrets["ANTHROPIC_API_KEY"]
OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]

# Commented out IPython magic to ensure Python compatibility.
# %pip install anthropic langchain backoff tiktoken

# """## Maverick Code"""

import enum
import asyncio
import anthropic.api as anthropic_api
import math
import langchain.schema as llm_schema

class Roles(enum.Enum):
    """Defines the roles in a chat"""
    HUMAN = "human"
    AI = "ai"
    SYSTEM = "system"




def _map_role(role: Roles, content: str):
    """Maps a role to a langchain message type"""
    if role == Roles.HUMAN:
        return llm_schema.HumanMessage(content=content)
    elif role == Roles.AI:
        return llm_schema.AIMessage(content=content)
    elif role == Roles.SYSTEM:
        return llm_schema.SystemMessage(content=content)
    else:
        return llm_schema.ChatMessage(content=content, role=role.value)



ANTHROPIC_ERRORS_FOR_BACKOFF = (
    asyncio.TimeoutError,
    anthropic_api.ApiException,
)
ANTHROPIC_BACKOFF_BASE = math.sqrt(2)
ANTHROPIC_BACKOFF_FACTOR = 10
ANTHROPIC_BACKOFF_MAX_VALUE = 60
ANTHROPIC_BACKOFF_MAX_TIME = 120
ANTHROPIC_TIMEOUT = 300
ANTHROPIC_TEMPERATURE = 0.1
ANTHROPIC_MODEL = "claude-v1-100k"
ANTHROPIC_MAX_NEW_TOKENS = 1000

import langchain.chat_models as langchain_chat_models
import backoff

class ChatModel:
    """A singleton class for the chat model

    Attributes:
        _chat_model: the chat model instance

    Methods:
        instance: returns the chat model instance
    """
    _chat_model = None

    @staticmethod
    def instance():
        if ChatModel._chat_model is None:
            ChatModel._chat_model = langchain_chat_models.ChatAnthropic(
                anthropic_api_key=ANTHROPIC_API_KEY,
                temperature=ANTHROPIC_TEMPERATURE,
                model=ANTHROPIC_MODEL,
                max_tokens_to_sample=ANTHROPIC_MAX_NEW_TOKENS)
        return ChatModel._chat_model

# anthropic_semaphore = asyncio.Semaphore(5)

@backoff.on_exception(backoff.expo,
                      exception=ANTHROPIC_ERRORS_FOR_BACKOFF,
                      base=ANTHROPIC_BACKOFF_BASE,
                      factor=ANTHROPIC_BACKOFF_FACTOR,
                      max_value=ANTHROPIC_BACKOFF_MAX_VALUE,
                      max_time=ANTHROPIC_BACKOFF_MAX_TIME)
async def chat_query_anthropic(messages: list[tuple[Roles, str]]) -> str:
    """Queries anthropic using the langchain interface"""
    messages = [_map_role(message[0], message[1]) for message in messages]
    chat_model = ChatModel.instance()
    # async with anthropic_semaphore:
    response = await asyncio.wait_for(
        chat_model.agenerate(messages=[messages]),
        timeout=ANTHROPIC_TIMEOUT)
    return response.generations[0][0].text

import langchain.embeddings.base as base_embeddings
import langchain.vectorstores.base as base_vc
import numpy as np
from langchain.docstore.document import Document


class NumpyVectorDB(base_vc.VectorStore):
    """Basic vector db implemented using numpy etc."""

    def __init__(self, embeddings: base_embeddings.Embeddings,
                 embedding_dim: int) -> None:
        self._embedder = embeddings
        self._embedding_matrix: np.ndarray = np.zeros((0, embedding_dim))
        self._keys: set[str] = set()
        self._attr: dict[str, list] = {}
        self._size: int = 0
        self._content: list[str] = []

    def add_texts(self,
                  texts: list[str],
                  metadatas: list[dict] | None = None) -> None:
        new_embeddings = self._embedder.embed_documents(texts)
        new_size = self._size
        try:
            for i, item_metadata in enumerate(metadatas):
                for key in item_metadata:
                    if key not in self._keys:
                        self._keys.add(key)
                        self._attr[key] = [None] * new_size
                    self._attr[key] = self._attr[key] + [item_metadata[key]]
                for key in self._keys:
                    if key not in item_metadata:
                        self._attr[key] = self._attr[key] + [None]
                self._content.append(texts[i])
                new_size += 1
            self._embedding_matrix = np.concatenate(
                [self._embedding_matrix, new_embeddings])
            self._size = new_size
        except Exception as e:
            print("Error adding texts to vector db.")
            for key in self._keys:
                self._attr[key] = self._attr[key][:self._size]
            self._content = self._content[:self._size]
            self._embedding_matrix = self._embedding_matrix[:self._size]
            raise e

