Parsing Your Data
Science & Technology
Introduction
Garbage in, Garbage out – this saying is especially true when developing RAG applications. Parsing and chunking your documents correctly has a large impact on retrieval results, but can be hard to get right. Parsing tables in PDF or stripping web pages of HTML markup are just a few of the challenges involved. In this article, we will explore some of the new solutions that have been developed to solve these challenges.
Llama Pars is a product by the creators of Lama Index, offering seamless integration. It supports all languages and many other formats besides PDF, such as HTML or PPTX. You can provide output instructions in natural language. Another alternative is Unstructured, which also integrates into other frameworks such as Long Chain or even Verba from WV8 for an open-source self-host option. Check out Sycamore from A.AI, which can perform advanced transformations such as schema and property extractions. All of these solutions are easy to use with WV8. Check out the links in the description to see the Python notebooks and other resources for this.
Keyword:
- Parsing
- Chunking
- RAG applications
- Llama Pars
- Unstructured data
- Sycamore
- Advanced transformations
FAQ:
- What is the importance of parsing and chunking in data retrieval?
- How do solutions like Llama Pars and Sycamore help in data processing?
- Can these tools handle multiple languages and formats besides PDF?