What is LangChain? A Complete Comprehensive Overview

What is LangChain?

LangChain is a Python framework designed to streamline AI application development, focusing on real-time data processing and integration with large language models (LLMs). It offers features for data communication, generating vector embeddings, and simplifying the interaction with LLMs, making it efficient for AI developers.

AI applications usually follow certain steps to process data as it’s received. When you click to view an item on an e-commerce website, that click event is sent to the website and is processed through an AI application to decide on other “suggested items” that should be shown on the page. The application would be given the context of what the item being viewed is, what’s in your cart, and the previous things you have viewed and shown interest in. All that data would be given to an LLM to decide on other items you might be interested in.

When you’re building an application like this, the steps in the pipeline choreograph what services should be included, how they will be “fed” the data, and what “shape” the data will take. As you can imagine, these complex actions require APIs, data structure, network access, security, and much more.

LangChain is a Python framework that helps someone build an AI application and simplify all the requirements without coding all the little details.

For example, take interacting with an LLM. Once all the information is together in a prompt, you’ll want to submit it to the LLM for completion. LangChain provides pre-built libraries for popular LLMs (like OpenAI GPT) so all a programmer has to do is provide their credentials and the prompt and wait for a response. They don’t need to worry about any of OpenAI’s API specifics like endpoints, protocols, and authentication.

But LLM interaction is just the beginning of what LangChain can help with. Let’s look at why it is important before diving into some of the framework’s key features.

Why is LangChain important?

LangChain represents a significant advancement in natural language processing (NLP). It offers an innovative framework for integrating various language models and tools. This integration enables the construction of more complex and sophisticated AI applications.

By leveraging LangChain, developers can create AI systems capable of understanding, reasoning, and interacting in ways that closely mimic human communication. This capability is crucial for applications requiring a deep comprehension of language and context.

The significance of LangChain extends to its potential impact across multiple industries. In areas such as customer service, content creation, and data analysis, LangChain can enhance the efficiency, accuracy, and contextual relevance of AI interactions. Its ability to bridge the communication gap between humans and machines opens up new possibilities for integrating AI into everyday tasks and processes. LangChain makes AI more accessible, intuitive, and useful in practical applications.

What are the key components of LangChain?

LangChain is a sophisticated framework comprising several key components that work in synergy to enhance natural language processing tasks. These components enable the system to effectively understand, process, and generate human-like language responses.

LLMs (large language models)

LLMs are the backbone of LangChain, providing the core capability for understanding and generating language. They are trained on vast datasets to produce text that is coherent and contextually relevant.

Prompt templates

Prompt templates in LangChain are designed to efficiently interact with LLMs. They structure the input in a way that maximizes the effectiveness of the language models in understanding and responding to user queries.

Indexes

Indexes in LangChain serve as databases, organizing and storing information in a structured manner so relevant data is retrieved efficiently when the system processes language queries.

Retrievers

Retrievers work alongside indexes. Their function is to quickly fetch the relevant information from the indexes based on the user input query, ensuring that the response generation is informed and accurate.

Output parsers

Output parsers process the language generated by LLMs. They help in refining the output into a format that is useful and relevant to the specific task at hand.

Vector store

The vector store in LangChain handles the embedding of words or phrases into numerical vectors. This is crucial for tasks involving semantic analysis and understanding the nuances of language.

Agents

Agents in LangChain are the decision-making components. They determine the best course of action based on the input, context, development environment, and available resources within the system.

What are the main features of LangChain?

Below is a summary of the most popular features of LangChain, curated to call out some of its best features.

Communicating with models

When building AI applications, you’ll need a way to communicate with a model. You might also want to generate vector embeddings when your app follows a RAG pattern, submit a prompt for an LLM to complete, or open a chat session with the model.

LangChain has specifically focused on common interactions with a model and made it very easy to create a complete solution. Learn more about models and which LLMs LangChain supports.

Typically, the application is going to be doing the same action again and again. The prompt used to communicate with a model will have certain text that stays the same no matter what the real-time data is.

LangChain has a special prompts library to help parameterize common prompt text. That means the prompts can have placeholders for areas that will be filled in right before submitting the LLM. Instead of doing an inefficient string replacement on the prompt, you provide LangChain with a map of each placeholder and its value. It efficiently handles getting the prompt ready for completion.

