Unlocking the Power of LLM Chains with GPT 3.5 and LangChain

Table of Contents

1. Introduction to Chains
2. Overview of Chains
   • 2.1 Types of Chains
     • 2.1.1 Generic Chains
     • 2.1.2 Combined Document Chains
     • 2.1.3 Utility Chains
     • 2.1.4 LM Math Chain
     • 2.1.5 SQL Chain
     • 2.1.6 API Chain
     • 2.1.7 Bash Commands Chain
3. Exploring Utility Chains
   • 3.1 Prompt Templates for Utility Chains
   • 3.2 Customizing Utility Chains
4. Building Custom Solutions with Generic Chains
   • 4.1 Transform Chain
   • 4.2 LLM Chain
   • 4.3 Sequential Chain
5. Using Langtune Hub to Load Chains

Introduction to Chains

Chains in the Line Chain Library are an essential skill for anyone looking to utilize the library effectively. Chains are thin wrappers around different components in the Line Chain Library, with the ability to combine different primitives within the library to perform various tasks. This article will provide an overview of chains, including the different types of chains, such as generic chains, combined document chains, and utility chains.

Overview of Chains

Chains in the Line Chain Library serve as wrappers around various components, known as primitives. These primitives can include Prompts, large language models, utilities, and even other chains. One of the simplest chains is the Large Language Model (LLM) chain, which takes input from the user, passes it through a prompt template, and then utilizes a large language model to generate output Based on the prompt. Chains in Line Chain can be much more complex than this simple example.

Types of Chains

Generic Chains

Generic chains are versatile chains used to build other chains within the library. While not designed for standalone use, they play a vital role in constructing custom solutions. The library includes various generic chains, such as the Transform Chain, which applies a function to inputs and returns outputs.

Combined Document Chains

Combined document chains are ideal for working with other documents, such as question answering or summarization tasks. These chains enable users to build SQL commands for natural language queries using the SQL Chain. The API Chain helps in making correct API calls based on API documentation. The Bash Commands Chain allows the creation of batch commands on the fly.

Utility Chains

Utility chains serve specific purposes and consist of a large language model chain paired with another specific line chain utility. By utilizing prompts, users can program utility chains to behave in a certain way, avoiding common pitfalls. Examples of utility chains include the LLM Math Chain, which enables complex math calculations, and the SQL Chain.

Exploring Utility Chains

To leverage utility chains, prompts play a crucial role in instructing the chain on how to perform specific tasks. By fine-tuning prompts, users can force the line language model to behave in precise ways. This is demonstrated in the LLM Math Chain, where prompts are used to generate Python code and avoid incorrect calculations.

Building Custom Solutions with Generic Chains

Generic chains, such as the Transform Chain, the LLM Chain, and the Sequential Chain, act as building blocks for constructing custom solutions. The Transform Chain applies a function to inputs, allowing users to clean input text, remove extra spaces, and avoid unnecessary token charges. The LLM Chain utilizes a prompt template and interacts with the LLN to generate output based on specific styles. The Sequential Chain combines multiple chains to perform sequential tasks.

Using Langtune Hub to Load Chains

Langtune Hub is where chains, prompts, and agents are serialized and loaded for use. By importing the Load Chain function and specifying the appropriate path, users can load specific chains from Langtune Hub. Parameters can also be customized, such as enabling or disabling verbosity.

Conclusion

The Line Chain Library offers a wide range of chains that users can leverage to perform various tasks. The versatility and customization options provided by generic chains, combined document chains, and utility chains make it powerful for building custom solutions. By understanding prompts and utilizing Langtune Hub, users can maximize the potential of the Line Chain Library. Explore the documentation and example notebooks to gain a deeper understanding of the available utility chains and Create innovative applications.