Building AI applications requires having easy, fast access to data. The more you can simplify basic tasks such as accessing private data and interacting with foundational models, the faster you can build powerful, production-ready AI apps.
Both LlamaIndex and LangChain reduce the effort required to build AI apps - just in different ways. LlamaIndex offers basic context retention capabilities suitable for simple tasks, while LangChain provides advanced context retention features essential for applications requiring coherent and relevant responses over extended conversations.
In this article, we’ll look at what both frameworks do, their similarities and differences, and how to leverage both to assemble AI apps in less time.
What is LlamaIndex for large language models?
LlamaIndex is an orchestration framework for Python and TypeScript that simplifies integrating private and public data together to build AI apps off of an existing large language model (LLM). It is a data framework specifically designed to enhance the capabilities of large language models (LLMs). Using LlamaIndex, you can integrate internal data from a variety of sources and convert it into vector format for easy search and retrieval.
In other words, LlamaIndex focuses primarily on retrieval-augmented generation (RAG). LLMs are trained on generalized data that are typically several years out of date. RAG supplements the foundational data provided by LLMs with recent, domain-specific information from your internal data stores. Data indexing is a critical process that enables efficient organization and retrieval of various types of data, transforming unstructured and structured data into numerical embeddings to enhance semantic understanding.
The challenge with supplying data for RAG is that the relevant data is usually unstructured and siloed. It’s often in undocumented APIs or formats such as PDF, NoSQL, etc. LamaIndex supports RAG through a multi-step process:
- Extracting: Use structured data extraction to pull out key data from unstructured sources using Pydantic expressions.
- Indexing: Create vector embeddings, which capture semantic relationships and similarities between data points using numerical representations.
- Querying: Leverage LlamaIndex’s query engine to perform natural language queries over this data, which you can include as context in an LLM prompt. Natural language query support means AI app developers don’t have to learn a domain-specific query language - not even SQL - to access data.
LlamaIndex also supports agents, which simplify building complex AI apps by orchestrating calls between LLMs and LlamaIndex query engines.
What is LangChain for retrieval algorithms?
LangChain is a programming framework, available in both Python and JavaScript, that application developers use to compose new AI applications from basic building blocks. The framework supports stringing together a number of different components using a straightforward, low-code expression syntax. Using LangChain, developers can create extremely sophisticated AI apps capable of understanding and reasoning, with responses that closely mimic human communication. LangChain integrates retrieval algorithms with large language models (LLMs) to enhance context-aware outputs.
For example, when calling an LLM, you typically need at least three components: a prompt to the LLM, a call to the LLM itself, and an output parser to read the return result. Traditionally, you’d code all of this yourself from scratch. Using the LangChain Expression Language (LCEL), you can instantiate these components as classes and compose your application as a series of pipes:
chain = prompt | llm | output_parser
LangChain supports calling and retrieving responses from dozens of the most popular LLMs. This simplifies switching out LLMs based on performance and capabilities, as well as chaining multiple LLMs together to implement advanced functionality. Examples include implementing prompt chaining or using the output from a specialized LLM as the input to another.
LangChain supports a large number of components in addition to LLMs, including memory, prompt templates, indexes, vector stores, metadata, and decision-making agents. The importance of 'load data' is emphasized in the context of integrating diverse data sources into applications. LangChain provides various data connectors that facilitate the process of loading data from different formats and repositories, making it easy to ingest data from multiple sources like Google Docs, PDF files, and other databases.
LangChain Templates make constructing applications like chatbots, structure data extraction tools, and agents even easier by providing pre-built reference architectures for common use cases.
Key features and components
Both LlamaIndex and LangChain offer features and components that make them powerful tools for building AI applications. Understanding these key aspects helps developers choose the right framework.
LlamaIndex features
LlamaIndex handles search and retrieval tasks with robust data extraction, indexing, and querying capabilities. One of its standout features is the ability to extract structured data from unstructured sources using Pydantic expressions. This makes it easier to pull key information from formats like PDFs, NoSQL databases, and undocumented APIs.
