從 LLMRouterChain 遷移

LLMRouterChain 會將輸入查詢路由到多個目的地之一——也就是說，給定一個輸入查詢，它會使用 LLM 從目的地鏈列表中選擇一個，並將其輸入傳遞到選定的鏈。

LLMRouterChain 不支援常見的 聊天模型 功能，例如訊息角色和 工具調用。在底層，LLMRouterChain 通過指示 LLM 生成 JSON 格式的文字，並解析出預期的目的地來路由查詢。

考慮一個來自 MultiPromptChain 的範例，它使用了 LLMRouterChain。以下是（範例）預設提示

from langchain.chains.router.multi_prompt import MULTI_PROMPT_ROUTER_TEMPLATE

destinations = """
animals: prompt for animal expert
vegetables: prompt for a vegetable expert
"""

router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations)

print(router_template.replace("`", "'"))  # for rendering purposes

Given a raw text input to a language model select the model prompt best suited for the input. You will be given the names of the available prompts and a description of what the prompt is best suited for. You may also revise the original input if you think that revising it will ultimately lead to a better response from the language model.

<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
'''json
{{
    "destination": string \ name of the prompt to use or "DEFAULT"
    "next_inputs": string \ a potentially modified version of the original input
}}
'''

REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
REMEMBER: "next_inputs" can just be the original input if you don't think any modifications are needed.

<< CANDIDATE PROMPTS >>

animals: prompt for animal expert
vegetables: prompt for a vegetable expert


<< INPUT >>
{input}

<< OUTPUT (must include '''json at the start of the response) >>
<< OUTPUT (must end with ''') >>

大多數行為都是通過單一自然語言提示來確定的。支援 工具調用 功能的聊天模型為此任務帶來了許多優勢

• 支援聊天提示範本，包括具有 system 和其他角色的訊息；
• 工具調用模型經過微調，可以生成結構化輸出；
• 支援 runnable 方法，例如串流和非同步操作。

現在讓我們並排查看 LLMRouterChain 和使用工具調用的 LCEL 實作。請注意，對於本指南，我們將使用 langchain-openai >= 0.1.20

%pip install -qU langchain-core langchain-openai

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

舊版

詳細資訊

from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

router_prompt = PromptTemplate(
    # Note: here we use the prompt template from above. Generally this would need
    # to be customized.
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)

chain = LLMRouterChain.from_llm(llm, router_prompt)

API 參考：LLMRouterChain | RouterOutputParser | PromptTemplate | ChatOpenAI

result = chain.invoke({"input": "What color are carrots?"})

print(result["destination"])

vegetables

LCEL

詳細資訊

from operator import itemgetter
from typing import Literal

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from typing_extensions import TypedDict

llm = ChatOpenAI(model="gpt-4o-mini")

route_system = "Route the user's query to either the animal or vegetable expert."
route_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", route_system),
        ("human", "{input}"),
    ]
)


# Define schema for output:
class RouteQuery(TypedDict):
    """Route query to destination expert."""

    destination: Literal["animal", "vegetable"]


# Instead of writing formatting instructions into the prompt, we
# leverage .with_structured_output to coerce the output into a simple
# schema.
chain = route_prompt | llm.with_structured_output(RouteQuery)

API 參考：ChatPromptTemplate | RunnablePassthrough | ChatOpenAI

result = chain.invoke({"input": "What color are carrots?"})

print(result["destination"])

vegetable

後續步驟

請參閱 本教學課程，以獲取有關使用提示範本、LLM 和輸出解析器進行建構的更多詳細資訊。

查看 LCEL 概念文件，以獲取更多背景資訊。