LlamaIndex vs LangChain: What's the difference?

RAG equips large language models (LLMs) with domain-specific knowledge to increase the accuracy and utility of the chatbots and other artificial intelligence (AI) apps they power. While LlamaIndex and LangChain have overlapping use cases, they each approach the task of RAG system creation in differing ways. 

LlamaIndex focuses on indexing, data ingestion and information retrieval from text-based data sources, making it ideal for simpler workflows and straightforward AI applications. Meanwhile, LangChain’s modular framework excels in the building of a wide range of natural language processing (NLP) and agentic AI applications.

RAG is a technique that connects LLM applications with proprietary data sources. RAG-powered data retrieval expands a machine learning (ML) model’s knowledge base beyond its training data. RAG gives generative AI models access to domain-specific knowledge, such as an organization’s internal data, they would otherwise lack. 

RAG systems respond to user queries by retrieving relevant information from designated data sources in real time, then augmenting the LLM’s generative capabilities for better answers. RAG-equipped models generate more accurate answers with a higher degree of contextual relevance. 

When time and cost constraints prevent lengthy fine-tuning processes, RAG can be an efficient and convenient alternative—though the two techniques can be combined for even stronger performance.

LlamaIndex, formerly known as GPT Index, is an open-source framework for data ingestion, indexing and retrieval. Available in Python and TypeScript, it enables users to connect LLMs with large datasets and create retrieval applications.  

LlamaIndex’s primary use case is document management and utility: it applies the power of LLMs to automate the process of organizing, querying and summarizing documents. By transforming huge textual datasets into easily queryable indexes, LlamaIndex streamlines RAG-enabled content generation.

LlamaIndex is structured around a core set of key components, which include: 

• Data ingestion and LlamaHub

• Data indexing

• Querying and retrieval 

• Postprocessing 

• Response synthesis

Data ingestion is the first stage of the LlamaIndex RAG pipeline. In LlamaIndex, this stage is known as data loading and uses data loaders, or data connectors, to fetch and process data. LlamaIndex covers over 160 types of data formats and supports data structures including structured, semistructured and unstructured data. 

When using LlamaIndex for retrieval tasks not covered by a single source of data, users can turn to LlamaHub: a versatile, open-source pool of data loaders covering a range of specific needs. LlamaHub users can blend multiple data sources, such as APIs, SQL databases and even Google Workspaces, into a single workflow. Some LlamaHub connectors even support audio and video files.

After all the data sources have been connected, the next step in the LlamaIndex workflow is data indexing: making sense of all the data and organizing it for future use. LlamaIndex uses embeddings to convert user-supplied data into a searchable, vector-based data index. Embedding models are AI algorithms that convert data into mathematical vectors plotted within a multidimensional vector database. 

The embedding process captures semantic relationships between data points to enhance context-aware searches. The user’s query is also converted into an embedding. Query engines can identify data points that are similar in meaning to the query, enhancing the search for relevant information. 

LlamaIndex can compose an index from other indexes to build complex workflows and queries with preexisting indexes. Rather than query separate vector stores one by one, users can create a combined index to make queries as efficient as possible.

When querying, the user’s query is also converted into an embedding. Query engines can identify data points that are similar in meaning to the query, aiding in the search for relevant information. LlamaIndex uses the principle of semantic similarity to retrieve the most relevant data from the vector stores in response to the query. 

LlamaIndex’s query transformation feature simplifies complex queries or breaks them up into more manageable queries. Query transformation is an optimization technique that increases the RAG system’s chances of finding the most relevant data and generating the best possible response.

After the relevant data has been retrieved, its component segments, or nodes, can be reranked and filtered to further enhance the forthcoming response. Node postprocessing makes it possible to handle data sources that require further structuring and transformation postretrieval.

The retrieved data is combined with the query and the original user prompt and passed to the connected LLM, which then generates a response.

LangChain is an agentic AI app creation framework with a suite of modular options that enable users to “chain” components together into complex workflows. LangChain supports the import of nearly any gen AI model, including OpenAI’s GPT and Anthropic’s Claude, with a focus on end-to-end automation. LangChain supports Python and JavaScript libraries.

The LangChain platform centers around a core suite of unique features designed to streamline the prototyping and creation of agentic AI applications such as chatbots and virtual agents. 

• Models 

• Prompt templates 

• Indexes 

• Memory 

• Tools 

• Chains 

• Agents 

• LangSmith and LangServe

LangChain’s standardized user interface simplifies the process of interacting and working with numerous LLMs. Model access is typically obtained through the provider’s API, which can cost the user money depending on the provider. 

