LLM Frameworks | LlamaIndex

Docs: https://ts.llamaindex.ai/
GitHub: https://github.com/run-llama/LlamaIndexTS
Community: Discord : https://discord.gg/dGcwcsnxhU Twitter: https://twitter.com/llama_index

LlamaIndex is a framework for building context-augmented generative AI applications with LLMs, including agents and workflows.

Pricing

LlamaIndex pricing is based on the computational costs of running models, data processing, and storage, allowing for flexible cost management depending on the complexity and scale of your AI application.

Things To Consider

LlamaIndex’s documentation is limited, with most content focused on examples. If your use case isn’t covered, you may need to dig into the source code, as I had to when implementing metadata filters.

Benefits

LlamaIndex effectively manages the chunking process for large text inputs.
It supports sophisticated retrieval techniques, such as breaking down a question into sub-questions or conducting multiple queries against the LLM, with the ability to self-evaluate and iterate if the initial output isn't optimal.
LlamaIndex provides built-in embedding retrieval, which is especially useful if your vector store doesn't natively support this feature.
It allows for the option to stream responses from the LLM in real-time, giving a more interactive experience similar to ChatGPT's web version, where text appears as it's being typed.
The framework supports shorter, more efficient implementations, streamlining the overall development process.