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# app.py
from typing import List, Union, Optional
from dotenv import load_dotenv, find_dotenv
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
from langchain.llms import LlamaCpp
from langchain.embeddings import LlamaCppEmbeddings
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.text_splitter import TokenTextSplitter
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Qdrant
from PyPDF2 import PdfReader
import streamlit as st
# Use a pipeline as a high-level helper
#from transformers import pipeline
#pipe = pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML")
# Load model directly
#from transformers import AutoModel
#model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML")
from transformers import AutoModelForCausalLM
model_id="TheBloke/Llama-2-7B-Chat-GGML"
tokenizer=AutoTokenizer.from_pretrained(model_id)
model=AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
PROMPT_TEMPLATE = """
Use the following pieces of context enclosed by triple backquotes to answer the question at the end.
\n\n
Context:
```
{context}
```
\n\n
Question: [][][][]{question}[][][][]
\n
Answer:"""
def init_page() -> None:
st.set_page_config(
page_title="Personal ChatGPT"
)
st.sidebar.title("Options")
def init_messages() -> None:
clear_button = st.sidebar.button("Clear Conversation", key="clear")
if clear_button or "messages" not in st.session_state:
st.session_state.messages = [
SystemMessage(
content=(
"You are a helpful AI QA assistant. "
"When answering questions, use the context enclosed by triple backquotes if it is relevant. "
"If you don't know the answer, just say that you don't know, "
"don't try to make up an answer. "
"Reply your answer in mardkown format.")
)
]
st.session_state.costs = []
def get_pdf_text() -> Optional[str]:
"""
Function to load PDF text and split it into chunks.
"""
st.header("Document Upload")
uploaded_file = st.file_uploader(
label="Here, upload your PDF file you want ChatGPT to use to answer",
type="pdf"
)
if uploaded_file:
pdf_reader = PdfReader(uploaded_file)
text = "\n\n".join([page.extract_text() for page in pdf_reader.pages])
text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=0)
return text_splitter.split_text(text)
else:
return None
def build_vectore_store(
texts: str, embeddings: Union[OpenAIEmbeddings, LlamaCppEmbeddings]) \
-> Optional[Qdrant]:
"""
Store the embedding vectors of text chunks into vector store (Qdrant).
"""
if texts:
with st.spinner("Loading PDF ..."):
qdrant = Qdrant.from_texts(
texts,
embeddings,
path=":memory:",
collection_name="my_collection",
force_recreate=True
)
st.success("File Loaded Successfully!!")
else:
qdrant = None
return qdrant
def select_llm() -> Union[ChatOpenAI, LlamaCpp]:
"""
Read user selection of parameters in Streamlit sidebar.
"""
model_name = st.sidebar.radio("Choose LLM:",
("llama-2-7b-chat.ggmlv3.q2_K",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
"gpt-4"))
temperature = st.sidebar.slider("Temperature:", min_value=0.0,
max_value=1.0, value=0.0, step=0.01)
return model_name, temperature
def load_llm(model_name: str, temperature: float) -> Union[ChatOpenAI, LlamaCpp]:
"""
Load LLM.
"""
if model_name.startswith("gpt-"):
return ChatOpenAI(temperature=temperature, model_name=model_name)
elif model_name.startswith("llama-2-"):
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
'''return LlamaCpp(
model_path=f"./models/{model_name}.bin",
input={"temperature": temperature,
"max_length": 2048,
"top_p": 1
},
n_ctx=2048,
callback_manager=callback_manager,
verbose=False, # True
)'''
return model
def load_embeddings(model_name: str) -> Union[OpenAIEmbeddings, LlamaCppEmbeddings]:
"""
Load embedding model.
"""
if model_name.startswith("gpt-"):
return OpenAIEmbeddings()
elif model_name.startswith("llama-2-"):
return LlamaCppEmbeddings(model_path=f"./models/{model_name}.bin")
def get_answer(llm, messages) -> tuple[str, float]:
"""
Get the AI answer to user questions.
"""
if isinstance(llm, ChatOpenAI):
with get_openai_callback() as cb:
answer = llm(messages)
return answer.content, cb.total_cost
if isinstance(llm, LlamaCpp):
return llm(llama_v2_prompt(convert_langchainschema_to_dict(messages))), 0.0
def find_role(message: Union[SystemMessage, HumanMessage, AIMessage]) -> str:
"""
Identify role name from langchain.schema object.
"""
if isinstance(message, SystemMessage):
return "system"
if isinstance(message, HumanMessage):
return "user"
if isinstance(message, AIMessage):
return "assistant"
raise TypeError("Unknown message type.")
def convert_langchainschema_to_dict(
messages: List[Union[SystemMessage, HumanMessage, AIMessage]]) \
-> List[dict]:
"""
Convert the chain of chat messages in list of langchain.schema format to
list of dictionary format.
"""
return [{"role": find_role(message),
"content": message.content
} for message in messages]
def llama_v2_prompt(messages: List[dict]) -> str:
"""
Convert the messages in list of dictionary format to Llama2 compliant
format.
"""
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
BOS, EOS = "<s>", "</s>"
DEFAULT_SYSTEM_PROMPT = f"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
if messages[0]["role"] != "system":
messages = [
{
"role": "system",
"content": DEFAULT_SYSTEM_PROMPT,
}
] + messages
messages = [
{
"role": messages[1]["role"],
"content": B_SYS + messages[0]["content"] + E_SYS + messages[1]["content"],
}
] + messages[2:]
messages_list = [
f"{BOS}{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} {EOS}"
for prompt, answer in zip(messages[::2], messages[1::2])
]
messages_list.append(
f"{BOS}{B_INST} {(messages[-1]['content']).strip()} {E_INST}")
return "".join(messages_list)
def extract_userquesion_part_only(content):
"""
Function to extract only the user question part from the entire question
content combining user question and pdf context.
"""
content_split = content.split("[][][][]")
if len(content_split) == 3:
return content_split[1]
return content
def main() -> None:
_ = load_dotenv(find_dotenv())
init_page()
model_name, temperature = select_llm()
llm = load_llm(model_name, temperature)
embeddings = load_embeddings(model_name)
texts = get_pdf_text()
qdrant = build_vectore_store(texts, embeddings)
init_messages()
st.header("Personal ChatGPT")
# Supervise user input
if user_input := st.chat_input("Input your question!"):
if qdrant:
context = [c.page_content for c in qdrant.similarity_search(
user_input, k=10)]
user_input_w_context = PromptTemplate(
template=PROMPT_TEMPLATE,
input_variables=["context", "question"]) \
.format(
context=context, question=user_input)
else:
user_input_w_context = user_input
st.session_state.messages.append(
HumanMessage(content=user_input_w_context))
with st.spinner("ChatGPT is typing ..."):
answer, cost = get_answer(llm, st.session_state.messages)
st.session_state.messages.append(AIMessage(content=answer))
st.session_state.costs.append(cost)
# Display chat history
messages = st.session_state.get("messages", [])
for message in messages:
if isinstance(message, AIMessage):
with st.chat_message("assistant"):
st.markdown(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("user"):
st.markdown(extract_userquesion_part_only(message.content))
costs = st.session_state.get("costs", [])
st.sidebar.markdown("## Costs")
st.sidebar.markdown(f"**Total cost: ${sum(costs):.5f}**")
for cost in costs:
st.sidebar.markdown(f"- ${cost:.5f}")
# streamlit run app.py
if __name__ == "__main__":
main() |