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Vector Search

GenAIScript provides various vector database to support embeddings search and retrieval augmented generation (RAG).

// 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)

Index creation

The retrieve.index creates or loads an existing index. The index creation takes a number of options which should not change between executions.

// index creation
const index = await retrieval.index("animals")

Local Index

By default, vector are stored locally in files under the .genaiscript/vector folder using a local vector database based on vectra. The embeddings are computed using the embeddings model alias

Play

The embeddings can also be configured through the options.

const index = await retrieval.index("animals", {
    embeddingsModel: "ollama:nomic-embed-text",
})

The index is serialized by default. If you wish to reset it on every execution, set deleteIfExists: true.

GenAIScript also supports using an Azure AI Search service. The Azure AI Search uses the simple query syntax.

const index = retrieval.index("animals", { type: "azure_ai_search" })

To configure the service, you will need to set the AZURE_AI_SEARCH_ENDPOINT and AZURE_AI_SEARCH_API_KEY environment variables in your .env file. Please refer to the Authentication documentation for more details.

AZURE_AI_SEARCH_ENDPOINT=https://{{service-name}}.search.windows.net/
AZURE_AI_SEARCH_API_KEY=...

Further index management can be done through the Azure Portal.

Model and chunking configuration

The computation of embeddings is done through the LLM APIs using the same authorization token as the LLM API.

const index = await retrieval.index("animals", {
    embeddingsModel: "ollama:all-minilm",
})

You can also configure the chunking of the input files. You can change this by setting the chunkSize and chunkOverlap options.

const index = await retrieval.index("animals", {
    chunkSize: 512,
    chunkOverlap: 0,
})

Indexing

The index.insertOrUpdate functiont takes care of chunking, vectorizing and updating the vector database.

// indexing
await index.insertOrUpdate(env.files)

Searching

The index.search performs a search (vector or hybrid) using the index.

const hits = await retrieval.search("keyword")

The returned value is an array of files with the resconstructed content from the matching chunks.

const hits = await retrieval.search("keyword")
def("FILE", files)