Retrieval for Semi-Structured Data
Motivation
Language models (LLMs) are trained on vast but fixed datasets, which limits their ability to access up-to-date or domain-specific information.
To enhance their performance on specific tasks, we can augment their knowledge using retrieval systems.
Retrieval systems fetch relevant information from external sources, which can then be included in the prompt given to the model.
Key benefits of using retrieval systems include:
- Access to recent or private information
- Improved accuracy on domain-specific tasks
- Reduced hallucination by grounding responses in retrieved facts
- Cost-effective alternative to fine-tuning for factual recall
Semi-Structured Data
Some retrieval systems, such as vectorstore, work with semi-structured data.
In the case of vectorstores, it is common to have unstructured data (e.g., raw text documents) and structured data (e.g., metadata associated with the documents).
This allows for querying in two different ways:
- Semantic search: Query the unstructured data directly, often using via embedding or keyword similarity.
- Metadata search: Apply structured query to the metadata, filering specific documents.
Query Construction
A popular approach to interacting with this type of semi-structured data is to use an LLM to convert a natural language query into a metadata filter.
This allows for structured filtering of documents based on metadata, which can then be followed by a semantic search across the filtered documents.
| Name | When to Use | Description |
|---|---|---|
| Self Query | If users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text. | This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filter to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself). |
See our tutorial on query analysis for metadata filters for more details.
See our blog post overview and RAG from Scratch video on query construction.