Delete rag.py

rag.py

@@ -1,44 +0,0 @@
-import os
-import openai
-from langchain.chat_models import ChatOpenAI
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.chains.question_answering import load_qa_chain
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import UnstructuredPDFLoader
-
-# OpenAI API Key Setup
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-# Load The Goal PDF
-loader = UnstructuredPDFLoader("data/The Goal - A Process of Ongoing Improvement (Third Revised Edition).pdf") # , mode="elements"
-docs = loader.load()
-
-# Split Text Chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-splits = text_splitter.split_documents(docs)
-
-# Embed Chunks into Chroma Vector Store
-vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
-retriever = vectorstore.as_retriever()
-
-# Use RAG Prompt Template
-prompt = hub.pull("rlm/rag-prompt")
-llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) # or gpt-3.5-turbo
-
-
-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()
-)
-
-for chunk in rag_chain.stream("What is a Bottleneck Constraint?"):
-    print(chunk, end="", flush=True)
-
-rag_chain.invoke("What is a Bottleneck Constraint?")