import type { CallToolResult } from "@modelcontextprotocol/sdk/types.js";
import { describeAccuracyTests } from "./sdk/describeAccuracyTests.js";
import { Matcher } from "./sdk/matcher.js";
const embeddingParameters = {
model: "voyage-3.5",
outputDimension: Matcher.anyOf(
Matcher.undefined,
Matcher.number((n) => n === 1024)
),
outputDType: Matcher.anyOf(Matcher.undefined, Matcher.value("float")),
};
const mockInsertMany = (): CallToolResult => {
return {
content: [
{
type: "text",
text: "Documents were inserted successfully.",
},
],
};
};
/**
* Accuracy tests for inserting documents with automatic vector embeddings generation.
*/
describeAccuracyTests(
[
{
prompt: "Insert 2 documents in one call into 'mflix.movies' collection - document should have a 'title' field that has generated embeddings using the voyage-3.5 model: 'The Matrix' and 'Blade Runner'. Assume the collection already exists and has vector index on the 'title' field.",
expectedToolCalls: [
{
toolName: "insert-many",
parameters: {
database: "mflix",
collection: "movies",
documents: [
{
// The title field might be specified, sometimes as "The Matrix" or "Placeholder". This will be overwritten by the embeddings so this is fine.
title: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
},
{
title: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
},
],
embeddingParameters: {
...embeddingParameters,
input: [
{
title: "The Matrix",
},
{
title: "Blade Runner",
},
],
},
},
},
],
},
{
prompt: "Insert a document into 'mflix.movies' collection with following fields: title is 'The Matrix', plotSummary is 'A computer hacker learns about the true nature of his reality', embeddings for plotSummary is stored in plotSummaryEmbeddings field, generate the necesssary vector embeddings for the 'plotSummaryEmbeddings' field using the voyage-3.5 model. Assume the collection already exists and has vector index on the 'plotSummaryEmbeddings' field.",
expectedToolCalls: [
{
toolName: "insert-many",
parameters: {
database: "mflix",
collection: "movies",
documents: [
{
title: "The Matrix",
plotSummary: "A computer hacker learns about the true nature of his reality",
plotSummaryEmbeddings: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
},
],
embeddingParameters: {
...embeddingParameters,
input: [
{
plotSummaryEmbeddings:
"A computer hacker learns about the true nature of his reality",
},
],
},
},
},
],
},
{
prompt: "Insert 2 documents in one call into 'mflix.movies' collection - the movie titles are 1. 'The Matrix' and 2. 'Blade Runner'. The documents should only have an info field which will have 2 subfields: 'title' and 'titleEmbeddings'. Generate the embeddings for the 'info.titleEmbeddings' subfield using the voyage-3.5 model. Assume the collection already exists and has vector index on the 'info.titleEmbeddings' field.",
expectedToolCalls: [
{
toolName: "insert-many",
parameters: {
database: "mflix",
collection: "movies",
documents: [
{
info: {
titleEmbeddings: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
title: "The Matrix",
},
},
{
info: {
titleEmbeddings: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
title: "Blade Runner",
},
},
],
embeddingParameters: {
...embeddingParameters,
input: [
{
"info.titleEmbeddings": "The Matrix",
},
{
"info.titleEmbeddings": "Blade Runner",
},
],
},
},
},
],
mockedTools: {
"insert-many": mockInsertMany,
},
},
{
prompt: "Insert a document into 'mflix.movies' collection with title 'The Matrix' and generate the necesssary vector embeddings for the current vector search fields using the voyage-3.5 model.",
expectedToolCalls: [
{
toolName: "collection-indexes",
parameters: {
database: "mflix",
collection: "movies",
},
},
{
toolName: "insert-many",
parameters: {
database: "mflix",
collection: "movies",
documents: [
{
title: "The Matrix",
title_embeddings: Matcher.anyOf(Matcher.undefined, Matcher.null, Matcher.string()),
},
],
embeddingParameters: {
...embeddingParameters,
input: [
{
title_embeddings: "The Matrix",
},
],
},
},
},
],
mockedTools: {
"insert-many": mockInsertMany,
"collection-indexes": (): CallToolResult => {
return {
content: [
{
type: "text",
text: JSON.stringify({
name: "title_embeddings",
type: "vectorSearch",
status: "READY",
queryable: true,
latestDefinition: {
type: "vector",
path: "title_embeddings",
numDimensions: 1024,
quantization: "none",
similarity: "euclidean",
},
}),
},
],
};
},
},
},
],
{
userConfig: { voyageApiKey: "valid-key", previewFeatures: "search" },
clusterConfig: {
search: true,
},
}
);
