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import gradio as gr
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain_community.chat_models.huggingface import ChatHuggingFace
from langchain.schema import SystemMessage, HumanMessage, AIMessage
from langchain_community.llms import HuggingFaceEndpoint
model_name = "sentence-transformers/all-mpnet-base-v2"
embedding_llm = SentenceTransformerEmbeddings(model_name=model_name)
db = FAISS.load_local("faiss_index", embedding_llm, allow_dangerous_deserialization=True)
# Set up Hugging Face model
llm = HuggingFaceEndpoint(
repo_id="HuggingFaceH4/starchat2-15b-v0.1",
task="text-generation",
max_new_tokens=4096,
temperature=0.6,
top_p=0.9,
top_k=40,
repetition_penalty=1.2,
do_sample=True,
)
chat_model = ChatHuggingFace(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content="Hi AI, how are you today?"),
AIMessage(content="I'm great thank you. How can I help you?")
]
def handle_query(query: str, mode: str):
# Check if query is empty
if not query.strip():
return "Enter a valid message."
if mode == "Chat":
return chat_mode(query)
elif mode == "Web-Search":
return web_search(query)
else:
return "Select a valid mode."
def chat_mode(query: str):
global messages
prompt = HumanMessage(content=query)
messages.append(prompt)
response = chat_model.invoke(messages)
messages.append(response.content)
if len(messages) >= 6:
messages = messages[-6:]
return f"You: {query}\nIT-Assistant: {response.content}"
def web_search(query: str):
global messages
similar_docs = db.similarity_search(query, k=3)
if similar_docs:
source_knowledge = "\n".join([x.page_content for x in similar_docs])
else:
source_knowledge = ""
augmented_prompt = f"""
If the answer to the next query is not contained in the Web Search say 'No Answer Available' and then give advice.
Query: {query}
Web Search:
{source_knowledge}
"""
prompt = HumanMessage(content=augmented_prompt)
messages.append(prompt)
response = chat_model.invoke(messages)
messages.append(response.content)
if len(messages) >= 6:
messages = messages[-6:]
return f"You: {query}\nIT-Assistant: {response.content}"
demo = gr.Interface(
fn=handle_query,
inputs=["text", gr.Radio(["Chat", "Web-Search"], label="Mode", info="Choose a mode and enter your message, then click submit to interact.")],
outputs="text",
title="IT Assistant")
demo.launch() |