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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
embeddings = HuggingFaceEmbeddings()
db = FAISS.load_local('db_full', embeddings)
mp_docs = {}
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)
df['_id'] = df['_id'].apply(lambda x: str(x))
df.sort_values("score", inplace = True)
# TO-DO: What if user query doesn't match what we provide as documents
tier_1 = df[df['score'] < 1]
chunks_1 = tier_1.groupby(['_id' ]).apply(lambda x: {f"chunk_{i}": row for i, row in enumerate(x.sort_values('id')[['id', 'score','page_content']].to_dict('records'))}).values
tier_1_adjusted = tier_1.groupby(['_id']).first().reset_index()[['_id', 'title', 'url', 'score']]
tier_1_adjusted['ref'] = range(1, len(tier_1_adjusted) + 1 )
tier_1_adjusted['chunks'] = chunks_1
score = tier_1.groupby(['_id' ]).apply(lambda x: x['score'].mean()).values
tier_1_adjusted['score'] = score
tier_1_adjusted.sort_values("score", inplace = True)
if n:
tier_1_adjusted = tier_1_adjusted[:min(len(tier_1_adjusted), n)]
return {'tier 1':tier_1_adjusted, }
def qa_retrieve(query,):
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']
reference = tier_1[['_id', 'url', 'title', 'chunks', 'score']].to_dict('records')
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="json",examples=examples)
demo.launch() |