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import os
from dotenv import load_dotenv
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.agents import Tool, AgentExecutor
from langchain.tools.retriever import create_retriever_tool
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import AzureOpenAIEmbeddings
from langchain_community.chat_models import AzureChatOpenAI
from openai import AzureOpenAI
import warnings
# Load environment variables
load_dotenv()
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
AZURE_OPENAI_LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT")
AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")
if not all([AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_LLM_DEPLOYMENT, AZURE_OPENAI_EMBEDDING_DEPLOYMENT]):
raise ValueError("Missing one or more Azure OpenAI environment variables.")
warnings.filterwarnings("ignore")
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
if not AZURE_OPENAI_API_KEY:
raise ValueError("Missing AZURE_OPENAI_API_KEY in environment variables.")
chunk_size = 500
# Extract Data from the PDFs
def load_pdf_file(data_path):
loader = DirectoryLoader(data_path, glob="*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()
return documents
# Split the data into chunks
def text_split(docs):
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=20)
return splitter.split_documents(docs)
# Set up LLM and Embedding
llm = AzureChatOpenAI(
deployment_name=AZURE_OPENAI_LLM_DEPLOYMENT,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_version="2023-12-01-preview" # or your supported version
# temperature=0.5 # Only if supported by your deployment
)
embeddings = AzureOpenAIEmbeddings(
azure_deployment=AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_version="2023-12-01-preview",
chunk_size=chunk_size # or another value up to 2048
)
# Load PDF, chunk it, embed it, and store in FAISS
pdf_docs = load_pdf_file("Dataset/") # Update this to your PDF folder
chunks = text_split(pdf_docs)
vectorstore = FAISS.from_documents(chunks, embeddings)
vectorstore.save_local("faiss_index_sysml")
# Load FAISS and create retriever QA chain
# new_vectorstore = FAISS.load_local("faiss_index_sysml", embeddings, allow_dangerous_deserialization=True)
# qa = RetrievalQA.from_chain_type(
# llm=llm,
# chain_type="stuff",
# retriever=new_vectorstore.as_retriever()
# )
# # Run a sample query
# query = "What is SysML used for?"
# print("User:", query)
# print("Bot:", qa.run(query))
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