app.py

from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings

from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import ArxivLoader
from faiss import IndexFlatL2
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain.document_transformers import LongContextReorder
from langchain_core.runnables import RunnableLambda
from langchain_core.runnables.passthrough import RunnableAssign
from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

import gradio as gr
from functools import partial
from operator import itemgetter
from langchain_community.document_loaders import PyPDFLoader




text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=100,
    separators=["\n\n", "\n", ".", ";", ",", " ", ""],
)


print("Loading Documents")
# docs = [
#     ArxivLoader(query="1706.03762").load(),  ## Attention Is All You Need Paper
#     ArxivLoader(query="1810.04805").load(),  ## BERT Paper
#     ArxivLoader(query="2005.11401").load(),  ## RAG Paper
#     # ArxivLoader(query="2205.00445").load(),  ## MRKL Paper
#     ArxivLoader(query="2310.06825").load(),  ## Mistral Paper
#     ArxivLoader(query="2306.05685").load(),  ## LLM-as-a-Judge
#     ## Some longer papers
#     # ArxivLoader(query="2210.03629").load(),  ## ReAct Paper
#     # ArxivLoader(query="2112.10752").load(),  ## Latent Stable Diffusion Paper
#     # ArxivLoader(query="2103.00020").load(),  ## CLIP Paper
# ]

# Open the PDF
# with pdfplumber.open("Data_Quality_Matters_A_Case_Study_on_Data_Label_Correctness_for_Security_Bug_Report_Prediction.pdf") as pdf:
#     # Extract the text
#     text = pdf.extract_text()
#     print(text)
#

# reader = PyPDFLoader("Data_Quality_Matters_A_Case_Study_on_Data_Label_Correctness_for_Security_Bug_Report_Prediction.pdf")
reader = PyPDFLoader("Inconsistent_Defect_Labels_Essence_Causes_and_Influence.pdf")
docs = [reader.load_and_split()]

    
for doc in docs:
    content = doc[0].page_content
    if "References" in content:
        doc[0].page_content = content[:content.index("References")]

## Split the documents and also filter out stubs (overly short chunks)
print("Chunking Documents")
docs_chunks = [text_splitter.split_documents(doc) for doc in docs]
docs_chunks = [[c for c in dchunks if len(c.page_content) > 200] for dchunks in docs_chunks]

## Make some custom Chunks to give big-picture details
# doc_string = '''Inconsistent_Defect_Labels_Essence_Causes_and_Influence
# Data_Quality_Matters_A_Case_Study_on_Data_Label_Correctness_for_Security_Bug_Report_Prediction
# '''

doc_string = '''
Inconsistent_Defect_Labels_Essence_Causes_and_Influence
'''
doc_metadata = []
for chunks in docs_chunks:
    metadata = getattr(chunks[0], 'metadata', {})
    # doc_string += "\n - " + metadata.get('Title')
    doc_metadata += [str(metadata)]

extra_chunks = [doc_string] + doc_metadata

embedder = NVIDIAEmbeddings(model="nvolveqa_40k", model_type=None)

## Construct series of document vector stores
print("Constructing Vector Stores")
vecstores = [FAISS.from_texts(extra_chunks, embedder)]
vecstores += [FAISS.from_documents(doc_chunks, embedder) for doc_chunks in docs_chunks]

embed_dims = len(embedder.embed_query("test"))
def default_FAISS():
    '''Useful utility for making an empty FAISS vectorstore'''
    return FAISS(
        embedding_function=embedder,
        index=IndexFlatL2(embed_dims),
        docstore=InMemoryDocstore(),
        index_to_docstore_id={},
        normalize_L2=False
    )

def aggregate_vstores(vectorstores):
    ## Initialize an empty FAISS Index and merge others into it
    ## We'll use default_faiss for simplicity, though it's tied to your embedder by reference
    agg_vstore = default_FAISS()
    for vstore in vectorstores:
        agg_vstore.merge_from(vstore)
    return agg_vstore

if 'docstore' not in globals():
    ## Unintuitive optimization; merge_from seems to optimize constituent vector stores away
    docstore = aggregate_vstores(vecstores)

print(f"Constructed aggregate docstore with {len(docstore.docstore._dict)} chunks")


########################################################################
## Utility Runnables/Methods
def RPrint(preface=""):
    """Simple passthrough "prints, then returns" chain"""
    def print_and_return(x, preface):
        print(f"{preface}{x}")
        return x
    return RunnableLambda(partial(print_and_return, preface=preface))

def docs2str(docs, title="Document"):
    """Useful utility for making chunks into context string. Optional, but useful"""
    out_str = ""
    for doc in docs:
        doc_name = getattr(doc, 'metadata', {}).get('Title', title)
        if doc_name:
            out_str += f"[Quote from {doc_name}] "
        out_str += getattr(doc, 'page_content', str(doc)) + "\n"
    return out_str

## Optional; Reorders longer documents to center of output text
long_reorder = RunnableLambda(LongContextReorder().transform_documents)
########################################################################

llm = ChatNVIDIA(model="mixtral_8x7b") | StrOutputParser()
convstore = default_FAISS()

def save_memory_and_get_output(d, vstore):
    """Accepts 'input'/'output' dictionary and saves to convstore"""
    vstore.add_texts([
        f"User previously responded with {d.get('input')}",
        f"Agent previously responded with {d.get('output')}"
    ])
    return d.get('output')

initial_msg = (
    "Hello! I am a research paper assistant here to help Ejaz!"
    f" These are the papers you are studying this week! {doc_string}\n\nPlease ask questions Ejaz !"
)

chat_prompt = ChatPromptTemplate.from_messages([("system",
    "You are a document chatbot. Help the user as they ask questions about documents."
    " User messaged just asked: {input}\n\n"
    " From this, we have retrieved the following potentially-useful info: "
    " Conversation History Retrieval:\n{history}\n\n"
    " Document Retrieval:\n{context}\n\n"
    " (Answer only from retrieval. Only cite sources that are used. Make your response conversational.)"
), ('user', '{input}')])

retrieval_chain = (
    {'input' : (lambda x: x)}
    | RunnableAssign({'history' : itemgetter('input') | convstore.as_retriever() | long_reorder | docs2str})
    | RunnableAssign({'context' : itemgetter('input') | docstore.as_retriever()  | long_reorder | docs2str})
    | RPrint()
)


stream_chain = chat_prompt | llm

def chat_gen(message, history=[], return_buffer=True):
    buffer = ""
    ## First perform the retrieval based on the input message
    retrieval = retrieval_chain.invoke(message)
    line_buffer = ""

    ## Then, stream the results of the stream_chain
    for token in stream_chain.stream(retrieval):
        buffer += token
        ## If you're using standard print, keep line from getting too long
        if not return_buffer:
            line_buffer += token
            if "\n" in line_buffer:
                line_buffer = ""
            if ((len(line_buffer)>84 and token and token[0] == " ") or len(line_buffer)>100):
                line_buffer = ""
                yield "\n"
                token = "  " + token.lstrip()
        yield buffer if return_buffer else token

    ## Lastly, save the chat exchange to the conversation memory buffer
    save_memory_and_get_output({'input':  message, 'output': buffer}, convstore)


chatbot = gr.Chatbot(value = [[None, initial_msg]])
demo = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()

try:
    demo.launch(debug=True, share=True, show_api=False)
    demo.close()
except Exception as e:
    demo.close()
    print(e)
    raise e