Retrieval

GenAIScript provides various utilities to retrieve content and augment the prompt. This technique is typically referred to as RAG (Retrieval-Augmentation-Generation) in the literature.

Vector Search

GenAIScript provides a tiny vector database based on vectra. The retrieve.vectorSearch performs a embeddings search to find the most similar documents to the prompt.

const files = await retrieval.vectorSearch("cat dog", env.files)
def("RAG", files)

The files variable contains a list of files, with concatenated fragments, that are most similar to the prompt. The fragments variable contains a list of fragments from the files that are most similar to the prompt.

Fuzz Search

The retrieve.fuzzSearch performs a “traditional” fuzzy search to find the most similar documents to the prompt.

const files = await retrieval.fuzzSearch("cat dog", env.files)

Web Search

The retrieval.webSearch performs a web search using a search engine API. You will need to provide API keys for the search engine you want to use.

const { webPages } = await retrieval.webSearch("cat dog")
def("RAG", webPages)

Bing

To enable Bing search, configure the BING_SEARCH_API_KEY secret in your .env file. Learn more about configuring the Bing Search API.