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Table Of Contents
- Introduction
- What is LangChain?
- Decoding LangChain: Exploring its Basic Components
- Summary
- References
Introduction
In this post, we discuss LangChain and try to do Decoding LangChain: Exploring its Basic Components. Here, We explore what a langchain is and what are the core components of the langchain.
What is LangChain?
langChain is an open-source development framework for LLM applications, there are two different packages available Python and Javascript (TypeScript).LangChain is focused on composition and modularity.
Key values of LangChain,
- Modular components
- Use cases: common ways to combine components
Langchain also be,
- Data-aware:
- connect a language model to other sources of data
- Agentic:
- allow a language model to interact with its environment
Decoding LangChain: Exploring its Basic Components
Important Components/Modules of LangChain
Models
- LLMs:
- Large Language Models (LLMs) take a text string as input and return a text string as output.
- Chat Models:
- Chat Models are usually backed by a language model, but their APIs are more structured. Specifically, these models take a list of Chat Messages as input and return a Chat Message.
Prompts
The new way of programming models is through prompts. A prompt refers to the input to the model. This input is often constructed from multiple components. A PromptTemplate is responsible for the construction of this input. LangChain provides several classes and functions to make constructing and working with prompts easy.
- Output Parsers
- Language models (and Chat Models) output text. But many times you may want to get more structured information. This is where output parsers come in.
- instruct the model how output should be formatted,
- parse output into the desired formatting (including retrying if necessary
Memory
By default, Chains, and Agents are stateless, meaning that they treat each incoming query independently (as are the underlying LLMs and chat models). In some applications (chatbots being a GREAT example) it is highly important to remember previous interactions, both at a short-term but also at a long-term level. Memory does exactly that.
LangChain provides memory components in two forms. First, LangChain provides helper utilities for managing and manipulating previous chat messages. These are designed to be modular and useful regardless of how they are used. Secondly, LangChain provides easy ways to incorporate these utilities into chains.
Indexes
Indexes play a crucial role in optimizing the interaction between language models (LLMs) and structured documents. In the context of chains, indexes primarily facilitate the retrieval of relevant documents based on user queries. While indexes can have various applications beyond retrieval, and retrieval itself can employ alternative methods, the focus lies on the Retriever interface, which forms the backbone of most chains.
When discussing indexes and retrieval, the primary emphasis is on managing unstructured data, such as text documents. However, for structured data (such as SQL tables) or APIs, specific use case sections provide relevant functionality links.
Chains
For simple applications, using a single language model (LLM) in isolation is often sufficient. However, when it comes to more complex tasks, chaining LLMs together becomes essential. This can involve connecting LLMs or integrating them with other domain experts. Enter LangChain, the solution that offers a standardized interface for Chains and multiple readily available chain implementations.
Agents
In certain applications, it’s not enough to rely on a fixed sequence of calls to language models (LLMs) or other tools. Instead, there is a need for adaptable chains that can dynamically adjust based on user input. These chains involve an agent equipped with a range of tools at its disposal. Depending on the specific user input, the agent intelligently decides which tools, if any, should be utilized.
Callbacks
LangChain introduces a powerful callbacks system that empowers developers to seamlessly integrate with different stages of their language model (LLM) applications. This feature proves invaluable for tasks like logging, monitoring, streaming, and more.
By leveraging the callbacks argument provided across the API, developers can subscribe to various events and customize their application behavior accordingly. The callbacks argument expects a list of handler objects, This way, developers have full control over the execution flow and can tailor it to their specific needs.
Summary
Stay tuned to explore how LangChain revolutionizes application development, unlocking the immense potential that arises from chaining LLMs and leveraging the expertise of various domain specialists.
To learn more about LangChain and Prompt Engineering with LangChain,