🗂 Llama Index - Making documents searchable
One of the major challenges with LLMs (which we've gone over many times now), is that they have no grasp on the truth. The areas where the most impact for AI is in data that lies within Silos. This is proprietary data, that companies will want to make more accessible for their stakeholders but haven't yet been able to
Why Indexing is important
Indexing is how we can organize a data source to make it more effecient to query. It is like a phonebook for your data. A well indexed source of data creates far faster and more efficient lookups.
In the context of generative AI, it allows a user to reduce the size of prompts, cut down boiler plate code, and provide a common data source.
Llama Index Use Case
- Connecting Large Language Model to external data source
- Summarize over Documents
- Combining information across multiple indices
- Routing Queries to the correct index
- Using keywords
Llama Index Process
- Load in documents - Use the GPT-Index data loader, or Llama Hub to take data from your source of truth. This can be software, like; Google Docs, Slack, Discord, a web page and more.
- Build an Index - Building an index is the essential processs that GPT-Index offers. Here it organizes the data and makes it callable
- Query the index - Querying is where you can actually put use to the index.
- Update the index - As your knowledge base expands, you can update your index. This makes the tool incredibly useful for growing organizations
What you can do
Coming Soon