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LangChain is a Python framework built around Large Language Model (LLM) that can be used for chatbots, Generative Question-Answering (GQA), summarization, and more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. LLMs are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge.

LangChain offers a way to interact with and fine-tuning LLMs on local data, providing a secure and efficient alternative to sending private data through external APIs. It allows companies to extract knowledge from their own data and develop chatbots or other applications that comprehend complex domain-specific information. By combining user input with prompts and interacting with LLMs, LangChain enables seamless integration and enhances the capabilities of applications. It utilizes vector databases as memory, allowing for efficient access to relevant information during the application's execution.

The benefits of using LangChain for fine-tuning language models include:

  • Seamless switching between different LLM providers: LangChain offers the flexibility to easily switch between different large language model providers. This allows developers to leverage the unique capabilities and strengths of various language models, tailoring their applications to specific needs.
  • Dynamic and immersive user experiences: LangChain enables applications to create dynamic and immersive user experiences by allowing language models to intelligently interact and respond to their surroundings. This feature enhances the user experience and makes applications more engaging.
  • Prompt management and optimization: LangChain provides capabilities for prompt management and optimization. Developers can efficiently manage prompts and optimize their performance to achieve better results from the language models.
  • Memory integration: LangChain allows for the integration of memory into user interactions with the language models. This feature enables applications to easily access relevant information from previous interactions, enhancing the capabilities of the application
  • Secure and efficient data handling: LangChain provides a secure and efficient alternative to sending private data through external APIs. It allows developers to fine-tune language models on local data, ensuring data privacy and reducing reliance on external services.
  • Simplified application development: LangChain simplifies the process of building applications powered by large language models. It provides a framework that empowers developers, including non-NLP specialists, to create applications that were previously difficult and required extensive expertise.

Getting Started

Data Independent - tutorial videos

reference videos throughout page


Long documents



Tabular Data





Visual ChatGPT


Hugging Face



Comparing Large Language Models (LLM)


Filtering LLM

Weights & Biases (W&B)

W&B Sweeps and LangChain integration is a feature that allows you to fine-tune LLMs with your own data using W&B Sweeps and LangChain visualization and debugging. W&B Sweeps is a hyperparameter optimization tool that helps you find the best combination of hyperparameters for your model. W&B Sweeps and LangChain integration can:

  • Create a LangChain model, chain, or agent that uses an LLM as a backend.
  • Import WandbTracer from wandb.integration.langchain and use it to continuously log calls to your LangChain object.
  • Use W&B dashboard to visualize and debug your LangChain object, such as viewing the prompts, responses, metrics, and errors.
  • Use W&B Sweeps to optimize the hyperparameters of your LangChain object, such as the prompt template, the context length, the temperature, and the top-k.

Weights & Biases Logging/LLMops is a feature of the Weights & Biases platform, which is a developer-first MLOps platform that provides enterprise-grade, end-to-end MLOps workflow to accelerate ML activities. Weights & Biases Logging/LLMops enables you to optimize LLM operations and prompt engineering with W&B.