Ultimate access to all questions.
Explanation:
A. This option correctly uses the LLMChain to create a conversational chain with a prompt template. The PromptTemplate takes a user's question and passes it to the language model (in this case, OpenAl), generating a response based on the provided input.
A. The SimpleChain does not exist in Langchain for conversational purposes; it's not suitable for chatbot interactions. • C. ConversationalRetrievalChain is used when combining retrieval-based systems with language models, but it's not appropriate for simple chatbot queries without retrieval functionality. • D. The ConversationalChain does not exist in Langchain; it's not valid for creating a chatbot interaction. Thus, B provides the correct implementation of a conversational chain using LLMChain and a prompt template.
Question: 14
You are building a chatbot application using Langchain in Databricks that takes a user's query and provides a response from a language model. You want to deploy a simple conversational chain to respond to user queries. Choose the correct implementation for this chain. Which of the following code snippets correctly implements a conversational chain for chatbot interaction using Langchain?
A
from langchain.chains import SimpleChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = SimpleChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
from langchain.chains import SimpleChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = SimpleChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
B
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
llm = OpenAI()
prompt = PromptTemplate(
input_variables=["question"],
template="Answer the following question: {question}",
)
chatbot_chain = LLMChain(prompt=prompt, llm=llm)
response = chatbot_chain.run(
{"question": "What is the capital of France?"}
)
print(response)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
llm = OpenAI()
prompt = PromptTemplate(
input_variables=["question"],
template="Answer the following question: {question}",
)
chatbot_chain = LLMChain(prompt=prompt, llm=llm)
response = chatbot_chain.run(
{"question": "What is the capital of France?"}
)
print(response)
C
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = ConversationalRetrievalChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = ConversationalRetrievalChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
D
from langchain.chains import ConversationalChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = ConversationalChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
from langchain.chains import ConversationalChain
from langchain.llms import OpenAI
llm = OpenAI()
chatbot_chain = ConversationalChain(llm)
response = chatbot_chain.run("What is the capital of France?")
print(response)