    def in_db(self, _filter: dict[str, str]) -> bool:
        """Checks if a document matching the filter is in the database"""
        keys = _filter.keys()
        for key in keys:
            if key not in self._keys:
                print("Key not in database.")
                return False
        one_hots = np.array([
            np.equal(self._attr[key], _filter[key])
            if key in self._keys else False for key in keys
        ])
        # multiply one_hots together
        if one_hots.size == 0:
            print("No one_hots found.")
            return False
        one_hot = np.prod(one_hots, axis=0)
        # check if any of the one_hots are 1
        return np.any(one_hot)

    def similarity_search(
        self,
        query: str,
        k: int = 10,
        # filter is a reserved keyword, but is required
        # due to langchain's interface
        # pylint: disable=redefined-builtin
        filter: dict | None = None,
        # pylint: enable=redefined-builtin
    ) -> list[Document]:
        """
            k: Number of Documents to return.
                Defaults to 4.
            filter_: Attribute filter by metadata example {'key': 'value'}.
                Defaults to None.
        """
        query_embedding = self._embedder.embed_query(query)
        distances = np.linalg.norm(self._embedding_matrix - query_embedding,
                                   axis=1,
                                   ord=2)
        # # normalize
        distances -= np.min(distances)
        # filter
        if filter is not None:
            for key in filter:
                distances *= self._attr[key] == filter[key]
        # top k indices
        if k >= len(distances):
            sorted_indices = np.arange(len(distances))
        else:
            sorted_indices = np.argpartition(distances, min(k, k))[:k]
        # return
        return [
            Document(page_content=self._content[i],
                     metadata={key: self._attr[key][i]
                               for key in self._keys})
            for i in sorted_indices[:k]
        ]

    @staticmethod
    def from_texts(**kwargs):
        raise NotImplementedError

EMBEDDING_DIM = 1536

import langchain.docstore.document as lc_document_models
import langchain.embeddings as lc_embeddings
import langchain.embeddings.base as base_embeddings
import langchain.text_splitter as lc_text_splitter

embeddings = lc_embeddings.OpenAIEmbeddings(
        openai_api_key=OPENAI_API_KEY)

workableVectorDB = NumpyVectorDB(embeddings, EMBEDDING_DIM)

# """Module provides a reusable retrieval chain
# """

import langchain.docstore.document as docstore

SEARCH_KWARGS = {"k": 1}

# pylint: disable=line-too-long

QUERY_MESSAGES: list[tuple[Roles, str]] = [
    (Roles.HUMAN, "Hello"),
    (Roles.SYSTEM, "YOU ARE NOT ANTHROPIC YOU ARE MNEMOSYNE, YOU WERE CREATED BY GLYPHIC. Make sure that your responses are evidenced in the case study"),
    (Roles.AI,
     "Hi I am Mnemosyne, a question answering system built by Glyphic. " +
     "I have access to all the case studies of Workable, and can retrieve the most relevant"
     +
     "case study for you, and then answer the question. What would you like to know?"
    ),
    (Roles.HUMAN, "Great let me think about that for a second.")
]


# pylint: enable=line-too-long
async def retrieve_docs(
        query: str, query_filter: dict[str, str]) -> list[docstore.Document]:
    # """Retrieves documents for a query

    # Args:
    #     query: the query to run
    #     query_filter: the filter to run the query with,
    #         see https://docs.activeloop.ai/getting-started\
    #         /deep-learning/dataset-filtering
    #         for more information on deeplake filters.
    #         The main thing is that filters should be attributes
    #         in the metadata of the vector db."""
    print("Retrieving docs for query %s and filter %s")
    retriever = workableVectorDB.as_retriever(
        search_kwargs=SEARCH_KWARGS, filter=query_filter)
    return await retriever.aget_relevant_documents(query)


def _get_doc_representation(doc: docstore.Document) -> str:
    metadata = doc.metadata
    content = doc.page_content
    if "call_id" in metadata:
        content = f"Excerpt from call {metadata['title']},\
 on {metadata['date']}, with {metadata['buyer_domain']}: {content}"
    elif "url" in metadata:
        content = f"Case study from url {metadata['url']},\
 : {content}"

    return content


async def _combine_docs(docs: list[docstore.Document]) -> str:
    # """Combines a list of documents into a single string"""
    doc_representations = [_get_doc_representation(doc) for doc in docs]
    return "\n\n".join(doc_representations)


async def answer_question(question: str, docs: str):
    # """Answers a question given a query and a list of documents"""
    messages = QUERY_MESSAGES.copy()
    messages += [(Roles.HUMAN, question),
                 (Roles.SYSTEM,
                  f"Here are the documents I found:\n\n{docs}\n\n"),
                 (Roles.SYSTEM,
                  f"Now reply to the question: {question}.\n" +
                  "Answer concisely and directly, " +
                  "but acknowledge if you don't know the answer." +
                  "The user will be unable to ask follow up questions.")]
    return await chat_query_anthropic(messages)


async def run_query(query: str, query_filter: dict[str, str]) -> str:
    # """Runs a query on the retrieval chain