If using a chat-based model, LangChain clarifies communication to and from the model with four message classifications. HumanMessage represents human-created messages, and AIMessage denotes messages from the AI model. SystemMessage is for instructional content to the model, while ChatMessage is a more niche option for role-setting.

LangChain offers a range of prompt templates to simplify the process of communicating with LLMs. Users can customize prompts as needed and recycle them between applications. LangChain’s prompt templates are geared to engender context-aware outputs from the LLMs in the chain.

Like other RAG platforms, LangChain connects LLMs with external data sources. It achieves this goal through the creation of vector-based indexes. LangChain’s library of document loaders can pull data from external sources including Google Workspaces, online collaboration tools such as Figma, web content including YouTube videos, databases and more. 

LangChain supports over 25 embedding methods to convert and load this data into vector stores. Users can split documents into “chunks” to make vector database queries even more efficient.

One of LangChain’s standout features is its memory capability. LangChain enables LLMs to refer to previous interactions and add that context to current and future conversations. When implementing memory management in LangChain, users can choose to retain entire conversations, summarize past conversations or keep a selected number of the most recent exchanges.

Tools in LangChain are functions, such as search engines and various APIs, that models can use to fulfill tasks in the chain. For example, a model that needs to include weather forecasts in its workflow can connect to a service provided by a meteorological agency.

Chains connect LLMs to other tools and make autonomous workflows possible within LangChain. If LLMs and tools are the objects in a flowchart, chains are the arrows and lines linking them together. Users can combine chains in a workflow, with each chain potentially including different prompt templates, models, tools and parameters.

AI agents are autonomous models that determine a course of action depending on current conditions and the input they receive. Rather than have a human prompt every stage of the process, as one experiences when interacting with a chatbot, agents work toward carrying out entire workflows autonomously. Users can deploy LangChain’s premade agents as-is or customize as needed.

LangSmith is LangChain’s evaluation suite, while LangServe covers deployment. LangSmith offers a range of testing and optimization features to aid users in evaluating their apps. Users can manually create test datasets, compile them from user feedback or generate them with LLMs. 

After evaluation, users can deploy their apps through LangServe, which converts chains into APIs. LangSmith can be combined with LangServe to provide monitoring over time, essential adherence to explainable AI standards.

LlamaIndex and LangChain both allow users to build RAG-enabled LLM applications, but offer two distinct approaches to the project. While LlamaIndex shines when querying databases to retrieve relevant information, LangChain’s broader flexibility allows for a wider variety of use cases, especially when chaining models and tools into complex workflows.

LlamaIndex is ideal for straightforward RAG applications with a lighter development lift. It excels with efficient, precise data retrieval based on semantic relevance. Its strengths include: 

• Search and retrieval apps: LlamaIndex’s efficient data storage and focus on data retrieval based on semantic similarity makes it a good choice for streamlined RAG applications. Use cases include internal organizational reference systems and knowledge management. 

• Speed and precision: With its advanced search algorithms, LlamaIndex is optimized for efficient data retrieval with a high degree of accuracy. 

• Minimal, streamlined app development: LlamaIndex’s tight focus leads to an efficient app creation process. Users can get up and running with RAG applications in minimal time. 

• Hierarchical documents: LlamaIndex is a good choice for text-heavy projects, such as implementing a knowledge management system within an enterprise in which document hierarchy is paramount.

LangChain’s focus on multipurpose use, customizability and versatility leads to a broader set of use cases. Users can chain multiple models and tools together to fit the needs of their projects, all from within a single user interface. 

LangChain’s advantages include: 

• Diverse use cases: LangChain is a sandbox of LLMs, tools and integrations, and users can chain them together to fit specific project requirements. 

• Multimodal data sources: While LlamaIndex can support images and text, LangChain’s media support is much more versatile. Apps created in LangChain can load data from online video and APIs along with images and PDFs. 

• Granular control: LangChain’s brick-by-brick approach to app creation gives users maximum control over functionality at every step of each chain in the process. 

• Context retention: Sophisticated memory management capabilities mean that apps created in LangChain can refer to previous interactions and maintain accuracy over longer conversations. 

• Complex queries and data structures: While LlamaIndex is built for semantic similarity, LangChain allows users to combine search techniques, such as by adding keyword search. It also more capably handles complex data structures with its modular interface, multimodal support and numerous integrations.