diff --git a/pgml-sdks/rust/pgml/javascript/tests/typescript-tests/test.ts b/pgml-sdks/rust/pgml/javascript/tests/typescript-tests/test.ts
index 133d91198..5e5b76061 100644
--- a/pgml-sdks/rust/pgml/javascript/tests/typescript-tests/test.ts
+++ b/pgml-sdks/rust/pgml/javascript/tests/typescript-tests/test.ts
@@ -18,7 +18,9 @@ const generate_dummy_documents = (count: number) => {
docs.push({
id: i,
text: `This is a test document: ${i}`,
+ project: "a10",
uuid: i * 10,
+ floating_uuid: i * 1.1,
name: `Test Document ${i}`,
});
}
@@ -36,7 +38,7 @@ it("can create collection", () => {
it("can create model", () => {
let model = pgml.newModel("test", "openai", {
- "tester": "test 0123948712394871234987"
+ some_example_parameter: "test 0123948712394871234987",
});
expect(model).toBeTruthy();
});
@@ -74,7 +76,7 @@ it("can vector search with local embeddings", async () => {
await collection.archive();
});
-it("can vector search with remote embeddings", async() => {
+it("can vector search with remote embeddings", async () => {
let model = pgml.newModel("text-embedding-ada-002", "openai");
let splitter = pgml.newSplitter();
let pipeline = pgml.newPipeline("test_j_p_cvswre_0", model, splitter);
@@ -86,26 +88,34 @@ it("can vector search with remote embeddings", async() => {
await collection.archive();
});
-it("can vector search with query builder", async() => {
+it("can vector search with query builder", async () => {
let model = pgml.newModel();
let splitter = pgml.newSplitter();
let pipeline = pgml.newPipeline("test_j_p_cvswqb_0", model, splitter);
let collection = pgml.newCollection("test_j_c_cvswqb_1");
await collection.upsert_documents(generate_dummy_documents(3));
await collection.add_pipeline(pipeline);
- let results = await collection.query().vector_recall("Here is some query", pipeline).limit(10).fetch_all();
+ let results = await collection
+ .query()
+ .vector_recall("Here is some query", pipeline)
+ .limit(10)
+ .fetch_all();
expect(results).toHaveLength(3);
await collection.archive();
});
-it("can vector search with query builder with remote embeddings", async() => {
+it("can vector search with query builder with remote embeddings", async () => {
let model = pgml.newModel("text-embedding-ada-002", "openai");
let splitter = pgml.newSplitter();
let pipeline = pgml.newPipeline("test_j_p_cvswqbwre_0", model, splitter);
let collection = pgml.newCollection("test_j_c_cvswqbwre_1");
await collection.upsert_documents(generate_dummy_documents(3));
await collection.add_pipeline(pipeline);
- let results = await collection.query().vector_recall("Here is some query", pipeline).limit(10).fetch_all();
+ let results = await collection
+ .query()
+ .vector_recall("Here is some query", pipeline)
+ .limit(10)
+ .fetch_all();
expect(results).toHaveLength(3);
await collection.archive();
});
@@ -122,10 +132,12 @@ it("can vector search with query builder and metadata filtering", async () => {
.vector_recall("Here is some query", pipeline)
.filter({
metadata: {
- $or: [{ uuid: { $eq: 0 } }, { uuid: { $eq: 20 } }],
+ $or: [{ uuid: { $eq: 0 } }, { floating_uuid: { $lt: 2 } }],
+ project: { $eq: "a10" },
},
})
- .limit(10).fetch_all();
+ .limit(10)
+ .fetch_all();
expect(results).toHaveLength(2);
await collection.archive();
});
@@ -141,7 +153,6 @@ it("pipeline to dict", async () => {
let collection = pgml.newCollection("test_j_c_ptd_2");
await collection.add_pipeline(pipeline);
let pipeline_dict = await pipeline.to_dict();
- console.log(JSON.stringify(pipeline_dict))
expect(pipeline_dict["name"]).toBe("test_j_p_ptd_0");
await collection.archive();
});
diff --git a/pgml-sdks/rust/pgml/python/tests/test.py b/pgml-sdks/rust/pgml/python/tests/test.py
index c6e85e3b6..88c19685d 100644
--- a/pgml-sdks/rust/pgml/python/tests/test.py
+++ b/pgml-sdks/rust/pgml/python/tests/test.py
@@ -29,7 +29,8 @@ def generate_dummy_documents(count: int) -> List[Dict[str, Any]]:
{
"id": i,
"text": "This is a test document: {}".format(i),
- "some_random_thing": "This will be metadata on it",
+ "project": "a10",
+ "floating_uuid": i * 1.01,
"uuid": i * 10,
"name": "Test Document {}".format(i),
}
@@ -147,17 +148,18 @@ async def test_can_vector_search_with_query_builder_and_metadata_filtering():
results = (
await collection.query()
.vector_recall("Here is some query", pipeline)
- .filter({
- "metadata": {
- "uuid": {
- "$eq": 0
- }
+ .filter(
+ {
+ "metadata": {
+ "$or": [{"uuid": {"$eq": 0}}, {"floating_uuid": {"$lt": 2}}],
+ "project": {"$eq": "a10"},
+ },
}
- })
+ )
.limit(10)
.fetch_all()
)
- assert len(results) == 1
+ assert len(results) == 2
await collection.archive()
diff --git a/pgml-sdks/rust/pgml/src/filter_builder.rs b/pgml-sdks/rust/pgml/src/filter_builder.rs
index a156405e5..b9792191b 100644
--- a/pgml-sdks/rust/pgml/src/filter_builder.rs
+++ b/pgml-sdks/rust/pgml/src/filter_builder.rs
@@ -117,9 +117,9 @@ fn get_value_type(value: &serde_json::Value) -> String {
} else if value.is_string() {
"text".to_string()
} else if value.is_i64() {
- "bigint".to_string()
+ "float8".to_string()
} else if value.is_f64() {
- "double".to_string()
+ "float8".to_string()
} else if value.is_boolean() {
"bool".to_string()
} else {
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