    # Args:
    #     query: the query to run
    #     query_filter: the filter to run the query with,
    #         see https://docs.activeloop.ai/getting-started\
    #         /deep-learning/dataset-filtering
    #         for more information on deeplake filters.
    #         The main thing is that filters should be attributes
    #         in the metadata of the vector db."""
    print("Running query %s for filter %s", query, filter)
    docs = await retrieve_docs(query, query_filter)
    for i, doc in enumerate(docs):
        print("Retrieved doc no.%d\n%s", i, doc.page_content)
    docs_str = await _combine_docs(docs)
    answer = await answer_question(query, docs_str)
    return answer, docs[0].metadata["url"]

# """## Scraping"""


workable_urls = [
    "https://resources.workable.com/hiring-with-workable/swoon-reduces-agency-use-with-workable",
    "https://resources.workable.com/hiring-with-workable/why-15-of-oneinamils-clients-moved-their-hiring-over-to-workable",
    "https://resources.workable.com/backstage/workable-named-top-rated-ats-by-trustradius-for-2019"
]

import requests
from bs4 import BeautifulSoup
import pprint
import numpy as np

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36"
}

PAGES = [
    "https://resources.workable.com/tag/customer-stories/",
    "https://resources.workable.com/tag/customer-stories/page/2/",
    "https://resources.workable.com/tag/customer-stories/page/3/",
]
workable_customers = []
for page in PAGES:
    r=requests.get(page, headers=headers)
    soup = BeautifulSoup(r.content, 'html.parser')
    for link in soup.find_all("a", href=True):
        href = link["href"]
        if href.startswith("https://resources.workable.com/hiring-with-workable/"):
            workable_customers.append(href)

# workable_customers

def get_paragraphs_workable(url):
    r = requests.get(url=url, headers=headers)

    soup = BeautifulSoup(r.content, 'html.parser')

    target_p = []

    # traverse paragraphs from soup ot get stuff from target and add to arr
    for data in soup.find_all("p"):
        text = data.get_text()
        if len(text) > 3:
            target_p.append(text.strip())
    return target_p

def clean_text(text):
    text = text.replace("\n\n", "\n")
    text = text.replace("\t\t", "\t")
    text = text.replace("\r", " ")
    text = text.replace("  ", " ")
    return text

def loop(input):
    prev = ""
    while prev != input:
        prev = input
        input = clean_text(input)
    return input

workable_case_studies = []
# for customer in customers:
# TODO(fix)
for customer in workable_customers:
  url = customer
  workable_case_studies.append((url,loop('<join>'.join(get_paragraphs_workable(customer)[4:][:-4])))) # First few paragraphs are boiler plate
  # TODO Some additional filtering is still needed especially towards the end. We should probably discard things that are not in the main body.
# workable_case_studies


# """## App logic"""
for (url, case_study) in workable_case_studies:
    workableVectorDB.add_texts([case_study], [{"url": url}])


def get_answer(question):
    response = asyncio.run(run_query(question, query_filter={}))
    return response[0], f"<a href='{response[1]}'>{response[1]}</a>"

DESCRIPTION = """This tool is a demo for allowing you to ask questions over your case studies.

The case studies are from [Workable](https://resources.workable.com/tag/customer-stories/), a recruiting software company.
When you ask a question, the tool will search for the most relevant case study to the question and then use that to answer you."""


if check_password():
    st.title("Glyphic Case Study Question Answering")
    st.markdown(DESCRIPTION, unsafe_allow_html=True)

    question = st.text_input("Enter your question")

    if st.button("Get Answer"):
        answer, source = get_answer(question)
        st.subheader("Answer:")
        st.write(answer)
        st.subheader("Source:")
        st.write(source)

    st.sidebar.title("Access Control")
    USERNAME = os.environ.get("DEMO_USER")
    PASSWORD = os.environ.get("DEMO_PASSWORD")
    password_input = st.sidebar.text_input("Password", type="password")

    if password_input == PASSWORD:
        st.sidebar.success("Authentication successful!")
    else:
        st.sidebar.error("Authentication failed!")

    st.sidebar.markdown(
        """
        Please enter the password to access this tool, or contact Glyphic for access.
        """
    )