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import gradio as gr
from gradio_pdf import PDF
from qdrant_client import models, QdrantClient
from sentence_transformers import SentenceTransformer
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.manager import CallbackManager
from langchain.llms import LlamaCpp
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Qdrant
from transformers import AutoModelForCausalLM
# Load the embedding model
encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
print("Embedding model loaded...")
# Load the LLM
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
'''
llm = AutoModelForCausalLM.from_pretrained(
"TheBloke/Llama-2-7B-Chat-GGUF",
model_file="llama-2-7b-chat.Q3_K_S.gguf",
model_type="llama",
temperature=0.2,
repetition_penalty=1.5,
max_new_tokens=300,
)
'''
llm = LlamaCpp(
model_path="./llama-2-7b-chat.Q3_K_S.gguf",
temperature = 0.2,
n_ctx=2048,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
max_tokens = 500,
callback_manager=callback_manager,
verbose=True,
)
print("LLM loaded...")
client = QdrantClient(path="./db")
def setup_database(files):
all_chunks = []
for file in files:
pdf_path = file
reader = PdfReader(pdf_path)
text = "".join(page.extract_text() for page in reader.pages if page.extract_text())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50, length_function=len)
chunks = text_splitter.split_text(text)
all_chunks.extend(chunks)
print(f"Total chunks: {len(all_chunks)}")
client.recreate_collection(
collection_name="my_facts",
vectors_config=models.VectorParams(
size=encoder.get_sentence_embedding_dimension(),
distance=models.Distance.COSINE,
),
)
print("Collection created...")
for idx, chunk in enumerate(all_chunks):
client.upload_record(
collection_name="my_facts",
record=models.Record(
id=idx,
vector=encoder.encode(chunk).tolist(),
payload={"text": chunk}
)
)
print("Records uploaded...")
def answer(question):
hits = client.search(
collection_name="my_facts",
query_vector=encoder.encode(question).tolist(),
limit=3
)
context = " ".join(hit.payload["text"] for hit in hits)
system_prompt = "You are a helpful co-worker. Use the provided context to answer user questions. Do not use any other information."
prompt = f"Context: {context}\nUser: {question}\n{system_prompt}"
response = llm(prompt)
return response
def chat(messages):
if not messages:
return "Please upload PDF documents to initialize the database."
last_message = messages[-1]
return answer(last_message["message"])
screen = gr.Interface(
fn=chat,
inputs=gr.Textbox(placeholder="Type your question here..."),
outputs="chatbot",
title="Q&A with PDFs 👩🏻💻📓✍🏻💡",
description="This app facilitates a conversation with PDFs uploaded💡",
theme="soft",
live=True,
allow_screenshot=False,
allow_flagging=False,
)
# Add a way to upload and setup the database before starting the chat
screen.launch()
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