RAG / app_RAG.py
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Rename app.py to app_RAG.py
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import torch
import transformers
from transformers import RagRetriever, RagSequenceForGeneration, AutoModelForCausalLM, pipeline
import gradio as gr
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dataset_path = "./5k_index_data/my_knowledge_dataset"
index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
passages_path = dataset_path,
index_path = index_path,
n_docs = 5)
rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
rag_model.retriever.init_retrieval()
rag_model.to(device)
pipe = pipeline(
"text-generation",
model="google/gemma-2-2b-it",
model_kwargs={"torch_dtype": torch.bfloat16},
device=device,
)
def strip_title(title):
if title.startswith('"'):
title = title[1:]
if title.endswith('"'):
title = title[:-1]
return title
def retrieved_info(query, rag_model = rag_model):
# Tokenize Query
retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
[query],
return_tensors = 'pt',
padding = True,
truncation = True,
)['input_ids'].to(device)
# Retrieve Documents
question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
question_encoder_pool_output = question_encoder_output[0]
result = rag_model.retriever(
retriever_input_ids,
question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
prefix = rag_model.rag.generator.config.prefix,
n_docs = rag_model.config.n_docs,
return_tensors = 'pt',
)
# Preparing query and retrieved docs for model
all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
retrieved_context = []
for docs in all_docs:
titles = [strip_title(title) for title in docs['title']]
texts = docs['text']
for title, text in zip(titles, texts):
retrieved_context.append(f'{title}: {text}')
# Generating answer using gemma model
messages = [
{"role": "user", "content": f"{query}"},
{"role": "system" , "content": f"Context: {retrieved_context}. Use the links and information from the Context to answer the query in brief. Provide links in the answer."}
]
outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
return assistant_response
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens ,
temperature,
top_p,
):
if message: # If there's a user query
response = retrieved_info(message) # Get the answer from your local FAISS and Q&A model
return response
# In case no message, return an empty string
return ""
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
# Custom title and description
title = "🧠 Welcome to Your AI Knowledge Assistant"
description = """
HI!!, I am your loyal assistant, y functionality is based on RAG model, I retrieves relevant information and provide answers based on that. Ask me any question, and let me assist you.
My capabilities are limited because I am still in development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
"""
demo = gr.ChatInterface(
respond,
type = 'messages',
additional_inputs=[
gr.Textbox(value="You are a helpful and friendly assistant.", 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)",
),
],
title=title,
description=description,
submit_btn = True,
textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
examples=[["Future of AI"], ["App Development"]],
theme="compact",
)
if __name__ == "__main__":
demo.launch(share = True )