LlamaIndex is a central interface to connect your LLM's with external data. It provides a suite of in-memory indices over your unstructured and structured data for use with ChatGPT. Unlike standard vector databases, LlamaIndex supports a wide range of indexing strategies (e.g. tree, keyword table, knowledge graph) optimized for different use-cases. It is light-weight, easy-to-use, and requires no additional deployment. All you need to do is specifying a few environment variables (optionally point to an existing saved Index json file). Note that metadata filters in queries are not yet supported.
Currently, LlamaIndex requires no additional deployment and runs as a part of the Retrieval Plugin. It is super easy to setup and great for quick prototyping with ChatGPT and your external data.
Retrieval App Environment Variables
| Name | Required | Description |
|---|---|---|
DATASTORE |
Yes | Datastore name. Set this to llama |
BEARER_TOKEN |
Yes | Your secret token |
OPENAI_API_KEY |
Yes | Your OpenAI API key |
Llama Datastore Environment Variables
| Name | Required | Description | Default |
|---|---|---|---|
LLAMA_INDEX_TYPE |
Optional | Index type (see below for details) | simple_dict |
LLAMA_INDEX_JSON_PATH |
Optional | Path to saved Index json file | None |
LLAMA_QUERY_KWARGS_JSON_PATH |
Optional | Path to saved query kwargs json file | None |
LLAMA_RESPONSE_MODE |
Optional | Response mode for query | no_text |
Different Index Types
By default, we use a GPTVectorStoreIndex to store document chunks in memory,
and retrieve top-k nodes by embedding similarity.
Different index types are optimized for different data and query use-cases.
See this guide on How Each Index Works to learn more.
You can configure the index type via the LLAMA_INDEX_TYPE, see here for the full list of accepted index type identifiers.
Read more details on readthedocs, and engage with the community on discord.
You can launch the test suite with this command:
pytest ./tests/datastore/providers/llama/test_llama_datastore.py