LCEL-Teacher

Introduction

LangChain Expression Language has a number of important benefits, including transparent composability of LLM components, seamless support for prototyping and production (with LangServe) using the same code, and a common interface for every chain. But, there a learning curve for using LCEL. Here, we aim to build a coding assistant for LCEL.

Architecture

We explore three several architectures for LCEL-teacher in this repo, including:

• Context stuffing of LCEL docs into the LLM context window
• RAG using retrieval from a vector databases of all LangChain documentation
• RAG using multi-question and answer generation using retrieval from a vector databases of all LangChain documentation
• Context stuffing with recovery using LangGraph for code execution and re-try

Code for each can be found in the /app directory.

Environment

We use Poetry for dependency management.

Context stuffing requires no vectorstore access because we will directly read the docs and stuff them into the LLM context windopw.

Both RAG approaches rely on an vectorstore index of LangChain documentation (Weaviate) with fine-tuned embeddings from Voyage:

• WEAVIATE_URL
• WEAVIATE_API_KEY
• VOYAGE_API_KEY
• VOYAGE_AI_MODEL

Using the app

This repo is a LangServe app. We host it using hosted LangServe. To learn more see this video.

You can access it here.

The deployment in the LangChain org within LangSmith is here.

The steps to deploy it for yourself are shown below.

---

Running locally and deployment

This repo was created following these steps:

(1) Create a LangChain app.

Run:

langchain app new .  

This creates two folders:

app: This is where LangServe code will live
packages: This is where your chains or agents will live

It also creates:

Dockerfile: App configurations
pyproject.toml: Project configurations

Add app dependencies to pyproject.toml and poetry.lock:

poetry add weaviate-client
poetry add langchainhub
poetry add openai
poetry add pandas
poetry add jupyter
poetry add tiktoken
poetry add scikit-learn
poetry add langchain_openai

Update enviorment based on the updated lock file:

poetry install

(2) Add the chains

Add our custom retrieval code to the app directory.

In our case, I add the various _chain.py files.

Each file simply has a LCEL chain defined. For example:

chain = (
    {
        "context": lambda x: concatenated_content,
        "question": RunnablePassthrough(),
    }
    | prompt
    | model
    | StrOutputParser()
)

# Add typing for input
class Question(BaseModel):
    __root__: str


chain = chain.with_types(input_type=Question)

Now, we simply import the chain in server.py:

from app.deploy_chain import chain as chain_to_deploy
add_routes(app, chain_to_deploy, path="/lcel-teacher")

Run locally

poetry run langchain serve

Simply, the invocation methods of our LCEl chain are mapped to HTTP endpoints in the LangServe app:

For hosted LangServe, sign up in your LangSmith console on the Deployments tab and connect to your fork of this repo.

Eval

In eval/ you will see eval.csv.

Use this to create a LangSmith as a dataset, lcel-teacher-eval.

Run notebook to kick off eval:

poetry run jupyter notebook

Use eval/eval_lcel_teacher.ipynb to run evals.