Once the data is extracted, LlamaIndex converts it into vector embeddings, capturing semantic relationships and similarities between data points. This indexing process is crucial for efficient data retrieval, allowing developers to perform natural language queries over the indexed data. The query engine in LlamaIndex supports these natural language queries, giving developers access to relevant documents without needing to learn complex query languages.
LlamaIndex supports agents, orchestrating calls between large language models and the LlamaIndex query engine. This simplifies the development of complex AI applications by automating the interaction between different components, making it a powerful tool for search and retrieval applications.
LangChain components
LangChain’s comprehensive components are designed to streamline AI-powered applications. One of its core strengths is integrating multiple large language models, so developers can switch between LLMs based on performance and capabilities. This flexibility is essential for creating sophisticated AI applications that require different types of reasoning and understanding.
LangChain also supports a variety of other components, including memory, prompt templates, indexes, vector stores, metadata, and decision-making agents. These components are easily composed using the LangChain Expression Language (LCEL): developers build complex applications with minimal code.
The framework’s support for retrieval algorithms and data connectors further enhances its capabilities, making it easier to integrate and reuse custom components across different AI applications. LangChain handles indexing and retrieval tasks, and its support for multiple tools makes it a versatile choice for developers looking to build advanced AI solutions.
LlamaIndex vs. LangChain: Similarities
You’ve probably already noticed some overlap between LlamaIndex and LangChain. Both frameworks simplify accessing the data required to drive AI-powered apps. Because of this, there’s some shared feature overlap between LlamaIndex and LangChain. For example, both support composing AI applications using agents that can integrate with LLMs and RAG data sources.
From a mile-high viewpoint, both LlamaIndex and LangChain aim to simplify building AI apps by removing undifferentiated heavy lifting. They abstract away some of the common problems that every AI app builder has to solve.
Both frameworks also make it easier for application developers who aren’t data scientists or AI experts to perform common AI tasks - e.g., utilizing structured and unstructured data across the organization. This democratizes AI app development, decreasing ramp-up time for developers with less experience building data-driven apps.
LlamaIndex vs. LangChain: Differences
That said, LlamaIndex and LangChain solve slightly different problems and with different approaches.
LlamaIndex shines as a framework for extracting, indexing, and querying data from various sources. It simplifies solving the universal problem of how to repurpose the data your organization already has locked away in various siloes and making it available for use in an LLM.
LlamaIndex provides fast, efficient access to petabyte-scale data, no matter where it originally lives or how it’s formatted. Its repository for data connectors, LlamaHub, hosts hundreds of connectors supporting different systems and document formats. LlamaHub excels in integrating multiple data sources into application workflows, emphasizing the convenience of using various data loaders to handle diverse formats and repositories.
By contrast, LangChain’s primary focus is on building AI-powered apps themselves. In other words, it focuses on doing something with that data - e.g., generating code, driving decisions, or answering customer’s questions.
LangChain’s power lies in creating abstractions for input and output processing for AI components, such as LLMs and agents. Using these abstractions, developers can string together complex apps with just a few lines of code. LangChain also provides a framework for integrating and reusing custom components (e.g., internal systems) across your company’s AI-powered apps.
Using LlamaIndex and LangChain together for retrieval-augmented generation
In short, LlamaIndex is best used for search-intensive apps that require a large amount of data and rapid data processing. LangChain is best used for prototyping and deploying production-ready LLM and RAG-powered apps with a number of components that may need to change and evolve rapidly over time.
However, the two frameworks aren’t mutually exclusive. You can use both in tandem, leveraging their unique strengths. LlamaIndex is a data framework specifically designed to enhance the capabilities of large language models (LLMs) and can be used in tandem with LangChain.
