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import os
from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain.vectorstores import FAISS
from sentence_transformers import SentenceTransformer
import faiss
import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from datetime import datetime
import gradio as gr
class DocumentRetrievalAndGeneration:
def __init__(self, embedding_model_name, lm_model_id, data_folder, faiss_index_path):
self.documents = self.load_documents(data_folder)
self.embeddings = SentenceTransformer(embedding_model_name)
self.gpu_index = self.load_faiss_index(faiss_index_path)
self.llm = self.initialize_llm(lm_model_id)
def load_documents(self, folder_path):
loader = DirectoryLoader(folder_path, loader_cls=TextLoader)
documents = loader.load()
print('Length of documents:', len(documents))
return documents
def load_faiss_index(self, faiss_index_path):
cpu_index = faiss.read_index(faiss_index_path)
gpu_resource = faiss.StandardGpuResources()
gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, cpu_index)
return gpu_index
def initialize_llm(self, model_id):
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
generate_text = pipeline(
model=model,
tokenizer=tokenizer,
return_full_text=True,
task='text-generation',
temperature=0.6,
max_new_tokens=2048,
)
return generate_text
def query_and_generate_response(self, query):
query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy()
distances, indices = self.gpu_index.search(np.array([query_embedding]), k=5)
content = ""
for idx in indices[0]:
content += "-" * 50 + "\n"
content += self.documents[idx].page_content + "\n"
print(self.documents[idx].page_content)
print("############################")
prompt = f"Query: {query}\nSolution: {content}\n"
# Encode and prepare inputs
messages = [{"role": "user", "content": prompt}]
encodeds = self.llm.tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(self.llm.device)
# Perform inference and measure time
start_time = datetime.now()
generated_ids = self.llm.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
elapsed_time = datetime.now() - start_time
# Decode and return output
decoded = self.llm.tokenizer.batch_decode(generated_ids)
generated_response = decoded[0]
print("Generated response:", generated_response)
print("Time elapsed:", elapsed_time)
print("Device in use:", self.llm.device)
return generated_response
def qa_infer_gradio(self, query):
response = self.query_and_generate_response(query)
return response
if __name__ == "__main__":
# Example usage
embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12'
lm_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
data_folder = 'sample_embedding_folder'
faiss_index_path = 'faiss_index_new_model3.index'
doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder, faiss_index_path)
# Define Gradio interface function
def launch_interface():
css_code = """
.gradio-container {
background-color: #daccdb;
}
/* Button styling for all buttons */
button {
background-color: #927fc7; /* Default color for all other buttons */
color: black;
border: 1px solid black;
padding: 10px;
margin-right: 10px;
font-size: 16px; /* Increase font size */
font-weight: bold; /* Make text bold */
}
"""
EXAMPLES = ["TDA4 product planning and datasheet release progress? ",
"I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?",
"Master core in TDA2XX is a15 and in TDA3XX it is m4,so we have to shift all modules that are being used by a15 in TDA2XX to m4 in TDA3xx."]
file_path = "ticketNames.txt"
# Read the file content
with open(file_path, "r") as file:
content = file.read()
ticket_names = json.loads(content)
dropdown = gr.Dropdown(label="Sample queries", choices=ticket_names)
# Define Gradio interface
interface = gr.Interface(
fn=doc_retrieval_gen.qa_infer_gradio,
inputs=[gr.Textbox(label="QUERY", placeholder="Enter your query here")],
allow_flagging='never',
examples=EXAMPLES,
cache_examples=False,
outputs=gr.Textbox(label="SOLUTION"),
css=css_code
)
# Launch Gradio interface
interface.launch(debug=True)
# Launch the interface
launch_interface()
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