LlamaIndex

Similar to Semantic Kernel and LangChain, LlamaIndex is a programming data framework for applications that use LLMs, allowing one to ingest, manage, and retrieve not only domain-specific data (such as industry-specific) but also private data using natural language. LlamaIndex is Python-based.

LlamaIndex has two main stages: the indexing stage and the querying stage, which can be incorporated into an LLMOps process, and we will cover this a bit later:

• Indexing stage: In this stage, LlamaIndex creates a vector index of your private data. This makes it possible to search through your own organization’s domain-specific knowledge base. You can input text documents, database records, knowledge graphs, and other data types.

• Querying stage: In this stage, the RAG pipeline finds the most relevant information based on the user’s query. This information is then passed to the LLM, along with the query, to generate a more accurate response.

Finally, LlamaIndex has three main components:

• Data connectors: They allow you to pull data from wherever it is stored, such as APIs, PDFs, databases, or external apps, such as Meta or X.

• Data indexes: The data index component organizes your data so that they are readily available.

• Engines: The heart of this is the engine component, which enables you to use natural language to interact with your data and create applications, agents, and workflows. We will cover exactly what agents and workflows are in the next section.

Now, the question arises: when should each be used? SK, Langchain, and LlamaIndex are architecturally distinct. SK and Langchain are broader frameworks that excel in scenarios requiring more complex interactions with agents and adding that AI orchestration layer when building chatbots.

Conversely, LlamaIndex stands out in RAG-based search-focused applications due to its optimization for swift and efficient search capabilities. Employing unique indexing methods significantly improves the pace of data retrieval.

If you would like to see more details on LlamaIndex, you can visit the following link: https:// docs.llamaindex.ai/en/stable/.