# Knowledge retrieval API

A knowledge retrieval API is a service that takes a query and returns the most relevant content from a knowledge source, with relevance scores and source references rather than a generated answer.

## What is a knowledge retrieval API?

It queries organizational or web knowledge sources and returns ranked, relevant passages with citations. It hands your application the source content it needs rather than a finished answer from a model.

A general search API matches keywords and returns links. It adds semantic understanding, a backing knowledge base, reranking, and source references, so results stay accurate and easy to audit.

This matters because it grounds AI apps in verified content. Retrieval-augmented generation, or RAG, is now common in production. [RAG reached 51% adoption](https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise/) among enterprises in late 2024, up from 31%, per a Menlo Ventures survey of 600 U.S. IT decision-makers.

## Key characteristics

- **Semantic matching:** It reads the intent behind a query rather than matching exact keywords, a technique known as semantic search.
- **Knowledge source backing:** It draws results from indexes, stored documents, or the live web.
- **Relevance scoring and reranking:** It scores each candidate passage and returns only results that clear a reranker threshold.
- **Source references:** It attaches citations to every passage so you can trace where an answer came from.

![Parallel web search API accuracy benchmarks](https://cdn.sanity.io/images/5hzduz3y/production/50aa4294bd61a539e76907fcb537c7db5266ebd7-1920x1080.png)

_Retrieval quality sets the ceiling on how well grounded AI answers can be. Parallel’s __[benchmarks page](https://parallel.ai/benchmarks)__ compares web search API accuracy across providers._

## Example

Picture a support assistant built as one of your AI agents. A user asks how to rotate an API key. The assistant sends that question to a retrieval API, which returns the three most relevant passages from your docs with links.

The assistant passes those passages to a large language model (LLM), which writes a short reply and cites each source. Your user gets a grounded answer, and your team can trace every claim to a document (or web page).

[Parallel’s Search API](https://parallel.ai/products/search) is one example of a real retrieval API that AI agents call this way.

## Related terms

- [Web search API](https://parallel.ai/articles/what-is-a-web-search-api): a retrieval API that queries the live web.
- [Semantic search](https://parallel.ai/articles/what-is-semantic-search): the matching technique behind it.
- [Deep research](https://parallel.ai/articles/what-is-deep-research): chained multi-source retrieval.
- [AI agents](https://parallel.ai/articles/what-is-an-ai-agent): common consumers of retrieval APIs.

## FAQ

**How is a knowledge retrieval API different from RAG?**

Retrieval is the first step, finding relevant content. RAG adds a generation step where an LLM writes an answer from that content, a pattern [NIST describes](https://csrc.nist.gov/glossary/term/retrieval_augmented_generation).

**Does a knowledge retrieval API stop AI from making things up?**

It reduces errors by grounding answers in real sources, though it doesn’t remove them entirely. A [Stanford legal research study](https://dho.stanford.edu/wp-content/uploads/Legal_RAG_Hallucinations.pdf) found general LLMs hallucinate on legal queries 58% to 82%, versus 17% to 33% for retrieval tools built for law.

**What can a knowledge retrieval API query?**

It can query search indexes, stored documents, and web sources, returning ranked passages with references.

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