Learn more about prompts

Once the model has done its job and responded with the completion, there’s a good chance the application will need to perform post-processing. There might be some simple cleanup of characters or (most likely) the completion needs to be included in a parameter of a class object. LangChain's output parsers make quick work of this by offering a deep integration between how the LLM will respond and the application’s custom classes.

Learn more about output parsers

Data retrieval

When a model is created, it's trained with some data set. A model is considered small or large based on the amount of data that was used during training. For example, LLMs like GPT have been trained on the entire public internet. That amount of data deserves the “large” label. The challenge is that no matter how much data a model is trained with, it's still a fixed amount. The flow of data doesn't stop after the model's training is finished.

Retrieval-augmented generation (RAG) is an application design where the application first compares input data to other recent data to gain a better context. Then, that up-to-date context is provided to the LLM for an up-to-date response.

RAG isn’t as simple as making a quick query and continuing to interact with the LLM. There are a few intermediate steps that have to happen. Also, data has to be stored in an understood format so that retrieval is simple and consistent.

LangChain offers a few libraries to aid in implementing the RAG pattern in an application. To gain the proper context, data might need to come from different sources. Each source likely follows a different schema. LangChain offers the “document” object to “normalize” data from multiple sources. As a document, data can easily be passed between different chains in a structured way.

The store used to look up up-to-date context is not your typical relational database. It’s a special version of the database that offers to store and retrieve vectors. Most modern databases like Astra DB offer a vector option. There are also vector-specific databases that are built for only the purpose of storing vector embeddings.

LangChain offers integrations for all of the popular vector stores, including AstraDB. It also takes things further by offering some very convenient features specific to vector search, like similarity search and similarity search by vector.

You can also tune your vector searches with maximum marginal relevance search. This comes in handy when you want to get that context just right - not too much data and not too little data provided to the LLM.

Chat memory

These days chatbots are all the rage.

It’s the “hello world” of AI application development. Thinking about the “chat” concept, when you are chatting with a friend, the conversation has a certain flow to it. As it progresses, you share the history of what has already been said. This helps move the conversation forward.

When an AI application is chatting with an LLM, it needs to give the LLM some historical information (context) about what is being asked. If no context is given, the conversation will be dry and unproductive. In the previous data retrieval section, the RAG pattern was described as a way to add this context.

Sometimes, though, the context needs to be in the moment. When you are chatting with your friend chances are you are not taking notes, trying to remember every word that was said. The conversation becomes a lecture if you were to do this - which is not that much fun. The same goes for a chat with the LLM.

LangChain offers a special memory library where the application can save a chat’s history to a fast lookup store. Typically is not a relational database. It’s usually a NoSQL or in-memory store. Having the ability to quickly retrieve a chat’s history and inject it into the LLM’s prompt is a powerful way of creating very intelligent contextual applications.

How does LangChain work?

The “chain” in LangChain brings the idea of putting AI actions together in order to create a processing pipeline. Each action, or chain, is a needed step in the pipeline to complete the overall goal.

For example, a RAG pattern starts with the user submitting a question. An embedding is created from that text; a lookup is done on a vector database to gain more context about the question, then a prompt is created using both the original question and the context from the retrieval. The prompt is submitted to the LLM, which responds with an intelligent completion of the prompt.

All the steps in the RAG pattern need to happen in succession. If there are any errors along the way, the processing should stop. LangChain offers the “chain” construct to attach steps in a specific way with a specific configuration. All LangChain libraries follow this chain construct to make it easy to move steps around and create powerful processing pipelines.

What are the benefits of using LangChain?

Using LangChain offers several benefits for natural language processing.

Enhanced language understanding and generation

LangChain's integration of various language models, vector databases and tools allows for more sophisticated language processing. This leads to better understanding and generation of human-like language, especially in complex tasks.

Customization and flexibility

The modular structure of LangChain offers significant customization options. This adaptability makes it suitable for various applications, allowing users to tailor language processing solutions to specific needs.

Streamlined development process

LangChain's framework simplifies the development of language processing systems. Chaining different components reduces complexity and accelerates the creation of advanced applications.

