Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Fixed postgres floating point type#952

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to ourterms of service andprivacy statement. We’ll occasionally send you account related emails.

Already on GitHub?Sign in to your account

Merged
SilasMarvin merged 4 commits intomasterfromsilas-sdk-filter-builder-patch
Aug 25, 2023
Merged
Show file tree
Hide file tree
Changes fromall commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
29 changes: 20 additions & 9 deletionspgml-sdks/rust/pgml/javascript/tests/typescript-tests/test.ts
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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}`,
});
}
Expand All@@ -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();
});
Expand DownExpand Up@@ -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);
Expand All@@ -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();
});
Expand All@@ -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: { $eq: "a10" },
},
})
.limit(10).fetch_all();
.limit(10)
.fetch_all();
expect(results).toHaveLength(2);
await collection.archive();
});
Expand All@@ -141,7 +153,6 @@ it("pipeline to dict", async () => {
let collection = pgml.newCollection("test_j_c_ptd_2");
await collection.add_pipeline(pipeline);
let pipeline_dict = await pipeline.to_dict();
console.log(JSON.stringify(pipeline_dict))
expect(pipeline_dict["name"]).toBe("test_j_p_ptd_0");
await collection.archive();
});
18 changes: 10 additions & 8 deletionspgml-sdks/rust/pgml/python/tests/test.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -29,7 +29,8 @@ def generate_dummy_documents(count: int) -> List[Dict[str, Any]]:
{
"id": i,
"text": "This is a test document: {}".format(i),
"some_random_thing": "This will be metadata on it",
"project": "a10",
"floating_uuid": i * 1.01,
"uuid": i * 10,
"name": "Test Document {}".format(i),
}
Expand DownExpand Up@@ -147,17 +148,18 @@ async def test_can_vector_search_with_query_builder_and_metadata_filtering():
results = (
await collection.query()
.vector_recall("Here is some query", pipeline)
.filter({
"metadata": {
"uuid": {
"$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()


Expand Down
4 changes: 2 additions & 2 deletionspgml-sdks/rust/pgml/src/filter_builder.rs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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 {
Expand Down

[8]ページ先頭

©2009-2025 Movatter.jp