Learning LangChain by Building: 7 Tiny Examples from Chat Models to RAG
I've spent about 12 years building data pipelines on AWS, most recently for financial-crimes compliance in banking. When I started working with LLMs, I kept hitting the same wall. Every tutorial was either a one-line "hello world" or a 500-line agent framework I couldn't take apart. So I did what data engineers do with any new tool: I broke it into the smallest pieces I could run, inspect, and…
1. LangChain framework: LangChain is an open-source framework that standardizes various components needed for an LLM application, such as prompts, conversation state, output parsing, access to private data, and function calls. It provides a chat-model interface, reusable prompt templates, and tools for handling multi-step logic.
2. Chat models: LangChain offers a unified interface for any LLM provider, allowing developers to swap between providers without changing the code. The framework supports Hugging Face, OpenAI, and Gemini models, all of which can be used with a single line of code. The chat-model interface simplifies the process of calling the model and processing the response.
3. Memory management: LLMs are stateless, meaning each call only knows what is sent to it. To maintain conversation history, memory must be managed by resending the conversation with each turn. While this approach is simple, it can lead to increased token usage and latency as the conversation grows. In production apps, older messages can be trimmed or summarized to manage memory more efficiently.
4. Chains with LCEL: LangChain's LCEL (Language Chain Expression Language) allows developers to create pipelines for prompts, where the output of one component feeds the input of the next. This approach is similar to how data engineers think about ETL (Extract, Transform, Load) processes. LCEL simplifies the creation of multi-step logic and supports batch and streaming operations without requiring significant changes.
5. Structured output: When consuming the model's output as data rather than prose, structured output is beneficial. LangChain allows developers to define a Pydantic model, which enforces the structure of the output. This feature ensures that the data is clean and easily consumable by other parts of the application. Additionally, the Field descriptions in the Pydantic model are sent to the model, guiding the model in generating the desired output format.
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