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 various vector database to support embeddings (vector) search.

// index creation
const index = await retrieval.index("animals")
// indexing
await index.insertOrUpdate(env.files)
// search
const res = await index.search("cat dog")
def("RAG", res)

• Read more about vector search and how to use it.

Fuzzy 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.