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Update vector search example in README.md #216

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Aug 29, 2024
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Update vector search example in README.md #216

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66 changes: 33 additions & 33 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -582,7 +582,7 @@ const typesenseInstantsearchAdapter = new TypesenseInstantSearchAdapter({

The general idea is to first hook into the query life-cycle of Instantsearch, intercept the typed query and send it to an embedding API, fetch the embeddings and then send the vectors to Typesense to do a nearest neighbor vector search.

Here's a demo that you can run locally to see this in action: [https://github.com/typesense/typesense-instantsearch-semantic-search-demo](https://github.com/typesense/typesense-instantsearch-semantic-search-demo).
Here's a demo that you can run locally to see this in action: [https://github.com/typesense/showcase-hn-comments-semantic-search](https://github.com/typesense/showcase-hn-comments-semantic-search).

Here's how to do this in Instantsearch.js:

Expand All @@ -601,39 +601,39 @@ const typesenseInstantsearchAdapter = new TypesenseInstantSearchAdapter({
},
additionalSearchParameters,
});
// from https://github.com/typesense/showcase-hn-comments-semantic-search/blob/8a33006cae58b425c53f56a64e1273e808cd9375/src/js/index.js#L101
const searchClient = typesenseInstantsearchAdapter.searchClient;
const search = instantsearch({
searchClient,
indexName: "products",
routing: true,
async searchFunction(helper) {
const query = helper.getQuery().query;
const page = helper.getPage(); // Retrieve the current page
const totalNearestNeighborsToFetch = 1000;

if (query !== "") {
// Get embedding for the query
let response = await fetch(
"http://localhost:8000/embedding?" + new URLSearchParams({ q: query }), // <=== Embedding API
);

let parsedResponse = await response.json();

console.log(parsedResponse);

// Send the embedding to Typesense to do a nearest neighbor search
helper
.setQueryParameter(
"typesenseVectorQuery", // <=== Special parameter that only works in [email protected] and above
`vectors:([${parsedResponse["embedding"].join(",")}], k:${totalNearestNeighborsToFetch})`,
)
.setPage(page)
.search();
} else {
helper.setQueryParameter("typesenseVectorQuery", null).setPage(page).search();
}
},
});
search = instantsearch({
searchClient,
indexName: INDEX_NAME,
routing: true,
async searchFunction(helper) {
// This fetches 200 (nearest neighbor) results for semantic / hybrid search

let query = helper.getQuery().query;
const page = helper.getPage(); // Retrieve the current page

if (
query !== "" &&
["semantic", "hybrid"].includes($("#search-type-select").val())
) {
console.log(helper.getQuery().query);
helper
.setQueryParameter(
"typesenseVectorQuery", // <=== Special parameter that only works in [email protected] and above
`embedding:([], k:200)`,
)
.setPage(page)
.search();
console.log(helper.getQuery().query);
} else {
helper
.setQueryParameter("typesenseVectorQuery", null)
.setPage(page)
.search();
}
},
});
```

## Compatibility
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