diff --git a/.gitignore b/.gitignore index 1b2e60fe..0e8bd916 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,7 @@ wheels/ # tests .coverage* evaluations/evaluations/data/ +evaluations/scripts/ tests/data/ .pytest_cache/ .ruff_cache/ diff --git a/tests/cassettes/test_client/test_client_search.yaml b/tests/cassettes/test_client/test_client_search.yaml index f6724f3f..79d12fb0 100644 --- a/tests/cassettes/test_client/test_client_search.yaml +++ b/tests/cassettes/test_client/test_client_search.yaml @@ -168,7 +168,7 @@ interactions: connection: - keep-alive content-length: - - '91' + - '97' content-type: - application/json host: @@ -177,7 +177,7 @@ interactions: parsed_body: encoding_format: base64 input: - - machine learning data + - machine learning algorithms model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -188,7 +188,7 @@ interactions: - chunked parsed_body: data: - - embedding: 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 index: 0 object: embedding model: qwen3-embedding:4b diff --git a/tests/test_client.py b/tests/test_client.py index dd9a347e..4c3f03f3 100644 --- a/tests/test_client.py +++ b/tests/test_client.py @@ -736,7 +736,7 @@ async def test_client_search(temp_db_path): assert first_result.document_id == doc1.id # Test search with different query - ml_results = await client.search("machine learning data", limit=2) + ml_results = await client.search("machine learning algorithms", limit=2) assert len(ml_results) > 0 # Verify first result is from the machine learning document (doc2)