main.py

from langchain_community.document_loaders import OnlinePDFLoader
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_core.prompts import PromptTemplate
from langchain_community.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import RetrievalQA
import sys
import os

class SuppressStdout:
    def __enter__(self):
        self._original_stdout = sys.stdout
        self._original_stderr = sys.stderr
        sys.stdout = open(os.devnull, 'w')
        sys.stderr = open(os.devnull, 'w')

    def __exit__(self, exc_type, exc_val, exc_tb):
        sys.stdout.close()
        sys.stdout = self._original_stdout
        sys.stderr = self._original_stderr

# load the pdf and split it into chunks
loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
data = loader.load()

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)

with SuppressStdout():
    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())

while True:
    query = input("\nQuery: ")
    if query == "exit":
        break
    if query.strip() == "":
        continue

    # Prompt
    template = """Use the following pieces of context to answer the question at the end.
    If you don't know the answer, just say that you don't know, don't try to make up an answer.
    Use three sentences maximum and keep the answer as concise as possible.
    {context}
    Question: {question}
    Helpful Answer:"""
    QA_CHAIN_PROMPT = PromptTemplate(
        input_variables=["context", "question"],
        template=template,
    )

    llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
    qa_chain = RetrievalQA.from_chain_type(
        llm,
        retriever=vectorstore.as_retriever(),
        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
    )

    result = qa_chain({"query": query})