LangChain Chains

This repository demonstrates four different chain types available in LangChain, showing how they can be used to build powerful, composable AI workflows.

📂 Chain Types Included

1. Simple Chain
2. Sequential Chain
3. Parallel Chain
4. Conditional Chain

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1. Simple Chain

A Simple Chain passes input through a single sequence of operations.

Advantages

• Easy to implement
• Minimal configuration
• Ideal for small, focused tasks

Use Case

• Taking a question and returning a concise answer
• Summarizing a short text snippet

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2. Sequential Chain

A Sequential Chain runs multiple chains one after another, passing the output of one chain as the input to the next.

Advantages

• Clear control over execution order
• Good for multi-step reasoning
• Easy to debug

Use Case

• First summarizing a document, then translating it
• Generating an outline, then expanding it into a full draft

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3. Parallel Chain

A Parallel Chain runs multiple chains at the same time using the same input, then aggregates their outputs.

Advantages

• Faster execution for independent tasks
• Can handle multiple perspectives or processing methods in parallel

Use Case

• Getting answers from multiple LLM prompts for comparison
• Running classification and sentiment analysis simultaneously

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4. Conditional Chain

A Conditional Chain decides which chain to run based on input conditions.

Advantages

• Flexible, dynamic behavior
• Saves computation by avoiding unnecessary steps

Use Case

• Routing a user query to either a summarizer or a translator based on query type
• Detecting language and choosing the appropriate processing chain