https://github.com/manav54321/chains
This repository demonstrates how to use Chains in LangChain to build modular AI pipelines.
https://github.com/manav54321/chains
chains groq huggingface langchain pydantic python runnable
Last synced: about 1 month ago
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This repository demonstrates how to use Chains in LangChain to build modular AI pipelines.
- Host: GitHub
- URL: https://github.com/manav54321/chains
- Owner: Manav54321
- License: mit
- Created: 2025-07-11T10:23:20.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-14T13:11:48.000Z (3 months ago)
- Last Synced: 2025-07-14T17:10:16.193Z (3 months ago)
- Topics: chains, groq, huggingface, langchain, pydantic, python, runnable
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LangChain Chains
This repository demonstrates how to use Chains in LangChain to build modular AI pipelines.
## What are Chains?
In LangChain, a Chain is a sequence of components connected together to perform a task. Each component in the chain does one job, and the output of one step becomes the input of the next.
For example:
Prompt → LLM → Output Parser
LangChain chains help break down tasks into manageable steps. You can combine models, prompts, tools, and logic using chains to create flexible AI applications.
## How This Works
This project uses LangChain's `Runnable` interfaces to build a conditional chain. Here's what it does:
1. Takes a feedback text input.
2. Uses a prompt and LLM to classify the feedback as Positive or Negative.
3. Parses the classification using a Pydantic model.
4. Based on the sentiment, routes the input to different prompts to generate an appropriate response.
5. Returns the final response.
The routing logic is handled using `RunnableBranch`, which acts like an if-else statement.
## Key Components Used
- `PromptTemplate`: To define prompt templates for the classifier and response generator.
- `ChatGroq`: LLM wrapper for making API calls to Groq-hosted LLMs.
- `PydanticOutputParser`: To parse the output of the model into structured format.
- `RunnableBranch`: For conditional logic based on sentiment.
- `RunnableLambda`: For simple transformations inside chains.
## Example Use Case
Input:
"I love the new features in the app, they are amazing."
Step-by-step flow:
1. Classify sentiment → Positive
2. Choose the positive response prompt
3. Generate and return the appropriate reply
## Why Use Chains
- Clear and modular code
- Easier debugging and testing
- Scalable logic using simple building blocks
- Useful for tasks like routing, classification, tool use, and complex workflows