Introduction to LangChain
A guide to LangChain, including its definition, workflow, benefits, use cases, and available resources to get started.
Read the entire series
- OpenAI's ChatGPT
- Introduction to LangChain
- Falcon 180B: Advancing Language Models in the AI Frontier
- LangChain Memory: Enhancing AI Conversational Capabilities
- OpenAI Whisper: Transforming Speech-to-Text with Advanced AI
- Unlocking the Secrets of GPT-4.0 and Large Language Models
- Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning
- Large Language Models and Search
- What are Private LLMs? Running Large Language Models Privately - privateGPT and Beyond
- Mastering Cohere's Reranker for Enhanced AI Performance
- Top LLMs of 2024: Only the Worthy
Introduction to LangChain
LangChain stands at the forefront of large language model-driven application development, offering a versatile framework that revolutionizes how we interact with text-based systems. At its heart, LangChain empowers applications to seamlessly integrate large language models, enabling context awareness and effective reasoning to deliver grounded responses. By bridging the gap between language models and contextual cues, LangChain facilitates informed decision-making, unlocking a new realm of possibilities for developers. The framework comprises key components meticulously crafted to streamline development and deployment processes:
- LangChain Libraries: These Python and JavaScript libraries provide the backbone for LangChain, offering a comprehensive suite of interfaces and integrations. These libraries empower developers to assemble intricate chains and agents effortlessly, with prebuilt implementations catering to diverse tasks.
- LangChain Templates: A repository of deployable reference architectures simplifies application development, offering streamlined solutions for various use cases. With LangChain Templates, developers can confidently kick start projects.
- LangServe: A dedicated library tailored to deploy LangChain chains as RESTful APIs. LangServe facilitates effortless integration into existing systems, ensuring compatibility and scalability for diverse application environments.
- LangSmith: A hosted developer platform designed to enhance the development and management of language-powered applications. Offering robust debugging, testing, evaluation, and monitoring capabilities, LangSmith empowers developers to fine-tune their creations with precision. Seamlessly integrating with any large language model (LLM) framework, LangSmith improves the capabilities of LangChain, driving innovation and efficiency in language-centric development endeavors.
Getting Started with LangChain
Getting started with the base installation package of LangChain for Python is quick and easy. The base install is barebones and does not include any of the dependencies for specific integrations you may be interested in. Please note, that the default LLM used with LangChain is OpenAI and you will need to have an OpenAI API key.
Pip
pip install langchain
If you are interested in a package containing their-party integrations, use the following community version:
pip install langchain-community
You can also create a free LangSmith account from the LangChain website.
LangChain Features
LangChain offers features designed to empower developers in building sophisticated language-powered applications. Here's a detailed explanation of the core features of LangChain:
- Context-Aware Responses: LangChain enables applications to generate context-aware responses by connecting large language models to various context sources, often stored in a vector database. This feature allows applications to understand and respond to user queries more meaningfully and reasonably.
- ** Effective Reasoning:** With LangChain, applications can leverage language models to make informed decisions based on the provided context. This feature enables applications to reason effectively and deliver more accurate and insightful responses to user queries.
- Integration Flexibility: LangChain provides flexible integration options, allowing developers to integrate language models into their applications seamlessly. Whether through prompt instructions, few-shot examples, or other sources of context, LangChain offers a variety of integration methods to suit different use cases.
- Customizable Components: LangChain offers customizable components that developers can use to tailor the framework to their needs. From prebuilt implementations to customizable templates, LangChain provides developers with the tools to build powerful language-powered applications.
What problem does LangChain solve?
LangChain solves the problem of developing applications that require sophisticated language understanding and reasoning capabilities. In many natural language processing (NLP) tasks, such as chatbots, virtual assistants, or content summarization systems, understanding the context and nuances of human language is crucial for providing accurate and relevant responses and avoiding presenting reasonable but false answers (AI Hallucinations) to users. However, building such applications from scratch can be complex and time-consuming, especially when integrating large language models (LLMs) and managing contextual information.
LangChain addresses this challenge by providing a versatile framework that facilitates the development of applications powered by language models. It enables developers to create context-aware applications that can connect language models to various sources of context, such as prompt instructions, few-shot examples, or other content. By leveraging LangChain and a vector database, developers can build applications that can comprehend and respond to natural language input more effectively, thereby improving user interactions and overall user experience.
How to Use LangChain by Use Case
There are many use cases supported by LangChain. In this section, we will review three of the most popular use cases: Retrieval Augmented Generation (RAG), RAG Chatbot, and Summarization.
