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Overview

LemonData works well with LangChain’s ChatOpenAI and OpenAIEmbeddings integrations when you stay on the standard OpenAI-compatible chat and embeddings surface.
Current LangChain docs note that ChatOpenAI targets official OpenAI-compatible request/response shapes. If you need provider-specific, non-standard response fields, use a provider-specific LangChain integration instead of relying on ChatOpenAI.

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Installation

pip install langchain langchain-openai langchain-community faiss-cpu

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Basic Configuration

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-your-lemondata-key",
    base_url="https://api.lemondata.cc/v1",
)

response = llm.invoke("Explain LemonData in one sentence.")
print(response.content)

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Using Different Models

from langchain_openai import ChatOpenAI

gpt = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

claude = ChatOpenAI(
    model="claude-sonnet-4-6",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

gemini = ChatOpenAI(
    model="gemini-2.5-flash",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

deepseek = ChatOpenAI(
    model="deepseek-r1",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

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Message History

from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the capital of France?")
]

response = llm.invoke(messages)
print(response.content)

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Streaming

for chunk in llm.stream("Write a short poem about coding."):
    if chunk.content:
        print(chunk.content, end="", flush=True)

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Embeddings

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

vector = embeddings.embed_query("Hello world")
print(vector[:5])

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Simple RAG Example

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1",
)

texts = [
    "LemonData provides one API for many AI models.",
    "LemonData supports OpenAI-compatible integrations."
]

vectorstore = FAISS.from_texts(texts, embeddings)
retriever = vectorstore.as_retriever()

prompt = ChatPromptTemplate.from_template(
    "Answer using the context below.\\n\\nContext:\\n{context}\\n\\nQuestion:\\n{question}"
)

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

response = rag_chain.invoke("What does LemonData provide?")
print(response.content)

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Agents

For new agentic projects, LangChain recommends considering LangGraph for more explicit control over long-running and tool-using workflows.
from langchain.agents import create_openai_tools_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Search results for: {query}"

tools = [search]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to tools."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

agent = create_openai_tools_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "Search for LemonData pricing"})
print(result["output"])

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Best Practices

The most reliable LemonData setup is to pass base_url="https://api.lemondata.cc/v1" directly to ChatOpenAI and OpenAIEmbeddings instead of depending on older environment-variable aliases.
Stick to standard chat, tool calling, streaming, and embeddings on ChatOpenAI. If you need vendor-native extras, switch to the vendor’s own LangChain integration.
Use embedding models like text-embedding-3-small for retrieval and reserve stronger chat models for the final answer step.