# Data modelclass RouteQuery(BaseModel):    """Route a user query to the most relevant datasource."""
    datasource: Literal["python_docs", "js_docs", "golang_docs"] = Field(        ...,        description="Given a user question choose which datasource would be most relevant for answering their question",    )
# LLM with function call llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)structured_llm = llm.with_structured_output(RouteQuery)
# Prompt system = """You are an expert at routing a user question to the appropriate data source.
Based on the programming language the question is referring to, route it to the relevant data source."""
prompt = ChatPromptTemplate.from_messages(    [        ("system", system),        ("human", "{question}"),    ])
# Define router router = prompt | structured_llm

question = """Why doesn't the following code work:
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(["human", "speak in {language}"])prompt.invoke("french")"""
result = router.invoke({"question": question})result.datasource
def choose_route(result):    if "python_docs" in result.datasource.lower():        ### Logic here         return "chain for python_docs"    elif "js_docs" in result.datasource.lower():        ### Logic here         return "chain for js_docs"    else:        ### Logic here         return "golang_docs"
from langchain_core.runnables import RunnableLambda
full_chain = router | RunnableLambda(choose_route)full_chain.invoke({"question": question})

# Two promptsphysics_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:{query}"""
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:{query}"""
embeddings = OpenAIEmbeddings()prompt_templates = [physics_template, math_template]prompt_embeddings = embeddings.embed_documents(prompt_templates)

# Route question to prompt def prompt_router(input):    # Embed question    query_embedding = embeddings.embed_query(input["query"])    # Compute similarity    similarity = cosine_similarity([query_embedding], prompt_embeddings)[0]    most_similar = prompt_templates[similarity.argmax()]    # Chosen prompt     print("Using MATH" if most_similar == math_template else "Using PHYSICS")    return PromptTemplate.from_template(most_similar)

chain = (    {"query": RunnablePassthrough()}    | RunnableLambda(prompt_router)    | ChatOpenAI()    | StrOutputParser())
print(chain.invoke("What's a black hole"))