Retrieval Augmented Generation (RAG)
One of the techniques that LangChain can help you use with large language models is Retrieval Augmented Generation (RAG). RAG enables developers to build applications that understand and respond to user queries with depth and accuracy. RAG blends information stored and retrieved from a vector database with large language models to generate a response. By leveraging external knowledge, this approach uses relevant information from vast knowledge sources to produce more accurate, coherent, and contextually relevant outputs.
RAG use case with LangChain and Zilliz Cloud
- Start by pulling together your collection of documents you want to use for your RAG solution. LangChain has over 100 different document loaders for all types of documents (html, pdf, code), from all types of locations (S3, public websites) and integrations with AirByte and Unstructured.
- Next, split the text up into small, semantically meaningful chunks (often sentences). There are two different axes along which you can customize your text splitter: How the text is split or How the chunk size is measured. In addition, there are different types of text splitters when you use LangChain. They are characterized by Name, Splits On (how the text is split), Adds Metadata (whether or not metadata is added) and Description (this also includes recommendations on use case).
- Generate embeddings of your splits and store them in a vector database like Zilliz Cloud or open source Milvus. There are a number of text embedding models you can choose from, please note that the default for LangChain is OpenAI which will require an OpenAI key and a cost. During the prototype phase, you might want to consider an open source model instead.
- At query time, the query is converted into a vector embedding (Note: use the same model that you used for your text chunks that you originally stored) which is then used to trigger a semantic search in the vector database. The response is then sent to the LLM to generate a reliable answer.
LangSmith trace for RAG use case with Zilliz.png
Chatbot
Chatbot with RAG use case with LangChain and Zilliz Cloud
The Chatbot utilizes the RAG technique discussed earlier, integrating the concept of "Memory" to enhance its functionality. Unlike traditional RAG use cases, the Chatbot with RAG incorporates a memory feature, enabling it to recall and incorporate past conversations into current interactions. For instance, if a user inquires about a topic previously discussed, the Chatbot can retrieve the summary of that conversation and incorporate it into the ongoing dialogue. By storing past chat sequences in a vector database, the Chatbot can efficiently retrieve and leverage historical information to enrich present interactions, even if the conversation occurred days or weeks earlier.
LangSmith trace for RAG Chatbot use case | Zilliz
Summarization
Summarization use case with LangChain and Zilliz Cloud
- For the Summarization use case, there are three things to add to your implementation so you can ensure a good user experience.
- If the content is small enough to fit in the LLM’s context window, you can simply send it in its entirety to the LLM with the prompt to generate the summary
- If the content is too large for the context window, you can split the text, generate the embeddings, and store in a vector database. You can then conduct a semantic similarity search and use the results with your prompt and LLM to generate the summary (similar to the RAG approach discussed above).
- Finally, you can also use the Map Reduce approach available with LangChain where you can send a distilled version of the content to the prompt and LLM.
LangChain & Zilliz Cloud and Milvus Resources
- Building an Open Source Chatbot Using LangChain and Milvus in Under 5 Minutes
- How LangChain Implements Self Querying
- Retrieval Augmented Generation on Notion Docs via LangChain
- Experimenting with Different Chunking Strategies via LangChain
- Using LangChain to Self-Query a Vector Database
- Prompting in LangChain
- Enhancing ChatGPT's Intelligence and Efficiency: The Power of LangChain and Milvus
- Query Multiple Documents Using LlamaIndex, LangChain, and Milvus
- Prompting in LangChain
- GPTCache, LangChain, Strong Alliance
- Ultimate Guide to Getting Started with LangChain
- LangChain and Zilliz Cloud and Milvus Integration
Frequently Asked LangChain Questions (FAQs)
What does LangChain do?
LangChain is a framework designed to facilitate the development of applications powered by language models. It enables applications to be context-aware, connecting language models to various sources of context for grounded responses. Additionally, LangChain empowers applications to reason effectively by leveraging language models to make informed decisions based on the provided context.
What problem does LangChain solve?
LangChain addresses the challenge of developing applications that require sophisticated language understanding and reasoning capabilities. By providing a framework that seamlessly integrates with large language models and allows for contextual awareness, LangChain enables developers to build applications that can comprehend and respond to natural language input more effectively.
What is the difference between an LLM and LangChain?
While a Large Language Model (LLM) focuses on the language understanding and generation capabilities, LangChain serves as a framework that enables the integration and orchestration of LLMs within applications. In other words, LLMs provide the language processing capabilities, while LangChain provides the infrastructure and tools to harness and utilize these capabilities in various applications.