lib/python3.10/site-packages/langchain/agents/conversational/output_parser.py", line 26, in parse     raise OutputParserException(f"Could not parse LLM output: `{text}`") langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `Do I need to use a tool? No
Labels: name: auto:bug, name: area: models

### System Info

langchain==0.0.249
Python 3.10.9

### Who can help?

_No response_

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction


import re
import json
from langchain.document_loaders import CSVLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import SpacyTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.agents import initialize_agent, tool
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
#  VectorDBQA 更新为 RetrievalQA
from langchain.chains import RetrievalQA

llm = OpenAI(temperature=0)

loader = TextLoader('./data/faq/ecommerce_faq.txt')

documents = loader.load()

text_splitter = SpacyTextSplitter(chunk_size=256, pipeline="zh_core_web_sm")

texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

docsearch = FAISS.from_documents(texts, embeddings)

faq_chain = VectorDBQA.from_chain_type(
    llm=llm, vectorstore=docsearch, verbose=True)

product_loader = CSVLoader('./data/faq/ecommerce_products.csv')
product_documents = product_loader.load()
product_text_splitter = CharacterTextSplitter(chunk_size=1024, separator="\n")
product_texts = product_text_splitter.split_documents(product_documents)
product_search = FAISS.from_documents(product_texts, OpenAIEmbeddings())
product_chain = VectorDBQA.from_chain_type(
    llm=llm, vectorstore=product_search, verbose=True)

ORDER_1 = "20230101ABC"
ORDER_2 = "20230101EFG"

ORDER_1_DETAIL = {
    "order_number": ORDER_1,
    "status": "已发货",
    "shipping_date": "2023-01-03",
    "estimated_delivered_date": "2023-01-05",
}

ORDER_2_DETAIL = {
    "order_number": ORDER_2,
    "status": "未发货",
    "shipping_date": None,
    "estimated_delivered_date": None,
}


answer_order_info = PromptTemplate(
    template="请把下面的订单信息回复给用户： \n\n {order}?", input_variables=["order"]
)
answer_order_llm = LLMChain(llm=ChatOpenAI(
    temperature=0),  prompt=answer_order_info)


@tool("Search Order", return_direct=True)
def search_order(input: str) -> str:

    """useful for when you need to answer questions about customers orders"""
    pattern = r"\d+[A-Z]+"
    match = re.search(pattern, input)

    order_number = input
    if match:
        order_number = match.group(0)
    else:
        return "请问您的订单号是多少？"
    if order_number == ORDER_1:
        return answer_order_llm.run(json.dumps(ORDER_1_DETAIL))
    elif order_number == ORDER_2:
        return answer_order_llm.run(json.dumps(ORDER_2_DETAIL))
    else:
        return f"对不起，根据{input}没有找到您的订单"


@tool("FAQ")
def faq(intput: str) -> str:
    """"useful for when you need to answer questions about shopping policies, like return policy, shipping policy, etc."""
    return faq_chain.run(intput)


@tool("Recommend Product")
def recommend_product(input: str) -> str:
    """"useful for when you need to search and recommend products and recommend it to the user"""
    return product_chain.run(input)


tools = [
    search_order,
    recommend_product,
    faq
]

chatllm = ChatOpenAI(temperature=0)

memory = ConversationBufferMemory(
    memory_key="chat_history", return_messages=True)

conversation_agent = initialize_agent(
    tools,
    chatllm,
    agent="conversational-react-description",
    memory=memory,
    verbose=True
)


question = "请问你们的货，能送到三亚吗？大概需要几天？"
result = conversation_agent.run(question)
print(result)





### Expected behavior

Hope no error is reported