LLMOps with Langchain

After the release of OpenAI ChatGPT in early 2023, we have seen flood gates open for innovation using the Large Language Models (GPT4, LLAMA2 etc). We all have been blown away by the LLM demos we saw for code writing, generating document summaries, poems and stories etc. Enterprises have created multiple teams to build use cases using Large Language Models. Key for enterprise success is to leverage in house data. Enterprise use case vary from enhancing customer service to help improve employee productivity.

Typical Enterprise Use Case looks something as below:

As enterprise embark on this journey to build LLM use cases, need for LLMOps tools and framework has grown rapidly.

Introducing Langchain

LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). It provides tools and abstractions to improve the customization, accuracy, and relevancy of information generated by these models. LangChain allows developers to connect language models with external data sources, enabling the development of diverse applications such as chatbots, question-answering systems, content generators, and more. It was launched in 2022 as an open-source project, offering Python and JavaScript libraries for developers to work with.

Langchain Key Modules

LangChain comprises several key modules that play essential roles in developing applications powered by language models. These modules provide specific functionalities and tools to streamline the creation of advanced language model applications. Here is a breakdown of the key modules of LangChain:

Model I/O:

This module facilitates interaction with various language models, managing their inputs and outputs efficiently. It includes interfaces for connecting to language models, handling their inputs and outputs effectively.

Retrieval:

The Retrieval module enables access to and interaction with application-specific data, crucial for dynamic data utilization also called RAG(Retrieve Augment and Generate). It allows applications to retrieve and utilize data from external sources, enhancing the contextual understanding of language models.

Agents:

Agents empower applications to select appropriate tools based on high-level directives, enhancing decision-making capabilities. Agents help in determining which tools or actions should be taken by the application based on predefined directives or instructions.

Chains:

Chains offer pre-defined, reusable compositions that serve as building blocks for application development. These chains consist of sequences of calls that help in structuring the flow of operations within the application.

Prompt:

The Prompt module involves user input guiding the model to generate relevant and coherent language-based responses. Prompts play a crucial role in shaping the behavior and responses of language models, influencing the output generated by the model.

These modules collectively form a comprehensive toolkit within LangChain, providing developers with the necessary components to create sophisticated applications leveraging large language models effectively

References

https://python.langchain.com/docs/get_started/introduction

Some of the text on this page was generated using https://www.perplexity.ai/

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Disclaimer: This is a personal blog. The opinions expressed here represent my own and not those of my current or any previous employers.