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import gradio as gr
import numpy as np
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import LLMChain
from langchain import PromptTemplate
import re
import pandas as pd
from langchain.vectorstores import FAISS
import requests
from typing import List
from langchain.schema import (
SystemMessage,
HumanMessage,
AIMessage
)
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any
import ast
from utils import ClaudeLLM, extract_website_name, remove_numbers
embeddings = HuggingFaceEmbeddings()
db = FAISS.load_local('db_full', embeddings)
mp_docs = {}
# llm = ClaudeLLM()
# ChatOpenAI(
# temperature=0,
# model='gpt-3.5-turbo-16k'
# )
def add_text(history, text):
print(history)
history = history + [(text, None)]
return history, ""
# pipeline = {'claude': (ClaudeLLM(), 0), 'gpt-3.5': (ChatOpenAI(temperature=0,model='gpt-3.5-turbo-16k'), 65), 'gpt-4': (ChatOpenAI(temperature=0, model='gpt-4'), 30)}
def retrieve_thoughts(query, n):
# print(db.similarity_search_with_score(query = query, k = k, fetch_k = k*10))
docs_with_score = db.similarity_search_with_score(query = query, k = len(db.index_to_docstore_id.values()), fetch_k = len(db.index_to_docstore_id.values()))
df = pd.DataFrame([dict(doc[0])['metadata'] for doc in docs_with_score], )
df = pd.concat((df, pd.DataFrame([dict(doc[0])['page_content'] for doc in docs_with_score], columns = ['page_content'])), axis = 1)
df = pd.concat((df, pd.DataFrame([doc[1] for doc in docs_with_score], columns = ['score'])), axis = 1)
# TO-DO: What if user query doesn't match what we provide as documents
tier_1 = df[df['score'] < 0.95]
tier_1 = tier_1[:min(len(tier_1),150)]
# tier_2 = df[(df['score'] < 0.95) * (df["score"] > 0.7)]
chunks_1 = tier_1.groupby(['title', 'url']).apply(lambda x: "\n...\n".join(x.sort_values('id')['page_content'].values)).values
tier_1_adjusted = tier_1.groupby(['title', 'url']).first().reset_index()[['title', 'url', 'score']]
tier_1_adjusted['ref'] = range(1, len(tier_1_adjusted) + 1 )
tier_1_adjusted['content'] = chunks_1
# chunks_2 = tier_2.groupby(['title', 'url', '_id']).apply(lambda x: "\n...\n".join(x.sort_values('id')['page_content'].values)).values
# tier_2_adjusted = tier_2.groupby(['title', 'url', '_id']).first().reset_index()[['_id', 'title', 'url']]
# tier_2_adjusted['content'] = chunks_2
if n:
tier_1_adjusted = tier_1_adjusted[:min(len(tier_1_adjusted), n)]
print(len(tier_1_adjusted))
# tier_1 = [doc[0] for doc in docs if ((doc[1] < 1))][:5]
# tier_2 = [doc[0] for doc in docs if ((doc[1] > 0.7)*(doc[1] < 1.5))][10:15]
return {'tier 1':tier_1_adjusted, }
def qa_retrieve(query, llm):
# llm = pipeline["claude"][0]
docs = ""
global db
print(db)
global mp_docs
thoughts = retrieve_thoughts(query, 0)
if not(thoughts):
if mp_docs:
thoughts = mp_docs
else:
mp_docs = thoughts
tier_1 = thoughts['tier 1']
# tier_2 = thoughts['tier 2']
reference = tier_1[['ref', 'url', 'title', 'content','score']].to_dict('records')
# tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n Content: {x.content}", axis = 1).values)
# print(len(tier_1))
# tier_2 = list(tier_2.apply(lambda x: f"title: {x['title']}\n Content: {x.content}", axis = 1).values)
return {'Reference': reference}
def flush():
return None
examples = [
["Will Russia win the war in Ukraine?"],
]
demo = gr.Interface(fn=qa_retrieve, title="cicero-qa-api",
inputs=gr.inputs.Textbox(lines=5, label="what would you like to learn about?"),
outputs=[
gr.components.JSON( label="Reference")],examples=examples)
demo.queue(concurrency_count = 4)
demo.launch()
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