rag.py

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

# Load, chunk and index the contents of the blog.
loader = WebBaseLoader(
    web_paths=("https://securade.ai/blog/securade-ai-generative-ai-transforming-workplace-safety.html",),
    bs_kwargs=dict(parse_only=bs4.SoupStrainer('article')),
)
docs = loader.load()

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

# Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


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

print(rag_chain.invoke("How can Generative AI help in video analytics?"))

# cleanup
vectorstore.delete_collection()