Build a Custom LangChain Chat Model from Scratch

Abstract / Overview

The open-source repository tranngocphu/custom_langchain_chat_model demonstrates how to build a custom conversational AI model using the LangChain framework. It extends LangChain’s modular pipeline for prompt management, response generation, and memory integration.

This guide provides a detailed walkthrough of the repository, including architecture, setup, example code, and implementation strategies for developers who want full control over their chatbot logic.

Conceptual Background

LangChain is a Python framework designed to simplify Large Language Model (LLM) integration into applications. It offers:

Prompt templating for dynamic message creation.
Memory systems to persist conversation context.
Chains and agents for orchestrating logical task flows.

Creating a custom chat model in LangChain allows developers to override generation logic, tailor message formatting, or introduce specialized validation layers.

Architecture Overview

custom-langchain-chat-model-architecture

Step-by-Step Walkthrough

1. Clone the Repository

git clone https://github.com/tranngocphu/custom_langchain_chat_model.git
cd custom_langchain_chat_model

2. Install Dependencies

pip install -r requirements.txt

Common dependencies include:

langchain
openai
python-dotenv
pydantic

3. Configure Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=YOUR_API_KEY

4. Define the Custom Model

In custom_model.py, the CustomChatModel class extends LangChain’s BaseChatModel to define how responses are generated.

from langchain.chat_models.base import BaseChatModel
from typing import List
from langchain.schema import AIMessage, HumanMessage

class CustomChatModel(BaseChatModel):
    def _generate(self, messages: List[HumanMessage], **kwargs):
        responses = []
        for message in messages:
            text = message.content.upper()
            responses.append(AIMessage(content=f"Echo: {text}"))
        return responses

The _generate method is the heart of LangChain chat logic. It handles message parsing and AI response construction.

5. Integrate with a LangChain Chain

You can now integrate your model within an LLMChain for streamlined interaction.

from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("You are a helpful assistant. Answer: {question}")
chain = LLMChain(llm=CustomChatModel(), prompt=prompt)

print(chain.run("What is LangChain?"))

This setup defines a prompt template and executes it through your custom model.

6. Add Memory for Context Retention

LangChain’s memory modules allow contextual conversation continuity.

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help?"})

Pass the memory object to your chain instance to maintain ongoing context:

chain_with_memory = LLMChain(llm=CustomChatModel(), prompt=prompt, memory=memory)

Use Cases / Scenarios

Custom Personas: Build assistants with unique tones or domain-specific styles.
Enterprise Bots: Integrate internal data sources securely within responses.
Offline/Hybrid Chat Systems: Use self-hosted or private APIs for sensitive deployments.
Prototyping: Quickly test different conversational flows or formats.

Limitations / Considerations

Dependent on third-party APIs (e.g., OpenAI).
Token limits restrict context length.
Response latency may increase with complex prompt logic.
Requires prompt testing for reliability and consistency.

Fixes and Troubleshooting

Problem: Invalid API Key
Fix: Ensure .env file includes a valid key and is loaded correctly with dotenv.load_dotenv().

Problem: No output or empty response
Fix: Verify _generate returns AIMessage objects and not plain text.

Problem: Context not remembered
Fix: Pass a memory object into the LLMChain initialization.

Problem: Module import errors
Fix: Activate your Python virtual environment and reinstall dependencies.

FAQs

Q1: Can I use this model with local LLMs instead of OpenAI?
Yes. Modify _generate to call your preferred API or local inference engine.

Q2: What’s the benefit of subclassing instead of using built-in chat models?
Subclassing allows fine-grained control over pre/post-processing, API calls, and internal message handling.

Q3: How can I deploy this chat model?
Wrap it in a FastAPI or Flask server to create an API endpoint for web or mobile clients.

Q4: Is LangChain necessary for a simple chatbot?
No, but LangChain simplifies chaining prompts, managing memory, and extending logic in a modular way.

Q5: Can I add logging or analytics?
Yes. Integrate Python’s logging module or use tools like Prometheus for metrics.

References

GitHub Repository – custom_langchain_chat_model

Conclusion

The custom_langchain_chat_model repository provides a modular framework for extending LangChain’s chat capabilities. By subclassing and integrating custom logic, developers can create chat models optimized for specific workflows, data environments, or application domains.

This approach strikes a balance between flexibility and simplicity, making it suitable for both experimentation and production-grade chatbot systems.