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import os
import streamlit as st
import streamlit.components.v1 as components
import openai
from llama_index.llms.openai import OpenAI
import os
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext, PropertyGraphIndex
from llama_index.core.indices.property_graph import (
ImplicitPathExtractor,
SimpleLLMPathExtractor,
)
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.core.retrievers import BaseRetriever
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.openai import OpenAIEmbedding
# from llama_index.llms.mistralai import MistralAI
from llmlingua import PromptCompressor
from rouge_score import rouge_scorer
from semantic_text_similarity.models import WebBertSimilarity
import nest_asyncio
# Apply nest_asyncio
nest_asyncio.apply()
# OpenAI credentials
key = os.getenv('OPENAI_API_KEY')
openai.api_key = key
os.environ["OPENAI_API_KEY"] = key
# key = os.getenv('MISTRAL_API_KEY')
# os.environ["MISTRAL_API_KEY"] = key
# Anthropic credentials
# key = os.getenv('CLAUDE_API_KEY')
# os.environ["ANTHROPIC_API_KEY"] = key
# Streamlit UI
st.title("Prompt Optimization for a Policy Bot")
uploaded_files = st.file_uploader("Upload a Policy document in pdf format", type="pdf", accept_multiple_files=True)
if uploaded_files:
for uploaded_file in uploaded_files:
with open(f"./data/{uploaded_file.name}", 'wb') as f:
f.write(uploaded_file.getbuffer())
reader = SimpleDirectoryReader(input_files=[f"./data/{uploaded_file.name}"])
documents = reader.load_data()
st.success("File uploaded...")
# # Indexing
# index = PropertyGraphIndex.from_documents(
# documents,
# embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
# kg_extractors=[
# ImplicitPathExtractor(),
# SimpleLLMPathExtractor(
# llm=OpenAI(model="gpt-3.5-turbo", temperature=0.3),
# num_workers=4,
# max_paths_per_chunk=10,
# ),
# ],
# show_progress=True,
# )
# # Save Knowlege Graph
# index.property_graph_store.save_networkx_graph(name="./data/kg.html")
# # Display the graph in Streamlit
# st.success("File Processed...")
# st.success("Creating Knowledge Graph...")
# HtmlFile = open("./data/kg.html", 'r', encoding='utf-8')
# source_code = HtmlFile.read()
# components.html(source_code, height= 500, width=700)
# # Retrieval
# kg_retriever = index.as_retriever(
# include_text=True, # include source text, default True
# )
# Indexing
splitter = SentenceSplitter(chunk_size=256)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)
# Retrieval
bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=1)
vector_retriever = index.as_retriever(similarity_top_k=3)
# Hybrid Retriever class
class HybridRetriever(BaseRetriever):
def __init__(self, vector_retriever, bm25_retriever):
# self.vector_retriever = vector_retriever
self.bm25_retriever = bm25_retriever
super().__init__()
# def _retrieve(self, query, **kwargs):
# bm25_nodes = self.bm25_retriever.retrieve(query, **kwargs)
# vector_nodes = self.vector_retriever.retrieve(query, **kwargs)
# all_nodes = []
# node_ids = set()
# for n in bm25_nodes + vector_nodes:
# if n.node.node_id not in node_ids:
# all_nodes.append(n)
# node_ids.add(n.node.node_id)
# return all_nodes
def _retrieve(self, query, **kwargs):
bm25_nodes = self.bm25_retriever.retrieve(query, **kwargs)
# vector_nodes = self.vector_retriever.retrieve(query, **kwargs)
all_nodes = []
node_ids = set()
for n in bm25_nodes:
if n.node.node_id not in node_ids:
all_nodes.append(n)
node_ids.add(n.node.node_id)
return all_nodes
hybrid_retriever = HybridRetriever(vector_retriever, bm25_retriever)
# Generation
model = "gpt-3.5-turbo"
# model = "claude-3-opus-20240229"
# def get_context(query):
# contexts = kg_retriever.retrieve(query)
# context_list = [n.text for n in contexts]
# return context_list
def get_context(query):
contexts = hybrid_retriever.retrieve(query)
context_list = [n.get_content() for n in contexts]
return context_list
def res(prompt):
response = openai.chat.completions.create(
model=model,
messages=[
{"role":"system",
"content":"You are a helpful assistant who answers from the following context. If the answer can't be found in context, politely refuse"
},
{"role": "user",
"content": prompt,
}
]
)
return [response.usage.prompt_tokens, response.usage.completion_tokens, response.usage.total_tokens, response.choices[0].message.content]
# Summary
def summary(prompt, temp):
response = openai.chat.completions.create(
model=model,
temperature=temp,
messages=[
{"role":"system",
"content":"Summarize the following context:"
},
{"role": "user",
"content": prompt,
}
]
)
return response.choices[0].message.content
full_prompt = documents[0].text
st.success("Input text")
st.markdown(full_prompt)
st.success("Generated summary")
gen_summ = summary(full_prompt, temp = 0.6)
st.markdown(gen_summ)
st.success("Reference summary")
ref_summ = summary(full_prompt, temp = 0.8)
st.markdown(ref_summ)
# Initialize session state for token summary, evaluation details, and chat messages
if "token_summary" not in st.session_state:
st.session_state.token_summary = []
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input("Enter your query:"):
st.success("Fetching info...")
