app.py

import gradio as gr
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.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# from langchain.llms import LlamaCpp
from langchain.vectorstores import Qdrant
from qdrant_client.http import models
# from langchain.llms import CTransformers
from ctransformers import AutoModelForCausalLM





# loading the embedding model - 

encoder = SentenceTransformer("all-MiniLM-L6-v2")

print("embedding model loaded.............................")
print("####################################################")

# loading the LLM 

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

print("loading the LLM......................................")

# llm = LlamaCpp(
#     model_path="TheBloke/Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q8_0.gguf",
#     n_ctx=2048,
#     f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
#     callback_manager=callback_manager,
#     verbose=True,
# )

llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGUF", 
                                           model_file="llama-2-7b-chat.Q8_0.gguf", 
                                           model_type="llama", 
                                          #  config = ctransformers.hub.AutoConfig,
                                           hf = True
                                           )



print("LLM loaded........................................")
print("################################################################")

def get_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(
        # seperator = "\n",
        chunk_size = 500,
        chunk_overlap = 100,
        length_function = len,
    )

    chunks = text_splitter.split_text(text)
    return chunks


pdf_path = '/home/devangpagare/llm/qdrant-cloud-rag-main/100 Weird Facts About the Human Body.pdf'


reader = PdfReader(pdf_path)
text = ""
num_of_pages = len(reader.pages)
for page in range(num_of_pages):
    current_page = reader.pages[page]
    text += current_page.extract_text()


chunks = get_chunks(text)

print("Chunks are ready.....................................")
print("######################################################")

qdrant = QdrantClient(path = "/home/devangpagare/llm/qdrant-cloud-rag-main/gradio/db")
print("db  created................................................")
print("#####################################################################")

qdrant.recreate_collection(
    collection_name="my_facts",
    vectors_config=models.VectorParams(
        size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
        distance=models.Distance.COSINE,
    ),
)

print("Collection created........................................")
print("#########################################################")



li = []
for i in range(len(chunks)):
    li.append(i)
 
dic = zip(li, chunks)
dic= dict(dic)

qdrant.upload_records(
    collection_name="my_facts",
    records=[
        models.Record(
            id=idx,
            vector=encoder.encode(dic[idx]).tolist(),
            payload= {dic[idx][:5] : dic[idx]}
        ) for idx in dic.keys()
    ],
)

print("Records uploaded........................................")
print("###########################################################")

def chat(question):
    # question = input("ask question from pdf.....")


    hits = qdrant.search(
        collection_name="my_facts",
        query_vector=encoder.encode(question).tolist(),
        limit=3
    )
    context = []
    for hit in hits:
      context.append(list(hit.payload.values())[0])
    
    context = context[0] + context[1] + context[2]

    system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
    Read the given context before answering questions and think step by step. If you can not answer a user question based on 
    the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""


    B_INST, E_INST = "[INST]", "[/INST]"

    B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

    SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS

    instruction = f""" 
    Context: {context}
    User: {question}"""

    prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST

    result = llm(prompt_template)
    return result 

gr.Interface(
    fn = chat,
    inputs = gr.Textbox(lines = 10, placeholder = "Enter your question here 👉"),
    outputs = gr.Textbox(lines = 10, placeholder = "Your answer will be here soon 🚀"),
    title="Q&N with PDF 👩🏻‍💻📓✍🏻💡",
    description="This app facilitates a conversation with PDFs available on https://www.delo.si/assets/media/other/20110728/100%20Weird%20Facts%20About%20the%20Human%20Body.pdf💡",
    theme="soft",
    examples=["Hello", "what is the speed of human nerve impulses?"],
    cache_examples=True,
).launch(share = True, auth=("username", "password"))