Improved efficiency and accuracy

Using LangChain in language tasks enhances efficiency and accuracy. The framework's ability to combine multiple language processing components leads to quicker and more accurate outcomes.

Versatility across sectors

LangChain's adaptability makes it valuable across various sectors, including content creation, customer service, artificial intelligence and data analytics. Its ability to handle diverse language tasks makes it a versatile tool in the AI toolkit.

Examples of LangChain applications

LangChain has been purpose built to facilitate a number of different types of GenAI applications.

Retrieval-augmented generation (RAG)

LangChain offers a few scenarios implementing the RAG pattern. There is a simple question/answer example that is a classic use of adding context to a prompt. There is also the RAG over code example that aids developers while writing code. The Chat History example creates a very intelligent chat experience with the LLM. Visit LangChain’s tutorials for more.

Chatbots

Chatbots are a fundamental application of LLMs, showcasing the practical implementation of AI-driven conversations. LangChain offers a getting started example, to help you create an AI application in just a few minutes.

Synthetic data generation

While developing applications, an engineer writes tests to ensure all their functions are working as expected. These are referred to as unit tests. There are also load tests a developer does to ensure the application can handle the load it will see in production. To run either of these tests you need data. Sometimes lots of data. LangChain's synthetic data generation example uses an LLM to artificially generate data. A developer could then use that data to test their application.

Interacting with a database using natural language

Writing SQL queries is not as much fun as it sounds. As one writes a query, they are taking a real-world question and transforming it into SQL syntax. A simple question like “How many chunks can a woodchuck chuck” would be transformed into “select count(chunk) from chunks where animal like ‘woodchuck'”. LangChain's SQL example uses an LLM to transform a natural language question into a SQL dialect.

While these examples showcase the versatility of LangChain, getting started with such a powerful framework can seem daunting. Fortunately, the LangChain ecosystem includes tools designed to simplify the development process and make it more accessible to developers of all skill levels. One such tool that has gained popularity is Langflow, which provides a visual interface for building LangChain applications.

Using Langflow with LangChain: Low-code RAG app builder

Langflow is a visual IDE (integrated development environment). It’s a game-changer for rapidly building AI pipelines and agents, with prebuilt connections to any API or data source. It’s Python-based, open source, and agnostic.

Langflow significantly lowers the barrier to entry for building LangChain applications.

It's designed to make it easier to prototype and build applications with LangChain, providing a visual approach to what would otherwise be a code-heavy process.

Langflow’s drag-and-drop visual interface makes it easy for developers to create LangChain pipelines and experiment with different components without writing extensive code.

Visualizing the structure and flow of LangChain applications speeds up the development process.

Key features of Langflow include:

1. Visual Builder: Drag-and-drop interface for creating LangChain flows
2. Component Library: Access LangChain components like LLMs, prompts, and agents
3. Real-Time Testing: Test flows directly in the interface
4. Code Export: Export flows as Python code for further development

By combining Langflow with LangChain, you accelerate your development process and more easily explore the capabilities of language models in your applications. Whether you're a beginner learning about LLM applications or an experienced developer looking to quickly prototype ideas, Langflow provides a valuable addition to the LangChain ecosystem.

Getting started with LangChain

Maybe you have an existing Python app that needs to be taught new AI tricks? Or maybe you're starting from scratch. Either way, you'll want to add the LangChain library to the requirements.

pip install langchain
# conda install langchain -c conda-forge

Assuming that you plan to interact with an LLM, the next step would be to install its supporting library. View the full list of LLMs here.

pip install openai

With that, the fun begins!

You could create a PromptTemplate + LLM + OutputParser or you could create a simple Prompt template application. Read more about interaction with models, data retrieval, chains, and memory in LangChain documentation.

Discover how DataStax and LangChain work better together

DataStax provides the real-time vector data and RAG capabilities that GenAI apps need. Seamlessly integrate with stacks of your choice (RAG, LangChain, LlamaIndex, OpenAI, GCP, AWS, Azure, etc).

To get started with LangChain and to see how DataStax and LangChain are better together, check out “Build LLM-Powered Applications Faster with LangChain Templates and Astra DB.”

Find more information on how to set up and deploy Astra DB on one of the world's highest-performing vector stores, built on Apache Cassandra, which is designed for handling massive volumes of data at scale.