From Prompts to Pipelines: An Overview of LangChain Orchestration

From Prompts to Pipelines

The Evolution of LLM Interaction

In previous lessons, we focused on single-prompt interactions. However, real-world applications require more than just a one-off question and answer. To build scalable AI systems, we must transition to Orchestration. This involves linking multiple LLM calls together, branching logic based on user input, and allowing the model to interact with external data.

The Building Blocks of Orchestration

LLMChain: The fundamental unit. It combines a Prompt Template with a Language Model.
Sequential Chains: These allow you to create a multi-step workflow where the output of one step becomes the input for the next.
Router Chains: These act as "traffic controllers," using an LLM to decide which specialized sub-chain should handle a specific request (e.g., sending a math question to a "Math Chain" and a history question to a "History Chain").

The Core Principle: The Chain Rule

Chains allow multiple components—models, prompts, and memory—to be combined into a single, coherent application. This modularity ensures that complex tasks can be broken down into manageable, debuggable steps.

Pro-Tip: Debugging Pipelines

When your pipelines grow complex, use langchain.debug = True. This "X-ray vision" allows you to see the exact prompts being sent and the raw outputs received behind the scenes at every stage of the chain.

TERMINAL bash — 80x24

> Ready. Click "Run" to execute.

Question 1

In LangChain, what is the primary difference between a SimpleSequentialChain and a standard SequentialChain?

SimpleSequentialChain supports multiple input variables, while SequentialChain does not.

SimpleSequentialChain only supports a single input and single output flowing between steps.

Only SequentialChain can be used with ChatOpenAI models.

Challenge: Library Support Router

Design a routing mechanism for a specialized bot.

You are building a support bot for a library.

Define the logic for a RouterChain that distinguishes between "Book Recommendations" and "Operating Hours."

Step 1

Create two prompt templates: one for book suggestions and one for library schedule info.

Solution:

book_template = """You are a librarian. Recommend books based on: {input}"""
schedule_template = """You are a receptionist. Answer hours queries: {input}"""

prompt_infos = [
    {"name": "books", "description": "Good for recommending books", "prompt_template": book_template},
    {"name": "schedule", "description": "Good for answering operating hours", "prompt_template": schedule_template}
]

Step 2

Define the router_template to guide the LLM on how to classify the user's intent, and initialize the chain.

Solution:

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
)