PerplexitySearch 集成

pip install -qU langchain-perplexity

import getpass
import os

if not os.environ.get("PPLX_API_KEY"):
    os.environ["PPLX_API_KEY"] = getpass.getpass("Perplexity API key:\n")

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

from langchain_perplexity import PerplexitySearchRetriever

retriever = PerplexitySearchRetriever(k=3)

query = "what year was breath of the wild released?"

retriever.invoke(query)

from langchain_perplexity import PerplexitySearchRetriever

retriever = PerplexitySearchRetriever(
    k=5,
    country="US",
    search_recency_filter="week",
    search_domain_filter=["nytimes.com", "reuters.com", "-pinterest.com"],
)

retriever.invoke("US Federal Reserve interest rate decisions")

from langchain_perplexity import PerplexitySearchRetriever

# 将结果限制在特定日期范围内
retriever = PerplexitySearchRetriever(
    k=5,
    search_after_date="01/01/2025",
    search_before_date="06/30/2025",
)

retriever.invoke("US Federal Reserve interest rate decisions")

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)

llm = ChatOpenAI(model="gpt-5.5")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke("What were the major outcomes of the most recent Federal Reserve meeting?")