DOC: <ValueError: OpenAIChat currently only supports single prompt, got ["Use the following portion of a long document to see if any of the text is relevant to answer the question."]'DOC: ' prefix>
Labels: name: auto:bug, name: auto:question

### Issue with current documentation:

I got this code that i want to move to the model gpt3.5-turbo since its 10 times cheaper than the text-davinci-003 but i get this error 
```
ValueError: OpenAIChat currently only supports single prompt, got ["Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nW H I T E PA P E R\nTHE FUTURE OF ART\nRelease\nV 3 . 1\nTable of Contents\n01 Abstract 02 Riwa Tech\nPg 3\nPg 
4\n03 Market research 04 Technology\nPg 5\nPg 7\n05 Why we do this 06 How Riwa Tech works\nPg 12\nPg 14\n07 The future 08 Team 09 Coin distribution 10 Business model 11 Timeline\nPg 20\nPg 21\nPg 22\nPg 24\nPg 26\n2\nAbstract\nArt and antiques have always been an integral part of the global economy, and\nthis remains true 
today. With the rise of digital platforms and technologies,\ntransaction methods have been revolutionized, but challenges such as\nprovenance, authentication, protection and preservation of cultural heritage\npersist. This white paper proposes integrating blockchain technology to improve\nthe industry's landscape and protect its unique value. Blockchain can provide\nsecure, transparent, and tamper-proof records for art and antiques, addressing\nnumerous issues. By combining traditional values and innovative technology, we\nQuestion: hi\nRelevant text, if any:", 'Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nTag. The owners can know the current location anytime they want.\nBlockchain\nAppreciation, trading and collecting of artworks are gradually becoming a part of\npeople’s life pursuits. In the development of the art market industry, collectibles\nlack clear records of transactions and evidence systems that can be veri\x00ed,\nmaking it almost impossible to determine the source information of collectibles.\nCollectibles do not have an “ID” system, resulting in no records for artworks. This\nlack of traceability in the industry can easily lead to counterfeiters taking\nadvantage of the situation, resulting in a proliferation of counterfeit artworks and\naffecting the development of the industry.\nOwners who deposit collectibles to Riwa’s ecosystem will get NFTs backed by the\ncollectible. The NFT smart contract will inherit the basic and anti-counterfeit\ndetails. For every future transaction of 
the collectible, the smart contract will\nQuestion: hi\nRelevant text, if any:', "Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nRiwa's advanced 3D technology to create 3D NFT collections and establish their\nown virtual spaces for exhibitions and other purposes.\n20\nTeam\nVIONA ZHANG Founder & chairman Experienced in commercializing artists and artworks, with a successful record in corporate and brand management.\nPIERRE BRUNETOT CEO Ex-CEO and founder of Sante Nature with extensive experience in marketing and strategy.\nYINJI DAI Co-Founder 
& Sales Manager Manager of the Asia Region and Co-founder. Over 17 years of experience in art and antiques industry management.\nAASHIR IFTIKHAR Chief Technical Of\x00cer at HashPotato Over 3 years of experience in mobile application development. Expert in Full Stack development.\nEDOUARD BRUNETOT COO CEO of Cobound helps 
businesses grow through marketing, sales, and customer service tools.\nFABIEN CERCLET Sales manager Over 7 years in blockchain tech & economics, established strong marketing foundation.\n21\nCoin distribution\nInitial Coin Offering (ICO)\nQuestion: hi\nRelevant text, if any:", "Use the following portion of a long document 
to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nand transaction reliability of artworks within the market.\n1. Riwa dual anti-counterfeiting\n1.1 Electronic Tag (E-Tag) management system\nRiwa's E-Tag technology management system is ef\x00cient, accurate and reliable.\nThe system can automatically read real-time artwork\ninformation and\ndynamically track and detect artwork locations through an electronic map,\nimproving the timeliness and accuracy of issue detection. Each Riwa E-Tag has a\nunique identity code assigned to the artwork or antique it represents, and the\ntags are physically 
non-replicable and indestructible. With large storage capacity,\nlong service life, and adaptability to indoor and outdoor environments, Riwa's E-\nTag also allows contactless information collection, pollution resistance, and high\nreliability.\n7\nUsers can access the Riwa system by sensing an item's E-Tag with a smartphone,\nobtaining detailed features, inspection count, origin, ownership change records,\nQuestion: hi\nRelevant text, if any:"]
``` 

I'm using RetrievalQAWithSourcesChain and FAISS, this is the code
```python
import os
from langchain.document_loaders import UnstructuredURLLoader
from langchain.text_splitter import CharacterTextSplitter
import pickle
import faiss
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.question_answering import load_qa_chain
from langchain import OpenAI



os.environ["OPENAI_API_KEY"] = "Apikey"


urls = [
    'https://riwa.nftify.network/collection/riwa-nft'
]

loaders = UnstructuredURLLoader(urls=urls)
data = loaders.load()


docs = text_splitter.split_documents(data)

embeddings = OpenAIEmbeddings()

vectorStore_openAI = FAISS.from_documents(docs, embeddings)

with open("faiss_store_openai.pkl", "wb") as f:
    pickle.dump(vectorStore_openAI, f)
with open("faiss_store_openai.pkl", "rb") as f:
    VectorStore = pickle.load(f)

llm=OpenAI(temperature=0,model_name="gpt-3.5-turbo",max_tokens=32)

chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=VectorStore.as_retriever())

question=input("What question you want to ask? : ")

print(chain({"question": str(question)}, return_only_outputs=True)["answer"])
``` 
I would really appreciate if someone could give me some guidance, i've been blocked in this problem for a while

### Idea or request for content:

_No response_