Create ragfile2.py

ragfile2.py

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+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+from langchain_community.document_loaders import CSVLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+# from langchain_community.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+from langchain_core.prompts import PromptTemplate
+
+
+loader = CSVLoader("Events_SameDay.csv",encoding='iso-8859-1')
+
+# Create an index using the loaded documents
+index_creator = VectorstoreIndexCreator()
+docsearch = index_creator.from_loaders([loader])
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.1"
+llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 1024})
+
+# Create a question-answering chain using the index
+chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.vectorstore.as_retriever(), input_key="question")
+
+question = """Retreive latest news on OPEC? """
+result = chain({"question": question})