import type { CallToolResult } from "@modelcontextprotocol/sdk/types.js";
import { describeAccuracyTests } from "./sdk/describeAccuracyTests.js";
describeAccuracyTests(
[
{
prompt: `\
Insert 2 movie documents in 'mflix.movies' namespace with the following fields:
1. \
title: "Matrix" \
plot: "A computer hacker learns about the true nature of his reality" \
2. \
title: "Jurassic Park" \
plot: "Pre-historic creatures come to life in this epic thrilling drama" \
'plot' field is covered with a vector search index so only if necessary, generate embeddings for it.
`,
mockedTools: {
"collection-indexes": (): CallToolResult => {
return {
content: [
{
type: "text",
text: 'Found 1 search and vector search indexes in the collection "movies"',
},
{
type: "text",
text: JSON.stringify({
name: "plot_auto_embed_index",
type: "vectorSearch",
status: "READY",
latestDefinition: {
type: "autoEmbed",
path: "plot",
model: "voyage-4-large",
modality: "text",
},
}),
},
],
};
},
},
expectedToolCalls: [
{
toolName: "collection-indexes",
parameters: {
database: "mflix",
collection: "movies",
},
optional: true,
},
{
toolName: "insert-many",
parameters: {
database: "mflix",
collection: "movies",
documents: [
{
title: "Matrix",
plot: "A computer hacker learns about the true nature of his reality",
},
{
title: "Jurassic Park",
plot: "Pre-historic creatures come to life in this epic thrilling drama",
},
],
},
},
],
},
],
{
userConfig: { voyageApiKey: process.env.MDB_VOYAGE_API_KEY, previewFeatures: "search" },
clusterConfig: {
autoEmbed: true,
mongotPassword: process.env.MDB_MONGOT_PASSWORD as string,
voyageIndexingKey: process.env.MDB_VOYAGE_API_KEY as string,
voyageQueryKey: process.env.MDB_VOYAGE_API_KEY as string,
},
}
);
