app_old.py

import os
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEndpoint
from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
import gradio as gr


############################## OLD CODE FOR 1 PDF ####################################

# Load and split documents
FILE_PATH = ""
loader = PyPDFLoader(FILE_PATH)
docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
)
splits = text_splitter.split_documents(docs)

# Set up embeddings and vector store
HF_EMBED_MODEL_ID = "BAAI/bge-small-en-v1.5"
embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)
vectorstore = FAISS.from_documents(splits, embeddings)

############################## NEW CODE FOR 5 PDFs ####################################
# Load and split documents
# FILE_PATHS = ["vol1.pdf", "vol2.pdf"]

# # Initialize an empty list to store all documents
# all_docs = []

# # Iterate through each file path in FILE_PATHS
# for file_path in FILE_PATHS:
#     # Load the PDF using PyPDFLoader
#     loader = PyPDFLoader(file_path)  
#     # Split the loaded document using RecursiveCharacterTextSplitter
#     text_splitter = RecursiveCharacterTextSplitter(
#         chunk_size=1000,
#         chunk_overlap=200,
#     )
#     # Load and split the current document and append to all_docs
#     documents = loader.load_and_split(text_splitter)
#     all_docs.extend(documents)


# # Set up embeddings and vector store
# HF_EMBED_MODEL_ID = "BAAI/bge-small-en-v1.5"
# embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)
# vectorstore = FAISS.from_documents(all_docs, embeddings)

# Set up LLM
HF_API_KEY = os.environ.get("HF_API_KEY")  # Ensure this is set in Hugging Face Space settings
HF_LLM_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.3"
llm = HuggingFaceEndpoint(
    repo_id=HF_LLM_MODEL_ID,
    huggingfacehub_api_token=HF_API_KEY,
)

# Create RAG pipeline
retriever = vectorstore.as_retriever()
prompt = PromptTemplate.from_template(
    "Context information is below.\\n---------------------\\n{context}\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: {question}\\nAnswer:\\n"
)

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Gradio interface
def ask_question(question):
    return rag_chain.invoke(question)

iface = gr.Interface(fn=ask_question, inputs="text", outputs="text")
iface.launch()