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End to End Examples

We include several end-to-end examples using LlamaIndex.TS in the repository

Chat Engine​

Read a file and chat about it with the LLM.

Vector Index​

Create a vector index and query it. The vector index will use embeddings to fetch the top k most relevant nodes. By default, the top k is 2.

Summary Index​

Create a list index and query it. This example also use the LLMRetriever, which will use the LLM to select the best nodes to use when generating answer.

Save / Load an Index​

Create and load a vector index. Persistance to disk in LlamaIndex.TS happens automatically once a storage context object is created.

Customized Vector Index​

Create a vector index and query it, while also configuring the the LLM, the ServiceContext, and the similarity_top_k.

OpenAI LLM​

Create an OpenAI LLM and directly use it for chat.

Llama2 DeuceLLM​

Create a Llama-2 LLM and directly use it for chat.

SubQuestionQueryEngine​

Uses the SubQuestionQueryEngine, which breaks complex queries into multiple questions, and then aggreates a response across the answers to all sub-questions.

Low Level Modules​

This example uses several low-level components, which removes the need for an actual query engine. These components can be used anywhere, in any application, or customized and sub-classed to meet your own needs.