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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,34 +1,44 @@ | ||
| [tool.poetry] | ||
| name = "easy-letters" | ||
| version = "0.1.8" | ||
| description = "A Python package for generating draft application letters using generative AI" | ||
| description = "A Python library for generating draft application letters using generative AI" | ||
| authors = ["Hassan Abedi <[email protected]>"] | ||
| maintainers = ["Hassan Abedi <[email protected]>"] | ||
| readme = "README.md" | ||
| packages = [{ include = "easy_letters", from = "." }] | ||
| include = ["README.md", "LICENSE"] | ||
| include = ["README.md"] | ||
| license = "MIT" | ||
| repository = "https://github.com/habedi/easy-letters" | ||
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| [tool.poetry.dependencies] | ||
| python = "^3.10" | ||
| openai = "^1.16.1" | ||
| qdrant-client = "^1.8.2" | ||
| numpy = "^2.2.3" | ||
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| [tool.poetry.group.dev.dependencies] | ||
| jupyter = "^1.0.0" | ||
| poetry-dynamic-versioning = "^1.4.0" | ||
| pytest = "^8.2.2" | ||
| black = ">=24.4.2,<26.0.0" | ||
| pytest-mock = "^3.14.0" | ||
| pytest-cov = ">=5,<7" | ||
| poetry-dynamic-versioning = "^1.4.0" | ||
| tiktoken = ">=0.7,<0.10" | ||
| pandas = "^2.2.2" | ||
| pytest-mock = "^3.14.0" | ||
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| [build-system] | ||
| requires = ["poetry-core"] | ||
| build-backend = "poetry.core.masonry.api" | ||
| jupyter = "^1.0.0" | ||
| tiktoken = ">=0.7,<0.10" | ||
| ruff = "^0.9.9" | ||
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| [tool.poetry-dynamic-versioning] | ||
| enable = true | ||
| vcs = "git" | ||
| versioning = "semver" # Semantic Versioning | ||
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| #[build-system] | ||
| #requires = ["poetry-core"] | ||
| #build-backend = "poetry.core.masonry.api" | ||
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| #[build-system] | ||
| #requires = ["pdm-backend"] | ||
| #build-backend = "pdm.backend" | ||
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| [build-system] | ||
| requires = ["hatchling"] | ||
| build-backend = "hatchling.build" |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| import numpy as np | ||
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| from easy_letters import Ranker | ||
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| # Sample documents and their embeddings for testing | ||
| documents_with_embeddings = { | ||
| 'text': ["Document 1", "Document 2"], | ||
| 'embedding': [np.array([0.1, 0.2, 0.3]), np.array([0.4, 0.5, 0.6])] | ||
| } | ||
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| # Sample embedding to search for similar documents | ||
| embedding_to_search = np.array([0.1, 0.2, 0.3]) | ||
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| # The expected response (score is Cosine similarity) | ||
| search_response = [ | ||
| {"id": 0, "score": 1.0, "payload": {"text": "Document 1"}}, | ||
| {"id": 1, "score": 0.9746, "payload": {"text": "Document 2"}} | ||
| ] | ||
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| def test_make_collection(): | ||
| """ | ||
| Test the make_collection method of the Ranker class. | ||
| This test checks if the collection is created successfully with the correct | ||
| parameters. | ||
| """ | ||
| # Arrange | ||
| ranker = Ranker() | ||
| collection_name = "test_collection" | ||
|
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| # Act | ||
| ranker.make_collection(documents_with_embeddings, collection_name) | ||
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| # Assert | ||
| coll = ranker.client.get_collection(collection_name) | ||
| assert coll is not None | ||
| assert coll.points_count == 2 | ||
| assert coll.config.params.vectors.size == 3 | ||
| assert coll.config.params.vectors.distance == "Cosine" |
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