LlamaIndex makes this easier by providing direct support for LangChain. You can use LlamaIndex data loaders as on-demand query tools from within a LangChain agent. The following code snippet from the LlamaIndex documentation shows how you would call a vector index built with LamaIndex to obtain data via RAG for use in an LLM query made with LangChain:
from llama_index.core.langchain_helpers.agents import (
IndexToolConfig,
LlamaIndexTool,
)
tool_config = IndexToolConfig(
query_engine=query_engine,
name=f"Vector Index",
description=f"useful for when you want to answer queries about X",
tool_kwargs={"return_direct": True},
)
tool = LlamaIndexTool.from_tool_config(tool_config)You can also utilize LamaIndex as a memory module in LangChain to give additional context to LangChain apps - e.g., adding arbitrary amounts of conversation history to a LangChain-powered chatbot.
Use cases and applications
Both LlamaIndex and LangChain are designed to address a wide range of use cases and applications, particularly in the realm of data retrieval and AI-powered decision-making. By leveraging their unique features, developers can create powerful applications that solve real-world problems.
Retrieval-augmented generation
Retrieval-augmented generation (RAG) is a technique that enhances the performance of large language models by supplementing their foundational data with recent, domain-specific information. This approach is particularly useful for generating contextually relevant responses in applications such as chatbots, virtual assistants, and customer support systems.
LlamaIndex supports RAG by providing efficient data extraction, indexing, and querying capabilities. By converting internal data into vector embeddings and enabling natural language queries, LlamaIndex ensures that the most relevant documents are retrieved and used to augment the responses generated by large language models.
LangChain, on the other hand, integrates retrieval algorithms and supports the chaining of multiple LLMs to implement advanced RAG techniques. By combining the strengths of different LLMs and leveraging LangChain’s components, developers can create applications that deliver accurate and contextually relevant responses.
In summary, both LlamaIndex and LangChain offer powerful tools for implementing retrieval augmented generation, making them invaluable for developers looking to enhance the performance of their AI applications. Whether it’s for document search, data management, or generating human-like responses, these frameworks provide the necessary components to build sophisticated and efficient AI solutions.
Using DataStax with LlamaIndex and LangChain
Like LlamaIndex and LangChain, DataStax enables you to build AI-powered applications faster than ever before. DataStax provides a real-time API for RAG and an opinionated data stack for building faster and more accurate AI apps with less overhead.
DataStax provides full support for both LangChain and LlamaIndex. Using LangChain, you can build out RAG-enabled apps using Astra DB, our Apache Cassandra-powered database service that enables low-latency access to vector data, by adding it as a RAG provider to a LangChain Template. You can build your LangChain apps with Python or JavaScript from the ground up or leverage LangFlow to build LangChain apps visually.
You can also use Astra DB in conjunction with LlamaIndex using the Astra DB LlamaIndex connector. Create a query engine connection from LlamaIndex to Astra DB to query your Astra DB vector data using LlamaIndex’s natural language query capabilities:
query_engine = index.as_query_engine()
query_engine.query('What sort of experiments did Galileo conduct?')DataStax simplifies the process of loading data from multiple sources like Google Docs, PDF files, and other databases, making data ingestion seamless. You don’t need to write code to take advantage of LangChain, Astra DB, and LlamaIndex.
DataStax offers Langflow for visually building GenAI apps. It currently supports Astra DB and LangChain as a part of its visual Integrated Development Environment (IDE).
Langflow is a tool-agnostic platform. That means you can use it with any tool or software package you choose—including LlamaIndex.
Conclusion
LlamaIndex and LangChain both reduce a lot of the heavy lifting required to prototype, develop, test, and deploy new AI applications. Used in conjunction with DataStax, you can democratize AI app dev, leveraging components for accessing unstructured data, implementing advanced RAG techniques, handling LLM request/response semantics, and building agents.
Learn more about our LangChain integrations on the DataStax Integrations page, and dive deeper into Langflow.
