Router

本笔记本演示了如何使用 RouterChain 范例来创建一个根据给定输入动态选择下一个链条的链条。

路由器链条由两个组件组成:

RouterChain (负责选择下一个要调用的链条)
destination_chains: 路由器链条可以路由到的链条

在本笔记本中，我们将重点讨论不同类型的路由链条。我们将展示这些路由链条在 MultiPromptChain 中的使用方式，以创建一个问答链条，该链条根据给定的问题选择最相关的提示，并使用该提示回答问题。

from langchain.chains.router import MultiPromptChain
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate

physics_template = """You are a very smart physics professor. 
You are great at answering questions about physics in a concise and easy to understand manner. 
When you don't know the answer to a question you admit that you don't know.

Here is a question:
{input}"""

math_template = """You are a very good mathematician. You are great at answering math questions. 
You are so good because you are able to break down hard problems into their component parts, 
answer the component parts, and then put them together to answer the broader question.

Here is a question:
{input}"""

prompt_infos = [
    {
        "name": "physics",
        "description": "Good for answering questions about physics",
        "prompt_template": physics_template,
    },
    {
        "name": "math",
        "description": "Good for answering math questions",
        "prompt_template": math_template,
    },
]

llm = OpenAI()

destination_chains = {}
for p_info in prompt_infos:
    name = p_info["name"]
    prompt_template = p_info["prompt_template"]
    prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
    chain = LLMChain(llm=llm, prompt=prompt)
    destination_chains[name] = chain
default_chain = ConversationChain(llm=llm, output_key="text")

LLMRouterChain

此链条使用LLM来确定如何路由事物。

from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE

destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)

chain = MultiPromptChain(
    router_chain=router_chain,
    destination_chains=destination_chains,
    default_chain=default_chain,
    verbose=True,
)

print(chain.run("What is black body radiation?"))

[1m> Entering new MultiPromptChain chain...[0m
physics: {'input': 'What is black body radiation?'}
[1m> Finished chain.[0m


Black body radiation is the term used to describe the electromagnetic radiation emitted by a “black body”—an object that absorbs all radiation incident upon it. A black body is an idealized physical body that absorbs all incident electromagnetic radiation, regardless of frequency or angle of incidence. It does not reflect, emit or transmit energy. This type of radiation is the result of the thermal motion of the body's atoms and molecules, and it is emitted at all wavelengths. The spectrum of radiation emitted is described by Planck's law and is known as the black body spectrum.

print(
    chain.run(
        "What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"
    )
)

[1m> Entering new MultiPromptChain chain...[0m
math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
[1m> Finished chain.[0m
?

The answer is 43. One plus 43 is 44 which is divisible by 3.

print(chain.run("What is the name of the type of cloud that rins"))

[1m> Entering new MultiPromptChain chain...[0m
None: {'input': 'What is the name of the type of cloud that rains?'}
[1m> Finished chain.[0m
 The type of cloud that rains is called a cumulonimbus cloud. It is a tall and dense cloud that is often accompanied by thunder and lightning.

EmbeddingRouterChain

EmbeddingRouterChain 使用嵌入和相似性在目标链条之间进行路由。

from langchain.chains.router.embedding_router import EmbeddingRouterChain
from langchain.embeddings import CohereEmbeddings
from langchain.vectorstores import Chroma

names_and_descriptions = [
    ("physics", ["for questions about physics"]),
    ("math", ["for questions about math"]),
]

router_chain = EmbeddingRouterChain.from_names_and_descriptions(
    names_and_descriptions, Chroma, CohereEmbeddings(), routing_keys=["input"]
)

Using embedded DuckDB without persistence: data will be transient

chain = MultiPromptChain(
    router_chain=router_chain,
    destination_chains=destination_chains,
    default_chain=default_chain,
    verbose=True,
)

print(chain.run("What is black body radiation?"))

[1m> Entering new MultiPromptChain chain...[0m
physics: {'input': 'What is black body radiation?'}
[1m> Finished chain.[0m

Black body radiation is the emission of energy from an idealized physical body (known as a black body) that is in thermal equilibrium with its environment. It is emitted in a characteristic pattern of frequencies known as a black-body spectrum, which depends only on the temperature of the body. The study of black body radiation is an important part of astrophysics and atmospheric physics, as the thermal radiation emitted by stars and planets can often be approximated as black body radiation.

print(
    chain.run(
        "What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"
    )
)

[1m> Entering new MultiPromptChain chain...[0m
math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
[1m> Finished chain.[0m
?

Answer: The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43.

Router

LLMRouterChain​

EmbeddingRouterChain​

LLMRouterChain

EmbeddingRouterChain