大模型应用与LUI（自然语言交互）落地的关键模块——语义路由实现总结

from langchain_anthropic import ChatAnthropic
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate


# Set up the LLM Chain to return a single word based on the query,
# and based on a list of words we provide to it in the prompt template
llm_completion_select_route_chain = (
        PromptTemplate.from_template("""
Given the user question below, classify it as either
being about `LangChain`, `Anthropic`, or `Other`.


Do not respond with more than one word.


<question>
{question}
</question>


Classification:"""
                                     )
        | ChatAnthropic(model_name="claude-3-haiku")
        | StrOutputParser()
)




# We setup an IF/Else condition to route the query to the correct chain 
# based on the LLM completion call above
def route_to_chain(route_name):
    if "anthropic" == route_name.lower():
        return anthropic_chain
    elif "langchain" == route_name.lower():
        return langchain_chain
    else:
        return general_chain


...


# Later on in the application, we can use the response from the LLM
# completion chain to control (i.e route) the flow of the application 
# to the correct chain via the route_to_chain method we created
route_name = llm_completion_select_route_chain.invoke(user_query)
chain = route_to_chain(route_name)
chain.invoke(user_query)

from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.selectors.pydantic_selectors import Pydantic
from llama_index.core.tools import QueryEngineTool
from llama_index.core import VectorStoreIndex, SummaryIndex


# define query engines
...


# initialize tools
list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    descriptinotallow="Useful for summarization questions related to the data source",
)
vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    descriptinotallow="Useful for retrieving specific context related to the data source",
)


# initialize router query engine (single selection, pydantic)
query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)
query_engine.query("<query>")

from semantic_router import Route


# we could use this as a guide for our chatbot to avoid political
# conversations
politics = Route(
    name="politics",
    utterances=[
        "isn't politics the best thing ever",
        "why don't you tell me about your political opinions",
        "don't you just love the president",
        "they're going to destroy this country!",
        "they will save the country!",
    ],
)


# this could be used as an indicator to our chatbot to switch to a more
# conversational prompt
chitchat = Route(
    name="chitchat",
    utterances=[
        "how's the weather today?",
        "how are things going?",
        "lovely weather today",
        "the weather is horrendous",
        "let's go to the chippy",
    ],
)


# we place both of our decisions together into single list
routes = [politics, chitchat]


#创建路由层


encoder = OpenAIEncoder()


from semantic_router.layer import RouteLayer


route_layer = RouteLayer(encoder=encoder, routes=routes)

route_layer("don't you love politics?").name
# -> 'politics'