Implementation:Langchain ai Langchain BaseChatOpenAI Generate

Knowledge Sources	LangChain OpenAI API Reference
Domains	NLP, API_Integration
Last Updated	2026-02-11 00:00 GMT

Overview

Concrete tool for invoking the OpenAI chat completions API provided by the LangChain OpenAI integration.

Description

The BaseChatOpenAI._generate() method is the non-streaming code path for OpenAI-compatible chat models. It converts LangChain BaseMessage objects to OpenAI message dictionaries (via _convert_messages_to_dicts()), builds the request payload, calls self.client.create() (the OpenAI SDK client), and delegates response parsing to _create_chat_result().

Usage

This method is called automatically when invoke() determines that non-streaming execution should be used. It is the provider-specific implementation of the abstract _generate() method defined in BaseChatModel.

Code Reference

Source Location

Repository: langchain
File: libs/partners/openai/langchain_openai/chat_models/base.py
Lines: L1379-1478

Signature

def _generate(
    self,
    messages: list[BaseMessage],
    stop: list[str] | None = None,
    run_manager: CallbackManagerForLLMRun | None = None,
    **kwargs: Any,
) -> ChatResult:

Import

# Internal method — accessed via ChatOpenAI instance
from langchain_openai import ChatOpenAI

I/O Contract

Inputs

Name	Type	Required	Description
messages	list[BaseMessage]	Yes	LangChain messages to send to the model
stop	list[str] or None	No	Stop sequences to halt generation
run_manager	CallbackManagerForLLMRun or None	No	Callback manager for tracing
**kwargs	Any	No	Additional parameters passed to the OpenAI API

Outputs

Name	Type	Description
return	ChatResult	Contains ChatGeneration objects with AIMessage responses and metadata

Usage Examples

Standard Non-Streaming Invocation

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

# invoke() internally calls _generate() for non-streaming path
response = llm.invoke([HumanMessage(content="Explain quantum computing in one sentence.")])
print(response.content)
# "Quantum computing uses quantum bits (qubits) that can exist in superposition..."
print(response.usage_metadata)
# {'input_tokens': 12, 'output_tokens': 25, 'total_tokens': 37}

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