Demo-Long / app.py
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Update app.py
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import together
import os
# set your API key
together.api_key = "c9909567768fbf1a69fbd94c758e432f0a05a6755c32dced992ac6640a8cfd79"
# list available models and descriptons
models = together.Models.list()
together.Models.start("togethercomputer/llama-2-7b-chat")
from langchain.llms import Together
llm = Together(
model="togethercomputer/llama-2-7b-chat",
temperature=0.7,
max_tokens=128,
top_k=1,
together_api_key="c9909567768fbf1a69fbd94c758e432f0a05a6755c32dced992ac6640a8cfd79"
)
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import DirectoryLoader
pdf_path = os.path.join(os.path.dirname(__file__), 'Production-Table - Sheet1 (2).pdf')
loader = PyPDFLoader(pdf_path)
documents = loader.load()
#splitting the text into
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-base-en"
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model_norm = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs={'device': 'cuda'},
encode_kwargs=encode_kwargs
)
# Embed and store the texts
# Supplying a persist_directory will store the embeddings on disk
persist_directory = 'db'
## Here is the nmew embeddings being used
embedding = model_norm
vectordb = Chroma.from_documents(documents=texts,
embedding=embedding,
persist_directory=persist_directory)
retriever = vectordb.as_retriever(search_kwargs={"k": 5})
## Default LLaMA-2 prompt style
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant of a production company. You should honestly answer the user's query using the knowledge of the company's production documents uploaded.
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."""
def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT ):
SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS
prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
return prompt_template
sys_prompt = """You are a helpful, respectful and honest assistant of a production company. You should honestly answer the user's query using the knowledge of the company's production documents uploaded.
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."""
instruction = """CONTEXT:/n/n {context}/n
Question: {question}"""
get_prompt(instruction, sys_prompt)
from langchain.prompts import PromptTemplate
prompt_template = get_prompt(instruction, sys_prompt)
llama_prompt = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": llama_prompt}
from langchain.schema import prompt
# create the chain to answer questions
qa_chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs=chain_type_kwargs,
return_source_documents=True)
## Cite sources
import textwrap
def wrap_text_preserve_newlines(text, width=110):
# Split the input text into lines based on newline characters
lines = text.split('\n')
# Wrap each line individually
wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
# Join the wrapped lines back together using newline characters
wrapped_text = '\n'.join(wrapped_lines)
return wrapped_text
def process_llm_response(llm_response):
print(wrap_text_preserve_newlines(llm_response['result']))
print('\n\nSources:')
for source in llm_response["source_documents"]:
print(source.metadata['source'])
import gradio as gr
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
print("Question: ", history[-1][0])
#wrap_text_preserve_newlines(llm_response['result'])
#bot_message = process_llm_response(qa_chain(history[-1][0]))
bot_message = wrap_text_preserve_newlines((qa_chain(history[-1][0]))['result'])
print("Response: ", bot_message)
history[-1][1] = ""
history[-1][1] += bot_message
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch(debug = True)