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import gradio as gr
from huggingface_hub import InferenceClient
# RAG imports
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# We'll load the existing FAISS index at the start
INDEX_FOLDER = "faiss_index"
_vectorstore = None
def load_vectorstore():
"""Loads FAISS index from local folder."""
global _vectorstore
if _vectorstore is None:
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
_vectorstore = FAISS.load_local(INDEX_FOLDER, embeddings, allow_dangerous_deserialization=True)
return _vectorstore
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
"""
Called on each user message. We'll do a retrieval step (RAG)
to get relevant context, then feed it into the system message
before calling the InferenceClient.
"""
# 1. Retrieve top documents from FAISS
vectorstore = load_vectorstore()
top_docs = vectorstore.similarity_search(message, k=3)
# Build context string from the docs
context_texts = []
for doc in top_docs:
context_texts.append(doc.page_content)
KnowledgeBase = "\n".join(context_texts)
# 2. Augment the original system message with retrieved context
augmented_system_message = system_message + "\n\n" + f"Relevant context:\n{KnowledgeBase}"
# 3. Convert (history) into messages
messages = [{"role": "system", "content": augmented_system_message }]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Finally, add the new user message
messages.append({"role": "user", "content": message})
# 4. Stream from the InferenceClient
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly, knowledgeable assistant acting as Prakash Naikade."
"You have access to a rich set of documents and references collectively called KnowledgeBase, which you should call and treat as your current knowledge base. "
"Always use the facts, details, and stories from KnowledgeBase to ground your answers. "
"If a question goes beyond what KnowledgeBase covers, politely explain that you don’t have enough information to answer. "
"Remain friendly, empathetic, and helpful, providing clear, concise, and context-driven responses. "
"Stay consistent with any personal or professional details found in KnowledgeBase. "
"If KnowledgeBase lacks any relevant detail, avoid making up new information—be honest about the gap. "
"Your goal is to accurately represent Prakash Naikade: his background, expertise, and experiences, using only the data from KnowledgeBase to support your answers.", 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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