Welcome to Day 2 of our LangChain learning series! Yesterday, we explored the basics of LangChain. Today, we’re going to dive deeper into one of its most powerful building blocks — the Chat Model — and see how to use it with Amazon Bedrock.
🤖 What is a Chat Model?
A Chat Model is an interface to interact with Large Language Models (LLMs) in a conversational format. Instead of sending just a plain prompt, you send a list of messages (like a chat history). The model replies just like a human would in a conversation.
It’s like having a conversation with an intelligent AI assistant that remembers the context and responds accordingly.
🧠 Why Use Chat Models?
Chat models offer a wide range of benefits:
- Keep track of multi-turn conversations
- Enable contextual AI assistants
- Perform step-by-step reasoning
- Output structured data like JSON
- Make tool/API calls for dynamic actions
These capabilities make them ideal for realistic applications like AI chatbots, agents, summarizers, and form-fillers.
🧩 Key Capabilities of Modern Chat Models
Here are some advanced features of modern chat models:
- Tool Calling: Models can trigger external tools or APIs to fetch dynamic information like weather, stock prices, etc.
- Structured Output: Models can return results in formats like JSON — helpful for feeding into apps or workflows.
- Multimodality: Some models understand not just text, but also images, audio, or video.
🌐 Chat Models in LangChain
LangChain provides a clean interface to many models across providers. These come in two flavors:
-
langchain: Core models officially supported. -
langchain-community: Community-maintained models and integrations. -
langchain-aws: Bedrock and AWS-native integrations (optional).
Chat models in LangChain follow a naming convention with a Chat prefix, such as:
ChatOpenAIChatAnthropic-
ChatBedrock(for Amazon Bedrock)
⚙️ Key Methods of Chat Models
Here are the primary methods you’ll encounter while working with chat models:
-
invoke: The core method for sending chat messages and receiving a reply. -
stream: Streams the AI’s response as it’s generated (great for real-time UIs). -
batch: Handles multiple chat requests in parallel. -
bind_tools: Connects external tools to the model (function calling). -
with_structured_output: Wraps the result in a clean, structured format like JSON.
✅ Summary
Today you learned:
- What Chat Models are and why they matter
- The core features of modern chat models (tool calling, structured output, streaming)
- How LangChain makes it easy to use Bedrock via ChatBedrock
- How to plug chat models into LangChain’s chains, memory, and tools
📅 What’s Coming in Day 3?
Tomorrow, we’ll explore Memory in LangChain — allowing your AI to “remember” past messages across sessions, and enabling contextual agents and smarter applications.
👨 About Me
👨 Hi, I’m Utkarsh Rastogi – a Cloud Specialist & AWS Community Builder passionate about AI and serverless innovation.
🔗 Let’s connect on LinkedIn
#LangChain #AI #LLM #ChatGPT #AmazonBedrock #Python #PromptEngineering #DevTools #Cloud #Serverless #AIApps #DailyLearning #UtkarshRastogi
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