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import gradio as gr
import os
import openai
import pandas as pd
from langchain.vectorstores import FAISS
from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
from langchain.chains import LLMChain
from langchain_core.output_parsers.string import StrOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
#from langchain.embeddings import HuggingFaceBgeEmbeddings
import nltk
nltk.download('wordnet')
from sentence_transformers import SentenceTransformer
#embeddings = OpenAIEmbeddings()
#model_name = "BAAI/bge-large-en-v1.5"
#model_kwargs = {'device':'cuda'}
#encode_kwargs = {'normalize_embeddings':True}
#embedding_function = HuggingFaceBgeEmbeddings(
# model_name = model_name,
# model_kwargs = model_kwargs,
# encode_kwargs = encode_kwargs
#)
embedder = SentenceTransformer('all-mpnet-base-v2')
# Set the OpenAI API key
#openai.api_key = os.getenv("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")
# Load the FAISS index using LangChain's FAISS implementation
db = FAISS.load_local("Faiss_index", embedder, allow_dangerous_deserialization=True)
parser = StrOutputParser()
# Load your data (e.g., a DataFrame)
df = pd.read_pickle('df_news (1).pkl')
# Search function to retrieve relevant documents
def search(query):
query_embedding = embedder.embed_query(query).reshape(1, -1).astype('float32')
D, I = db.similarity_search_with_score(query_embedding, k=10)
results = []
for idx in I[0]:
if idx < 3327: # Adjust this based on your indexing
doc_index = idx
results.append({
'type': 'metadata',
'title': df.iloc[doc_index]['title'],
'author': df.iloc[doc_index]['author'],
'full_text': df.iloc[doc_index]['full_text'],
'source': df.iloc[doc_index]['url']
})
else:
chunk_index = idx - 3327
metadata = metadata_info[chunk_index]
doc_index = metadata['index']
chunk_text = metadata['chunk']
results.append({
'type': 'content',
'title': df.iloc[doc_index]['title'],
'author': df.iloc[doc_index]['author'],
'content': chunk_text,
'source': df.iloc[doc_index]['url']
})
return results
# Generate an answer based on the retrieved documents
def generate_answer(query):
context = search(query)
context_str = "\n\n".join([f"Title: {doc['title']}\nContent: {doc.get('content', doc.get('full_text', ''))}" for doc in context])
prompt = f"""
Answer the question based on the context below. If you can't answer the question, answer with "I don't know".
Context: {context_str}
Question: {query}
"""
# Set up the ChatOpenAI model with temperature and other parameters
chat = ChatOpenAI(
model="gpt-4",
temperature=0.2,
max_tokens=1500,
api_key=openai.api_key
)
messages = [
SystemMessagePromptTemplate.from_template("You are a helpful assistant."),
HumanMessagePromptTemplate.from_template(prompt)
]
chat_chain = LLMChain(
llm=chat,
prompt=ChatPromptTemplate.from_messages(messages)
)
# Get the response from the chat model
response = chat_chain.run(messages)
return response.strip()
# Gradio chat interface
def respond(message, history, system_message, max_tokens, temperature, top_p):
response = generate_answer(message)
yield response
# Gradio demo setup
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch()
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