pdfreader.py

from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import FAISS    
import os
from langchain_openai import ChatOpenAI


openai_api_key = os.getenv("OPENAI_API_KEY")

pdfreader = PyPDFLoader("langchain-academy/module-1/studio/Leadership-Development.pdf")
docs = pdfreader.load()

llm = ChatOpenAI(
    openai_api_key=openai_api_key,
    model="gpt-4")

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = InMemoryVectorStore.from_documents(
    documents=splits, embedding=OpenAIEmbeddings()
)

retriever = vectorstore.as_retriever()

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

system_prompt = (
    "You are an assistant for question-answering tasks. "
    "Use the following pieces of retrieved context to answer "
    "the question. If you don't know the answer, say that you "
    "don't know. Use three sentences maximum and keep the "
    "answer concise."
    "\n\n"
    "{context}"
)

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)


question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)

results = rag_chain.invoke({"input": "When to adjust your style?"})

results

print(results["context"][0].page_content)