Frameworks are a collection of tools and libraries that provide a structured foundation for developing applications. These frameworks have reusable components that enable customization and integration of LLMs into AI applications. These frameworks have been used to create Document Summarizers, Virtual Assistants, Chatbots and AI agents.
In this blog, I will provide you with a brief overview of the frameworks, their main features and what makes them unique.
LANGCHAIN
LangChain began as an open-source project in October 2022, but as the GitHub stars piled up, it was quickly turned into a company led by Harrison Chase. It provides tools for chaining together LLMs, APIs, and other components to create more complex workflows. It also emphasizes the use of external knowledge sources (like databases, APIs, or documents) with LLMs to enhance their capabilities. LangChain is fascinating because it lets you augment existing LLMs with memory and context. They can artificially add “reasoning” and complete more complex tasks with greater precision and accuracy. Reasoning is the process of using information acquired prior to the communication act in order to reach new conclusions.
Things
that make LangChain beneficial are:
·
Scalability – LangChain may be used to create applications capable
of handling massive volumes of data.
·
Adaptability – The framework’s adaptability allows it to be
used to develop a wide range of applications, from chatbots to
question-answering systems.
·
Ease of use – LangChain offers a high-level API for connecting
language models to various data sources and building complicated applications.
·
Vibrant community – There is a huge and active
community of LangChain users and developers that can assist and support you.
·
Great documentation – The documentation is thorough
and simple to understand.
·
Integrations – LangChain may be integrated with various
frameworks and libraries, such as Flask and TensorFlow.
Tutorial to start learning LangChain: https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/
LlamaIndex
It
is an AI tool that is used to connect different types of data such as PDFs,
URLs and Powerpoint apps such as Notion and Slack to LLMs. It build data
structures(indexes) that enable efficient retrieval from large document
collections and integrate with LLMs.
Key Features
- Data Connectors: Bring in data from
different sources and formats easily.
- Document Operations: You can add, delete,
update, and refresh documents in the index.
- Data Synthesis: Combine information
from multiple documents or different sources.
- Router Feature: Choose between
different query engines to get the best results.
- Hypothetical Document
Embeddings: Improve the quality of the answers you get.
- Integrations: Compatible with a
wide range of tools, including LangChain, ChatGPT plugins, vector storage,
and tracing tools.
BENEFITS
·
LlamaIndex provides more flexibility, allowing developers to
structure their code as they see fit. But this comes at the cost of having to
make more decisions and potentially inconsistent code across projects.
·
LlamaIndex has a modular approach to dependencies leading to
increased flexibility of replacing individual components and reduces potential
conflict with other libraries.
·
LlamaIndex has a large, more community which leads to a wide
range of components and integrations available.
HAYSTACK
This is an open-source, end-to-end Python framework made by deepset. It leverages Transformers, which are the deep learning architectures behind LLMs like ChatGPT.
Haystack are made up of:
· Components: Building blocks that can perform tasks like
document retrieval, text generation or summarization. A chain of components
form a pipeline.
·
Pipeline: They are the standard structure used for connecting
data and performing your NLP tasks.
Example
of a Haystack Pipeline
Haystack
opinionated approach makes it easier to get started and ensures consistent
patterns across projects. Haystack has a small community which will lead to decreased
sustainability and development. Haystack uses a monolithic approach with the “haystack-ai”
package. It simplifies initial setup and provides less flexibility when using custom
components.
Tutorial: https://www.deeplearning.ai/short-courses/building-ai-applications-with-haystack/
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