mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
|
from langchain.chat_models import ChatOpenAI
|
|||
|
|
from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
|
|||
|
|
from langchain.schema.output_parser import StrOutputParser
|
|||
|
|
from langchain.schema.runnable import RunnableLambda
|
|||
|
|
from langchain.utilities import DuckDuckGoSearchAPIWrapper
|
|||
|
|
|
|||
|
|
|
|||
|
|
search = DuckDuckGoSearchAPIWrapper(max_results=4)
|
|||
|
|
|
|||
|
|
def retriever(query):
|
|||
|
|
return search.run(query)
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Few Shot Examples
|
|||
|
|
examples = [
|
|||
|
|
{
|
|||
|
|
"input": "Could the members of The Police perform lawful arrests?",
|
|||
|
|
"output": "what can the members of The Police do?"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"input": "Jan Sindel’s was born in what country?",
|
|||
|
|
"output": "what is Jan Sindel’s personal history?"
|
|||
|
|
},
|
|||
|
|
]
|
|||
|
|
# We now transform these to example messages
|
|||
|
|
example_prompt = ChatPromptTemplate.from_messages(
|
|||
|
|
[
|
|||
|
|
("human", "{input}"),
|
|||
|
|
("ai", "{output}"),
|
|||
|
|
]
|
|||
|
|
)
|
|||
|
|
few_shot_prompt = FewShotChatMessagePromptTemplate(
|
|||
|
|
example_prompt=example_prompt,
|
|||
|
|
examples=examples,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
prompt = ChatPromptTemplate.from_messages([
|
|||
|
|
("system", """You are an expert at world knowledge. Your task is to step back and paraphrase a question to a more generic step-back question, which is easier to answer. Here are a few examples:"""),
|
|||
|
|
# Few shot examples
|
|||
|
|
few_shot_prompt,
|
|||
|
|
# New question
|
|||
|
|
("user", "{question}"),
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
question_gen = prompt | ChatOpenAI(temperature=0) | StrOutputParser()
|
|||
|
|
|
|||
|
|
response_prompt_template = """You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant.
|
|||
|
|
|
|||
|
|
{normal_context}
|
|||
|
|
{step_back_context}
|
|||
|
|
|
|||
|
|
Original Question: {question}
|
|||
|
|
Answer:"""
|
|||
|
|
response_prompt = ChatPromptTemplate.from_template(response_prompt_template)
|
|||
|
|
|
|||
|
|
chain = {
|
|||
|
|
# Retrieve context using the normal question
|
|||
|
|
"normal_context": RunnableLambda(lambda x: x['question']) | retriever,
|
|||
|
|
# Retrieve context using the step-back question
|
|||
|
|
"step_back_context": question_gen | retriever,
|
|||
|
|
# Pass on the question
|
|||
|
|
"question": lambda x: x["question"]
|
|||
|
|
} | response_prompt | ChatOpenAI(temperature=0) | StrOutputParser()
|
|||
|
|
|
|||
|
|
|