add-chain

name	add-chain
description	Step-by-step procedure to create a new LangChain LCEL chain in a genai-tk project.

Add an LCEL Chain

Follow these steps to add a new LangChain Expression Language (LCEL) chain to a genai-tk project.

Prerequisites

• The project was initialized with cli init
• The project has a <package>/chains/ directory

Step 1: Create the Chain Module

Create a new file in <package>/chains/my_chain.py:

"""My custom chain — describe what it does."""

from genai_tk.core.factories.llm_factory import get_llm
from genai_tk.core.prompts import def_prompt
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import Runnable


def get_chain(config: dict | None = None) -> Runnable:
    """Create the chain: prompt | llm | parser."""
    llm = get_llm()
    prompt = def_prompt(
        system="You are an expert at [domain]. Be concise and accurate.",
        user="{input}",
    )
    return prompt | llm | StrOutputParser()

Step 2: Test the Chain

Via CLI (using the core run command)

uv run cli core run "My Chain" "example input"

Via Webapp

Launch make webapp and navigate to the Runnable Playground page. The chain will appear in the dropdown.

Programmatically

from <package_name>.chains.my_chain import get_chain

chain = get_chain()
result = chain.invoke({"input": "test"})
print(result)

Common LCEL Patterns

Chain with Multiple Steps

from langchain_core.runnables import RunnablePassthrough

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

Chain with Branching

from langchain_core.runnables import RunnableBranch

chain = RunnableBranch(
    (lambda x: "math" in x["topic"], math_chain),
    (lambda x: "code" in x["topic"], code_chain),
    default_chain,
)

Chain with Structured Output

from pydantic import BaseModel

class Answer(BaseModel):
    reasoning: str
    answer: str
    confidence: float

chain = prompt | llm.with_structured_output(Answer)

RAG Chain

from genai_tk.core.factories.llm_factory import get_llm
from genai_tk.core.factories.embeddings_factory import get_embeddings_store

store = get_embeddings_store()
retriever = store.as_retriever(search_kwargs={"k": 4})

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | def_prompt(
        system="Answer based on the context provided.",
        user="Context: {context}\n\nQuestion: {question}",
    )
    | get_llm()
    | StrOutputParser()
)

Registration Options

register_runnable(
    RunnableItem(
        tag="Category",          # Groups chains in the UI
        name="Display Name",     # Unique name for the chain
        runnable=get_chain,      # Factory function returning a Runnable
        examples=[               # Example inputs for the playground
            Example(query=["input 1"]),
            Example(query=["input 2"]),
        ],
    )
)