LiteParse v2.0 is out now, and it is blazing fast + runs everywhere!

We rewrote everything from scratch in Rust, and now:

• up to 100x faster parsing
• install natively in Rust, JS/TS, and Python
• a custom WASM package enables browser and edge runtime usage

pip install liteparse

npm i ＠llamaindex/liteparse

npm i ＠llamaindex/liteparse-wasm

cargo install liteparse

Blog: llamaindex.ai/blog/liteparse-v2-0-runs-everywhere?utm_medium=socials&utm_source=twitter&utm_campaign=2026-may-

Repo: github.com/run-llama/liteparse

We recently added 3 finetuning projects 🔥

• Finetuning embeddings
• @OpenAI finetuning gpt-3.5-turbo to distill GPT-4
• Finetuning Llama 2 for text-to-SQL

We now have a brand-new guide ✨showing how to include all these components when building RAG:

gpt-index.readthedocs.io/en/latest/end_to_end_tutorials/finetuning.html

Finetuning embeddings: github.com/run-llama/finetune-embedding

Finetuning gpt-3.5-turbo:

colab.research.google.com/drive/1vWeJBXdFEObuihO7Z8ui2CAYkdHQORqo?usp=sharing

We now have the most comprehensive cookbook on building LLMs with Knowledge Graphs (credits @wey_gu).

• Key query techniques: text2cypher, graph RAG
• Automated KG construction
• vector db RAG vs. KG RAG

Check out the full 1.5 hour tutorial:

The full Colab notebook is here:

There was so much content beyond the live webinar that we recorded a part 2 🔥

We stitched it together in the video.colab.research.google.com/drive/1tLjOg2ZQuIClfuWrAC2LdiZHCov8oUbs?usp=sharing

To reiterate, there’s a ton of content in here - it basically qualifies as a mini-course 🧑‍🏫

First, we learn the concepts through helpful visual explanations and links.

Learn both about KGs and the traditional RAG stack.

Introducing “One-click Observability” 🔭

With one line of code, you can now seamlessly integrate @llama_index with rich observability/eval tools offered by our partners (@weights_biases, @arizeai, @truera_ai).

Easily debug/eval your LLM app for prod 💪 https://t.co/tia41IgsT6gpt-index.readthedocs.io/en/latest/end_to_end_tutorials/one_click_observability.html

[1] @weights_biases Prompts lets users log/trace/inspect the LlamaIndex execution flow during index construction/querying.

You automatically get traces, and can also choose to version/load indices.

https://t.co/iGDkmxybzggpt-index.readthedocs.io/en/latest/end_to_end_tutorials/one_click_observability.html

[2] OpenInference (@arize_ai) is a standard for capturing/storing AI model inferences.

It allows you to experiment/visualize LLM apps using observability tools like @arize_phoenix.

Check out the notebook here! https://t.co/aT9PAP3jGhgpt-index.readthedocs.io/en/latest/examples/callbacks/OpenInferenceCallback.html

Tip for better RAG systems💡: don’t just store raw text chunks, augment them with structured data.

• Enables metadata filtering
• Helps bias embeddings

Here’s a guide on how to use the @huggingface span-marker to extract entities for this exact purpose📕: https://t.co/Gwwoeu3i9Hgpt-index.readthedocs.io/en/latest/examples/metadata_extraction/EntityExtractionClimate.html

In this example, we parse the 2023 IPPC Climate Report.

After text parsing to break the document into chunks, we use the span-marker extractor to extract relevant entities.

These entities can be used as metadata filters (in a vector db) or to help enhance the context embeddings.

In this guide, we do the latter. Adding/embedding the right metadata directly improves the generated answer (left), vs. without (right)