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Generate response
# st.success("Fetching info...")
context_list = get_context(prompt)
context = " ".join(context_list)
st.success("Getting context")
st.markdown(context)
# # Summarize
# full_prompt = "\n\n".join([context + prompt])
# orig_res = res(full_prompt)
# Original prompt response
full_prompt = "\n\n".join([context + prompt])
orig_res = res(full_prompt)
st.session_state.messages.append({"role": "assistant", "content": "Generating Original prompt response..."})
st.session_state.messages.append({"role": "assistant", "content": orig_res[3]})
st.success("Generating Original prompt response...")
with st.chat_message("assistant"):
st.markdown(orig_res[3])
# # Compressed Response
# st.session_state.messages.append({"role": "assistant", "content": "Generating Optimized prompt response..."})
# st.success("Generating Optimized prompt response...")
# llm_lingua = PromptCompressor(
# model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
# use_llmlingua2=True, device_map="cpu"
# )
# def prompt_compression(context, rate=0.5):
# compressed_context = llm_lingua.compress_prompt(
# context,
# rate=rate,
# force_tokens=["!", ".", "?", "\n"],
# drop_consecutive=True,
# )
# return compressed_context
# compressed_context = prompt_compression(context)
# full_opt_prompt = "\n\n".join([compressed_context['compressed_prompt'] + prompt])
# compressed_res = res(full_opt_prompt)
# st.session_state.messages.append({"role": "assistant", "content": compressed_res[3]})
# with st.chat_message("assistant"):
# st.markdown(compressed_res[3])
# # Save token summary and evaluation details to session state
# scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
# scores = scorer.score(compressed_res[3],orig_res[3])
# webert_model = WebBertSimilarity(device='cpu')
# similarity_score = webert_model.predict([(compressed_res[3], orig_res[3])])[0] / 5 * 100
# # Display token summary
# st.session_state.messages.append({"role": "assistant", "content": "Token Length Summary..."})
# st.success('Token Length Summary...')
# st.session_state.messages.append({"role": "assistant", "content": f"Original Prompt has {orig_res[0]} tokens"})
# st.write(f"Original Prompt has {orig_res[0]} tokens")
# st.session_state.messages.append({"role": "assistant", "content": f"Optimized Prompt has {compressed_res[0]} tokens"})
# st.write(f"Optimized Prompt has {compressed_res[0]} tokens")
# st.session_state.messages.append({"role": "assistant", "content": "Comparing Original and Optimized Prompt Response..."})
# st.success("Comparing Original and Optimized Prompt Response...")
# st.session_state.messages.append({"role": "assistant", "content": f"Rouge Score : {scores['rougeL'].fmeasure * 100}"})
# st.write(f"Rouge Score : {scores['rougeL'].fmeasure * 100}")
# st.session_state.messages.append({"role": "assistant", "content": f"Semantic Text Similarity Score : {similarity_score}"})
# st.write(f"Semantic Text Similarity Score : {similarity_score}")
# st.write(" ")
# # origin_tokens = compressed_context['origin_tokens']
# # compressed_tokens = compressed_context['compressed_tokens']
# origin_tokens = orig_res[0]
# compressed_tokens = compressed_res[0]
# gpt_saving = (origin_tokens - compressed_tokens) * 0.06 / 1000
# claude_saving = (origin_tokens - compressed_tokens) * 0.015 / 1000
# mistral_saving = (origin_tokens - compressed_tokens) * 0.004 / 1000
# # st.session_state.messages.append({"role": "assistant", "content": f"""The optimized prompt has saved ${gpt_saving:.4f} in GPT4, ${mistral_saving:.4f} in Mistral"""})
# # st.success(f"""The optimized prompt has saved ${gpt_saving:.4f} in GPT4, ${mistral_saving:.4f} in Mistral""")
# st.session_state.messages.append({"role": "assistant", "content": f"The optimized prompt has ${gpt_saving:.4f} saved in GPT-4."})
# st.success(f"The optimized prompt has ${gpt_saving:.4f} saved in GPT-4.")
# st.success("Downloading Optimized Prompt...")
# st.download_button(label = "Download Optimized Prompt",
# data = full_opt_prompt, file_name='./data/optimized_prompt.txt')
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