End to End Examples

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

Check out the examples below or try them out and complete them in minutes with interactive Github Codespace tutorials provided by Dev-Docs here:

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.

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.

JSON Entity Extraction

Features OpenAI's chat API (using json_mode) to extract a JSON object from a sales call transcript.